From 19d9550223934983fd45d762e300f2941869e84e Mon Sep 17 00:00:00 2001 From: Alex Strashny Date: Thu, 5 Sep 2024 17:18:19 -0400 Subject: [PATCH] mode = "NCHS" --- NAMESPACE | 1 + NEWS.md | 1 + R/set_mode.R | 31 +++ R/set_survey.R | 72 +++---- R/surveytable.R | 16 +- R/svyciprop_adjusted.R | 12 +- R/zzz.R | 36 ++-- README.Rmd | 4 +- README.md | 67 +++---- docs/articles/Advanced-topics.html | 6 +- ...tory-Medical-Care-Survey-NAMCS-tables.html | 74 ++++---- ...ty-Services-User-NSLTCP-RCC-SU-report.html | 89 ++++++--- docs/articles/surveytable.html | 82 ++++---- docs/authors.html | 6 +- docs/index.html | 65 +++---- docs/news/index.html | 1 + docs/pkgdown.yml | 2 +- docs/reference/codebook.html | 2 + docs/reference/index.html | 2 +- docs/reference/print.surveytable_table.html | 129 +++++++------ docs/reference/set_count_1k.html | 29 +-- docs/reference/set_output.html | 2 +- docs/reference/set_survey.html | 52 +++--- docs/reference/show_options.html | 8 +- docs/reference/survey_subset.html | 22 +-- docs/reference/surveytable-options.html | 14 +- docs/reference/svyciprop_adjusted.html | 18 +- docs/reference/tab.html | 168 ++++++++--------- docs/reference/tab_rate.html | 26 +-- docs/reference/tab_subset.html | 176 +++++++++--------- docs/reference/tab_subset_rate.html | 69 +++---- docs/reference/total.html | 15 +- docs/reference/total_rate.html | 15 +- docs/reference/var_all.html | 22 +-- docs/reference/var_any.html | 21 +-- docs/reference/var_case.html | 86 ++++----- docs/reference/var_collapse.html | 54 +++--- docs/reference/var_copy.html | 69 ++++--- docs/reference/var_cross.html | 98 +++++----- docs/reference/var_cut.html | 32 ++-- docs/reference/var_list.html | 1 + docs/reference/var_not.html | 1 + docs/search.json | 2 +- inst/CITATION | 8 + man/set_survey.Rd | 50 ++--- man/surveytable-options.Rd | 15 +- man/svyciprop_adjusted.Rd | 11 +- vignettes/Advanced-topics.Rmd | 2 +- ...atory-Medical-Care-Survey-NAMCS-tables.Rmd | 6 + ...ity-Services-User-NSLTCP-RCC-SU-report.Rmd | 16 +- vignettes/surveytable.Rmd | 6 + 51 files changed, 984 insertions(+), 828 deletions(-) create mode 100644 R/set_mode.R create mode 100644 inst/CITATION diff --git a/NAMESPACE b/NAMESPACE index a90bb41..a6896e3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,6 +5,7 @@ S3method(print,surveytable_table) export(codebook) export(set_count_1k) export(set_count_int) +export(set_mode) export(set_output) export(set_survey) export(show_options) diff --git a/NEWS.md b/NEWS.md index 7db62f2..9a92716 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,7 @@ # surveytable (development version) * `rccsu2018` +* `set_mode()` # surveytable 0.9.4 diff --git a/R/set_mode.R b/R/set_mode.R new file mode 100644 index 0000000..a54212f --- /dev/null +++ b/R/set_mode.R @@ -0,0 +1,31 @@ +#' @rdname set_survey +#' @order 2 +#' @export +set_mode = function(mode = "default") { + opts.table = c("nchs", "default", "general") + idx = mode %>% tolower %>% pmatch(opts.table) + assert_that(noNA(idx), msg = paste("Unknown mode:", mode)) + opts = opts.table[idx] + + if (opts == "nchs") { + message("* Mode: NCHS.") + options( + surveytable.tx_count = ".tx_count_1k" + , surveytable.names_count = c("n", "Number (000)", "SE (000)", "LL (000)", "UL (000)") + , surveytable.find_lpe = TRUE + , surveytable.adjust_svyciprop = TRUE + ) + } else if (opts %in% c("general", "default")) { + message("* Mode: General.") + options( + surveytable.tx_count = ".tx_count_int" + , surveytable.names_count = c("n", "Number", "SE", "LL", "UL") + , surveytable.find_lpe = FALSE + , surveytable.adjust_svyciprop = FALSE + ) + } else { + stop("!!") + } + + invisible(NULL) +} diff --git a/R/set_survey.R b/R/set_survey.R index 2bedfcb..04590ba 100644 --- a/R/set_survey.R +++ b/R/set_survey.R @@ -1,67 +1,47 @@ #' Specify the survey to analyze #' -#' You need to specify a survey before the other functions, such as [tab()], -#' will work. -#' -#' `opts`: -#' * `"nchs"`: -#' * Round counts to the nearest 1,000 -- see [set_count_1k()]. -#' * Identify low-precision estimates (`surveytable.find_lpe` option is `TRUE`). -#' * Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation (`surveytable.adjust_svyciprop` option is `TRUE`). -#' * `"general":` -#' * Round counts to the nearest integer -- see [set_count_int()]. -#' * Do not look for low-precision estimates (`surveytable.find_lpe` option is `FALSE`). -#' * Percentage CI's: use standard Korn-Graubard CI's (`surveytable.adjust_svyciprop` option is `FALSE`). +#' You must specify a survey before the other functions, such as [tab()], +#' will work. To convert a `data.frame` to a survey object, see [survey::svydesign()] +#' or [survey::svrepdesign()]. #' #' Optionally, the survey can have an attribute called `label`, which is the -#' long name of the survey. +#' long name of the survey. Optionally, each variable in the survey can have an +#' attribute called `label`, which is the variable's long name. #' -#' Optionally, each variable in the survey can have an attribute called `label`, -#' which is the variable's long name. +#' If you are not sure what the `mode` should be, leave it as `"default"`. Here is +#' what `mode` does: #' -#' @param design either a survey object (`survey.design` or `svyrep.design`) or a -#' `data.frame` for an unweighted survey. -#' @param opts set certain options. See below. +#' * `"general"` or `"default"`: +#' * Round counts to the nearest integer -- see [set_count_int()]. +#' * Do not look for low-precision estimates. +#' * Percentage CI's: use standard Korn-Graubard CI's. +#' +#' * `"nchs"`: +#' * Round counts to the nearest 1,000 -- see [set_count_1k()]. +#' * Identify low-precision estimates. +#' * Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation. +#' +#' @param design either a survey object (created with [survey::svydesign()] or +#' [survey::svrepdesign()]); or, for an unweighted survey, a `data.frame`. +#' @param mode set certain options. See below. #' @param csv name of a CSV file #' #' @family options -#' @return Info about the survey. +#' @return `set_survey`: info about the survey. `set_mode`: nothing. +#' @order 1 #' @export #' #' @examples #' set_survey(namcs2019sv) -set_survey = function(design, opts = "NCHS" - , csv = getOption("surveytable.csv")) { - - ## - opts.table = c("nchs", "general") - idx = opts %>% tolower %>% pmatch(opts.table) - assert_that(noNA(idx), msg = paste("Unknown value of opts:", opts)) - opts = opts.table[idx] - - if (opts == "nchs") { - options( - surveytable.tx_count = ".tx_count_1k" - , surveytable.names_count = c("n", "Number (000)", "SE (000)", "LL (000)", "UL (000)") - , surveytable.find_lpe = TRUE - , surveytable.adjust_svyciprop = TRUE - ) - } else if (opts == "general") { - options( - surveytable.tx_count = ".tx_count_int" - , surveytable.names_count = c("n", "Number", "SE", "LL", "UL") - , surveytable.find_lpe = FALSE - , surveytable.adjust_svyciprop = FALSE - ) - } else { - stop("!!") - } - +#' set_mode("general") +set_survey = function(design, mode = "default", csv = getOption("surveytable.csv")) { # In case there's an error below and we don't set a new survey, # don't retain the previous survey either. env$survey = NULL options(surveytable.survey_label = "") + set_mode(mode = mode) + if (is.string(design)) { label_default = design design %<>% get0 diff --git a/R/surveytable.R b/R/surveytable.R index 5e9b203..ffd7858 100644 --- a/R/surveytable.R +++ b/R/surveytable.R @@ -19,9 +19,20 @@ NULL #' #' ## Low-precision estimates. #' +#' Optionally, all of the tabulation functions can identify low-precision estimates. +#' To turn on this functionality, either set the `surveytable.find_lpe` option to `TRUE`, +#' or call [set_survey()] or [set_mode()] with the argument `mode = "NCHS"`. +#' +#' By default, low-precision estimates are identified using National Center for +#' Health Statistics (NCHS) algorithms. However, this can be changed, as described +#' below. +#' +#' Here is a description of the options related to the identification of low-precision +#' estimates. +#' #' * `surveytable.find_lpe`: should the tabulation functions look for low-precision -#' estimates? You can change this directly with `options()` or with the `opts` argument -#' to `set_survey()`. +#' estimates? You can change this directly with `options()` or with the `mode` argument +#' to [set_survey()] or [set_mode()]. #' * `surveytable.lpe_n`, `surveytable.lpe_counts`, `surveytable.lpe_percents`: names #' of 3 functions. #' @@ -61,7 +72,6 @@ NULL #' level, two different things were found, indicated by short codes `A1` and `A2`. In #' this case, `has.flag = c("A1", "A2")`, `descriptions = c(A1 = "A1: something", A2 = "A2: something else")`. #' -#' #' @name surveytable-options #' @family options "_PACKAGE" diff --git a/R/svyciprop_adjusted.R b/R/svyciprop_adjusted.R index 6b9b068..cf91542 100644 --- a/R/svyciprop_adjusted.R +++ b/R/svyciprop_adjusted.R @@ -1,13 +1,14 @@ #' Confidence intervals for proportions, adjusted for degrees of freedom #' -#' A version of `survey::svyciprop()` that adjusts for the degrees of freedom when `method = "beta"`. +#' A version of `survey::svyciprop()` that adjusts for the degrees of freedom +#' when `method = "beta"`. #' #' Written by Makram Talih in 2019. #' #' `df_method`: for `"default"`, `df = degf(design)`; for `"NHIS"`, `df = nrow(design) - 1`. #' -#' To use this function in tabulations, call [set_survey()] with the `opts = "NCHS"` argument, -#' or type: `options(surveytable.adjust_svyciprop = TRUE)`. +#' To use this function in tabulations, call [set_survey()] or [set_mode()] with the +#' `mode = "NCHS"` argument, or type: `options(surveytable.adjust_svyciprop = TRUE)`. #' #' @param formula see `survey::svyciprop()`. #' @param design see `survey::svyciprop()`. @@ -16,13 +17,14 @@ #' @param df_method how `df` should be calculated: `"default"` or `"NHIS"`. #' @param ... see `survey::svyciprop()`. #' -#' #' @return The point estimate of the proportion, with the confidence interval as an attribute. #' @export #' #' @examples -#' set_survey(namcs2019sv, opts = "NCHS") +#' set_survey(namcs2019sv) +#' set_mode("NCHS") #' tab("AGER") +#' set_mode("general") svyciprop_adjusted = function(formula , design , method = c("logit", "likelihood", "asin", "beta" diff --git a/R/zzz.R b/R/zzz.R index 8920ab9..bd9db99 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -1,21 +1,21 @@ -.onAttach = function(libname, pkgname) { - txt = paste0( - "Before you can tabulate estimates, you have to specify which survey object" - , " you would like to analyze. You can do this in a couple of ways:" - , "\n\na) This package comes with a survey object for use in examples called" - , " 'namcs2019sv'. This object has selected variables from the NAMCS 2019 PUF survey." - , " To use this survey object:" - , "\n\nset_survey(namcs2019sv)" - , "\n\nb) If you have a survey object stored in a file:" - , "\n\nmysurvey = readRDS('file_name.rds')" - , "\n\nset_survey(mysurvey)" - , "\n\nFor info on how to create a survey object from a data frame, see" - , " ?survey::svydesign or ?survey::svrepdesign ." - ) - - txt = paste(strwrap(txt), collapse = "\n") - packageStartupMessage(txt) -} +# .onAttach = function(libname, pkgname) { +# txt = paste0( +# "Before you can tabulate estimates, you have to specify which survey object" +# , " you would like to analyze. You can do this in a couple of ways:" +# , "\n\na) This package comes with a survey object for use in examples called" +# , " 'namcs2019sv'. This object has selected variables from the NAMCS 2019 PUF survey." +# , " To use this survey object:" +# , "\n\nset_survey(namcs2019sv)" +# , "\n\nb) If you have a survey object stored in a file:" +# , "\n\nmysurvey = readRDS('file_name.rds')" +# , "\n\nset_survey(mysurvey)" +# , "\n\nFor info on how to create a survey object from a data frame, see" +# , " ?survey::svydesign or ?survey::svrepdesign ." +# ) +# +# txt = paste(strwrap(txt), collapse = "\n") +# packageStartupMessage(txt) +# } env = new.env() diff --git a/README.Rmd b/README.Rmd index c33b00d..8d90e23 100644 --- a/README.Rmd +++ b/README.Rmd @@ -35,7 +35,7 @@ knitr::opts_chunk$set( * modify survey variables, and * save the output. -* All of the tabulation functions identify low-precision estimates using National Center for Health Statistics (NCHS) algorithms. +* Optionally, all of the tabulation functions can identify low-precision estimates using the National Center for Health Statistics (NCHS) algorithms (or other algorithms). * If the `surveytable` code is called from an R Markdown notebook or a Quarto document, it automatically generates HTML or LaTeX tables, as appropriate. * The package reduces the number of commands that users need to execute, which is especially helpful for users new to R or to programming. @@ -89,7 +89,7 @@ The table shows: * Estimated count with its SE and 95% CI * Estimated percentage with its SE and 95% CI * Sample size -* Whether any low-precision estimates were found +* Optionally, the table can show whether any low-precision estimates were found ## Public Domain Standard Notice diff --git a/README.md b/README.md index 7caffcd..996beb0 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,9 @@ - modify survey variables, and - save the output. -- All of the tabulation functions identify low-precision estimates using - National Center for Health Statistics (NCHS) algorithms. +- Optionally, all of the tabulation functions can identify low-precision + estimates using the National Center for Health Statistics (NCHS) + algorithms (or other algorithms). - If the `surveytable` code is called from an R Markdown notebook or a Quarto document, it automatically generates HTML or LaTeX tables, as @@ -77,6 +78,7 @@ library(surveytable) ``` r set_survey(namcs2019sv) +#> * Mode: General. ``` @@ -141,16 +143,16 @@ Level n
-Number (000) +Number -SE (000) +SE -LL (000) +LL -UL (000) +UL Percent @@ -173,16 +175,16 @@ Under 15 years 887 -117,917 +117,916,772 -14,097 +14,097,315 -93,229 +93,228,928 -149,142 +149,142,177 11.4 @@ -205,16 +207,16 @@ Under 15 years 542 -64,856 +64,855,698 -7,018 +7,018,359 -52,387 +52,386,950 -80,292 +80,292,164 6.3 @@ -237,16 +239,16 @@ Under 15 years 1,435 -170,271 +170,270,604 -13,966 +13,965,978 -144,925 +144,924,545 -200,049 +200,049,472 16.4 @@ -269,16 +271,16 @@ Under 15 years 2,283 -309,506 +309,505,956 -23,290 +23,289,827 -266,994 +266,994,092 -358,787 +358,786,727 29.9 @@ -301,16 +303,16 @@ Under 15 years 1,661 -206,866 +206,865,982 -14,366 +14,365,993 -180,481 +180,480,708 -237,109 +237,108,637 20   @@ -333,16 +335,16 @@ Under 15 years 1,442 -167,069 +167,069,344 -15,179 +15,179,082 -139,746 +139,746,193 -199,735 +199,734,713 16.1 @@ -359,7 +361,7 @@ Under 15 years
-N = 8250. Checked NCHS presentation standards. Nothing to report. +N = 8250.
@@ -373,7 +375,8 @@ The table shows: - Estimated count with its SE and 95% CI - Estimated percentage with its SE and 95% CI - Sample size -- Whether any low-precision estimates were found +- Optionally, the table can show whether any low-precision estimates + were found diff --git a/docs/articles/Advanced-topics.html b/docs/articles/Advanced-topics.html index 742d0c2..2a953df 100644 --- a/docs/articles/Advanced-topics.html +++ b/docs/articles/Advanced-topics.html @@ -266,7 +266,8 @@

Subsetting a survey
 newsurvey = survey_subset(namcs2019sv, NUMMED > 0
   , label = "NAMCS 2019 PUF: NUMMED 1+")
-set_survey(newsurvey)
+set_survey(newsurvey) +## * Mode: General.
Survey info {NAMCS 2019 PUF: NUMMED 1+} @@ -448,8 +449,7 @@

Advanced variable editing

  • surveytable provides a number of functions to create or modify survey variables.

    • -

  • Some examples include var_collapse() and -var_cut().

    • +

  • Some examples include [var_collapse()] and [var_cut()].

  • Occasionally, you might need to do advanced variable editing. Here’s how:

  • Every survey object has an element called diff --git a/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html b/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html index 31c64dd..ebf73ce 100644 --- a/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html +++ b/docs/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html @@ -108,7 +108,8 @@

    Begin library(surveytable)

    Now, specify the survey that you’d like to analyze.

    -set_survey(namcs2019sv)
    +set_survey(namcs2019sv) +## * Mode: General.
    Survey info {NAMCS 2019 PUF} @@ -143,6 +144,13 @@

    Begin

    Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.

    +

    For this example, we do want to turn on certain NCHS-specific +options, such as identifying low-precision estimates. If you do not care +about identifying low-precision estimates, you can skip this command. To +turn on the NCHS-specific options:

    +
    +set_mode("NCHS")
+## * Mode: NCHS.

    Table 1 @@ -155,7 +163,7 @@

    Counts and percentages

    The variables that are necessary for creating this table are already in the survey, making the commands very straightforward.

    -
    +
 
    
 
@@ -206,7 +214,7 @@ 
    Counts and percentages
 
 
    
-
    +
     tab("MDDO", "SPECCAT", "MSA")
    
 
    
 
@@ -600,19 +608,19 @@ 
    Rates
 into the correct format. The surveytable package comes with
 an object called uspop2019 that contains several population
 estimates for use in these examples.
    
-
    +
     class(uspop2019)
 ## [1] "list"
 names(uspop2019)
 ## [1] "total"       "MSA"         "AGER"        "Age group"   "SEX"        
 ## [6] "AGER x SEX"  "Age group 5"
    
 
    Here is the overall population estimate:
    
-
    +
     uspop2019$total
 ## [1] 323186697
    
 
    Once we have the overall population estimate, the overall rate
 is:
    
-
    +
     total_rate(uspop2019$total)
    
 
    
 
@@ -671,14 +679,14 @@ 
    Rates
 
    For MSA, we can see the levels of the variables just by
 using the tab() command, just as we did above. Thus, to
 calculate rates, we need a data frame as follows:
    
-
    +
     uspop2019$MSA
 ##                                 Level Population
 ## 1 MSA (Metropolitan Statistical Area)  277229518
 ## 2                             Non-MSA   45957179
    
 
    Now that we have the appropriate population estimates, the rate
 is:
    
-
    +
     tab_rate("MSA", uspop2019$MSA)
    
 
    
 
@@ -759,7 +767,7 @@ 
    Rates
 
    
 
    We can also calculate rates of a specific variable based on the
 entire population:
    
-
    +
     tab_rate("MDDO", uspop2019$total)
 ## * Rate based on the entire population.
    
 
@@ -838,7 +846,7 @@ 
    Rates
 
 
 
    
-
    +
     tab_rate("SPECCAT", uspop2019$total)
 ## * Rate based on the entire population.
    
 
@@ -948,7 +956,7 @@ 
    Counts and percentages
 
    This table presents estimates for each age group, as well as for each
 age group by sex.
    
-
    +
     var_list("age")
    
 
    
 
@@ -997,13 +1005,13 @@ 
    Counts and percentagesAGER, we need another age group variable,
 with different age categories. We create it using the
 var_cut function.
    
-
    +
     var_cut("Age group", "AGE"
         , c(-Inf, 0, 4, 14, 64, Inf)
         , c("Under 1", "1-4", "5-14", "15-64", "65 and over") )
    
 
    Now that we’ve created the Age group variable, we can
 create the tables:
    
-
    +
     tab("AGER", "Age group", "SEX")
    
 
    
 
@@ -1577,7 +1585,7 @@ 
    Counts and percentages
 
 
    
-
    +
     tab_cross("AGER", "SEX")
    
 
    
 
@@ -2019,7 +2027,7 @@ 
    Counts and percentages
 
    Rates
 
    
-
    +
     tab_rate("AGER", uspop2019$AGER)
    
 
    
 
@@ -2177,7 +2185,7 @@ 
    Rates
 
 
 
    
-
    +
     tab_rate("Age group", uspop2019$`Age group`)
 ## * Population for some levels not defined: 15-64
    
 
@@ -2316,7 +2324,7 @@ 
    Rates
 
 
 
    
-
    +
     tab_rate("SEX", uspop2019$SEX)
    
 
    
 
@@ -2397,7 +2405,7 @@ 
    Rates
 
    To calculate the rates for one variable (AGER) by
 another variable (SEX), we need population estimates in the
 following format:
    
-
    +
     uspop2019$`AGER x SEX`
 ##                Level Subset Population
 ## 1     Under 15 years Female   29604762
@@ -2413,7 +2421,7 @@ 
    Rates
 ## 11       65-74 years   Male   14586962
 ## 12 75 years and over   Male    9098236
    
 
    Once we have these population estimates, the rates are:
    
-
    +
     tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)
    
 
    
 
@@ -2741,7 +2749,7 @@ 
    Table 5importsurvey package, which automatically detects binary
 variables and imports them as logical variables.)
    
-
    +
     #
 var_all("Medicare and Medicaid", c("PAYMCARE", "PAYMCAID"))
 
@@ -3978,11 +3986,11 @@ 
    Table 6In the table, the “Unknown” and “Blank” values are collapsed into a
 single value. We can collapse two or more levels of a factor into a
 single level using the var_collapse function.
    
-
    +
     var_collapse("PRIMCARE", "Unknown if PCP", c("Unknown", "Blank"))
 var_collapse("REFER", "Unknown if referred", c("Unknown", "Blank"))
    
 
    Now, for the table:
    
-
    +
     tab("PRIMCARE", "REFER", "SENBEFOR")
    
 
    
 
@@ -4431,7 +4439,7 @@ 
    Table 6The percentages within each subset that is defined by
 SENBEFOR add up to 100% – for this reason, we want to use
 tab_subset(), not tab_cross().
    
-
    +
     tab_subset("PRIMCARE", "SENBEFOR")
    
 
    
 
@@ -4743,7 +4751,7 @@ 
    Table 6
 
 
    
-
    +
     tab_subset("REFER", "SENBEFOR")
    
 
    
 
@@ -5117,7 +5125,7 @@ 
    Table 11Let’s create Age group from AGE and cross
 AGER and SEX to create a variable called
 Age x Sex:
    
-
    +
     var_cut("Age group", "AGE"
         , c(-Inf, 0, 4, 14, 64, Inf)
         , c("Under 1", "1-4", "5-14", "15-64", "65 and over") )
@@ -5127,7 +5135,7 @@ 
    Table 11To see the possible values of MAJOR (Major reason for
 this visit), and to estimate the total count for preventive care
 visits:
    
-
    +
     tab("MAJOR")
    
 
    
 
@@ -5407,7 +5415,7 @@ 
    Table 11
 
    To create the tables of age, sex, and their interaction, and limit
 them to only the preventive care visits:
    
-
    +
     tab_subset("AGER", "MAJOR", "Preventive care")
    
 
    
 
@@ -5654,7 +5662,7 @@ 
    Table 11
 
 
    
-
    +
     tab_subset("Age group", "MAJOR", "Preventive care")
    
 
    
 
@@ -5869,7 +5877,7 @@ 
    Table 11
 
 
    
-
    +
     tab_subset("SEX", "MAJOR", "Preventive care")
    
 
    
 
@@ -5988,7 +5996,7 @@ 
    Table 11
 
 
    
-
    +
     tab_subset("Age x Sex", "MAJOR", "Preventive care")
    
 
    
 
@@ -6430,7 +6438,7 @@ 
    Table 11As each of the above commands is similar, and differs only in the
 first variable that is passed to the tab_subset() function,
 this code can be streamlined with a for loop:
    
-
    +
     for (vr in c("AGER", "Age group", "SEX", "Age x Sex")) {
     print( tab_subset(vr, "MAJOR", "Preventive care") )
 }
    
@@ -7457,7 +7465,7 @@ 
    More advanced coding
    +
     tmp_file = tempfile(fileext = ".csv")
 suppressMessages( set_output(csv = tmp_file) )
 
@@ -7501,7 +7509,7 @@ 
    More advanced coding
    +
     vr = "AGER"
 var_cross("tmp", "MAJOR", vr)
 ## Warning in var_cross("tmp", "MAJOR", vr): tmp: overwriting a variable that
diff --git a/docs/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html b/docs/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html
index decfb91..fc7517d 100644
--- a/docs/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html
+++ b/docs/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html
@@ -108,7 +108,8 @@ 
    Begin
 library(surveytable)
    
 
    Now, specify the survey that you’d like to analyze.
    
 
    -set_survey(rccsu2018)
    
+set_survey(rccsu2018)
+## * Mode: General.
    
 
    
 
 Survey info {RCC SU 2018 PUF}
@@ -143,6 +144,50 @@ 
    Begin
 
    
 
    Check the survey name, survey design variables, and the number of
 observations to verify that it all looks correct.
    
+
    For this example, we do want to turn on certain NCHS-specific
+options, such as identifying low-precision estimates. If you do not care
+about identifying low-precision estimates, you can skip this command. To
+turn on the NCHS-specific options:
    
+
    +set_mode("NCHS")
+## * Mode: NCHS.
    
+
    Alternatively, you can combine these two commands into a single
+command, like so:
    
+
    +set_survey(rccsu2018, mode = "NCHS")
+## * Mode: NCHS.
    
+
+
++++
+
+
+
+
+
+
+
+
+
+
    
+Survey info {RCC SU 2018 PUF}
+



    
+Variables
+
+Observations
+
+Design
+
    
+81
+
+904
+
+Stratified Independent Sampling design
    svydesign(ids = ~1, strata =
+~pufstrata2 + su_facid, fpc = ~pufpopfac2, 
     weights = ~suwt, data =
+d1)
+
    
 
    
 
    
 
    Figure 1
@@ -150,7 +195,7 @@ 
    Figure 1This figure shows the percentage of residents by sex, race /
 ethnicity, and age group.
    
 
    Sex.
    
-
    +
     tab("sex")
    
 
    
 
@@ -269,7 +314,7 @@ 
    Figure 1
 
    
 
    Race / ethnicity.
    
-
    +
     var_list("race")
    
 
    
 
@@ -301,7 +346,7 @@ 
    Figure 1
 
 
    
-
    +
     tab("raceeth2")
    
 
    
 
@@ -500,7 +545,7 @@ 
    Figure 1In the published figure, the Hispanic and Other categories have been
 merged into a single category called “Another race or ethnicity”. We can
 do that using the var_collapse() function.
    
-
    +
     var_collapse("raceeth2"
              , "Another race or ethnicity"
              , c("Hispanic", "Other"))
@@ -654,7 +699,7 @@ 
    Figure 1
 
    
 
    Age group.
    
-
    +
     var_list("age")
    
 
    
 
@@ -689,7 +734,7 @@ 
    Figure 1age2 is a numeric variable. We need to create a
 categorical variable based on this numeric variable. This is done using
 the var_cut() function.
    
-
    +
     var_cut("Age", "age2"
         , c(-Inf, 64, 74, 84, Inf)
         , c("Under 65", "65-74", "75-84", "85 and over") )
@@ -880,7 +925,7 @@ 
    Figure 2
 
    This figure shows the percentage of residents with Medicaid, overall
 and by age group.
    
-
    +
     tab("medicaid2")
    
 
    
 
@@ -1037,7 +1082,7 @@ 
    Figure 2NA’s from the calculation, use the drop_na
 argument:
    
-
    +
     tab("medicaid2", drop_na = TRUE)
    
 
    
 
@@ -1158,7 +1203,7 @@ 
    Figure 2Note that the table title alerts you to the fact that you are using
 known values only.
    
 
    By age group:
    
-
    +
     tab_subset("medicaid2", "Age", drop_na = TRUE)
    
 
    
 
@@ -1662,7 +1707,7 @@ 
    Figure 4
 
 
    Here’s a table for high blood pressure.
    
-
    +
     tab("hbp")
    
 
    
 
@@ -1815,7 +1860,7 @@ 
    Figure 4Once again, unknown values (NA) are present, while the
 figure is based on knowns only. Therefore, we again will use the
 drop_na argument:
    
-
    +
     tab("hbp", "alz", "depress", "arth", "diabetes", "heartdise", "osteo"
     , "copd", "stroke", "cancer"
     , drop_na = TRUE)
    
@@ -2996,7 +3041,7 @@ 
    Advanced variable editing
    This is a data frame where the survey’s variables are
 located
    
 
-
    +
     class(rccsu2018$variables)
 ## [1] "data.frame"
    
 
      
@@ -3008,14 +3053,15 @@ 
      Advanced variable editing
 
      We go through these steps to count how many chronic
 conditions were present.
      
-
      +
       rccsu2018$variables$num_cc = 0
 for (vr in c("hbp", "alz", "depress", "arth", "diabetes", "heartdise", "osteo"
              , "copd", "stroke", "cancer")) {
   idx = which(rccsu2018$variables[,vr])
   rccsu2018$variables$num_cc[idx] = rccsu2018$variables$num_cc[idx] + 1
 }
-set_survey(rccsu2018)
      
+set_survey(rccsu2018, mode = "NCHS")
+## * Mode: NCHS.
      
 
      
 
 Survey info {RCC SU 2018 PUF}
@@ -3053,7 +3099,7 @@ 
      Advanced variable editingvar_cut(), which
 converts numeric variables to categorical (factor)
 variables.
      
-
      +
       var_cut("Number of chronic conditions", "num_cc"
         , c(-Inf, 0, 1, 3, 10, Inf)
         , c("0", "1", "2-3", "4-10", "??"))
@@ -3250,7 +3296,7 @@ 
      Figure 3
 
 
      Here’s a table for bathhlp (help with bathing):
      
-
      +
       tab("bathhlp")
      
 
      
 
@@ -3492,7 +3538,7 @@ 
      Figure 3To do this, convert the variable to having 2 levels (needs help /
 does not need help) plus NA (for unknown); then use the
 drop_na argument to base percentages on knowns only.
      
-
      +
       for (vr in c("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp")) {
   var_collapse(vr
     , "Needs assistance"
@@ -4206,7 +4252,7 @@ 
      Figure 3
 
      Now, go through the “advanced variable editing” steps – very similar
 to Figure 4 – to count how many ADLs were present.
      
-
      +
       rccsu2018$variables$num_adl = 0
 for (vr in c("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp")) {
   idx = which(rccsu2018$variables[,vr] %in%
@@ -4215,7 +4261,8 @@ 
      Figure 3      , "BOTH"))
   rccsu2018$variables$num_adl[idx] = rccsu2018$variables$num_adl[idx] + 1
 }
-set_survey(rccsu2018)
      
+set_survey(rccsu2018, mode = "NCHS")
+## * Mode: NCHS.
      
 
      
 
 Survey info {RCC SU 2018 PUF}
@@ -4250,7 +4297,7 @@ 
      Figure 3
 
      For generating the figure, create a categorical variable based on
 num_adl, which is numeric.
      
-
      +
       var_cut("Number of ADLs", "num_adl"
         , c(-Inf, 0, 2, 6, Inf)
         , c("0", "1-2", "3-6", "??"))
diff --git a/docs/articles/surveytable.html b/docs/articles/surveytable.html
index 60a93a7..725d6ce 100644
--- a/docs/articles/surveytable.html
+++ b/docs/articles/surveytable.html
@@ -250,7 +250,8 @@ 
      Begin analysis
 
      First, specify the survey object that you’d like to analyze.
      
 
      -set_survey(namcs2019sv)
      
+set_survey(namcs2019sv)
+#> * Mode: General.
      
 
      
 
 Survey info {NAMCS 2019 PUF}
@@ -285,6 +286,13 @@ 
      Begin analysis
 
      Check the survey label, survey design variables, and the number of
 observations to verify that it all looks correct.
      
+
      For this example, we do want to turn on certain NCHS-specific
+options, such as identifying low-precision estimates. If you do not care
+about identifying low-precision estimates, you can skip this command. To
+turn on the NCHS-specific options:
      
+
      +set_mode("NCHS")
+#> * Mode: NCHS.
      
 
      
 
      List variables
 
      
@@ -293,7 +301,7 @@ 
      List variablesage, type:
      
-
      +
       var_list("age")
      
 
      
 
@@ -355,7 +363,7 @@ 
      Tabulate categorical and log
 with their standard errors (SEs) and 95% confidence intervals (CIs), and
 percentages, with their SEs and CIs. For example, to tabulate
 AGER, type:
      
-
      +
       tab("AGER")
      
 
      
 
@@ -621,7 +629,7 @@ 
      Tabulate categorical and log
 
      One does not need to do anything extra to perform presentation
 standards checking – it is performed automatically. For example, let’s
 tabulate PAYNOCHG:
      
-
      +
       tab("PAYNOCHG")
      
 
      
 
@@ -758,7 +766,7 @@ 
      Tabulate categorical and log
 missing values (NA). Consider the following variable, which
 is not part of the actual survey, but was constructed specifically for
 this example:
      
-
      +
       tab("SPECCAT.bad")
      
 
      
 
@@ -942,7 +950,7 @@ 
      Tabulate categorical and log
 
      
 
      To calculate percentages based on the non-missing values only, use
 the drop_na argument:
      
-
      +
       tab("SPECCAT.bad", drop_na = TRUE)
      
 
      
 
@@ -1096,7 +1104,7 @@ 
      Tabulate categorical and log
 based only on non-NA values.
      
 
      Multiple tables. Multiple tables can be created with
 a single command:
      
-
      +
       tab("MDDO", "SPECCAT", "MSA")
      
 
      
 
@@ -1484,7 +1492,7 @@ 
      Entire populationtotal() command:
      
-
      +
 
      
 
@@ -1541,7 +1549,7 @@ 
      Subsets or interactions
 
      To create a table of AGER for each value of the variable
 SEX, type:
      
-
      +
       tab_subset("AGER", "SEX")
      
 
      
 
@@ -2040,7 +2048,7 @@ 
      Subsets or interactionsAGER and
 SEX, type:
      
-
      +
       tab_cross("AGER", "SEX")
      
 
      
 
@@ -2496,7 +2504,7 @@ 
      Tabulate numeric variablesNUMMED (number of medications), a
 numeric variable, type:
      
-
      +
       tab("NUMMED")
      
 
      
 
@@ -2541,7 +2549,7 @@ 
      Tabulate numeric variablesNA), the mean, the standard error of the mean (SEM), and
 the standard deviation (SD).
      
 
      Subsetting works too:
      
-
      +
       tab_subset("NUMMED", "AGER")
      
 
      
 
@@ -2686,7 +2694,7 @@ 
      Categorical variables
 
      Consider the relationship between AGER an
 SPECCAT:
      
-
      +
       tab_subset("AGER", "SPECCAT", test = TRUE)
      
 
      
 
@@ -4584,7 +4592,7 @@ 
      Categorical variables
 
      As another example, consider the relationship between
 MRI and SPECCAT:
      
-
      +
       tab_subset("MRI", "SPECCAT", test = TRUE)
      
 
      
 
@@ -5270,7 +5278,7 @@ 
      Numeric variables
      +
       tab_subset("NUMMED", "AGER", test = TRUE)
      
 
      
 
@@ -5676,7 +5684,7 @@ 
      Numeric variables
      +
       tab_subset("NUMMED", "SPECCAT", test = TRUE)
      
 
      
 
@@ -5869,7 +5877,7 @@ 
      Categorical variables (single var
 
      
 
      To test whether any pair of SPECCAT levels is
 statistically similar or different, type:
      
-
      +
       tab("SPECCAT", test = TRUE)
      
 
      
 
@@ -6110,18 +6118,18 @@ 
      Calculate ratesuspop2019 that contains several population
 figures for use in these examples.
      
 
      Let’s examine uspop2019:
      
-
      +
       class(uspop2019)
 #> [1] "list"
 names(uspop2019)
 #> [1] "total"       "MSA"         "AGER"        "Age group"   "SEX"        
 #> [6] "AGER x SEX"  "Age group 5"
      
 
      The overall population size for the country as a whole is:
      
-
      +
       uspop2019$total
 #> [1] 323186697
      
 
      Once we have the overall population size, the overall rate is:
      
-
      +
       total_rate(uspop2019$total)
      
 
      
 
@@ -6178,7 +6186,7 @@ 
      Calculate ratesPopulation that gives the size of the population for that
 level.
      
 
      For example, for AGER, this data frame as follows:
      
-
      +
 
      Now that we have the appropriate population figures, the rates table
 is obtained by typing:
      
-
      +
       tab_rate("AGER", uspop2019$AGER)
      
 
      
 
@@ -6350,7 +6358,7 @@ 
      Calculate ratesTo calculate the rates for one variable (AGER) by
 another variable (SEX), we need population figures in the
 following format:
      
-
      +
 
      With this data frame, the rates table is obtained by typing:
      
-
      +
       tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)
      
 
      
 
@@ -6690,7 +6698,7 @@ 
      Create or modify variables
 
      Convert factor to logical. The variable
 MAJOR (major reason for this visit) has several levels.
      
-
      +
       tab("MAJOR")
      
 
      
 
@@ -6975,7 +6983,7 @@ 
      Create or modify variablesPreventive care visits that is TRUE for
 preventive care visits and FALSE for all other types of
 visits, as follows:
      
-
      +
       var_case("Preventive care visits", "MAJOR", "Preventive care")
 tab("Preventive care visits")
      
 
@@ -7103,7 +7111,7 @@ 
      Create or modify variablesThus, if an analyst is interested in surgery-related visits, which
 are indicated by two different levels of MAJOR, they could
 type:
      
-
      +
       var_case("Surgery-related visits"
   , "MAJOR"
   , c("Pre-surgery", "Post-surgery"))
@@ -7227,7 +7235,7 @@ 
      Create or modify variablesCollapse levels. The variable PRIMCARE
 (whether the physician is this patient’s primary care provider) has
 levels Unknown and Blank, among others.
      
-
      +
       tab("PRIMCARE")
      
 
      
 
@@ -7425,7 +7433,7 @@ 
      Create or modify variables
 
      To collapse Unknown and Blank into a single
 level, type:
      
-
      +
       var_collapse("PRIMCARE", "Unknown if PCP", c("Unknown", "Blank"))
 tab("PRIMCARE")
      
 
@@ -7578,7 +7586,7 @@ 
      Create or modify variables
 
      Convert numeric to factor. The variable
 AGE is numeric.
      
-
      +
       tab("AGE")
      
 
      
 
@@ -7619,7 +7627,7 @@ 
      Create or modify variables
 
      To create a new variable of age categories based on AGE,
 type:
      
-
      +
       var_cut("Age group"
    , "AGE"
    , c(-Inf, -0.1, 0, 4, 14, 64, Inf)
@@ -7861,7 +7869,7 @@ 
      Create or modify variablesMRI
 and XRAY. To create the Imaging services
 variable, type:
      
-
      +
       var_any("Imaging services"
   , c("ANYIMAGE", "BONEDENS", "CATSCAN", "ECHOCARD", "OTHULTRA"
   , "MAMMO", "MRI", "XRAY", "OTHIMAGE"))
@@ -7986,14 +7994,14 @@ 
      Create or modify variablesvar_cross() command:
      
-
      +
       var_cross("Age x Sex", "AGER", "SEX")
      
 
      Specify the name of the new variable as well as names of the two
 variables to interact.
      
 
      Copy a variable. Create a new variable that is a
 copy of another variable using var_copy(). You can modify
 the copy, while the original remains unchanged. For example:
      
-
      +
       var_copy("Age group", "AGER")
 #> Warning in var_copy("Age group", "AGER"): Age group: overwriting a variable
 #> that already exists.
@@ -8437,9 +8445,9 @@ 
      Save the outputset_output() function. For example, the following directs
 surveytable to send all future output to a CSV file, create
 some tables, and then turn off sending output to the file:
      
-
      -set_output(csv = "output.csv")
      
 
      +set_output(csv = "output.csv")
      
+
       tab("MDDO", "SPECCAT", "MSA")
      
 
      
 
@@ -8822,7 +8830,7 @@ 
      Save the output
 
 
      
-
      +
       set_output(csv = "")
 #> * Turning off CSV output.
 #> * ?set_output for other options.
      
diff --git a/docs/authors.html b/docs/authors.html
index c620960..a904d67 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -67,18 +67,18 @@ 
      Authors
      
 
     
      
       
      Citation
      
-      
      Source: DESCRIPTION
      
+      
      Source: inst/CITATION
      
 
       
      Strashny A (2023).
 surveytable: Formatted Survey Estimates.
-R package version 0.9.4.9000, https://github.com/CDCgov/surveytable, https://cdcgov.github.io/surveytable/. 
+doi:10.32614/CRAN.package.surveytable, https://cdcgov.github.io/surveytable/. 
 
      
       
      @Manual{,
   title = {surveytable: Formatted Survey Estimates},
   author = {Alex Strashny},
   year = {2023},
-  note = {R package version 0.9.4.9000, https://github.com/CDCgov/surveytable},
   url = {https://cdcgov.github.io/surveytable/},
+  doi = {10.32614/CRAN.package.surveytable},
 }
      
     
      
   
      
 
      
 
 Survey info {NAMCS 2019 PUF}
@@ -188,16 +189,16 @@ 
      Example
 
      
-Number (000)
+Number
 
 
-SE (000)
+SE
 
 
-LL (000)
+LL
 
 
-UL (000)
+UL
 
 
 Percent
@@ -220,16 +221,16 @@ 
      Example
 
      		
-117,917
+117,916,772
 
 
-14,097
+14,097,315
 
 
-93,229
+93,228,928
 
 
-149,142
+149,142,177
 
 
 11.4
@@ -252,16 +253,16 @@ 
      Example
 
      		
-64,856
+64,855,698
 
 
-7,018
+7,018,359
 
 
-52,387
+52,386,950
 
 
-80,292
+80,292,164
 
 
 6.3
@@ -284,16 +285,16 @@ 
      Example
 
      		
-170,271
+170,270,604
 
 
-13,966
+13,965,978
 
 
-144,925
+144,924,545
 
 
-200,049
+200,049,472
 
 
 16.4
@@ -316,16 +317,16 @@ 
      Example
 
      		
-309,506
+309,505,956
 
 
-23,290
+23,289,827
 
 
-266,994
+266,994,092
 
 
-358,787
+358,786,727
 
 
 29.9
@@ -348,16 +349,16 @@ 
      Example
 
      		
-206,866
+206,865,982
 
 
-14,366
+14,365,993
 
 
-180,481
+180,480,708
 
 
-237,109
+237,108,637
 
 
 20  
@@ -380,16 +381,16 @@ 
      Example
 
      		
-167,069
+167,069,344
 
 
-15,179
+15,179,082
 
 
-139,746
+139,746,193
 
 
-199,735
+199,734,713
 
 
 16.1
@@ -406,7 +407,7 @@ 
      Example
 
      
 
-N = 8250. Checked NCHS presentation standards. Nothing to report.
+N = 8250.
 
 
      
 
      
@@ -422,7 +423,7 @@ 
      Example
 
 
    1. Sample size
      2. 
-
    2. Whether any low-precision estimates were found
      3. 
+
    3. Optionally, the table can show whether any low-precision estimates were found
      4. 
 
 
 
      
diff --git a/docs/news/index.html b/docs/news/index.html
index b212f5a..bf2a616 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -60,6 +60,7 @@ 
      Advanced
      
     
      
 
      surveytable (development version)
      
 
      
     
      
 
      surveytable 0.9.4
      CRAN release: 2024-05-20
      
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 9f2f035..a79b67e 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -6,7 +6,7 @@ articles:
   Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables: Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html
   Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report: Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html
   surveytable: surveytable.html
-last_built: 2024-07-24T13:51Z
+last_built: 2024-09-05T21:12Z
 urls:
   reference: https://cdcgov.github.io/surveytable/reference
   article: https://cdcgov.github.io/surveytable/articles
diff --git a/docs/reference/codebook.html b/docs/reference/codebook.html
index e05561a..be3883d 100644
--- a/docs/reference/codebook.html
+++ b/docs/reference/codebook.html
@@ -87,6 +87,7 @@ 
      Value
      
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -100,6 +101,7 @@ 
      Examples#> └───────────┴──────────────┴────────────────────────────────────────────────┘
 #> 
 codebook()
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
diff --git a/docs/reference/index.html b/docs/reference/index.html
index acb9d49..6b93579 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -69,7 +69,7 @@ 
      Begin
      
           
-          set_survey() 
+          set_survey() set_mode() 
         
      
         
      Specify the survey to analyze
      
       
      
diff --git a/docs/reference/print.surveytable_table.html b/docs/reference/print.surveytable_table.html
index 5daa0f0..584d660 100644
--- a/docs/reference/print.surveytable_table.html
+++ b/docs/reference/print.surveytable_table.html
@@ -95,6 +95,7 @@ 
      Value
      
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -109,77 +110,73 @@ 
      Examples#> 
 table1 = tab("AGER")
 print(table1)
-#>                                 Patient age recode {NAMCS 2019 PUF}                                 
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │
-#> │ years       │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
-#> │ and over    │       │             │          │          │          │         │     │      │      │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                                     Patient age recode {NAMCS 2019 PUF}                                     
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │
+#> │ years       │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
+#> │ and over    │       │             │            │             │             │         │     │      │      │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 table_many = tab("MDDO", "SPECCAT", "MSA")
 print(table_many)
-#>                              Type of doctor (MD or DO) {NAMCS 2019 PUF}                              
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │           │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ M.D. -      │ 7,498 │     980,280 │   48,388 │  889,842 │ 1,079,910 │    94.6 │ 0.7 │ 93.1 │ 95.8 │
-#> │ Doctor of   │       │             │          │          │           │         │     │      │      │
-#> │ Medicine    │       │             │          │          │           │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ D.O. -      │   752 │      56,204 │    6,602 │   44,597 │    70,832 │     5.4 │ 0.7 │  4.2 │  6.9 │
-#> │ Doctor of   │       │             │          │          │           │         │     │      │      │
-#> │ Osteopathy  │       │             │          │          │           │         │     │      │      │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  
+#>                                  Type of doctor (MD or DO) {NAMCS 2019 PUF}                                 
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ M.D. -      │ 7,498 │ 980,280,219 │ 48,387,921 │ 889,841,831 │ 1,079,910,2 │    94.6 │ 0.7 │ 93.1 │ 95.8 │
+#> │ Doctor of   │       │             │            │             │          43 │         │     │      │      │
+#> │ Medicine    │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ D.O. -      │   752 │  56,204,137 │  6,601,909 │  44,596,891 │  70,832,404 │     5.4 │ 0.7 │  4.2 │  6.9 │
+#> │ Doctor of   │       │             │            │             │             │         │     │      │      │
+#> │ Osteopathy  │       │             │            │             │             │         │     │      │      │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
-#>                   Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                   
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Primary     │ 2,993 │     521,466 │   31,136 │  463,840 │  586,252 │    50.3 │ 2.6 │ 45.1 │ 55.5 │
-#> │ care        │       │             │          │          │          │         │     │      │      │
-#> │ specialty   │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Surgical    │ 3,050 │     214,832 │   31,110 │  161,661 │  285,490 │    20.7 │ 3   │ 15.1 │ 27.3 │
-#> │ care        │       │             │          │          │          │         │     │      │      │
-#> │ specialty   │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Medical     │ 2,207 │     300,186 │   43,497 │  225,806 │  399,067 │    29   │ 3.6 │ 22.1 │ 36.6 │
-#> │ care        │       │             │          │          │          │         │     │      │      │
-#> │ specialty   │       │             │          │          │          │         │     │      │      │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                       Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                       
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Primary     │ 2,993 │ 521,466,378 │ 31,136,212 │ 463,840,192 │ 586,251,877 │    50.3 │ 2.6 │ 45.1 │ 55.5 │
+#> │ care        │       │             │            │             │             │         │     │      │      │
+#> │ specialty   │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Surgical    │ 3,050 │ 214,831,829 │ 31,110,335 │ 161,661,415 │ 285,489,984 │    20.7 │ 3   │ 15.1 │ 27.3 │
+#> │ care        │       │             │            │             │             │         │     │      │      │
+#> │ specialty   │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Medical     │ 2,207 │ 300,186,150 │ 43,496,739 │ 225,806,019 │ 399,066,973 │    29   │ 3.6 │ 22.1 │ 36.6 │
+#> │ care        │       │             │            │             │             │         │     │      │      │
+#> │ specialty   │       │             │            │             │             │         │     │      │      │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
-#>              Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}             
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │           │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ MSA         │ 7,496 │     973,676 │   50,515 │  879,490 │ 1,077,947 │    93.9 │ 1.7 │ 89.7 │ 96.8 │
-#> │ (Metropolit │       │             │          │          │           │         │     │      │      │
-#> │ an          │       │             │          │          │           │         │     │      │      │
-#> │ Statistical │       │             │          │          │           │         │     │      │      │
-#> │ Area)       │       │             │          │          │           │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ Non-MSA     │   754 │      62,809 │   17,549 │   36,249 │   108,830 │     6.1 │ 1.7 │  3.2 │ 10.3 │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  
+#>                 Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}                 
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ MSA         │ 7,496 │ 973,675,566 │ 50,514,928 │ 879,490,192 │ 1,077,947,3 │    93.9 │ 1.7 │ 89.6 │ 96.8 │
+#> │ (Metropolit │       │             │            │             │          34 │         │     │      │      │
+#> │ an          │       │             │            │             │             │         │     │      │      │
+#> │ Statistical │       │             │            │             │             │         │     │      │      │
+#> │ Area)       │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Non-MSA     │   754 │  62,808,790 │ 17,549,184 │  36,248,698 │ 108,829,955 │     6.1 │ 1.7 │  3.2 │ 10.4 │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 
      
     
      
diff --git a/docs/reference/set_count_1k.html b/docs/reference/set_count_1k.html
index d40982b..d433788 100644
--- a/docs/reference/set_count_1k.html
+++ b/docs/reference/set_count_1k.html
@@ -93,6 +93,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -109,26 +110,26 @@ 
      Examples#> * Rounding counts to the nearest integer.
 #> * ?set_count_int for other options.
 total()
-#>                              Total {NAMCS 2019 PUF}                              
-#> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐
-#> │             n │        Number │            SE │            LL │            UL │
-#> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤
-#> │         8,250 │ 1,036,484,356 │    48,836,217 │   945,013,590 │ 1,136,808,860 │
-#> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.              
+#>                        Total {NAMCS 2019 PUF}                       
+#> ┌───────┬───────────────┬────────────┬─────────────┬───────────────┐
+#> │     n │        Number │         SE │          LL │            UL │
+#> ├───────┼───────────────┼────────────┼─────────────┼───────────────┤
+#> │ 8,250 │ 1,036,484,356 │ 48,836,217 │ 945,013,590 │ 1,136,808,860 │
+#> └───────┴───────────────┴────────────┴─────────────┴───────────────┘
+#>   N = 8250.                                                         
 #> 
 
 set_count_1k()
 #> * Rounding counts to the nearest 1,000.
 #> * ?set_count_1k for other options.
 total()
-#>                              Total {NAMCS 2019 PUF}                              
-#> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐
-#> │             n │  Number (000) │      SE (000) │      LL (000) │      UL (000) │
-#> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤
-#> │         8,250 │     1,036,484 │        48,836 │       945,014 │     1,136,809 │
-#> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.              
+#>                   Total {NAMCS 2019 PUF}                  
+#> ┌───────┬──────────────┬──────────┬──────────┬───────────┐
+#> │     n │ Number (000) │ SE (000) │ LL (000) │  UL (000) │
+#> ├───────┼──────────────┼──────────┼──────────┼───────────┤
+#> │ 8,250 │    1,036,484 │   48,836 │  945,014 │ 1,136,809 │
+#> └───────┴──────────────┴──────────┴──────────┴───────────┘
+#>   N = 8250.                                               
 #> 
 
      
     
      
diff --git a/docs/reference/set_output.html b/docs/reference/set_output.html
index fab05da..5190bfa 100644
--- a/docs/reference/set_output.html
+++ b/docs/reference/set_output.html
@@ -122,7 +122,7 @@ 
      Examples#> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
 #> │ and over    │       │             │          │          │          │         │     │      │      │
 #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>   N = 8250.                                                                                         
 #> 
 set_output(csv = "") # Turn off CSV output
 #> * Turning off CSV output.
diff --git a/docs/reference/set_survey.html b/docs/reference/set_survey.html
index ccab419..94c41c6 100644
--- a/docs/reference/set_survey.html
+++ b/docs/reference/set_survey.html
@@ -1,7 +1,9 @@
 
-Specify the survey to analyze — set_survey • surveytableSpecify the survey to analyze — set_survey • surveytable
@@ -56,28 +58,31 @@ 
      Advanced
      
 
      
   
      
       
      Specify the survey to analyze
      
-      Source:       R/set_survey.R
+      Source: R/set_survey.R, R/set_mode.R
       
      set_survey.Rd
      
     
      
 
     
      
-    
      You need to specify a survey before the other functions, such as tab(),
-will work.
      
+    
      You must specify a survey before the other functions, such as tab(),
+will work. To convert a data.frame to a survey object, see survey::svydesign()
+or survey::svrepdesign().
      
     
      
 
     
      
     
      Usage
      
-    
      set_survey(design, opts = "NCHS", csv = getOption("surveytable.csv"))
      
+    
      set_survey(design, mode = "default", csv = getOption("surveytable.csv"))
+
+set_mode(mode = "default")
      
     
      
 
     
      
     
      Arguments
      
     
      design
      
-
      either a survey object (survey.design or svyrep.design) or a
-data.frame for an unweighted survey.
      
+
      either a survey object (created with survey::svydesign() or
+survey::svrepdesign()); or, for an unweighted survey, a data.frame.
      
 
 
-
      opts
      
+
      mode
      
 
      set certain options. See below.
      
 
 
@@ -89,23 +94,23 @@ 
      ArgumentsValue
      
     
 
-
      Info about the survey.
      
+
      set_survey: info about the survey. set_mode: nothing.
      
     
      
     
      
     
      Details
      
-    
      opts:
      • "nchs":
        • Round counts to the nearest 1,000 -- see set_count_1k().
          • 
-
        • Identify low-precision estimates (surveytable.find_lpe option is TRUE).
          • 
-
        • Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation (surveytable.adjust_svyciprop option is TRUE).
          • 
+    
          Optionally, the survey can have an attribute called label, which is the
+long name of the survey. Optionally, each variable in the survey can have an
+attribute called label, which is the variable's long name.
          
+
          If you are not sure what the mode should be, leave it as "default". Here is
+what mode does:
          • "general" or "default":
            • Round counts to the nearest integer -- see set_count_int().
              • 
+
            • Do not look for low-precision estimates.
              • 
+
            • Percentage CI's: use standard Korn-Graubard CI's.
              • 
 
            • 
-
          • "general":
            • Round counts to the nearest integer -- see set_count_int().
              • 
-
            • Do not look for low-precision estimates (surveytable.find_lpe option is FALSE).
              • 
-
            • Percentage CI's: use standard Korn-Graubard CI's (surveytable.adjust_svyciprop option is FALSE).
              • 
+
            • "nchs":
              • Round counts to the nearest 1,000 -- see set_count_1k().
                • 
+
              • Identify low-precision estimates.
                • 
+
              • Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation.
                • 
 
              • 
-
            Optionally, the survey can have an attribute called label, which is the
-long name of the survey.
            
-
            Optionally, each variable in the survey can have an attribute called label,
-which is the variable's long name.
            
-    
      
+
      
     
      
     
      See also
      
     
      Other options: 
@@ -118,6 +123,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -130,6 +136,8 @@ 
      Examples#> │           │              │ , data = namcs2019sv_df)                       │
 #> └───────────┴──────────────┴────────────────────────────────────────────────┘
 #> 
+set_mode("general")
+#> * Mode: General.
 
      
     
      
   
      
 
     
      
-    
      A version of survey::svyciprop() that adjusts for the degrees of freedom when method = "beta".
      
+    
      A version of survey::svyciprop() that adjusts for the degrees of freedom
+when method = "beta".
      
     
      
 
     
      
@@ -110,13 +113,14 @@ 
      Value
      Details
      
     
      Written by Makram Talih in 2019.
      
 
      df_method: for "default", df = degf(design); for "NHIS", df = nrow(design) - 1.
      
-
      To use this function in tabulations, call set_survey() with the opts = "NCHS" argument,
-or type: options(surveytable.adjust_svyciprop = TRUE).
      
+
      To use this function in tabulations, call set_survey() or set_mode() with the
+mode = "NCHS" argument, or type: options(surveytable.adjust_svyciprop = TRUE).
      
     
      
 
     
      
     
      Examples
      
-    
      set_survey(namcs2019sv, opts = "NCHS")
+    
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -129,6 +133,8 @@ 
      Examples#> │           │              │ , data = namcs2019sv_df)                       │
 #> └───────────┴──────────────┴────────────────────────────────────────────────┘
 #> 
+set_mode("NCHS")
+#> * Mode: NCHS.
 tab("AGER")
 #>                                 Patient age recode {NAMCS 2019 PUF}                                 
 #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
@@ -151,6 +157,8 @@ 
      Examples#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
 #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
 #> 
+set_mode("general")
+#> * Mode: General.
 
      
     
      
   
      
diff --git a/docs/reference/total_rate.html b/docs/reference/total_rate.html
index 6af8e47..67de6c7 100644
--- a/docs/reference/total_rate.html
+++ b/docs/reference/total_rate.html
@@ -104,6 +104,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -117,13 +118,13 @@ 
      Examples#> └───────────┴──────────────┴────────────────────────────────────────────────┘
 #> 
 total_rate(uspop2019$total)
-#>                 Total (rate per 100 population) {NAMCS 2019 PUF}                 
-#> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐
-#> │             n │          Rate │            SE │            LL │            UL │
-#> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤
-#> │         8,250 │         320.7 │          15.1 │         292.4 │         351.7 │
-#> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.              
+#> Total (rate per 100 population) {NAMCS 2019 PUF}
+#> ┌───────┬───────┬──────┬───────┬───────┐
+#> │     n │  Rate │   SE │    LL │    UL │
+#> ├───────┼───────┼──────┼───────┼───────┤
+#> │ 8,250 │ 320.7 │ 15.1 │ 292.4 │ 351.7 │
+#> └───────┴───────┴──────┴───────┴───────┘
+#>   N = 8250.                             
 #> 
 
      
     
      
diff --git a/docs/reference/var_all.html b/docs/reference/var_all.html
index 74e1b61..6f43282 100644
--- a/docs/reference/var_all.html
+++ b/docs/reference/var_all.html
@@ -101,6 +101,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -115,17 +116,16 @@ 
      Examples#> 
 var_all("Medicare and Medicaid", c("PAYMCARE", "PAYMCAID"))
 tab("Medicare and Medicaid")
-#>                             Medicare and Medicaid {NAMCS 2019 PUF}                             
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │           │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 8,126 │   1,016,202 │   47,395 │  927,389 │ 1,113,520 │      98 │ 0.5 │ 96.9 │ 98.9 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │   124 │      20,282 │    5,177 │   12,120 │    33,941 │       2 │ 0.5 │  1.1 │  3.1 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to                                    
-#>   report.                                                                                      
+#>                                 Medicare and Medicaid {NAMCS 2019 PUF}                                
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 8,126 │ 1,016,202,0 │ 47,395,074 │ 927,388,977 │ 1,113,520,4 │      98 │ 0.5 │ 96.9 │ 98.9 │
+#> │       │       │          62 │            │             │          92 │         │     │      │      │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │   124 │  20,282,295 │  5,177,254 │  12,120,309 │  33,940,676 │       2 │ 0.5 │  1.1 │  3.1 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                           
 #> 
 
      
     
      
diff --git a/docs/reference/var_any.html b/docs/reference/var_any.html
index 2b6076e..da15fc3 100644
--- a/docs/reference/var_any.html
+++ b/docs/reference/var_any.html
@@ -101,6 +101,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -117,17 +118,15 @@ 
      Examples, c("ANYIMAGE", "BONEDENS", "CATSCAN", "ECHOCARD", "OTHULTRA"
 , "MAMMO", "MRI", "XRAY", "OTHIMAGE"))
 tab("Imaging services")
-#>                               Imaging services {NAMCS 2019 PUF}                               
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │          │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 7,148 │     901,115 │   43,298 │  820,085 │  990,151 │    86.9 │ 1.1 │ 84.6 │ 89.1 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │ 1,102 │     135,369 │   13,574 │  111,134 │  164,890 │    13.1 │ 1.1 │ 10.9 │ 15.4 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to                                   
-#>   report.                                                                                     
+#>                                   Imaging services {NAMCS 2019 PUF}                                   
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 7,148 │ 901,115,076 │ 43,298,146 │ 820,085,161 │ 990,151,291 │    86.9 │ 1.1 │ 84.6 │ 89.1 │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │ 1,102 │ 135,369,280 │ 13,573,736 │ 111,133,847 │ 164,889,838 │    13.1 │ 1.1 │ 10.9 │ 15.4 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                           
 #> 
 
      
     
      
diff --git a/docs/reference/var_case.html b/docs/reference/var_case.html
index c42988c..6597cdd 100644
--- a/docs/reference/var_case.html
+++ b/docs/reference/var_case.html
@@ -106,6 +106,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -121,66 +122,59 @@ 
      Examples
 var_case("Preventive care visits", "MAJOR", "Preventive care")
 tab("Preventive care visits")
-#>                            Preventive care visits {NAMCS 2019 PUF}                            
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │          │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 6,682 │     812,861 │   45,220 │  728,841 │  906,566 │    78.4 │ 1.7 │ 74.9 │ 81.7 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │ 1,568 │     223,624 │   18,520 │  190,068 │  263,103 │    21.6 │ 1.7 │ 18.3 │ 25.1 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to                                   
-#>   report.                                                                                     
+#>                                Preventive care visits {NAMCS 2019 PUF}                                
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 6,682 │ 812,860,686 │ 45,220,483 │ 728,841,389 │ 906,565,549 │    78.4 │ 1.7 │ 74.9 │ 81.7 │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │ 1,568 │ 223,623,671 │ 18,519,789 │ 190,068,005 │ 263,103,441 │    21.6 │ 1.7 │ 18.3 │ 25.1 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                           
 #> 
 
 var_case("Surgery-related visits"
 , "MAJOR"
 , c("Pre-surgery", "Post-surgery"))
 tab("Surgery-related visits")
-#>                             Surgery-related visits {NAMCS 2019 PUF}                            
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │           │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 7,432 │     969,451 │   47,976 │  879,793 │ 1,068,246 │    93.5 │ 0.8 │ 91.9 │ 94.9 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │   818 │      67,034 │    7,810 │   53,273 │    84,348 │     6.5 │ 0.8 │  5.1 │  8.1 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to                                    
-#>   report.                                                                                      
+#>                                Surgery-related visits {NAMCS 2019 PUF}                                
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 7,432 │ 969,450,753 │ 47,976,379 │ 879,792,684 │ 1,068,245,7 │    93.5 │ 0.8 │ 91.9 │ 94.9 │
+#> │       │       │             │            │             │          12 │         │     │      │      │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │   818 │  67,033,604 │  7,810,237 │  53,273,079 │  84,348,494 │     6.5 │ 0.8 │  5.1 │  8.1 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                           
 #> 
 
 var_case("Non-primary"
 , "SPECCAT.bad"
 , c("Surgical care specialty", "Medical care specialty"))
 tab("Non-primary")
-#>                                  Non-primary {NAMCS 2019 PUF}                                 
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │          │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 2,406 │     422,807 │   26,382 │  374,099 │  477,857 │    40.8 │ 2.2 │ 36.5 │ 45.2 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │ 4,194 │     406,216 │   32,643 │  346,937 │  475,622 │    39.2 │ 2.1 │ 35   │ 43.5 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ <N/A> │ 1,650 │     207,462 │   12,458 │  184,378 │  233,436 │    20   │ 0.8 │ 18.5 │ 21.6 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to                                   
-#>   report.                                                                                     
+#>                                      Non-primary {NAMCS 2019 PUF}                                     
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 2,406 │ 422,806,843 │ 26,381,877 │ 374,098,520 │ 477,857,080 │    40.8 │ 2.2 │ 36.5 │ 45.2 │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │ 4,194 │ 406,215,659 │ 32,642,950 │ 346,937,333 │ 475,622,385 │    39.2 │ 2.1 │ 35   │ 43.5 │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ <N/A> │ 1,650 │ 207,461,854 │ 12,457,774 │ 184,377,795 │ 233,436,032 │    20   │ 0.8 │ 18.5 │ 21.6 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                           
 #> 
 tab("Non-primary", drop_na = TRUE)
-#>                           Non-primary (knowns only) {NAMCS 2019 PUF}                          
-#> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │       │       │       (000) │          │          │          │         │     │      │      │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ FALSE │ 2,406 │     422,807 │   26,382 │  374,099 │  477,857 │      51 │ 2.6 │ 45.7 │ 56.3 │
-#> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ TRUE  │ 4,194 │     406,216 │   32,643 │  346,937 │  475,622 │      49 │ 2.6 │ 43.7 │ 54.3 │
-#> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 6600. Checked NCHS presentation standards. Nothing to                                   
-#>   report.                                                                                     
+#>                               Non-primary (knowns only) {NAMCS 2019 PUF}                              
+#> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ FALSE │ 2,406 │ 422,806,843 │ 26,381,877 │ 374,098,520 │ 477,857,080 │      51 │ 2.6 │ 45.7 │ 56.3 │
+#> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ TRUE  │ 4,194 │ 406,215,659 │ 32,642,950 │ 346,937,333 │ 475,622,385 │      49 │ 2.6 │ 43.7 │ 54.3 │
+#> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 6600.                                                                                           
 #> 
 
      
     
      
diff --git a/docs/reference/var_collapse.html b/docs/reference/var_collapse.html
index 12d13b5..b80f0f2 100644
--- a/docs/reference/var_collapse.html
+++ b/docs/reference/var_collapse.html
@@ -102,6 +102,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -115,37 +116,34 @@ 
      Examples#> └───────────┴──────────────┴────────────────────────────────────────────────┘
 #> 
 tab("PRIMCARE")
-#>                      Are you the patient's primary care provider? {NAMCS 2019 PUF}                     
-#> ┌─────────┬───────┬────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┬───────┐
-#> │ Level   │     n │     Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ Flags │
-#> │         │       │      (000) │          │          │          │         │     │      │      │       │
-#> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤
-#> │ Blank   │    16 │      1,150 │      478 │      440 │    3,005 │     0.1 │ 0   │  0   │  0.2 │ Cx    │
-#> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤
-#> │ Unknown │   300 │     39,519 │    9,507 │   24,520 │   63,692 │     3.8 │ 0.9 │  2.3 │  6   │       │
-#> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤
-#> │ Yes     │ 2,278 │    383,481 │   28,555 │  331,362 │  443,798 │    37   │ 2.6 │ 31.9 │ 42.3 │       │
-#> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤
-#> │ No      │ 5,656 │    612,335 │   43,282 │  533,050 │  703,413 │    59.1 │ 2.5 │ 53.9 │ 64.1 │       │
-#> └─────────┴───────┴────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┴───────┘
-#>   N = 8250. Checked NCHS presentation standards: Cx: suppress count                                    
-#>   (and rate).                                                                                          
+#>                      Are you the patient's primary care provider? {NAMCS 2019 PUF}                      
+#> ┌─────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level   │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Blank   │    16 │   1,150,066 │    478,377 │     440,081 │   3,005,475 │     0.1 │ 0   │  0   │  0.2 │
+#> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Unknown │   300 │  39,518,576 │  9,507,422 │  24,519,903 │  63,691,845 │     3.8 │ 0.9 │  2.3 │  6   │
+#> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Yes     │ 2,278 │ 383,480,893 │ 28,554,963 │ 331,361,656 │ 443,797,864 │    37   │ 2.6 │ 31.9 │ 42.3 │
+#> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ No      │ 5,656 │ 612,334,822 │ 43,282,478 │ 533,049,777 │ 703,412,608 │    59.1 │ 2.5 │ 53.9 │ 64.1 │
+#> └─────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                             
 #> 
 var_collapse("PRIMCARE", "Unknown if PCP", c("Blank", "Unknown"))
 tab("PRIMCARE")
-#>                    Are you the patient's primary care provider? {NAMCS 2019 PUF}                    
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Unknown if  │   316 │      40,669 │    9,479 │   25,619 │   64,560 │     3.9 │ 0.9 │  2.4 │  6.1 │
-#> │ PCP         │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Yes         │ 2,278 │     383,481 │   28,555 │  331,362 │  443,798 │    37   │ 2.6 │ 31.9 │ 42.3 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ No          │ 5,656 │     612,335 │   43,282 │  533,050 │  703,413 │    59.1 │ 2.5 │ 53.9 │ 64.1 │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                        Are you the patient's primary care provider? {NAMCS 2019 PUF}                        
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Unknown if  │   316 │  40,668,642 │  9,478,963 │  25,618,707 │  64,559,793 │     3.9 │ 0.9 │  2.4 │  6.1 │
+#> │ PCP         │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Yes         │ 2,278 │ 383,480,893 │ 28,554,963 │ 331,361,656 │ 443,797,864 │    37   │ 2.6 │ 31.9 │ 42.3 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ No          │ 5,656 │ 612,334,822 │ 43,282,478 │ 533,049,777 │ 703,412,608 │    59.1 │ 2.5 │ 53.9 │ 64.1 │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 
      
     
      
diff --git a/docs/reference/var_copy.html b/docs/reference/var_copy.html
index 4b35d14..1919ece 100644
--- a/docs/reference/var_copy.html
+++ b/docs/reference/var_copy.html
@@ -101,6 +101,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -117,42 +118,40 @@ 
      Examplesvar_collapse("Age group", "65+", c("65-74 years", "75 years and over"))
 var_collapse("Age group", "25-64", c("25-44 years", "45-64 years"))
 tab("AGER", "Age group")
-#>                                 Patient age recode {NAMCS 2019 PUF}                                 
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │
-#> │ years       │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
-#> │ and over    │       │             │          │          │          │         │     │      │      │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                                     Patient age recode {NAMCS 2019 PUF}                                     
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │
+#> │ years       │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │
+#> │ and over    │       │             │            │             │             │         │     │      │      │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
-#>                                      Age group {NAMCS 2019 PUF}                                     
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │
-#> │ years       │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 25-64       │ 3,718 │     479,777 │   32,175 │  420,624 │  547,247 │    46.3 │ 1.8 │ 42.7 │ 49.9 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65+         │ 3,103 │     373,935 │   24,523 │  328,777 │  425,296 │    36.1 │ 1.9 │ 32.3 │ 40   │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                                          Age group {NAMCS 2019 PUF}                                         
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │
+#> │ years       │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 25-64       │ 3,718 │ 479,776,560 │ 32,174,693 │ 420,624,423 │ 547,247,222 │    46.3 │ 1.8 │ 42.7 │ 49.9 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65+         │ 3,103 │ 373,935,326 │ 24,522,516 │ 328,776,878 │ 425,296,417 │    36.1 │ 1.9 │ 32.3 │ 40   │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 
      
     
diff --git a/docs/reference/var_cross.html b/docs/reference/var_cross.html
index 62cb9f0..0762532 100644
--- a/docs/reference/var_cross.html
+++ b/docs/reference/var_cross.html
@@ -105,6 +105,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -119,55 +120,54 @@ 
      Examples#> 
 var_cross("Age x Sex", "AGER", "SEX")
 tab("Age x Sex")
-#>                                      Age x Sex {NAMCS 2019 PUF}                                     
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 15    │   434 │      59,958 │    7,206 │   47,318 │   75,974 │     5.8 │ 0.7 │  4.5 │  7.3 │
-#> │ years:      │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-24       │   346 │      41,128 │    4,532 │   33,066 │   51,156 │     4   │ 0.4 │  3.2 │  4.9 │
-#> │ years:      │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 25-44       │   923 │     113,708 │   11,461 │   93,256 │  138,646 │    11   │ 1   │  9   │ 13.2 │
-#> │ years:      │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 45-64       │ 1,253 │     175,978 │   16,009 │  147,153 │  210,450 │    17   │ 1.1 │ 14.9 │ 19.3 │
-#> │ years:      │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65-74       │   891 │     120,099 │   11,066 │  100,171 │  143,992 │    11.6 │ 1   │  9.7 │ 13.7 │
-#> │ years:      │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 75 years    │   762 │      94,173 │   11,085 │   74,682 │  118,751 │     9.1 │ 0.9 │  7.3 │ 11.1 │
-#> │ and over:   │       │             │          │          │          │         │     │      │      │
-#> │ Female      │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 15    │   453 │      57,959 │    7,728 │   44,570 │   75,371 │     5.6 │ 0.7 │  4.3 │  7.2 │
-#> │ years: Male │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-24       │   196 │      23,728 │    4,344 │   16,457 │   34,210 │     2.3 │ 0.4 │  1.6 │  3.2 │
-#> │ years: Male │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 25-44       │   512 │      56,562 │    7,277 │   43,861 │   72,942 │     5.5 │ 0.6 │  4.3 │  6.8 │
-#> │ years: Male │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 45-64       │ 1,030 │     133,528 │   12,956 │  110,319 │  161,619 │    12.9 │ 1   │ 10.9 │ 15.1 │
-#> │ years: Male │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65-74       │   770 │      86,766 │    6,767 │   74,409 │  101,176 │     8.4 │ 0.6 │  7.2 │  9.7 │
-#> │ years: Male │       │             │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 75 years    │   680 │      72,896 │    6,661 │   60,872 │   87,296 │     7   │ 0.6 │  5.9 │  8.3 │
-#> │ and over:   │       │             │          │          │          │         │     │      │      │
-#> │ Male        │       │             │          │          │          │         │     │      │      │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                                          Age x Sex {NAMCS 2019 PUF}                                         
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 15    │   434 │  59,957,823 │  7,205,594 │  47,318,228 │  75,973,693 │     5.8 │ 0.7 │  4.5 │  7.3 │
+#> │ years:      │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-24       │   346 │  41,128,003 │  4,532,466 │  33,065,609 │  51,156,253 │     4   │ 0.4 │  3.2 │  4.9 │
+#> │ years:      │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 25-44       │   923 │ 113,708,461 │ 11,461,189 │  93,256,445 │ 138,645,797 │    11   │ 1   │  9   │ 13.2 │
+#> │ years:      │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 45-64       │ 1,253 │ 175,978,133 │ 16,008,541 │ 147,152,826 │ 210,449,940 │    17   │ 1.1 │ 14.8 │ 19.3 │
+#> │ years:      │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65-74       │   891 │ 120,099,493 │ 11,066,146 │ 100,171,315 │ 143,992,203 │    11.6 │ 1   │  9.7 │ 13.7 │
+#> │ years:      │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 75 years    │   762 │  94,173,155 │ 11,085,372 │  74,682,310 │ 118,750,789 │     9.1 │ 0.9 │  7.3 │ 11.1 │
+#> │ and over:   │       │             │            │             │             │         │     │      │      │
+#> │ Female      │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 15    │   453 │  57,958,950 │  7,727,594 │  44,569,688 │  75,370,504 │     5.6 │ 0.7 │  4.3 │  7.2 │
+#> │ years: Male │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-24       │   196 │  23,727,695 │  4,343,932 │  16,457,071 │  34,210,431 │     2.3 │ 0.4 │  1.6 │  3.2 │
+#> │ years: Male │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 25-44       │   512 │  56,562,143 │  7,276,983 │  43,860,836 │  72,941,520 │     5.5 │ 0.6 │  4.3 │  6.8 │
+#> │ years: Male │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 45-64       │ 1,030 │ 133,527,822 │ 12,956,239 │ 110,319,199 │ 161,619,006 │    12.9 │ 1   │ 10.9 │ 15.1 │
+#> │ years: Male │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65-74       │   770 │  86,766,489 │  6,766,876 │  74,409,284 │ 101,175,865 │     8.4 │ 0.6 │  7.2 │  9.7 │
+#> │ years: Male │       │             │            │             │             │         │     │      │      │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 75 years    │   680 │  72,896,189 │  6,660,855 │  60,871,965 │  87,295,593 │     7   │ 0.6 │  5.9 │  8.3 │
+#> │ and over:   │       │             │            │             │             │         │     │      │      │
+#> │ Male        │       │             │            │             │             │         │     │      │      │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 
      
     
diff --git a/docs/reference/var_cut.html b/docs/reference/var_cut.html
index fa32bec..a286070 100644
--- a/docs/reference/var_cut.html
+++ b/docs/reference/var_cut.html
@@ -106,6 +106,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
@@ -126,22 +127,21 @@ 
      Examples  , c(-Inf, -0.1, 0, 4, 14, 64, Inf)
   , c(NA, "Under 1", "1-4", "5-14", "15-64", "65 and over"))
 tab("Age group")
-#>                                      Age group {NAMCS 2019 PUF}                                     
-#> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐
-#> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │
-#> │             │       │       (000) │          │          │          │         │     │      │      │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ Under 1     │   203 │      31,148 │    5,282 │   22,269 │   43,566 │     3   │ 0.5 │  2.1 │  4.1 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 1-4         │   281 │      38,240 │    5,444 │   28,864 │   50,662 │     3.7 │ 0.5 │  2.7 │  4.8 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 5-14        │   403 │      48,529 │    5,741 │   38,430 │   61,282 │     4.7 │ 0.5 │  3.7 │  5.9 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 15-64       │ 4,260 │     544,632 │   36,082 │  478,254 │  620,223 │    52.5 │ 2   │ 48.6 │ 56.5 │
-#> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤
-#> │ 65 and over │ 3,103 │     373,935 │   24,523 │  328,777 │  425,296 │    36.1 │ 1.9 │ 32.3 │ 40   │
-#> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘
-#>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                 
+#>                                          Age group {NAMCS 2019 PUF}                                         
+#> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐
+#> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ Under 1     │   203 │  31,147,553 │  5,281,607 │  22,269,146 │  43,565,662 │     3   │ 0.5 │  2.1 │  4.1 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 1-4         │   281 │  38,240,087 │  5,443,933 │  28,863,791 │  50,662,237 │     3.7 │ 0.5 │  2.7 │  4.8 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 5-14        │   403 │  48,529,132 │  5,741,214 │  38,429,869 │  61,282,455 │     4.7 │ 0.5 │  3.7 │  5.9 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 15-64       │ 4,260 │ 544,632,258 │ 36,082,093 │ 478,254,001 │ 620,223,345 │    52.5 │ 2   │ 48.6 │ 56.5 │
+#> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤
+#> │ 65 and over │ 3,103 │ 373,935,326 │ 24,522,516 │ 328,776,878 │ 425,296,417 │    36.1 │ 1.9 │ 32.3 │ 40   │
+#> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘
+#>   N = 8250.                                                                                                 
 #> 
 
      
     
diff --git a/docs/reference/var_list.html b/docs/reference/var_list.html
index 39af02d..22f279e 100644
--- a/docs/reference/var_list.html
+++ b/docs/reference/var_list.html
@@ -91,6 +91,7 @@ 
      Value
      
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
diff --git a/docs/reference/var_not.html b/docs/reference/var_not.html
index aa382b6..cc6f6a8 100644
--- a/docs/reference/var_not.html
+++ b/docs/reference/var_not.html
@@ -98,6 +98,7 @@ 
      See also
     
      Examples
      
     
      set_survey(namcs2019sv)
+#> * Mode: General.
 #>                         Survey info {NAMCS 2019 PUF}                         
 #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐
 #> │ Variables │ Observations │ Design                                         │
diff --git a/docs/search.json b/docs/search.json
index 891b28d..d7541d6 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"subsetting-a-survey","dir":"Articles","previous_headings":"","what":"Subsetting a survey","title":"Advanced topics","text":"Consider example, estimate number medications age group: Survey info {NAMCS 2019 PUF} Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} ’d like estimate thing, visits NUMMED > 0? One way create another survey object NUMMED > 0, analyze new survey object. Survey info {NAMCS 2019 PUF: NUMMED 1+} Note called set_survey(), let R know now want analyze new object newsurvey, namcs2019sv. Now, let’s create table: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF: NUMMED 1+} sure check table title verify tabulating new survey object.","code":"library(surveytable) set_survey(namcs2019sv) tab_subset(\"NUMMED\", \"AGER\") newsurvey = survey_subset(namcs2019sv, NUMMED > 0   , label = \"NAMCS 2019 PUF: NUMMED 1+\") set_survey(newsurvey) tab_subset(\"NUMMED\", \"AGER\")"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"advanced-variable-editing","dir":"Articles","previous_headings":"Advanced variable editing and data flow","what":"Advanced variable editing","title":"Advanced topics","text":"First, let’s review call “advanced variable editing”. surveytable provides number functions create modify survey variables. examples include var_collapse() var_cut(). Occasionally, might need advanced variable editing. ’s : Every survey object element called variables data frame survey’s variables located Create new variable variables data frame (part survey object). Call set_survey() . time modify variables data frame, call set_survey(). Tabulate new variable. example , see vignette(\"Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report\").","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"data-flow","dir":"Articles","previous_headings":"Advanced variable editing and data flow","what":"Data flow","title":"Advanced topics","text":"explanation raises question set_survey() must called , variables modified. explanation: survey ’re analyzing actually exists three separate places: file computer data storage contains survey object. example, RDS file hard disk drive contains survey object named something like mysurvey.rds. survey object R’s global environment, named something like mysurvey. hidden copy survey object ’s used surveytable. surveytable analyzes. (3) ’s different (2), might ask. ’s due arcane issue R packages work – (2) (3) necessary. Normally, information flows forwards, (1) (2) (2) (3). Forwards flow: Going (1) (2): call readRDS(). Going (2) (3): call set_survey(). Backwards flow: Going (3) (2): probably don’t need , see . really need , call surveytable:::.load_survey(). Going (2) (1): call saveRDS(). Normally, probably don’t want . Normally, survey file (mysurvey.rds) probably changed. functions modifying creating variables part surveytable package (like var_cut() var_collapse()) modify (3). Since (3) surveytable works tabulates, can call var_collapse(), can call tab(). don’t need anything extra . modifying variables data frame directly, actually modifying (2). modify (2), need copy (3), surveytable can use . calling set_survey(). Thus, time modify variables , call set_survey(). modify (2), copy (2) -> (3) calling set_survey(). flip side, changes make (3) (using surveytable functions like var_cut() var_collapse()) reflected (2). make changes (3), call set_survey(), changes lost, set_survey() copies (2) -> (3). changes important, can just rerun code created . really need go (3) (2), use mysurvey = surveytable:::.load_survey().","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. omitting message . Now, specify survey ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) set_survey(namcs2019sv)"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages","dir":"Articles","previous_headings":"Table 1","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows overall estimated count well counts percentages type doctor, physician specialty, metropolitan statistical area. variables necessary creating table already survey, making commands straightforward. Total {NAMCS 2019 PUF} Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"total() tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates","dir":"Articles","previous_headings":"Table 1","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"published table also shows several rates. calculate rates, addition survey, need source information population estimates. typically use function read.csv() load population estimates get correct format. surveytable package comes object called uspop2019 contains several population estimates use examples. overall population estimate: overall population estimate, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame variable called Level matches levels variable survey, variable called Population gives population size (assumed constant rather random variable). MSA, can see levels variables just using tab() command, just . Thus, calculate rates, need data frame follows: Now appropriate population estimates, rate : Metropolitan Statistical Area Status physician location (rate per 100 population) {NAMCS 2019 PUF} can also calculate rates specific variable based entire population: Type doctor (MD ) (rate per 100 population) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) ## [1] \"list\" names(uspop2019) ## [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         ## [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total ## [1] 323186697 total_rate(uspop2019$total) uspop2019$MSA ##                                 Level Population ## 1 MSA (Metropolitan Statistical Area)  277229518 ## 2                             Non-MSA   45957179 tab_rate(\"MSA\", uspop2019$MSA) tab_rate(\"MDDO\", uspop2019$total) ## * Rate based on the entire population. tab_rate(\"SPECCAT\", uspop2019$total) ## * Rate based on the entire population."},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages-1","dir":"Articles","previous_headings":"Table 3","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table presents estimates age group, well age group sex. Variables beginning ‘age’ {NAMCS 2019 PUF} survey couple relevant age-related variables. AGE patient age years. AGER categorical variable based AGE. However, table, addition AGER, need another age group variable, different age categories. create using var_cut function. Now ’ve created Age group variable, can create tables: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} Patient sex {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}","code":"var_list(\"age\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"AGER\", \"Age group\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates-1","dir":"Articles","previous_headings":"Table 3","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Patient age recode (rate per 100 population) {NAMCS 2019 PUF} Age group (rate per 100 population) {NAMCS 2019 PUF} Patient sex (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population estimates following format: population estimates, rates : Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"tab_rate(\"AGER\", uspop2019$AGER) tab_rate(\"Age group\", uspop2019$`Age group`) ## * Population for some levels not defined: 15-64 tab_rate(\"SEX\", uspop2019$SEX) uspop2019$`AGER x SEX` ##                Level Subset Population ## 1     Under 15 years Female   29604762 ## 2        15-24 years Female   20730118 ## 3        25-44 years Female   43192143 ## 4        45-64 years Female   42508901 ## 5        65-74 years Female   16673240 ## 6  75 years and over Female   12421444 ## 7     Under 15 years   Male   30921894 ## 8        15-24 years   Male   20988582 ## 9        25-44 years   Male   42407267 ## 10       45-64 years   Male   40053148 ## 11       65-74 years   Male   14586962 ## 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-5","dir":"Articles","previous_headings":"","what":"Table 5","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table gives expected sources payment. use PAY* variables create several new variables required table. Note PAY* variables logical (TRUE FALSE), simplifies workflow. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Expected source payment visit: Private insurance {NAMCS 2019 PUF} Expected source payment visit: Medicare {NAMCS 2019 PUF} Expected source payment visit: Medicaid CHIP state-based program {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} insurance {NAMCS 2019 PUF} Self-pay {NAMCS 2019 PUF} charge {NAMCS 2019 PUF} Expected source payment visit: Workers Compensation {NAMCS 2019 PUF} Expected source payment visit: {NAMCS 2019 PUF} Unknown blank {NAMCS 2019 PUF} Check presentation standards flags! NCHS presentation standards rules, estimates shown.","code":"# var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\"))  # var_any(\"Payment used\", c(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\"   , \"PAYWKCMP\", \"PAYOTH\", \"PAYDK\")) var_not(\"No other payment used\", \"Payment used\")  var_all(\"Self-pay\", c(\"PAYSELF\", \"No other payment used\")) var_all(\"No charge\", c(\"PAYNOCHG\", \"No other payment used\")) var_any(\"No insurance\", c(\"Self-pay\", \"No charge\"))  # var_case(\"No pay\", \"NOPAY\", \"No categories marked\") var_any(\"Unknown or blank\", c(\"PAYDK\", \"No pay\"))  ## tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\"   , \"No insurance\", \"Self-pay\", \"No charge\"   , \"PAYWKCMP\", \"PAYOTH\", \"Unknown or blank\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-6","dir":"Articles","previous_headings":"","what":"Table 6","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows primary care provider referral status, prior-visit status. table, “Unknown” “Blank” values collapsed single value. can collapse two levels factor single level using var_collapse function. Now, table: patient’s primary care provider? {NAMCS 2019 PUF} patient referred visit? {NAMCS 2019 PUF} patient seen practice ? {NAMCS 2019 PUF} percentages within subset defined SENBEFOR add 100% – reason, want use tab_subset(), tab_cross(). patient’s primary care provider? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient’s primary care provider? (patient seen practice ? = , new patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = , new patient) {NAMCS 2019 PUF}","code":"var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) var_collapse(\"REFER\", \"Unknown if referred\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\", \"REFER\", \"SENBEFOR\") tab_subset(\"PRIMCARE\", \"SENBEFOR\") tab_subset(\"REFER\", \"SENBEFOR\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-11","dir":"Articles","previous_headings":"","what":"Table 11","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows information Table 3, preventive care visits. , estimates age group, well age group sex, preventive care visits. Let’s create Age group AGE cross AGER SEX create variable called Age x Sex: see possible values MAJOR (Major reason visit), estimate total count preventive care visits: Major reason visit {NAMCS 2019 PUF} create tables age, sex, interaction, limit preventive care visits: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age x Sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} commands similar, differs first variable passed tab_subset() function, code can streamlined loop: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age x Sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Note called inside loop, print() function needs called explicitly.","code":"var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) ## Warning in var_cut(\"Age group\", \"AGE\", c(-Inf, 0, 4, 14, 64, Inf), c(\"Under 1\", ## : Age group: overwriting a variable that already exists. var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"MAJOR\") tab_subset(\"AGER\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age group\", \"MAJOR\", \"Preventive care\") tab_subset(\"SEX\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age x Sex\", \"MAJOR\", \"Preventive care\") for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     print( tab_subset(vr, \"MAJOR\", \"Preventive care\") ) }"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"more-advanced-coding","dir":"Articles","previous_headings":"Table 11","what":"More advanced coding","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"addition, age-sex category, published table shows percentage preventive care visits made primary care physicians. calculate percentages, slightly involved loop needed. code, followed explanation: Since tab_subset() called within loop, wanted print screen, need use print( tab_subset(*) ). Since don’t want print screen, call print() omitted. Since many tables produced, output sent CSV file. , loop goes age, sex, age / sex interaction variables, calling variables vr. MAJOR vr crossed, result stored variable called tmp. Next, inner loop goes levels vr, calling levels lvl. code tabulates SPECCAT (Type specialty – Primary, Medical, Surgical) subset tmp (MAJOR crossed vr) restricted \"Preventive care: \" followed lvl, level vr, “15 years” AGER. Finally, CSV output turned . run code, tables stored CSV file. give idea tables look like, just one tables: Type specialty (Primary, Medical, Surgical) (tmp = Preventive care: 15 years) {NAMCS 2019 PUF} match percentage published table, see “Primary care specialty” row. sure check presentation standards flags.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) )  for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     var_cross(\"tmp\", \"MAJOR\", vr)     for (lvl in levels(surveytable:::env$survey$variables[,vr])) {         tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care: \", lvl))     } } ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. set_output(csv = \"\") ## * Turning off CSV output. ## * ?set_output for other options. vr = \"AGER\" var_cross(\"tmp\", \"MAJOR\", vr) ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. lvl = levels(surveytable:::env$survey$variables[,vr])[1] tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care: \", lvl))"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. Now, specify survey ’d like analyze. Survey info {RCC SU 2018 PUF} Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) set_survey(rccsu2018)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-1","dir":"Articles","previous_headings":"","what":"Figure 1","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents sex, race / ethnicity, age group. Sex. Resident’s gender {RCC SU 2018 PUF} Race / ethnicity. Variables beginning ‘race’ {RCC SU 2018 PUF} Resident’s race/ethnicity {RCC SU 2018 PUF} published figure, Hispanic categories merged single category called “Another race ethnicity”. can using var_collapse() function. Resident’s race/ethnicity {RCC SU 2018 PUF} Age group. Variables beginning ‘age’ {RCC SU 2018 PUF} age2 numeric variable. need create categorical variable based numeric variable. done using var_cut() function. Age {RCC SU 2018 PUF}","code":"tab(\"sex\") var_list(\"race\") tab(\"raceeth2\") var_collapse(\"raceeth2\"              , \"Another race or ethnicity\"              , c(\"Hispanic\", \"Other\")) tab(\"raceeth2\") var_list(\"age\") var_cut(\"Age\", \"age2\"         , c(-Inf, 64, 74, 84, Inf)         , c(\"Under 65\", \"65-74\", \"75-84\", \"85 and over\") ) tab(\"Age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-2","dir":"Articles","previous_headings":"","what":"Figure 2","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents Medicaid, overall age group. Used Medicaid pay services {RCC SU 2018 PUF} can see, observations, value variable unknown (’s missing NA). command calculates percentages based observations, including ones missing (NA) values. However, published figure, percentages based knowns . exclude NA’s calculation, use drop_na argument: Used Medicaid pay services (knowns ) {RCC SU 2018 PUF} Note table title alerts fact using known values . age group: Used Medicaid pay services (Age = 65) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 65-74) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 75-84) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 85 ) (knowns ) {RCC SU 2018 PUF} Note according NCHS presentation criteria, percentages suppressed.","code":"tab(\"medicaid2\") tab(\"medicaid2\", drop_na = TRUE) tab_subset(\"medicaid2\", \"Age\", drop_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-4","dir":"Articles","previous_headings":"","what":"Figure 4","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"(Figure 3 slightly involved, ’ll next.) figure shows percentage residents one select set chronic conditions. addition, shows distribution residents number conditions. ’s table high blood pressure. Resident diagnosed high blood pressure {RCC SU 2018 PUF} , unknown values (NA) present, figure based knowns . Therefore, use drop_na argument: Resident diagnosed high blood pressure (knowns ) {RCC SU 2018 PUF} Resident diagnosed Alzheimer’s/dementia (knowns ) {RCC SU 2018 PUF} Resident diagnosed depression (knowns ) {RCC SU 2018 PUF} Resident diagnosed arthritis (knowns ) {RCC SU 2018 PUF} Resident diagnosed diabetes (knowns ) {RCC SU 2018 PUF} Resident diagnosed heart disease (knowns ) {RCC SU 2018 PUF} Resident diagnosed osteoporosis (knowns ) {RCC SU 2018 PUF} Resident diagnosed COPD (knowns ) {RCC SU 2018 PUF} Resident diagnosed stroke (knowns ) {RCC SU 2018 PUF} Resident diagnosed cancer (knowns ) {RCC SU 2018 PUF}","code":"tab(\"hbp\") tab(\"hbp\", \"alz\", \"depress\", \"arth\", \"diabetes\", \"heartdise\", \"osteo\"     , \"copd\", \"stroke\", \"cancer\"     , drop_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"advanced-variable-editing","dir":"Articles","previous_headings":"Figure 4","what":"Advanced variable editing","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"surveytable provides number functions create modify survey variables. saw couple : var_collapse() var_cut(). Occasionally, might need advanced variable editing. ’s : Every survey object element called variables data frame survey’s variables located Create new variable variables data frame (part survey object). Call set_survey() . time modify variables data frame, call set_survey(). Tabulate new variable. go steps count many chronic conditions present. Survey info {RCC SU 2018 PUF} num_cc numeric variable number chronic conditions. published figure uses categorical variable based numeric variable. Use var_cut(), converts numeric variables categorical (factor) variables. Number chronic conditions {RCC SU 2018 PUF}","code":"class(rccsu2018$variables) ## [1] \"data.frame\" rccsu2018$variables$num_cc = 0 for (vr in c(\"hbp\", \"alz\", \"depress\", \"arth\", \"diabetes\", \"heartdise\", \"osteo\"              , \"copd\", \"stroke\", \"cancer\")) {   idx = which(rccsu2018$variables[,vr])   rccsu2018$variables$num_cc[idx] = rccsu2018$variables$num_cc[idx] + 1 } set_survey(rccsu2018) var_cut(\"Number of chronic conditions\", \"num_cc\"         , c(-Inf, 0, 1, 3, 10, Inf)         , c(\"0\", \"1\", \"2-3\", \"4-10\", \"??\")) tab(\"Number of chronic conditions\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-3","dir":"Articles","previous_headings":"","what":"Figure 3","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents need help one activities daily living (ADLs). addition, shows distribution residents number ADLs need help. ’s table bathhlp (help bathing): Type assistance resident needs bathe {RCC SU 2018 PUF} variable multiple levels. Several levels correspond resident needing help, One level (\"NEED ASSISTANCE\") = need help One level (\"MISSING\") = unknown want show (resident needing help) percentage knowns (, excluding unknowns). , convert variable 2 levels (needs help / need help) plus NA (unknown); use drop_na argument base percentages knowns . Type assistance resident needs bathe (knowns ) {RCC SU 2018 PUF} Type assistance resident needs locomotion (knowns ) {RCC SU 2018 PUF} Type assistance resident needs dress (knowns ) {RCC SU 2018 PUF} Type assistance resident needs transfer /chair (knowns ) {RCC SU 2018 PUF} Type assistance resident needs use bathroom (knowns ) {RCC SU 2018 PUF} Type assistance resident needs eat (knowns ) {RCC SU 2018 PUF} Now, go “advanced variable editing” steps – similar Figure 4 – count many ADLs present. Survey info {RCC SU 2018 PUF} generating figure, create categorical variable based num_adl, numeric. Number ADLs {RCC SU 2018 PUF}","code":"tab(\"bathhlp\") for (vr in c(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\")) {   var_collapse(vr     , \"Needs assistance\"     , c(\"NEED HELP OR SUPERVISION FROM ANOTHER PERSON\"       , \"USE OF AN ASSISTIVE DEVICE\"       , \"BOTH\"))   var_collapse(vr, NA, \"MISSING\") }  tab(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\", drop_na = TRUE) rccsu2018$variables$num_adl = 0 for (vr in c(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\")) {   idx = which(rccsu2018$variables[,vr] %in%     c(\"NEED HELP OR SUPERVISION FROM ANOTHER PERSON\"       , \"USE OF AN ASSISTIVE DEVICE\"       , \"BOTH\"))   rccsu2018$variables$num_adl[idx] = rccsu2018$variables$num_adl[idx] + 1 } set_survey(rccsu2018) var_cut(\"Number of ADLs\", \"num_adl\"         , c(-Inf, 0, 2, 6, Inf)         , c(\"0\", \"1-2\", \"3-6\", \"??\")) tab(\"Number of ADLs\")"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"concepts","dir":"Articles","previous_headings":"Preliminaries","what":"Concepts","title":"Introduction to surveytable","text":"two important concepts need learn distinguish: data frame standard way storing data R. data frame rectangular data. Variables columns, observations rows. Example: data frame, , represent complex survey. , just looking data frame, R know sampling weights , strata , etc. Even variables represent sampling weights, etc, part data frame, just looking data frame, R know variable represents weights survey design variables. can get data frame R many different ways. data currently comma-separated values (CSV) file, can use read.csv(). ’s SAS file, can use package like haven importsurvey. ’s already R format, use readRDS(), . survey object object describes survey. tells R sampling weights , strata , . data frame can converted survey object using survey::svydesign() function; survey uses replicate weights, survey::svrepdesign() function used. Generally speaking, need convert data frame survey object . converted, can save saveRDS() (similar). future, can load readRDS(). need re-convert data frame survey object every time.","code":"head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"namcs","dir":"Articles","previous_headings":"Preliminaries","what":"NAMCS","title":"Introduction to surveytable","text":"Examples tutorial use survey called National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS “annual nationally representative sample survey visits non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, pathologists.” Note unit observation visits, patients – distinction important since single patient can make multiple visits. surveytable package comes data frame selected variables NAMCS, called namcs2019sv_df (sv = selected variables; df = data frame). survey object survey called namcs2019sv. namcs2019sv object analyze. really need namcs2019sv. reason package namcs2019sv_df illustrate convert data frame survey object.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"more-concepts","dir":"Articles","previous_headings":"Preliminaries","what":"More concepts","title":"Introduction to surveytable","text":"importing data another source, SAS CSV, analysts aware standard way variables handled R. Specifically, categorical variables stored factor. true / false variables stored factor well, programming tasks easier stored logical. Unknown values stored missing (NA). variable contains “special values”, negative value indicating age missing, “special values” need converted NA. Variables namcs2019sv_df already stored correctly. Thus, AGER (patient’s age group) factor variable; PAYNOCHG (indicates whether charge physician visit) logical variable; AGE (patient’s age years) numeric variable.","code":"library(\"surveytable\") class(namcs2019sv_df$AGER) #> [1] \"factor\" class(namcs2019sv_df$PAYNOCHG) #> [1] \"logical\" class(namcs2019sv_df$AGE) #> [1] \"numeric\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-a-survey-object","dir":"Articles","previous_headings":"Preliminaries","what":"Create a survey object","title":"Introduction to surveytable","text":"seen , tables produced surveytable clearer either variable names descriptive, variables \"label\" attribute descriptive. namcs2019sv_df, variables already \"label\" attribute set. example, variable name AGE descriptive, variable descriptive \"label\" attribute: Documentation NAMCS survey provides names survey design variables. Specifically, NAMCS, cluster ID’s, also known primary sampling units (PSU’s), given CPSUM; strata given CSTRATM; sampling weights given PATWT. Thus, namcs2019sv_df data frame can turned survey object follows: Tables produced surveytable clearer either name survey object descriptive, object \"label\" attribute descriptive. Let’s set attribute mysurvey: mysurvey object now namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years (raw - use caution)\" mysurvey = survey::svydesign(ids = ~ CPSUM   , strata = ~ CSTRATM   , weights = ~ PATWT   , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" all.equal(namcs2019sv, mysurvey) #> [1] TRUE"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"begin-analysis","dir":"Articles","previous_headings":"","what":"Begin analysis","title":"Introduction to surveytable","text":"First, specify survey object ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey label, survey design variables, number observations verify looks correct.","code":"set_survey(namcs2019sv)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"list-variables","dir":"Articles","previous_headings":"Begin analysis","what":"List variables","title":"Introduction to surveytable","text":"var_list() function lists variables survey. avoid unintentionally listing variables survey, can many, starting characters variable names specified. example, list variables start letters age, type: Variables beginning ‘age’ {NAMCS 2019 PUF} table lists variable name; class, type variable; variable label, long name variable. Common classes factor (categorical variable), logical (yes / variable), numeric.","code":"var_list(\"age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-categorical-and-logical-variables","dir":"Articles","previous_headings":"","what":"Tabulate categorical and logical variables","title":"Introduction to surveytable","text":"main function surveytable package tab(), tabulates variables. operates categorical logical variables, presents estimated counts, standard errors (SEs) 95% confidence intervals (CIs), percentages, SEs CIs. example, tabulate AGER, type: Patient age recode {NAMCS 2019 PUF} table title shows variable label (long variable name) survey label. level variable, table shows: estimated count, standard error, 95% confidence interval; estimated percentage, standard error, 95% confidence interval. NCHS presentation standards. tab() function also applies National Center Health Statistics (NCHS) presentation standards counts percentages, flags estimates , according standards, suppressed, footnoted, reviewed analyst. CIs displayed ones used NCHS presentation standards. Specifically, counts, tables show log Student’s t 95% CI, adaptations complex surveys; percentages, show 95% Korn Graubard CI. One need anything extra perform presentation standards checking – performed automatically. example, let’s tabulate PAYNOCHG: Expected source payment visit: Charge/Charity {NAMCS 2019 PUF} table tells us , according NCHS presentation standards, estimated number visits charge visit suppressed due low precision. However, lack percentage flag indicates estimated percentage visits can shown. Drop missing values. variables might contain missing values (NA). Consider following variable, part actual survey, constructed specifically example: Type specialty (BAD - use) {NAMCS 2019 PUF} calculate percentages based non-missing values , use drop_na argument: Type specialty (BAD - use) (knowns ) {NAMCS 2019 PUF} table gives percentages based knowns, , based non-NA values. Multiple tables. Multiple tables can created single command: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"tab(\"AGER\") tab(\"PAYNOCHG\") tab(\"SPECCAT.bad\") tab(\"SPECCAT.bad\", drop_na = TRUE) tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"entire-population","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Entire population","title":"Introduction to surveytable","text":"Estimate total count entire population using total() command: Total {NAMCS 2019 PUF}","code":"total()"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"subsets-or-interactions","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Subsets or interactions","title":"Introduction to surveytable","text":"create table AGER value variable SEX, type: Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} addition giving long name variable tabulated, title table reflects value subsetting variable (case, either Female Male). tab_subset() command, table (, subset), percentages add 100%. tab_cross() function similar – crosses interacts two variables generates table using new variable. Thus, create table interaction AGER SEX, type: (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} estimated counts produced tab_subset() tab_cross() , percentages different. tab_subset() command, within table (, within subset), percentages add 100%. hand, tab_cross(), percentages across entire population add 100%.","code":"tab_subset(\"AGER\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-numeric-variables","dir":"Articles","previous_headings":"","what":"Tabulate numeric variables","title":"Introduction to surveytable","text":"tab() tab_subset() functions also work numeric variables, though variables, output different. tabulate NUMMED (number medications), numeric variable, type: Number medications coded {NAMCS 2019 PUF} , table title shows variable label (long variable name) survey label. table shows percentage values missing (NA), mean, standard error mean (SEM), standard deviation (SD). Subsetting works : Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF}","code":"tab(\"NUMMED\") tab_subset(\"NUMMED\", \"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"perform-statistical-hypothesis-testing","dir":"Articles","previous_headings":"","what":"Perform statistical hypothesis testing","title":"Introduction to surveytable","text":"tab_subset() function makes easy perform hypothesis testing using test argument. argument TRUE, test association performed. addition, t-tests pairs levels performed well.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables","title":"Introduction to surveytable","text":"Consider relationship AGER SPECCAT: Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association Patient age recode Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15-24 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 25-44 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 45-64 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 65-74 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 75 years ) {NAMCS 2019 PUF} According tables, association physician specialty type patient age. instance, patients 15 years, statistical difference primary care physician specialty medical care specialty. older patients, 45-64 age group, statistical difference two specialty types. another example, consider relationship MRI SPECCAT: MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association MRI Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = FALSE) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = TRUE) {NAMCS 2019 PUF} According tables, statistical association MRI physician specialty. 3 specialty types, minority visits MRI’s. visits MRI’s, statistical difference specialty types. general rule thumb, since statistical association MRI physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate MRI without subsetting SPECCAT.","code":"tab_subset(\"AGER\", \"SPECCAT\", test = TRUE) tab_subset(\"MRI\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"numeric-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Numeric variables","title":"Introduction to surveytable","text":"relationship NUMMED AGER: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} Association Number medications coded Patient age recode {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Patient age recode {NAMCS 2019 PUF} According tables, association number medications age category. NUMMED statistically similar “15 years” “15-24 years” AGER categories. statistically different pairs age categories. Finally, let’s look relationship NUMMED SPECCAT: Number medications coded (different levels Type specialty (Primary, Medical, Surgical)) {NAMCS 2019 PUF} Association Number medications coded Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According tables, association number medications physician specialty type. NUMMED statistically similar pairs physician specialties. general rule thumb, since statistical association number medications physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate NUMMED without subsetting SPECCAT.","code":"tab_subset(\"NUMMED\", \"AGER\", test = TRUE) tab_subset(\"NUMMED\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables-single-variable","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables (single variable)","title":"Introduction to surveytable","text":"test whether pair SPECCAT levels statistically similar different, type: Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According , surgical medical care specialties statistically similar, statistically different primary care.","code":"tab(\"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"calculate-rates","dir":"Articles","previous_headings":"","what":"Calculate rates","title":"Introduction to surveytable","text":"rate ratio count estimates based survey question divided population size, assumed known. example, number physician visits per 100 people population rate: number physician visits estimated namcs2019sv survey, number people population comes another source. calculate rates, addition survey, need source information population size. typically use function read.csv() load population figures get correct format. surveytable package comes object called uspop2019 contains several population figures use examples. Let’s examine uspop2019: overall population size country whole : overall population size, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame column called Level matches levels variable survey, column called Population gives size population level. example, AGER, data frame follows: Now appropriate population figures, rates table obtained typing: Patient age recode (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population figures following format: data frame, rates table obtained typing: Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) #> [1] \"list\" names(uspop2019) #> [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         #> [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total #> [1] 323186697 total_rate(uspop2019$total) uspop2019$AGER #>               Level Population #> 1    Under 15 years   60526656 #> 2       15-24 years   41718700 #> 3       25-44 years   85599410 #> 4       45-64 years   82562049 #> 5       65-74 years   31260202 #> 6 75 years and over   21519680 tab_rate(\"AGER\", uspop2019$AGER) uspop2019$`AGER x SEX` #>                Level Subset Population #> 1     Under 15 years Female   29604762 #> 2        15-24 years Female   20730118 #> 3        25-44 years Female   43192143 #> 4        45-64 years Female   42508901 #> 5        65-74 years Female   16673240 #> 6  75 years and over Female   12421444 #> 7     Under 15 years   Male   30921894 #> 8        15-24 years   Male   20988582 #> 9        25-44 years   Male   42407267 #> 10       45-64 years   Male   40053148 #> 11       65-74 years   Male   14586962 #> 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-or-modify-variables","dir":"Articles","previous_headings":"","what":"Create or modify variables","title":"Introduction to surveytable","text":"situations, might necessary modify survey variables, create new ones. section describes . Convert factor logical. variable MAJOR (major reason visit) several levels. Major reason visit {NAMCS 2019 PUF} Notice one levels called \"Preventive care\". Suppose analyst interested whether visit preventive care visit – interested visit types. can create new variable called Preventive care visits TRUE preventive care visits FALSE types visits, follows: Preventive care visits {NAMCS 2019 PUF} creates logical variable TRUE preventive care visits tabulates . using var_case() function, specify name new logical variable created, existing factor variable, one levels factor variable set TRUE logical variable. Thus, analyst interested surgery-related visits, indicated two different levels MAJOR, type: Surgery-related visits {NAMCS 2019 PUF} Collapse levels. variable PRIMCARE (whether physician patient’s primary care provider) levels Unknown Blank, among others. patient’s primary care provider? {NAMCS 2019 PUF} collapse Unknown Blank single level, type: patient’s primary care provider? {NAMCS 2019 PUF} Convert numeric factor. variable AGE numeric. Patient age years (raw - use caution) {NAMCS 2019 PUF} create new variable age categories based AGE, type: Age group {NAMCS 2019 PUF} var_cut() command, specify following information: name new categorical variable; name existing numeric variable; cut points – note intervals inclusive right; category labels. cognizant “special values” numeric variable might . data systems, negative values indicate unknowns, coded NA. ’s – value -Inf -0.1 gets coded missing (NA). Though particular data, unknowns “special values”. Check whether variable true. series logical variables, can check whether TRUE using var_any() command. physician visit considered “imaging services” visit number imaging services ordered provided. Imaging services indicated using logical variables, MRI XRAY. create Imaging services variable, type: Imaging services {NAMCS 2019 PUF} Interact variables. tab_cross() function creates table interaction two variables, save interacted variable. create interacted variable, use var_cross() command: Specify name new variable well names two variables interact. Copy variable. Create new variable copy another variable using var_copy(). can modify copy, original remains unchanged. example: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} , AGER variable remains unchanged, Age group variable fewer categories.","code":"tab(\"MAJOR\") var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") var_case(\"Surgery-related visits\"   , \"MAJOR\"   , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") tab(\"PRIMCARE\") var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\") tab(\"AGE\") var_cut(\"Age group\"    , \"AGE\"    , c(-Inf, -0.1, 0, 4, 14, 64, Inf)    , c(NA, \"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") var_any(\"Imaging services\"   , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\"   , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") var_cross(\"Age x Sex\", \"AGER\", \"SEX\") var_copy(\"Age group\", \"AGER\") #> Warning in var_copy(\"Age group\", \"AGER\"): Age group: overwriting a variable #> that already exists. var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"save-the-output","dir":"Articles","previous_headings":"","what":"Save the output","title":"Introduction to surveytable","text":"tab* total* functions argument called csv specifies name comma-separated values (CSV) file save output . Alternatively, can name default CSV output file using set_output() function. example, following directs surveytable send future output CSV file, create tables, turn sending output file: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF} tabulation functions called within R Markdown notebook Quarto document, produce HTML LaTeX tables, appropriate. makes easy incorporate output surveytable package directly documents, presentations, “shiny” web apps, output types. Finally, tabulation functions return tables produce. advanced analysts can use functionality integrate surveytable programming tasks.","code":"set_output(csv = \"output.csv\") tab(\"MDDO\", \"SPECCAT\", \"MSA\") set_output(csv = \"\") #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Alex Strashny. Author, maintainer.","code":""},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Strashny (2023). surveytable: Formatted Survey Estimates. R package version 0.9.4.9000, https://github.com/CDCgov/surveytable, https://cdcgov.github.io/surveytable/.","code":"@Manual{,   title = {surveytable: Formatted Survey Estimates},   author = {Alex Strashny},   year = {2023},   note = {R package version 0.9.4.9000, https://github.com/CDCgov/surveytable},   url = {https://cdcgov.github.io/surveytable/}, }"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"survey-table-formatted-survey-estimates","dir":"","previous_headings":"","what":"Formatted Survey Estimates","title":"Formatted Survey Estimates","text":"surveytable R package conveniently tabulating estimates complex surveys. deal survey objects R (created survey::svydesign()), package . Works complex surveys (data systems involve survey design variables, like weights strata). Works unweighted data well. surveytable package provides short understandable commands generate tabulated, formatted, rounded survey estimates. surveytable, can tabulate estimated counts percentages, standard errors confidence intervals, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, perform hypothesis tests, tabulate rates, modify survey variables, save output. tabulation functions identify low-precision estimates using National Center Health Statistics (NCHS) algorithms. surveytable code called R Markdown notebook Quarto document, automatically generates HTML LaTeX tables, appropriate. package reduces number commands users need execute, especially helpful users new R programming.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"Install CRAN: get development version GitHub:","code":"install.packages(\"surveytable\") install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Formatted Survey Estimates","text":"Find documentation surveytable : https://cdcgov.github.io/surveytable/","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Formatted Survey Estimates","text":"basic example, get started. Load package: Specify survey wish analyze. surveytable comes survey called namcs2019sv, use examples. Survey info {NAMCS 2019 PUF} Specify variable analyze. NAMCS, AGER age category variable: Patient age recode {NAMCS 2019 PUF} table shows: Descriptive variable name Survey name Number observations Estimated count SE 95% CI Estimated percentage SE 95% CI Sample size Whether low-precision estimates found","code":"library(surveytable) set_survey(namcs2019sv) tab(\"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"public-domain-standard-notice","dir":"","previous_headings":"","what":"Public Domain Standard Notice","title":"Formatted Survey Estimates","text":"repository constitutes work United States Government subject domestic copyright protection 17 USC § 105. repository public domain within United States, copyright related rights work worldwide waived CC0 1.0 Universal public domain dedication. contributions repository released CC0 dedication. submitting pull request agreeing comply waiver copyright interest.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"license-standard-notice","dir":"","previous_headings":"","what":"License Standard Notice","title":"Formatted Survey Estimates","text":"repository utilizes code licensed terms Apache Software License therefore licensed ASL v2 later. source code repository free: can redistribute /modify terms Apache Software License version 2, (option) later version. source code repository distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See Apache Software License details. received copy Apache Software License along program. , see https://www.apache.org/licenses/LICENSE-2.0.html source code forked open source projects inherit license.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"privacy-standard-notice","dir":"","previous_headings":"","what":"Privacy Standard Notice","title":"Formatted Survey Estimates","text":"repository contains non-sensitive, publicly available data information. material community participation covered Disclaimer Code Conduct. information CDC’s privacy policy, please visit https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"contributing-standard-notice","dir":"","previous_headings":"","what":"Contributing Standard Notice","title":"Formatted Survey Estimates","text":"Anyone encouraged contribute repository forking submitting pull request. 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(Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a codebook for the survey — codebook","title":"Create a codebook for the survey — codebook","text":"Create codebook survey","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a codebook for the survey — codebook","text":"","code":"codebook(all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a codebook for the survey — codebook","text":"tabulate variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a codebook for the survey — codebook","text":"list tables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a codebook for the survey — codebook","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  codebook() #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  #>                                     Codebook {NAMCS 2019 PUF}                                      #> ┌──────────┬─────────────┬───────────────────────┬─────────┬─────────────┬───────────────────────┐ #> │ Item no. │ Variable    │ Description           │ Class   │ Missing (%) │ Values                │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        1 │ CPSUM       │ Masked provider       │ numeric │           0 │ 100001 - 100398       │ #> │          │             │ marker                │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        2 │ CSTRATM     │ Masked sampling       │ numeric │           0 │ 10119101 - 10419115   │ #> │          │             │ stratum from which    │         │             │                       │ #> │          │             │ provider was selected │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        3 │ PATWT       │ Patient visit weight  │ numeric │           0 │ 7064.00718 -          │ #> │          │             │ used for national and │         │             │ 1120996.55599         │ #> │          │             │ subnational estimates │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        4 │ MDDO        │ Type of doctor (MD or │ factor  │           0 │ M.D. - Doctor of      │ #> │          │             │ DO)                   │         │             │ Medicine, D.O. -      │ #> │          │             │                       │         │             │ Doctor of Osteopathy  │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        5 │ SPECCAT     │ Type of specialty     │ factor  │           0 │ Primary care          │ #> │          │             │ (Primary, Medical,    │         │             │ specialty, Surgical   │ #> │          │             │ Surgical)             │         │             │ care specialty,       │ #> │          │             │                       │         │             │ Medical care          │ #> │          │             │                       │         │             │ specialty             │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        6 │ MSA         │ Metropolitan          │ factor  │           0 │ MSA (Metropolitan     │ #> │          │             │ Statistical Area      │         │             │ Statistical Area),    │ #> │          │             │ Status of physician   │         │             │ Non-MSA               │ #> │          │             │ location              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        7 │ AGER        │ Patient age recode    │ factor  │           0 │ Under 15 years, 15-24 │ #> │          │             │                       │         │             │ years, 25-44 years,   │ #> │          │             │                       │         │             │ 45-64 years, 65-74    │ #> │          │             │                       │         │             │ years, 75 years and   │ #> │          │             │                       │         │             │ over                  │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        8 │ SEX         │ Patient sex           │ factor  │           0 │ Female, Male          │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        9 │ AGE         │ Patient age in years  │ numeric │           0 │ 0 - 94                │ #> │          │             │ (raw - use caution)   │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       10 │ NOPAY       │ Expected source of    │ factor  │           0 │ One or more           │ #> │          │             │ payment for visit: No │         │             │ categories marked, No │ #> │          │             │ answer to item        │         │             │ categories marked     │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       11 │ PAYPRIV     │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Private insurance     │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       12 │ PAYMCARE    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Medicare              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       13 │ PAYMCAID    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Medicaid or CHIP or   │         │             │                       │ #> │          │             │ other state-based     │         │             │                       │ #> │          │             │ program               │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       14 │ PAYWKCMP    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Workers Compensation  │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       15 │ PAYOTH      │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Other                 │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       16 │ PAYDK       │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Unknown               │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       17 │ PAYSELF     │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Self-pay              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       18 │ PAYNOCHG    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit: No │         │             │                       │ #> │          │             │ Charge/Charity        │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       19 │ PRIMCARE    │ Are you the patient's │ factor  │           0 │ Blank, Unknown, Yes,  │ #> │          │             │ primary care          │         │             │ No                    │ #> │          │             │ provider?             │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       20 │ REFER       │ Was patient referred  │ factor  │           0 │ Blank, Unknown, Not   │ #> │          │             │ for visit?            │         │             │ applicable, Yes, No   │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       21 │ SENBEFOR    │ Has this patient been │ factor  │           0 │ Yes, established      │ #> │          │             │ seen in your practice │         │             │ patient, No, new      │ #> │          │             │ before?               │         │             │ patient               │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       22 │ MAJOR       │ Major reason for this │ factor  │           0 │ Blank, New problem    │ #> │          │             │ visit                 │         │             │ (less than 3 mos.     │ #> │          │             │                       │         │             │ onset), Chronic       │ #> │          │             │                       │         │             │ problem, routine,     │ #> │          │             │                       │         │             │ Chronic problem,      │ #> │          │             │                       │         │             │ flare-up,             │ #> │          │             │                       │         │             │ Pre-surgery,          │ #> │          │             │                       │         │             │ Post-surgery,         │ #> │          │             │                       │         │             │ Preventive care       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       23 │ NUMMED      │ Number of medications │ numeric │           0 │ 0 - 30                │ #> │          │             │ coded                 │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       24 │ ANYIMAGE    │ Any imaging           │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       25 │ BONEDENS    │ Bone mineral density  │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       26 │ CATSCAN     │ CT Scan               │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       27 │ ECHOCARD    │ Echocardiogram        │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       28 │ OTHULTRA    │ Ultrasound            │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       29 │ MAMMO       │ Mammography           │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       30 │ MRI         │ MRI                   │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       31 │ XRAY        │ X-ray                 │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       32 │ OTHIMAGE    │ Other imaging         │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       33 │ SPECCAT.bad │ Type of specialty     │ factor  │          20 │ Primary care          │ #> │          │             │ (BAD - do not use)    │         │             │ specialty, Surgical   │ #> │          │             │                       │         │             │ care specialty,       │ #> │          │             │                       │         │             │ Medical care          │ #> │          │             │                       │         │             │ specialty             │ #> └──────────┴─────────────┴───────────────────────┴─────────┴─────────────┴───────────────────────┘ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":null,"dir":"Reference","previous_headings":"","what":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"Selected variables data system visits office-based physicians. Note unit observation visits, patients - distinction important since single patient can make multiple visits.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"","code":"namcs2019sv  namcs2019sv_df"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"object class survey.design2 (inherits survey.design) 8250 rows 33 columns. object class data.frame 8250 rows 33 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/namcs2019_sas.zip Survey design variables: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/readme2019-sas.txt SAS formats: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/nam19for.txt Documentation: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2019-508.pdf National Summary Tables: https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"namcs2019sv_df data frame. namcs2019sv survey object created namcs2019sv_df using survey::svydesign().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print surveytable tables — print.surveytable_table","title":"Print surveytable tables — print.surveytable_table","text":"Print surveytable tables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print surveytable tables — print.surveytable_table","text":"","code":"# S3 method for surveytable_table print(x, .output = NULL, ...)  # S3 method for surveytable_list print(x, .output = NULL, ...)"},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print surveytable tables — print.surveytable_table","text":"x object class surveytable_table surveytable_list. .output output type. NULL = auto-detect. ... ignored","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print surveytable tables — print.surveytable_table","text":"x invisibly.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print surveytable tables — print.surveytable_table","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  table1 = tab(\"AGER\") print(table1) #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  table_many = tab(\"MDDO\", \"SPECCAT\", \"MSA\") print(table_many) #>                              Type of doctor (MD or DO) {NAMCS 2019 PUF}                               #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ M.D. -      │ 7,498 │     980,280 │   48,388 │  889,842 │ 1,079,910 │    94.6 │ 0.7 │ 93.1 │ 95.8 │ #> │ Doctor of   │       │             │          │          │           │         │     │      │      │ #> │ Medicine    │       │             │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ D.O. -      │   752 │      56,204 │    6,602 │   44,597 │    70,832 │     5.4 │ 0.7 │  4.2 │  6.9 │ #> │ Doctor of   │       │             │          │          │           │         │     │      │      │ #> │ Osteopathy  │       │             │          │          │           │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                   #>  #>                   Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                    #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Primary     │ 2,993 │     521,466 │   31,136 │  463,840 │  586,252 │    50.3 │ 2.6 │ 45.1 │ 55.5 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Surgical    │ 3,050 │     214,832 │   31,110 │  161,661 │  285,490 │    20.7 │ 3   │ 15.1 │ 27.3 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Medical     │ 2,207 │     300,186 │   43,497 │  225,806 │  399,067 │    29   │ 3.6 │ 22.1 │ 36.6 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  #>              Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}              #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ MSA         │ 7,496 │     973,676 │   50,515 │  879,490 │ 1,077,947 │    93.9 │ 1.7 │ 89.7 │ 96.8 │ #> │ (Metropolit │       │             │          │          │           │         │     │      │      │ #> │ an          │       │             │          │          │           │         │     │      │      │ #> │ Statistical │       │             │          │          │           │         │     │      │      │ #> │ Area)       │       │             │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ Non-MSA     │   754 │      62,809 │   17,549 │   36,249 │   108,830 │     6.1 │ 1.7 │  3.2 │ 10.3 │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                   #>"},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":null,"dir":"Reference","previous_headings":"","what":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"data system RCC residents.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"","code":"rccsu2018"},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"object class survey.design2 (inherits survey.design) 904 rows 81 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NPALS/final2018rcc_su_puf.sas7bdat Documentation: https://www.cdc.gov/nchs/npals/RCCresident-readme03152021vr.pdf Codebook: https://www.cdc.gov/nchs/data/npals/final2018rcc_su_puf_codebook.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounding counts — set_count_1k","title":"Rounding counts — set_count_1k","text":"Determines counts rounded.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounding counts — set_count_1k","text":"","code":"set_count_1k()  set_count_int()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounding counts — set_count_1k","text":"(Nothing.)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rounding counts — set_count_1k","text":"set_count_1k(): round counts nearest 1,000. set_count_int(): round counts nearest integer.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rounding counts — set_count_1k","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #>                              Total {NAMCS 2019 PUF}                               #> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐ #> │             n │        Number │            SE │            LL │            UL │ #> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤ #> │         8,250 │ 1,036,484,356 │    48,836,217 │   945,013,590 │ 1,136,808,860 │ #> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.               #>   set_count_1k() #> * Rounding counts to the nearest 1,000. #> * ?set_count_1k for other options. total() #>                              Total {NAMCS 2019 PUF}                               #> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐ #> │             n │  Number (000) │      SE (000) │      LL (000) │      UL (000) │ #> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤ #> │         8,250 │     1,036,484 │        48,836 │       945,014 │     1,136,809 │ #> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Set output defaults — set_output","title":"Set output defaults — set_output","text":"show_output() shows current defaults.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set output defaults — set_output","text":"","code":"set_output(drop_na = NULL, max_levels = NULL, csv = NULL)  show_output()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set output defaults — set_output","text":"drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file \"\" turn CSV output","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set output defaults — set_output","text":"(Nothing.)","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set output defaults — set_output","text":"","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"AGER\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  set_output(csv = \"\") # Turn off CSV output #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the survey to analyze — set_survey","title":"Specify the survey to analyze — set_survey","text":"need specify survey functions, tab(), work.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(design, opts = \"NCHS\", csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the survey to analyze — set_survey","text":"design either survey object (survey.design svyrep.design) data.frame unweighted survey. opts set certain options. See . csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the survey to analyze — set_survey","text":"Info survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify the survey to analyze — set_survey","text":"opts: \"nchs\": Round counts nearest 1,000 -- see set_count_1k(). Identify low-precision estimates (surveytable.find_lpe option TRUE). Percentage CI's: adjust Korn-Graubard CI's number degrees freedom, matching SUDAAN calculation (surveytable.adjust_svyciprop option TRUE). \"general\": Round counts nearest integer -- see set_count_int(). look low-precision estimates (surveytable.find_lpe option FALSE). Percentage CI's: use standard Korn-Graubard CI's (surveytable.adjust_svyciprop option FALSE). Optionally, survey can attribute called label, long name survey. Optionally, variable survey can attribute called label, variable's long name.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Show package options — show_options","title":"Show package options — show_options","text":"See surveytable-options discussion options.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show package options — show_options","text":"","code":"show_options(sw = \"surveytable\")"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show package options — show_options","text":"sw starting characters","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show package options — show_options","text":"List options values.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show package options — show_options","text":"","code":"show_options() #> $surveytable.adjust_svyciprop #> [1] TRUE #>  #> $surveytable.adjust_svyciprop.df_method #> [1] \"NHIS\" #>  #> $surveytable.csv #> [1] \"\" #>  #> $surveytable.drop_na #> [1] FALSE #>  #> $surveytable.find_lpe #> [1] TRUE #>  #> $surveytable.lpe_counts #> [1] \".lpe_counts\" #>  #> $surveytable.lpe_n #> [1] \".lpe_n\" #>  #> $surveytable.lpe_percents #> [1] \".lpe_percents\" #>  #> $surveytable.max_levels #> [1] 20 #>  #> $surveytable.names_count #> [1] \"n\"            \"Number (000)\" \"SE (000)\"     \"LL (000)\"     \"UL (000)\"     #>  #> $surveytable.names_prct #> [1] \"Percent\" \"SE\"      \"LL\"      \"UL\"      #>  #> $surveytable.p.adjust_method #> [1] \"bonferroni\" #>  #> $surveytable.rate_per #> [1] 100 #>  #> $surveytable.survey_label #> [1] \"NAMCS 2019 PUF\" #>  #> $surveytable.svychisq_statistic #> [1] \"F\" #>  #> $surveytable.tx_count #> [1] \".tx_count_1k\" #>  #> $surveytable.tx_numeric #> [1] \".tx_numeric\" #>  #> $surveytable.tx_prct #> [1] \".tx_prct\" #>  #> $surveytable.tx_rate #> [1] \".tx_rate\" #>"},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — surveytable-options","title":"Package options — surveytable-options","text":"Run show_options() see available options. description notable options.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":"low-precision-estimates-","dir":"Reference","previous_headings":"","what":"Low-precision estimates.","title":"Package options — surveytable-options","text":"surveytable.find_lpe: tabulation functions look low-precision estimates? can change directly options() opts argument set_survey(). surveytable.lpe_n, surveytable.lpe_counts, surveytable.lpe_percents: names 3 functions. argument surveytable.lpe_n vector number observations level variable. argument surveytable.lpe_counts data frame count-related estimates. Specifically, data frame following variables: x: point estimates counts s: SE ll, ul: CI samp.size: effective sample size counts: actual sample size degf: degrees freedom argument surveytable.lpe_percents data frame percent-related estimates. Specifically, data frame following variables: Proportion: point estimates proportions (0 1) SE: SE LL, UL: CI n numerator: number observations variable TRUE n denominator: total number observations functions must return list following elements: id: name algorithm used, \"NCHS presentation standards\" flags: vector. level variable, short codes indicating presence low-precision estimates. .flag: vector short codes present flags. descriptions: named vector. names must short codes, values longer descriptions. example, variable 3 levels, flags might c(\"\", \"A1 A2\", \"\"). indicates first third level, nothing found, whereas second level, two different things found, indicated short codes A1 A2. case, .flag = c(\"A1\", \"A2\"), descriptions = c(A1 = \"A1: something\", A2 = \"A2: something else\").","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Package options — surveytable-options","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":null,"dir":"Reference","previous_headings":"","what":"surveytable: Formatted Survey Estimates — surveytable-package","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Short understandable commands generate tabulated, formatted, rounded survey estimates. Mostly wrapper 'survey' package (Lumley (2004) doi:10.18637/jss.v009.i08  https://CRAN.R-project.org/package=survey) identifies low-precision estimates using National Center Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) doi:10.15620/cdc:124368 ).","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a survey, while preserving variable labels — survey_subset","title":"Subset a survey, while preserving variable labels — survey_subset","text":"Subset survey, preserving variable labels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"survey_subset(design, subset, label)"},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a survey, while preserving variable labels — survey_subset","text":"design survey object subset expression specifying sub-population label survey label newly created survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a survey, while preserving variable labels — survey_subset","text":"new survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"children = survey_subset(namcs2019sv, AGE < 18, \"Children < 18\") set_survey(children) #>                          Survey info {Children < 18}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        1,066 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (139) clusters.                           │ #> │           │              │ survey_subset(namcs2019sv, AGE < 18, \"Children │ #> │           │              │ < 18\")                                         │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"AGER\") #>                                 Patient age recode {Children < 18}                                 #> ┌─────────────┬─────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │   n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │     │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼─────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │ 887 │     117,917 │   14,097 │   93,229 │  149,142 │    86.1 │ 1.6 │ 82.6 │ 89.2 │ #> │ years       │     │             │          │          │          │         │     │      │      │ #> ├─────────────┼─────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │ 179 │      19,004 │    2,872 │   14,051 │   25,702 │    13.9 │ 1.6 │ 10.8 │ 17.4 │ #> └─────────────┴─────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 1066. Checked NCHS presentation standards. Nothing to report.                                #>"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"version survey::svyciprop() adjusts degrees freedom method = \"beta\".","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"svyciprop_adjusted(   formula,   design,   method = c(\"logit\", \"likelihood\", \"asin\", \"beta\", \"mean\", \"xlogit\"),   level = 0.95,   df_method,   ... )"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"formula see survey::svyciprop(). design see survey::svyciprop(). method see survey::svyciprop(). level see survey::svyciprop(). df_method df calculated: \"default\" \"NHIS\". ... see survey::svyciprop().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"point estimate proportion, confidence interval attribute.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"Written Makram Talih 2019. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1. use function tabulations, call set_survey() opts = \"NCHS\" argument, type: options(surveytable.adjust_svyciprop = TRUE).","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"set_survey(namcs2019sv, opts = \"NCHS\") #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"AGER\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate variables — tab","title":"Tabulate variables — tab","text":"Tabulate categorical (factor), logical, numeric variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate variables — tab","text":"","code":"tab(   ...,   test = FALSE,   alpha = 0.05,   p_adjust = FALSE,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate variables — tab","text":"... names variables (quotes) test perform hypothesis tests? alpha significance level tests p_adjust adjust p-values multiple comparisons? drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate variables — tab","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate variables — tab","text":"categorical logical variables, presents estimated counts, standard errors (SEs) confidence intervals (CIs), percentages, SEs CIs. Checks presentation guidelines counts percentages flags estimates , according guidelines, suppressed, footnoted, reviewed analyst. numeric variables, presents percentage observations known values, mean known values, standard error mean (SEM), standard deviation (SD). CIs calculated 95% confidence level. CIs count estimates log Student's t CIs, adaptations complex surveys. CIs percentage estimates Korn Graubard CIs.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate variables — tab","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"AGER\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  tab(\"MDDO\", \"SPECCAT\", \"MSA\") #>                              Type of doctor (MD or DO) {NAMCS 2019 PUF}                               #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ M.D. -      │ 7,498 │     980,280 │   48,388 │  889,842 │ 1,079,910 │    94.6 │ 0.7 │ 93.1 │ 95.8 │ #> │ Doctor of   │       │             │          │          │           │         │     │      │      │ #> │ Medicine    │       │             │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ D.O. -      │   752 │      56,204 │    6,602 │   44,597 │    70,832 │     5.4 │ 0.7 │  4.2 │  6.9 │ #> │ Doctor of   │       │             │          │          │           │         │     │      │      │ #> │ Osteopathy  │       │             │          │          │           │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                   #>  #>                   Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                    #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Primary     │ 2,993 │     521,466 │   31,136 │  463,840 │  586,252 │    50.3 │ 2.6 │ 45.1 │ 55.5 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Surgical    │ 3,050 │     214,832 │   31,110 │  161,661 │  285,490 │    20.7 │ 3   │ 15.1 │ 27.3 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Medical     │ 2,207 │     300,186 │   43,497 │  225,806 │  399,067 │    29   │ 3.6 │ 22.1 │ 36.6 │ #> │ care        │       │             │          │          │          │         │     │      │      │ #> │ specialty   │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  #>              Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}              #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ MSA         │ 7,496 │     973,676 │   50,515 │  879,490 │ 1,077,947 │    93.9 │ 1.7 │ 89.7 │ 96.8 │ #> │ (Metropolit │       │             │          │          │           │         │     │      │      │ #> │ an          │       │             │          │          │           │         │     │      │      │ #> │ Statistical │       │             │          │          │           │         │     │      │      │ #> │ Area)       │       │             │          │          │           │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ Non-MSA     │   754 │      62,809 │   17,549 │   36,249 │   108,830 │     6.1 │ 1.7 │  3.2 │ 10.3 │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                   #>   # Numeric variables tab(\"NUMMED\") #> Number of medications coded {NAMCS 2019 PUF} #> ┌─────────┬──────┬───────┬──────┐ #> │ % known │ Mean │   SEM │   SD │ #> ├─────────┼──────┼───────┼──────┤ #> │     100 │ 3.46 │ 0.268 │ 4.43 │ #> └─────────┴──────┴───────┴──────┘ #>   # Hypothesis testing with categorical variables tab(\"AGER\", test = TRUE) #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  #> Comparison of all possible pairs of Patient age recode {NAMCS 2019 PUF} #> ┌────────────────┬───────────────────┬─────────┬──────┐ #> │ Level 1        │ Level 2           │ p-value │ Flag │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 15-24 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 25-44 years       │   0.012 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 75 years and over │   0.022 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 25-44 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 65-74 years       │   0.065 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 75 years and over │   0.878 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 65-74 years    │ 75 years and over │   0.019 │ *    │ #> └────────────────┴───────────────────┴─────────┴──────┘ #>   Design-based t-test. *: p <= 0.05                     #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates — tab_rate","title":"Calculate rates — tab_rate","text":"Calculate rates categorical (factor) logical variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates — tab_rate","text":"","code":"tab_rate(   vr,   pop,   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates — tab_rate","text":"vr variable tabulate pop either single number data.frame columns named Level Population. Level must exactly match levels vr. Population population level vr. per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates — tab_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates — tab_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  # pop is a data frame tab_rate(\"MSA\", uspop2019$MSA) #> Metropolitan Statistical Area Status of physician location (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────────────────────┬───────┬───────┬──────┬───────┬───────┐ #> │ Level                 │     n │  Rate │   SE │    LL │    UL │ #> ├───────────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ MSA (Metropolitan     │ 7,496 │ 351.2 │ 18.2 │ 317.2 │ 388.8 │ #> │ Statistical Area)     │       │       │      │       │       │ #> ├───────────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ Non-MSA               │   754 │ 136.7 │ 38.2 │  78.9 │ 236.8 │ #> └───────────────────────┴───────┴───────┴──────┴───────┴───────┘ #>   N = 8250. Checked NCHS presentation                            #>   standards. Nothing to report.                                  #>   # pop is a single number tab_rate(\"MDDO\", uspop2019$total) #> * Rate based on the entire population. #>              Type of doctor (MD or DO) (rate per 100 population) {NAMCS 2019 PUF}               #> ┌───────────────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ #> │ Level                 │           n │        Rate │          SE │          LL │          UL │ #> ├───────────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ M.D. - Doctor of      │       7,498 │       303.3 │          15 │       275.3 │       334.1 │ #> │ Medicine              │             │             │             │             │             │ #> ├───────────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ D.O. - Doctor of      │         752 │        17.4 │           2 │        13.8 │        21.9 │ #> │ Osteopathy            │             │             │             │             │             │ #> └───────────────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                             #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate subsets or interactions — tab_cross","title":"Tabulate subsets or interactions — tab_cross","text":"Create subsets survey using one variable, tabulate another variable within subsets. Interact two variables tabulate.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"tab_cross(   vr,   vrby,   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )  tab_subset(   vr,   vrby,   lvls = c(),   test = FALSE,   alpha = 0.05,   p_adjust = FALSE,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate subsets or interactions — tab_cross","text":"vr variable tabulate vrby use variable subset survey max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file lvls (optional) show levels vrby test perform hypothesis tests? alpha significance level tests p_adjust adjust p-values multiple comparisons? drop_na drop missing values (NA)? Categorical variables .","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate subsets or interactions — tab_cross","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate subsets or interactions — tab_cross","text":"tab_subset creates subsets using levels vrby, tabulates vr subset. Optionally, use lvls levels vrby. vr can categorical (factor), logical, numeric. tab_cross crosses interacts vr vrby tabulates new variable. Tables created using tab_subset tab_cross counts different percentages. tab_subset, percentages within subset add 100%. tab_cross, percentages across entire population add 100%. Also see var_cross(). test = TRUE performs test association two variables. Also performs t-tests possible pairs levels vr vrby.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>   # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #>                      Patient age recode (Patient sex = Female) {NAMCS 2019 PUF}                      #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │      59,958 │    7,206 │   47,318 │   75,974 │     9.9 │ 1.2 │  7.6 │ 12.6 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   346 │      41,128 │    4,532 │   33,066 │   51,156 │     6.8 │ 0.7 │  5.4 │  8.4 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │   923 │     113,708 │   11,461 │   93,256 │  138,646 │    18.8 │ 1.6 │ 15.8 │ 22.1 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 1,253 │     175,978 │   16,009 │  147,153 │  210,450 │    29.1 │ 1.7 │ 25.8 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │   891 │     120,099 │   11,066 │  100,171 │  143,992 │    19.8 │ 1.5 │ 17   │ 22.9 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │      94,173 │   11,085 │   74,682 │  118,751 │    15.6 │ 1.5 │ 12.8 │ 18.7 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 4609. Checked NCHS presentation standards. Nothing to report.                                  #>  #>                       Patient age recode (Patient sex = Male) {NAMCS 2019 PUF}                       #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │      57,959 │    7,728 │   44,570 │   75,371 │    13.4 │ 1.7 │ 10.3 │ 17.1 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   196 │      23,728 │    4,344 │   16,457 │   34,210 │     5.5 │ 0.8 │  4   │  7.3 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │   512 │      56,562 │    7,277 │   43,861 │   72,942 │    13.1 │ 1.3 │ 10.7 │ 15.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 1,030 │     133,528 │   12,956 │  110,319 │  161,619 │    30.9 │ 1.6 │ 27.8 │ 34.3 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │   770 │      86,766 │    6,767 │   74,409 │  101,176 │    20.1 │ 1.5 │ 17.3 │ 23.1 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │      72,896 │    6,661 │   60,872 │   87,296 │    16.9 │ 1.5 │ 14   │ 20.2 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 3641. Checked NCHS presentation standards. Nothing to report.                                  #>   # Same counts as tab_subset(), but different percentages. tab_cross(\"AGER\", \"SEX\") #>                        (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                         #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │      59,958 │    7,206 │   47,318 │   75,974 │     5.8 │ 0.7 │  4.5 │  7.3 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   346 │      41,128 │    4,532 │   33,066 │   51,156 │     4   │ 0.4 │  3.2 │  4.9 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   923 │     113,708 │   11,461 │   93,256 │  138,646 │    11   │ 1   │  9   │ 13.2 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,253 │     175,978 │   16,009 │  147,153 │  210,450 │    17   │ 1.1 │ 14.9 │ 19.3 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   891 │     120,099 │   11,066 │  100,171 │  143,992 │    11.6 │ 1   │  9.7 │ 13.7 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │      94,173 │   11,085 │   74,682 │  118,751 │     9.1 │ 0.9 │  7.3 │ 11.1 │ #> │ and over:   │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │      57,959 │    7,728 │   44,570 │   75,371 │     5.6 │ 0.7 │  4.3 │  7.2 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   196 │      23,728 │    4,344 │   16,457 │   34,210 │     2.3 │ 0.4 │  1.6 │  3.2 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   512 │      56,562 │    7,277 │   43,861 │   72,942 │     5.5 │ 0.6 │  4.3 │  6.8 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,030 │     133,528 │   12,956 │  110,319 │  161,619 │    12.9 │ 1   │ 10.9 │ 15.1 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   770 │      86,766 │    6,767 │   74,409 │  101,176 │     8.4 │ 0.6 │  7.2 │  9.7 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │      72,896 │    6,661 │   60,872 │   87,296 │     7   │ 0.6 │  5.9 │  8.3 │ #> │ and over:   │       │             │          │          │          │         │     │      │      │ #> │ Male        │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>   # Numeric variables tab_subset(\"NUMMED\", \"AGER\") #> Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} #> ┌───────────────────┬─────────┬──────┬───────┬──────┐ #> │ Level             │ % known │ Mean │   SEM │   SD │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ Under 15 years    │     100 │ 1.58 │ 0.168 │ 1.75 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 15-24 years       │     100 │ 1.64 │ 0.112 │ 1.7  │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 25-44 years       │     100 │ 2.15 │ 0.225 │ 2.74 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 45-64 years       │     100 │ 3.49 │ 0.303 │ 4.49 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 65-74 years       │     100 │ 4.44 │ 0.431 │ 5.03 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 75 years and over │     100 │ 5.53 │ 0.494 │ 5.59 │ #> └───────────────────┴─────────┴──────┴───────┴──────┘ #>   # Hypothesis testing tab_subset(\"NUMMED\", \"AGER\", test = TRUE) #> Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} #> ┌───────────────────┬─────────┬──────┬───────┬──────┐ #> │ Level             │ % known │ Mean │   SEM │   SD │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ Under 15 years    │     100 │ 1.58 │ 0.168 │ 1.75 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 15-24 years       │     100 │ 1.64 │ 0.112 │ 1.7  │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 25-44 years       │     100 │ 2.15 │ 0.225 │ 2.74 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 45-64 years       │     100 │ 3.49 │ 0.303 │ 4.49 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 65-74 years       │     100 │ 4.44 │ 0.431 │ 5.03 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 75 years and over │     100 │ 5.53 │ 0.494 │ 5.59 │ #> └───────────────────┴─────────┴──────┴───────┴──────┘ #>  #> Association between Number of medications coded and Patient age recode {NAMCS 2019 PUF} #> ┌──────────────┬──────────────┐ #> │      p-value │ Flag         │ #> ├──────────────┼──────────────┤ #> │            0 │ *            │ #> └──────────────┴──────────────┘ #>   Wald test. *: p <= 0.05       #>  #> Comparison of Number of medications coded across all possible pairs of Patient age recode {NAMCS 2019 PUF} #> ┌────────────────┬───────────────────┬─────────┬──────┐ #> │ Level 1        │ Level 2           │ p-value │ Flag │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 15-24 years       │   0.739 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 25-44 years       │   0.043 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 25-44 years       │   0.029 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 65-74 years       │   0.007 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 65-74 years    │ 75 years and over │   0.002 │ *    │ #> └────────────────┴───────────────────┴─────────┴──────┘ #>   Design-based t-test. *: p <= 0.05                     #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates for subsets — tab_subset_rate","title":"Calculate rates for subsets — tab_subset_rate","text":"Create subsets survey using one variable, tabulate rates another variable within subsets.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"tab_subset_rate(   vr,   vrby,   pop,   lvls = c(),   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates for subsets — tab_subset_rate","text":"vr variable tabulate vrby use variable subset survey pop data.frame columns named Level, Subset, Population. Level must exactly match levels vr. Subset must exactly match levels vrby. Population population level vr vrby. lvls (optional) show levels vrby per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates for subsets — tab_subset_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`) #>    Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF}     #> ┌───────────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ #> │ Level             │           n │        Rate │          SE │          LL │          UL │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ Under 15 years    │         434 │       202.5 │        24.3 │       159.8 │       256.6 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 15-24 years       │         346 │       198.4 │        21.9 │       159.5 │       246.8 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 25-44 years       │         923 │       263.3 │        26.5 │       215.9 │       321   │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 45-64 years       │       1,253 │       414   │        37.7 │       346.2 │       495.1 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 65-74 years       │         891 │       720.3 │        66.4 │       600.8 │       863.6 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 75 years and over │         762 │       758.1 │        89.2 │       601.2 │       956   │ #> └───────────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ #>   N = 4609. Checked NCHS presentation standards. Nothing to report.                         #>  #>     Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}      #> ┌───────────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐ #> │ Level             │           n │        Rate │          SE │          LL │          UL │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ Under 15 years    │         453 │       187.4 │        25   │       144.1 │       243.7 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 15-24 years       │         196 │       113.1 │        20.7 │        78.4 │       163   │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 25-44 years       │         512 │       133.4 │        17.2 │       103.4 │       172   │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 45-64 years       │       1,030 │       333.4 │        32.3 │       275.4 │       403.5 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 65-74 years       │         770 │       594.8 │        46.4 │       510.1 │       693.6 │ #> ├───────────────────┼─────────────┼─────────────┼─────────────┼─────────────┼─────────────┤ #> │ 75 years and over │         680 │       801.2 │        73.2 │       669.1 │       959.5 │ #> └───────────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘ #>   N = 3641. Checked NCHS presentation standards. Nothing to report.                         #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":null,"dir":"Reference","previous_headings":"","what":"Total count — total","title":"Total count — total","text":"Total count","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total count — total","text":"","code":"total(csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total count — total","text":"csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total count — total","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total count — total","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  total() #>                              Total {NAMCS 2019 PUF}                               #> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐ #> │             n │  Number (000) │      SE (000) │      LL (000) │      UL (000) │ #> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤ #> │         8,250 │     1,036,484 │        48,836 │       945,014 │     1,136,809 │ #> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Overall rate — total_rate","title":"Overall rate — total_rate","text":"Overall rate","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Overall rate — total_rate","text":"","code":"total_rate(   pop,   per = getOption(\"surveytable.rate_per\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Overall rate — total_rate","text":"pop population per calculate rate per many items population csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Overall rate — total_rate","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Overall rate — total_rate","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  total_rate(uspop2019$total) #>                 Total (rate per 100 population) {NAMCS 2019 PUF}                  #> ┌───────────────┬───────────────┬───────────────┬───────────────┬───────────────┐ #> │             n │          Rate │            SE │            LL │            UL │ #> ├───────────────┼───────────────┼───────────────┼───────────────┼───────────────┤ #> │         8,250 │         320.7 │          15.1 │         292.4 │         351.7 │ #> └───────────────┴───────────────┴───────────────┴───────────────┴───────────────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":null,"dir":"Reference","previous_headings":"","what":"US Population in 2019 — uspop2019","title":"US Population in 2019 — uspop2019","text":"Population estimates civilian non-institutional population United States July 1, 2019. Used calculating rates. usage examples, see *_rate functions.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US Population in 2019 — uspop2019","text":"","code":"uspop2019"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US Population in 2019 — uspop2019","text":"object class list length 7.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Are all the variables true? (Logical AND) — var_all","title":"Are all the variables true? (Logical AND) — var_all","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"var_all(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are all the variables true? (Logical AND) — var_all","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are all the variables true? (Logical AND) — var_all","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\")) tab(\"Medicare and Medicaid\") #>                             Medicare and Medicaid {NAMCS 2019 PUF}                              #> ┌───────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │           │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 8,126 │   1,016,202 │   47,395 │  927,389 │ 1,113,520 │      98 │ 0.5 │ 96.9 │ 98.9 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │   124 │      20,282 │    5,177 │   12,120 │    33,941 │       2 │ 0.5 │  1.1 │  3.1 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to                                     #>   report.                                                                                       #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? (Logical OR) — var_any","title":"Is any variable true? (Logical OR) — var_any","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"var_any(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is any variable true? (Logical OR) — var_any","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is any variable true? (Logical OR) — var_any","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_any(\"Imaging services\" , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\" , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") #>                               Imaging services {NAMCS 2019 PUF}                                #> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │          │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 7,148 │     901,115 │   43,298 │  820,085 │  990,151 │    86.9 │ 1.1 │ 84.6 │ 89.1 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 1,102 │     135,369 │   13,574 │  111,134 │  164,890 │    13.1 │ 1.1 │ 10.9 │ 15.4 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to                                    #>   report.                                                                                      #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert factor to logical — var_case","title":"Convert factor to logical — var_case","text":"Convert factor logical","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert factor to logical — var_case","text":"","code":"var_case(newvr, vr, cases, retain_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert factor to logical — var_case","text":"newvr name new logical variable created vr factor variable cases one levels vr converted TRUE. levels converted FALSE. retain_na observations vr NA, newvr NA well?","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert factor to logical — var_case","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert factor to logical — var_case","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>   var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #>                            Preventive care visits {NAMCS 2019 PUF}                             #> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │          │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 6,682 │     812,861 │   45,220 │  728,841 │  906,566 │    78.4 │ 1.7 │ 74.9 │ 81.7 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 1,568 │     223,624 │   18,520 │  190,068 │  263,103 │    21.6 │ 1.7 │ 18.3 │ 25.1 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to                                    #>   report.                                                                                      #>   var_case(\"Surgery-related visits\" , \"MAJOR\" , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") #>                             Surgery-related visits {NAMCS 2019 PUF}                             #> ┌───────┬───────┬─────────────┬──────────┬──────────┬───────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │  UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │           │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 7,432 │     969,451 │   47,976 │  879,793 │ 1,068,246 │    93.5 │ 0.8 │ 91.9 │ 94.9 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼───────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │   818 │      67,034 │    7,810 │   53,273 │    84,348 │     6.5 │ 0.8 │  5.1 │  8.1 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴───────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to                                     #>   report.                                                                                       #>   var_case(\"Non-primary\" , \"SPECCAT.bad\" , c(\"Surgical care specialty\", \"Medical care specialty\")) tab(\"Non-primary\") #>                                  Non-primary {NAMCS 2019 PUF}                                  #> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │          │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 2,406 │     422,807 │   26,382 │  374,099 │  477,857 │    40.8 │ 2.2 │ 36.5 │ 45.2 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 4,194 │     406,216 │   32,643 │  346,937 │  475,622 │    39.2 │ 2.1 │ 35   │ 43.5 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │  │ 1,650 │     207,462 │   12,458 │  184,378 │  233,436 │    20   │ 0.8 │ 18.5 │ 21.6 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to                                    #>   report.                                                                                      #>  tab(\"Non-primary\", drop_na = TRUE) #>                           Non-primary (knowns only) {NAMCS 2019 PUF}                           #> ┌───────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │       │       │       (000) │          │          │          │         │     │      │      │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 2,406 │     422,807 │   26,382 │  374,099 │  477,857 │      51 │ 2.6 │ 45.7 │ 56.3 │ #> ├───────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 4,194 │     406,216 │   32,643 │  346,937 │  475,622 │      49 │ 2.6 │ 43.7 │ 54.3 │ #> └───────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 6600. Checked NCHS presentation standards. Nothing to                                    #>   report.                                                                                      #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels — var_collapse","title":"Collapse factor levels — var_collapse","text":"Collapse two levels factor variable single level.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels — var_collapse","text":"","code":"var_collapse(vr, newlevel, oldlevels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels — var_collapse","text":"vr factor variable newlevel name new level oldlevels vector old levels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels — var_collapse","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels — var_collapse","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"PRIMCARE\") #>                      Are you the patient's primary care provider? {NAMCS 2019 PUF}                      #> ┌─────────┬───────┬────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┬───────┐ #> │ Level   │     n │     Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ Flags │ #> │         │       │      (000) │          │          │          │         │     │      │      │       │ #> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤ #> │ Blank   │    16 │      1,150 │      478 │      440 │    3,005 │     0.1 │ 0   │  0   │  0.2 │ Cx    │ #> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤ #> │ Unknown │   300 │     39,519 │    9,507 │   24,520 │   63,692 │     3.8 │ 0.9 │  2.3 │  6   │       │ #> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤ #> │ Yes     │ 2,278 │    383,481 │   28,555 │  331,362 │  443,798 │    37   │ 2.6 │ 31.9 │ 42.3 │       │ #> ├─────────┼───────┼────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┼───────┤ #> │ No      │ 5,656 │    612,335 │   43,282 │  533,050 │  703,413 │    59.1 │ 2.5 │ 53.9 │ 64.1 │       │ #> └─────────┴───────┴────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┴───────┘ #>   N = 8250. Checked NCHS presentation standards: Cx: suppress count                                     #>   (and rate).                                                                                           #>  var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Blank\", \"Unknown\")) tab(\"PRIMCARE\") #>                    Are you the patient's primary care provider? {NAMCS 2019 PUF}                     #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Unknown if  │   316 │      40,669 │    9,479 │   25,619 │   64,560 │     3.9 │ 0.9 │  2.4 │  6.1 │ #> │ PCP         │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Yes         │ 2,278 │     383,481 │   28,555 │  331,362 │  443,798 │    37   │ 2.6 │ 31.9 │ 42.3 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ No          │ 5,656 │     612,335 │   43,282 │  533,050 │  703,413 │    59.1 │ 2.5 │ 53.9 │ 64.1 │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy a variable — var_copy","title":"Copy a variable — var_copy","text":"Create new variable copy another variable. can modify copy, original remains unchanged. See examples.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy a variable — var_copy","text":"","code":"var_copy(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Copy a variable — var_copy","text":"newvr name new variable created vr variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy a variable — var_copy","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy a variable — var_copy","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_copy(\"Age group\", \"AGER\") var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  #>                                      Age group {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-64       │ 3,718 │     479,777 │   32,175 │  420,624 │  547,247 │    46.3 │ 1.8 │ 42.7 │ 49.9 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65+         │ 3,103 │     373,935 │   24,523 │  328,777 │  425,296 │    36.1 │ 1.9 │ 32.3 │ 40   │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross or interact two variables — var_cross","title":"Cross or interact two variables — var_cross","text":"Create new variable interaction two variables. Also see tab_cross().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross or interact two variables — var_cross","text":"","code":"var_cross(newvr, vr, vrby)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross or interact two variables — var_cross","text":"newvr name new variable created vr first variable vrby second variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross or interact two variables — var_cross","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross or interact two variables — var_cross","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #>                                      Age x Sex {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │      59,958 │    7,206 │   47,318 │   75,974 │     5.8 │ 0.7 │  4.5 │  7.3 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   346 │      41,128 │    4,532 │   33,066 │   51,156 │     4   │ 0.4 │  3.2 │  4.9 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   923 │     113,708 │   11,461 │   93,256 │  138,646 │    11   │ 1   │  9   │ 13.2 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,253 │     175,978 │   16,009 │  147,153 │  210,450 │    17   │ 1.1 │ 14.9 │ 19.3 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   891 │     120,099 │   11,066 │  100,171 │  143,992 │    11.6 │ 1   │  9.7 │ 13.7 │ #> │ years:      │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │      94,173 │   11,085 │   74,682 │  118,751 │     9.1 │ 0.9 │  7.3 │ 11.1 │ #> │ and over:   │       │             │          │          │          │         │     │      │      │ #> │ Female      │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │      57,959 │    7,728 │   44,570 │   75,371 │     5.6 │ 0.7 │  4.3 │  7.2 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   196 │      23,728 │    4,344 │   16,457 │   34,210 │     2.3 │ 0.4 │  1.6 │  3.2 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   512 │      56,562 │    7,277 │   43,861 │   72,942 │     5.5 │ 0.6 │  4.3 │  6.8 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,030 │     133,528 │   12,956 │  110,319 │  161,619 │    12.9 │ 1   │ 10.9 │ 15.1 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   770 │      86,766 │    6,767 │   74,409 │  101,176 │     8.4 │ 0.6 │  7.2 │  9.7 │ #> │ years: Male │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │      72,896 │    6,661 │   60,872 │   87,296 │     7   │ 0.6 │  5.9 │  8.3 │ #> │ and over:   │       │             │          │          │          │         │     │      │      │ #> │ Male        │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert numeric to factor — var_cut","title":"Convert numeric to factor — var_cut","text":"Create new categorical variable based numeric variable.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert numeric to factor — var_cut","text":"","code":"var_cut(newvr, vr, breaks, labels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert numeric to factor — var_cut","text":"newvr name new factor variable created vr numeric variable breaks see cut() labels see cut()","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert numeric to factor — var_cut","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert numeric to factor — var_cut","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  # In some data systems, variables might contain \"special values\". For example, # negative values might indicate unknowns (which should be coded as `NA`). # Though in this particular data, there are no unknowns. var_cut(\"Age group\"   , \"AGE\"   , c(-Inf, -0.1, 0, 4, 14, 64, Inf)   , c(NA, \"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") #>                                      Age group {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 1     │   203 │      31,148 │    5,282 │   22,269 │   43,566 │     3   │ 0.5 │  2.1 │  4.1 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 1-4         │   281 │      38,240 │    5,444 │   28,864 │   50,662 │     3.7 │ 0.5 │  2.7 │  4.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 5-14        │   403 │      48,529 │    5,741 │   38,430 │   61,282 │     4.7 │ 0.5 │  3.7 │  5.9 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-64       │ 4,260 │     544,632 │   36,082 │  478,254 │  620,223 │    52.5 │ 2   │ 48.6 │ 56.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65 and over │ 3,103 │     373,935 │   24,523 │  328,777 │  425,296 │    36.1 │ 1.9 │ 32.3 │ 40   │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List variables in a survey. — var_list","title":"List variables in a survey. — var_list","text":"List variables survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List variables in a survey. — var_list","text":"","code":"var_list(sw = \"\", all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List variables in a survey. — var_list","text":"sw starting characters variable name (case insensitive) print variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List variables in a survey. — var_list","text":"table","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List variables in a survey. — var_list","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_list(\"age\") #>          Variables beginning with 'age' {NAMCS 2019 PUF}          #> ┌──────────┬─────────┬──────────────────────────────────────────┐ #> │ Variable │ Class   │ Long name                                │ #> ├──────────┼─────────┼──────────────────────────────────────────┤ #> │ AGE      │ numeric │ Patient age in years (raw - use caution) │ #> ├──────────┼─────────┼──────────────────────────────────────────┤ #> │ AGER     │ factor  │ Patient age recode                       │ #> └──────────┴─────────┴──────────────────────────────────────────┘ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical NOT — var_not","title":"Logical NOT — var_not","text":"Logical ","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical NOT — var_not","text":"","code":"var_not(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical NOT — var_not","text":"newvr name new variable created vr logical variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logical NOT — var_not","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical NOT — var_not","text":"","code":"set_survey(namcs2019sv) #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_not(\"Private insurance not used\", \"PAYPRIV\")"},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-development-version","dir":"Changelog","previous_headings":"","what":"surveytable (development version)","title":"surveytable (development version)","text":"rccsu2018","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-094","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.4","title":"surveytable 0.9.4","text":"CRAN release: 2024-05-20 Optionally adjust p-values multiple comparisons (p_adjust argument)","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-093","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.3","title":"surveytable 0.9.3","text":"codebook() Improved output. Allows unweighted survey data.frame. Can set certain options using argument. Tabulation functions show number observations. LaTeX printing.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-092","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.2","title":"surveytable 0.9.2","text":"CRAN release: 2024-01-18 Addressed CRAN comments.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-091","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.1","title":"surveytable 0.9.1","text":"Initial CRAN submission.","code":""}]
+[{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"subsetting-a-survey","dir":"Articles","previous_headings":"","what":"Subsetting a survey","title":"Advanced topics","text":"Consider example, estimate number medications age group: Survey info {NAMCS 2019 PUF} Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} ’d like estimate thing, visits NUMMED > 0? One way create another survey object NUMMED > 0, analyze new survey object. Survey info {NAMCS 2019 PUF: NUMMED 1+} Note called set_survey(), let R know now want analyze new object newsurvey, namcs2019sv. Now, let’s create table: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF: NUMMED 1+} sure check table title verify tabulating new survey object.","code":"library(surveytable) set_survey(namcs2019sv) tab_subset(\"NUMMED\", \"AGER\") newsurvey = survey_subset(namcs2019sv, NUMMED > 0   , label = \"NAMCS 2019 PUF: NUMMED 1+\") set_survey(newsurvey) ## * Mode: General. tab_subset(\"NUMMED\", \"AGER\")"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"advanced-variable-editing","dir":"Articles","previous_headings":"Advanced variable editing and data flow","what":"Advanced variable editing","title":"Advanced topics","text":"First, let’s review call “advanced variable editing”. surveytable provides number functions create modify survey variables. examples include [var_collapse()] [var_cut()]. Occasionally, might need advanced variable editing. ’s : Every survey object element called variables data frame survey’s variables located Create new variable variables data frame (part survey object). Call set_survey() . time modify variables data frame, call set_survey(). Tabulate new variable. example , see vignette(\"Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report\").","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/Advanced-topics.html","id":"data-flow","dir":"Articles","previous_headings":"Advanced variable editing and data flow","what":"Data flow","title":"Advanced topics","text":"explanation raises question set_survey() must called , variables modified. explanation: survey ’re analyzing actually exists three separate places: file computer data storage contains survey object. example, RDS file hard disk drive contains survey object named something like mysurvey.rds. survey object R’s global environment, named something like mysurvey. hidden copy survey object ’s used surveytable. surveytable analyzes. (3) ’s different (2), might ask. ’s due arcane issue R packages work – (2) (3) necessary. Normally, information flows forwards, (1) (2) (2) (3). Forwards flow: Going (1) (2): call readRDS(). Going (2) (3): call set_survey(). Backwards flow: Going (3) (2): probably don’t need , see . really need , call surveytable:::.load_survey(). Going (2) (1): call saveRDS(). Normally, probably don’t want . Normally, survey file (mysurvey.rds) probably changed. functions modifying creating variables part surveytable package (like var_cut() var_collapse()) modify (3). Since (3) surveytable works tabulates, can call var_collapse(), can call tab(). don’t need anything extra . modifying variables data frame directly, actually modifying (2). modify (2), need copy (3), surveytable can use . calling set_survey(). Thus, time modify variables , call set_survey(). modify (2), copy (2) -> (3) calling set_survey(). flip side, changes make (3) (using surveytable functions like var_cut() var_collapse()) reflected (2). make changes (3), call set_survey(), changes lost, set_survey() copies (2) -> (3). changes important, can just rerun code created . really need go (3) (2), use mysurvey = surveytable:::.load_survey().","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. omitting message . Now, specify survey ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey name, survey design variables, number observations verify looks correct. example, want turn certain NCHS-specific options, identifying low-precision estimates. care identifying low-precision estimates, can skip command. turn NCHS-specific options:","code":"library(surveytable) set_survey(namcs2019sv) ## * Mode: General. set_mode(\"NCHS\") ## * Mode: NCHS."},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages","dir":"Articles","previous_headings":"Table 1","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows overall estimated count well counts percentages type doctor, physician specialty, metropolitan statistical area. variables necessary creating table already survey, making commands straightforward. Total {NAMCS 2019 PUF} Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"total() tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates","dir":"Articles","previous_headings":"Table 1","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"published table also shows several rates. calculate rates, addition survey, need source information population estimates. typically use function read.csv() load population estimates get correct format. surveytable package comes object called uspop2019 contains several population estimates use examples. overall population estimate: overall population estimate, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame variable called Level matches levels variable survey, variable called Population gives population size (assumed constant rather random variable). MSA, can see levels variables just using tab() command, just . Thus, calculate rates, need data frame follows: Now appropriate population estimates, rate : Metropolitan Statistical Area Status physician location (rate per 100 population) {NAMCS 2019 PUF} can also calculate rates specific variable based entire population: Type doctor (MD ) (rate per 100 population) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) ## [1] \"list\" names(uspop2019) ## [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         ## [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total ## [1] 323186697 total_rate(uspop2019$total) uspop2019$MSA ##                                 Level Population ## 1 MSA (Metropolitan Statistical Area)  277229518 ## 2                             Non-MSA   45957179 tab_rate(\"MSA\", uspop2019$MSA) tab_rate(\"MDDO\", uspop2019$total) ## * Rate based on the entire population. tab_rate(\"SPECCAT\", uspop2019$total) ## * Rate based on the entire population."},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"counts-and-percentages-1","dir":"Articles","previous_headings":"Table 3","what":"Counts and percentages","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table presents estimates age group, well age group sex. Variables beginning ‘age’ {NAMCS 2019 PUF} survey couple relevant age-related variables. AGE patient age years. AGER categorical variable based AGE. However, table, addition AGER, need another age group variable, different age categories. create using var_cut function. Now ’ve created Age group variable, can create tables: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} Patient sex {NAMCS 2019 PUF} (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}","code":"var_list(\"age\") var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) tab(\"AGER\", \"Age group\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"rates-1","dir":"Articles","previous_headings":"Table 3","what":"Rates","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"Patient age recode (rate per 100 population) {NAMCS 2019 PUF} Age group (rate per 100 population) {NAMCS 2019 PUF} Patient sex (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population estimates following format: population estimates, rates : Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"tab_rate(\"AGER\", uspop2019$AGER) tab_rate(\"Age group\", uspop2019$`Age group`) ## * Population for some levels not defined: 15-64 tab_rate(\"SEX\", uspop2019$SEX) uspop2019$`AGER x SEX` ##                Level Subset Population ## 1     Under 15 years Female   29604762 ## 2        15-24 years Female   20730118 ## 3        25-44 years Female   43192143 ## 4        45-64 years Female   42508901 ## 5        65-74 years Female   16673240 ## 6  75 years and over Female   12421444 ## 7     Under 15 years   Male   30921894 ## 8        15-24 years   Male   20988582 ## 9        25-44 years   Male   42407267 ## 10       45-64 years   Male   40053148 ## 11       65-74 years   Male   14586962 ## 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-5","dir":"Articles","previous_headings":"","what":"Table 5","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table gives expected sources payment. use PAY* variables create several new variables required table. Note PAY* variables logical (TRUE FALSE), simplifies workflow. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Expected source payment visit: Private insurance {NAMCS 2019 PUF} Expected source payment visit: Medicare {NAMCS 2019 PUF} Expected source payment visit: Medicaid CHIP state-based program {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} insurance {NAMCS 2019 PUF} Self-pay {NAMCS 2019 PUF} charge {NAMCS 2019 PUF} Expected source payment visit: Workers Compensation {NAMCS 2019 PUF} Expected source payment visit: {NAMCS 2019 PUF} Unknown blank {NAMCS 2019 PUF} Check presentation standards flags! NCHS presentation standards rules, estimates shown.","code":"# var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\"))  # var_any(\"Payment used\", c(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\"   , \"PAYWKCMP\", \"PAYOTH\", \"PAYDK\")) var_not(\"No other payment used\", \"Payment used\")  var_all(\"Self-pay\", c(\"PAYSELF\", \"No other payment used\")) var_all(\"No charge\", c(\"PAYNOCHG\", \"No other payment used\")) var_any(\"No insurance\", c(\"Self-pay\", \"No charge\"))  # var_case(\"No pay\", \"NOPAY\", \"No categories marked\") var_any(\"Unknown or blank\", c(\"PAYDK\", \"No pay\"))  ## tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\"   , \"No insurance\", \"Self-pay\", \"No charge\"   , \"PAYWKCMP\", \"PAYOTH\", \"Unknown or blank\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-6","dir":"Articles","previous_headings":"","what":"Table 6","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows primary care provider referral status, prior-visit status. table, “Unknown” “Blank” values collapsed single value. can collapse two levels factor single level using var_collapse function. Now, table: patient’s primary care provider? {NAMCS 2019 PUF} patient referred visit? {NAMCS 2019 PUF} patient seen practice ? {NAMCS 2019 PUF} percentages within subset defined SENBEFOR add 100% – reason, want use tab_subset(), tab_cross(). patient’s primary care provider? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient’s primary care provider? (patient seen practice ? = , new patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = Yes, established patient) {NAMCS 2019 PUF} patient referred visit? (patient seen practice ? = , new patient) {NAMCS 2019 PUF}","code":"var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) var_collapse(\"REFER\", \"Unknown if referred\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\", \"REFER\", \"SENBEFOR\") tab_subset(\"PRIMCARE\", \"SENBEFOR\") tab_subset(\"REFER\", \"SENBEFOR\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-11","dir":"Articles","previous_headings":"","what":"Table 11","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table shows information Table 3, preventive care visits. , estimates age group, well age group sex, preventive care visits. Let’s create Age group AGE cross AGER SEX create variable called Age x Sex: see possible values MAJOR (Major reason visit), estimate total count preventive care visits: Major reason visit {NAMCS 2019 PUF} create tables age, sex, interaction, limit preventive care visits: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age x Sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} commands similar, differs first variable passed tab_subset() function, code can streamlined loop: Patient age recode (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age group (Major reason visit = Preventive care) {NAMCS 2019 PUF} Patient sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Age x Sex (Major reason visit = Preventive care) {NAMCS 2019 PUF} Note called inside loop, print() function needs called explicitly.","code":"var_cut(\"Age group\", \"AGE\"         , c(-Inf, 0, 4, 14, 64, Inf)         , c(\"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\") ) ## Warning in var_cut(\"Age group\", \"AGE\", c(-Inf, 0, 4, 14, 64, Inf), c(\"Under 1\", ## : Age group: overwriting a variable that already exists. var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"MAJOR\") tab_subset(\"AGER\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age group\", \"MAJOR\", \"Preventive care\") tab_subset(\"SEX\", \"MAJOR\", \"Preventive care\") tab_subset(\"Age x Sex\", \"MAJOR\", \"Preventive care\") for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     print( tab_subset(vr, \"MAJOR\", \"Preventive care\") ) }"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"more-advanced-coding","dir":"Articles","previous_headings":"Table 11","what":"More advanced coding","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"addition, age-sex category, published table shows percentage preventive care visits made primary care physicians. calculate percentages, slightly involved loop needed. code, followed explanation: Since tab_subset() called within loop, wanted print screen, need use print( tab_subset(*) ). Since don’t want print screen, call print() omitted. Since many tables produced, output sent CSV file. , loop goes age, sex, age / sex interaction variables, calling variables vr. MAJOR vr crossed, result stored variable called tmp. Next, inner loop goes levels vr, calling levels lvl. code tabulates SPECCAT (Type specialty – Primary, Medical, Surgical) subset tmp (MAJOR crossed vr) restricted \"Preventive care: \" followed lvl, level vr, “15 years” AGER. Finally, CSV output turned . run code, tables stored CSV file. give idea tables look like, just one tables: Type specialty (Primary, Medical, Surgical) (tmp = Preventive care: 15 years) {NAMCS 2019 PUF} match percentage published table, see “Primary care specialty” row. sure check presentation standards flags.","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) )  for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) {     var_cross(\"tmp\", \"MAJOR\", vr)     for (lvl in levels(surveytable:::env$survey$variables[,vr])) {         tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care: \", lvl))     } } ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. set_output(csv = \"\") ## * Turning off CSV output. ## * ?set_output for other options. vr = \"AGER\" var_cross(\"tmp\", \"MAJOR\", vr) ## Warning in var_cross(\"tmp\", \"MAJOR\", vr): tmp: overwriting a variable that ## already exists. lvl = levels(surveytable:::env$survey$variables[,vr])[1] tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care: \", lvl))"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"begin","dir":"Articles","previous_headings":"","what":"Begin","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"Begin loading surveytable package. , print message explaining specify survey ’d like analyze. Now, specify survey ’d like analyze. Survey info {RCC SU 2018 PUF} Check survey name, survey design variables, number observations verify looks correct. example, want turn certain NCHS-specific options, identifying low-precision estimates. care identifying low-precision estimates, can skip command. turn NCHS-specific options: Alternatively, can combine two commands single command, like : Survey info {RCC SU 2018 PUF}","code":"library(surveytable) set_survey(rccsu2018) ## * Mode: General. set_mode(\"NCHS\") ## * Mode: NCHS. set_survey(rccsu2018, mode = \"NCHS\") ## * Mode: NCHS."},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-1","dir":"Articles","previous_headings":"","what":"Figure 1","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents sex, race / ethnicity, age group. Sex. Resident’s gender {RCC SU 2018 PUF} Race / ethnicity. Variables beginning ‘race’ {RCC SU 2018 PUF} Resident’s race/ethnicity {RCC SU 2018 PUF} published figure, Hispanic categories merged single category called “Another race ethnicity”. can using var_collapse() function. Resident’s race/ethnicity {RCC SU 2018 PUF} Age group. Variables beginning ‘age’ {RCC SU 2018 PUF} age2 numeric variable. need create categorical variable based numeric variable. done using var_cut() function. Age {RCC SU 2018 PUF}","code":"tab(\"sex\") var_list(\"race\") tab(\"raceeth2\") var_collapse(\"raceeth2\"              , \"Another race or ethnicity\"              , c(\"Hispanic\", \"Other\")) tab(\"raceeth2\") var_list(\"age\") var_cut(\"Age\", \"age2\"         , c(-Inf, 64, 74, 84, Inf)         , c(\"Under 65\", \"65-74\", \"75-84\", \"85 and over\") ) tab(\"Age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-2","dir":"Articles","previous_headings":"","what":"Figure 2","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents Medicaid, overall age group. Used Medicaid pay services {RCC SU 2018 PUF} can see, observations, value variable unknown (’s missing NA). command calculates percentages based observations, including ones missing (NA) values. However, published figure, percentages based knowns . exclude NA’s calculation, use drop_na argument: Used Medicaid pay services (knowns ) {RCC SU 2018 PUF} Note table title alerts fact using known values . age group: Used Medicaid pay services (Age = 65) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 65-74) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 75-84) (knowns ) {RCC SU 2018 PUF} Used Medicaid pay services (Age = 85 ) (knowns ) {RCC SU 2018 PUF} Note according NCHS presentation criteria, percentages suppressed.","code":"tab(\"medicaid2\") tab(\"medicaid2\", drop_na = TRUE) tab_subset(\"medicaid2\", \"Age\", drop_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-4","dir":"Articles","previous_headings":"","what":"Figure 4","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"(Figure 3 slightly involved, ’ll next.) figure shows percentage residents one select set chronic conditions. addition, shows distribution residents number conditions. ’s table high blood pressure. Resident diagnosed high blood pressure {RCC SU 2018 PUF} , unknown values (NA) present, figure based knowns . Therefore, use drop_na argument: Resident diagnosed high blood pressure (knowns ) {RCC SU 2018 PUF} Resident diagnosed Alzheimer’s/dementia (knowns ) {RCC SU 2018 PUF} Resident diagnosed depression (knowns ) {RCC SU 2018 PUF} Resident diagnosed arthritis (knowns ) {RCC SU 2018 PUF} Resident diagnosed diabetes (knowns ) {RCC SU 2018 PUF} Resident diagnosed heart disease (knowns ) {RCC SU 2018 PUF} Resident diagnosed osteoporosis (knowns ) {RCC SU 2018 PUF} Resident diagnosed COPD (knowns ) {RCC SU 2018 PUF} Resident diagnosed stroke (knowns ) {RCC SU 2018 PUF} Resident diagnosed cancer (knowns ) {RCC SU 2018 PUF}","code":"tab(\"hbp\") tab(\"hbp\", \"alz\", \"depress\", \"arth\", \"diabetes\", \"heartdise\", \"osteo\"     , \"copd\", \"stroke\", \"cancer\"     , drop_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"advanced-variable-editing","dir":"Articles","previous_headings":"Figure 4","what":"Advanced variable editing","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"surveytable provides number functions create modify survey variables. saw couple : var_collapse() var_cut(). Occasionally, might need advanced variable editing. ’s : Every survey object element called variables data frame survey’s variables located Create new variable variables data frame (part survey object). Call set_survey() . time modify variables data frame, call set_survey(). Tabulate new variable. go steps count many chronic conditions present. Survey info {RCC SU 2018 PUF} num_cc numeric variable number chronic conditions. published figure uses categorical variable based numeric variable. Use var_cut(), converts numeric variables categorical (factor) variables. Number chronic conditions {RCC SU 2018 PUF}","code":"class(rccsu2018$variables) ## [1] \"data.frame\" rccsu2018$variables$num_cc = 0 for (vr in c(\"hbp\", \"alz\", \"depress\", \"arth\", \"diabetes\", \"heartdise\", \"osteo\"              , \"copd\", \"stroke\", \"cancer\")) {   idx = which(rccsu2018$variables[,vr])   rccsu2018$variables$num_cc[idx] = rccsu2018$variables$num_cc[idx] + 1 } set_survey(rccsu2018, mode = \"NCHS\") ## * Mode: NCHS. var_cut(\"Number of chronic conditions\", \"num_cc\"         , c(-Inf, 0, 1, 3, 10, Inf)         , c(\"0\", \"1\", \"2-3\", \"4-10\", \"??\")) tab(\"Number of chronic conditions\")"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.html","id":"figure-3","dir":"Articles","previous_headings":"","what":"Figure 3","title":"Example: Residential Care Community Services User (NSLTCP RCC SU) report","text":"figure shows percentage residents need help one activities daily living (ADLs). addition, shows distribution residents number ADLs need help. ’s table bathhlp (help bathing): Type assistance resident needs bathe {RCC SU 2018 PUF} variable multiple levels. Several levels correspond resident needing help, One level (\"NEED ASSISTANCE\") = need help One level (\"MISSING\") = unknown want show (resident needing help) percentage knowns (, excluding unknowns). , convert variable 2 levels (needs help / need help) plus NA (unknown); use drop_na argument base percentages knowns . Type assistance resident needs bathe (knowns ) {RCC SU 2018 PUF} Type assistance resident needs locomotion (knowns ) {RCC SU 2018 PUF} Type assistance resident needs dress (knowns ) {RCC SU 2018 PUF} Type assistance resident needs transfer /chair (knowns ) {RCC SU 2018 PUF} Type assistance resident needs use bathroom (knowns ) {RCC SU 2018 PUF} Type assistance resident needs eat (knowns ) {RCC SU 2018 PUF} Now, go “advanced variable editing” steps – similar Figure 4 – count many ADLs present. Survey info {RCC SU 2018 PUF} generating figure, create categorical variable based num_adl, numeric. Number ADLs {RCC SU 2018 PUF}","code":"tab(\"bathhlp\") for (vr in c(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\")) {   var_collapse(vr     , \"Needs assistance\"     , c(\"NEED HELP OR SUPERVISION FROM ANOTHER PERSON\"       , \"USE OF AN ASSISTIVE DEVICE\"       , \"BOTH\"))   var_collapse(vr, NA, \"MISSING\") }  tab(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\", drop_na = TRUE) rccsu2018$variables$num_adl = 0 for (vr in c(\"bathhlp\", \"walkhlp\", \"dreshlp\", \"transhlp\", \"toilhlp\", \"eathlp\")) {   idx = which(rccsu2018$variables[,vr] %in%     c(\"NEED HELP OR SUPERVISION FROM ANOTHER PERSON\"       , \"USE OF AN ASSISTIVE DEVICE\"       , \"BOTH\"))   rccsu2018$variables$num_adl[idx] = rccsu2018$variables$num_adl[idx] + 1 } set_survey(rccsu2018, mode = \"NCHS\") ## * Mode: NCHS. var_cut(\"Number of ADLs\", \"num_adl\"         , c(-Inf, 0, 2, 6, Inf)         , c(\"0\", \"1-2\", \"3-6\", \"??\")) tab(\"Number of ADLs\")"},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"concepts","dir":"Articles","previous_headings":"Preliminaries","what":"Concepts","title":"Introduction to surveytable","text":"two important concepts need learn distinguish: data frame standard way storing data R. data frame rectangular data. Variables columns, observations rows. Example: data frame, , represent complex survey. , just looking data frame, R know sampling weights , strata , etc. Even variables represent sampling weights, etc, part data frame, just looking data frame, R know variable represents weights survey design variables. can get data frame R many different ways. data currently comma-separated values (CSV) file, can use read.csv(). ’s SAS file, can use package like haven importsurvey. ’s already R format, use readRDS(), . survey object object describes survey. tells R sampling weights , strata , . data frame can converted survey object using survey::svydesign() function; survey uses replicate weights, survey::svrepdesign() function used. Generally speaking, need convert data frame survey object . converted, can save saveRDS() (similar). future, can load readRDS(). need re-convert data frame survey object every time.","code":"head(iris) #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1          5.1         3.5          1.4         0.2  setosa #> 2          4.9         3.0          1.4         0.2  setosa #> 3          4.7         3.2          1.3         0.2  setosa #> 4          4.6         3.1          1.5         0.2  setosa #> 5          5.0         3.6          1.4         0.2  setosa #> 6          5.4         3.9          1.7         0.4  setosa"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"namcs","dir":"Articles","previous_headings":"Preliminaries","what":"NAMCS","title":"Introduction to surveytable","text":"Examples tutorial use survey called National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS “annual nationally representative sample survey visits non-federal office-based patient care physicians, excluding anesthesiologists, radiologists, pathologists.” Note unit observation visits, patients – distinction important since single patient can make multiple visits. surveytable package comes data frame selected variables NAMCS, called namcs2019sv_df (sv = selected variables; df = data frame). survey object survey called namcs2019sv. namcs2019sv object analyze. really need namcs2019sv. reason package namcs2019sv_df illustrate convert data frame survey object.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"more-concepts","dir":"Articles","previous_headings":"Preliminaries","what":"More concepts","title":"Introduction to surveytable","text":"importing data another source, SAS CSV, analysts aware standard way variables handled R. Specifically, categorical variables stored factor. true / false variables stored factor well, programming tasks easier stored logical. Unknown values stored missing (NA). variable contains “special values”, negative value indicating age missing, “special values” need converted NA. Variables namcs2019sv_df already stored correctly. Thus, AGER (patient’s age group) factor variable; PAYNOCHG (indicates whether charge physician visit) logical variable; AGE (patient’s age years) numeric variable.","code":"library(\"surveytable\") class(namcs2019sv_df$AGER) #> [1] \"factor\" class(namcs2019sv_df$PAYNOCHG) #> [1] \"logical\" class(namcs2019sv_df$AGE) #> [1] \"numeric\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-a-survey-object","dir":"Articles","previous_headings":"Preliminaries","what":"Create a survey object","title":"Introduction to surveytable","text":"seen , tables produced surveytable clearer either variable names descriptive, variables \"label\" attribute descriptive. namcs2019sv_df, variables already \"label\" attribute set. example, variable name AGE descriptive, variable descriptive \"label\" attribute: Documentation NAMCS survey provides names survey design variables. Specifically, NAMCS, cluster ID’s, also known primary sampling units (PSU’s), given CPSUM; strata given CSTRATM; sampling weights given PATWT. Thus, namcs2019sv_df data frame can turned survey object follows: Tables produced surveytable clearer either name survey object descriptive, object \"label\" attribute descriptive. Let’s set attribute mysurvey: mysurvey object now namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years (raw - use caution)\" mysurvey = survey::svydesign(ids = ~ CPSUM   , strata = ~ CSTRATM   , weights = ~ PATWT   , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" all.equal(namcs2019sv, mysurvey) #> [1] TRUE"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"begin-analysis","dir":"Articles","previous_headings":"","what":"Begin analysis","title":"Introduction to surveytable","text":"First, specify survey object ’d like analyze. Survey info {NAMCS 2019 PUF} Check survey label, survey design variables, number observations verify looks correct. example, want turn certain NCHS-specific options, identifying low-precision estimates. care identifying low-precision estimates, can skip command. turn NCHS-specific options:","code":"set_survey(namcs2019sv) #> * Mode: General. set_mode(\"NCHS\") #> * Mode: NCHS."},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"list-variables","dir":"Articles","previous_headings":"Begin analysis","what":"List variables","title":"Introduction to surveytable","text":"var_list() function lists variables survey. avoid unintentionally listing variables survey, can many, starting characters variable names specified. example, list variables start letters age, type: Variables beginning ‘age’ {NAMCS 2019 PUF} table lists variable name; class, type variable; variable label, long name variable. Common classes factor (categorical variable), logical (yes / variable), numeric.","code":"var_list(\"age\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-categorical-and-logical-variables","dir":"Articles","previous_headings":"","what":"Tabulate categorical and logical variables","title":"Introduction to surveytable","text":"main function surveytable package tab(), tabulates variables. operates categorical logical variables, presents estimated counts, standard errors (SEs) 95% confidence intervals (CIs), percentages, SEs CIs. example, tabulate AGER, type: Patient age recode {NAMCS 2019 PUF} table title shows variable label (long variable name) survey label. level variable, table shows: estimated count, standard error, 95% confidence interval; estimated percentage, standard error, 95% confidence interval. NCHS presentation standards. tab() function also applies National Center Health Statistics (NCHS) presentation standards counts percentages, flags estimates , according standards, suppressed, footnoted, reviewed analyst. CIs displayed ones used NCHS presentation standards. Specifically, counts, tables show log Student’s t 95% CI, adaptations complex surveys; percentages, show 95% Korn Graubard CI. One need anything extra perform presentation standards checking – performed automatically. example, let’s tabulate PAYNOCHG: Expected source payment visit: Charge/Charity {NAMCS 2019 PUF} table tells us , according NCHS presentation standards, estimated number visits charge visit suppressed due low precision. However, lack percentage flag indicates estimated percentage visits can shown. Drop missing values. variables might contain missing values (NA). Consider following variable, part actual survey, constructed specifically example: Type specialty (BAD - use) {NAMCS 2019 PUF} calculate percentages based non-missing values , use drop_na argument: Type specialty (BAD - use) (knowns ) {NAMCS 2019 PUF} table gives percentages based knowns, , based non-NA values. Multiple tables. Multiple tables can created single command: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF}","code":"tab(\"AGER\") tab(\"PAYNOCHG\") tab(\"SPECCAT.bad\") tab(\"SPECCAT.bad\", drop_na = TRUE) tab(\"MDDO\", \"SPECCAT\", \"MSA\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"entire-population","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Entire population","title":"Introduction to surveytable","text":"Estimate total count entire population using total() command: Total {NAMCS 2019 PUF}","code":"total()"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"subsets-or-interactions","dir":"Articles","previous_headings":"Tabulate categorical and logical variables","what":"Subsets or interactions","title":"Introduction to surveytable","text":"create table AGER value variable SEX, type: Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} addition giving long name variable tabulated, title table reflects value subsetting variable (case, either Female Male). tab_subset() command, table (, subset), percentages add 100%. tab_cross() function similar – crosses interacts two variables generates table using new variable. Thus, create table interaction AGER SEX, type: (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} estimated counts produced tab_subset() tab_cross() , percentages different. tab_subset() command, within table (, within subset), percentages add 100%. hand, tab_cross(), percentages across entire population add 100%.","code":"tab_subset(\"AGER\", \"SEX\") tab_cross(\"AGER\", \"SEX\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"tabulate-numeric-variables","dir":"Articles","previous_headings":"","what":"Tabulate numeric variables","title":"Introduction to surveytable","text":"tab() tab_subset() functions also work numeric variables, though variables, output different. tabulate NUMMED (number medications), numeric variable, type: Number medications coded {NAMCS 2019 PUF} , table title shows variable label (long variable name) survey label. table shows percentage values missing (NA), mean, standard error mean (SEM), standard deviation (SD). Subsetting works : Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF}","code":"tab(\"NUMMED\") tab_subset(\"NUMMED\", \"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"perform-statistical-hypothesis-testing","dir":"Articles","previous_headings":"","what":"Perform statistical hypothesis testing","title":"Introduction to surveytable","text":"tab_subset() function makes easy perform hypothesis testing using test argument. argument TRUE, test association performed. addition, t-tests pairs levels performed well.","code":""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables","title":"Introduction to surveytable","text":"Consider relationship AGER SPECCAT: Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association Patient age recode Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Patient age recode (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 15-24 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 25-44 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 45-64 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 65-74 years) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (Patient age recode = 75 years ) {NAMCS 2019 PUF} According tables, association physician specialty type patient age. instance, patients 15 years, statistical difference primary care physician specialty medical care specialty. older patients, 45-64 age group, statistical difference two specialty types. another example, consider relationship MRI SPECCAT: MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Association MRI Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Primary care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Surgical care specialty) {NAMCS 2019 PUF} Comparison possible pairs MRI (Type specialty (Primary, Medical, Surgical) = Medical care specialty) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = FALSE) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) (MRI = TRUE) {NAMCS 2019 PUF} According tables, statistical association MRI physician specialty. 3 specialty types, minority visits MRI’s. visits MRI’s, statistical difference specialty types. general rule thumb, since statistical association MRI physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate MRI without subsetting SPECCAT.","code":"tab_subset(\"AGER\", \"SPECCAT\", test = TRUE) tab_subset(\"MRI\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"numeric-variables","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Numeric variables","title":"Introduction to surveytable","text":"relationship NUMMED AGER: Number medications coded (different levels Patient age recode) {NAMCS 2019 PUF} Association Number medications coded Patient age recode {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Patient age recode {NAMCS 2019 PUF} According tables, association number medications age category. NUMMED statistically similar “15 years” “15-24 years” AGER categories. statistically different pairs age categories. Finally, let’s look relationship NUMMED SPECCAT: Number medications coded (different levels Type specialty (Primary, Medical, Surgical)) {NAMCS 2019 PUF} Association Number medications coded Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison Number medications coded across possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According tables, association number medications physician specialty type. NUMMED statistically similar pairs physician specialties. general rule thumb, since statistical association number medications physician specialty, presenting tabulation particularly interesting, especially since subsetting decreases sample size therefore also decreases estimate reliability. Instead, generally make sense just tabulate NUMMED without subsetting SPECCAT.","code":"tab_subset(\"NUMMED\", \"AGER\", test = TRUE) tab_subset(\"NUMMED\", \"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"categorical-variables-single-variable","dir":"Articles","previous_headings":"Perform statistical hypothesis testing","what":"Categorical variables (single variable)","title":"Introduction to surveytable","text":"test whether pair SPECCAT levels statistically similar different, type: Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Comparison possible pairs Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} According , surgical medical care specialties statistically similar, statistically different primary care.","code":"tab(\"SPECCAT\", test = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"calculate-rates","dir":"Articles","previous_headings":"","what":"Calculate rates","title":"Introduction to surveytable","text":"rate ratio count estimates based survey question divided population size, assumed known. example, number physician visits per 100 people population rate: number physician visits estimated namcs2019sv survey, number people population comes another source. calculate rates, addition survey, need source information population size. typically use function read.csv() load population figures get correct format. surveytable package comes object called uspop2019 contains several population figures use examples. Let’s examine uspop2019: overall population size country whole : overall population size, overall rate : Total (rate per 100 population) {NAMCS 2019 PUF} calculate rates particular variable, need provide data frame column called Level matches levels variable survey, column called Population gives size population level. example, AGER, data frame follows: Now appropriate population figures, rates table obtained typing: Patient age recode (rate per 100 population) {NAMCS 2019 PUF} calculate rates one variable (AGER) another variable (SEX), need population figures following format: data frame, rates table obtained typing: Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF}","code":"class(uspop2019) #> [1] \"list\" names(uspop2019) #> [1] \"total\"       \"MSA\"         \"AGER\"        \"Age group\"   \"SEX\"         #> [6] \"AGER x SEX\"  \"Age group 5\" uspop2019$total #> [1] 323186697 total_rate(uspop2019$total) uspop2019$AGER #>               Level Population #> 1    Under 15 years   60526656 #> 2       15-24 years   41718700 #> 3       25-44 years   85599410 #> 4       45-64 years   82562049 #> 5       65-74 years   31260202 #> 6 75 years and over   21519680 tab_rate(\"AGER\", uspop2019$AGER) uspop2019$`AGER x SEX` #>                Level Subset Population #> 1     Under 15 years Female   29604762 #> 2        15-24 years Female   20730118 #> 3        25-44 years Female   43192143 #> 4        45-64 years Female   42508901 #> 5        65-74 years Female   16673240 #> 6  75 years and over Female   12421444 #> 7     Under 15 years   Male   30921894 #> 8        15-24 years   Male   20988582 #> 9        25-44 years   Male   42407267 #> 10       45-64 years   Male   40053148 #> 11       65-74 years   Male   14586962 #> 12 75 years and over   Male    9098236 tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-or-modify-variables","dir":"Articles","previous_headings":"","what":"Create or modify variables","title":"Introduction to surveytable","text":"situations, might necessary modify survey variables, create new ones. section describes . Convert factor logical. variable MAJOR (major reason visit) several levels. Major reason visit {NAMCS 2019 PUF} Notice one levels called \"Preventive care\". Suppose analyst interested whether visit preventive care visit – interested visit types. can create new variable called Preventive care visits TRUE preventive care visits FALSE types visits, follows: Preventive care visits {NAMCS 2019 PUF} creates logical variable TRUE preventive care visits tabulates . using var_case() function, specify name new logical variable created, existing factor variable, one levels factor variable set TRUE logical variable. Thus, analyst interested surgery-related visits, indicated two different levels MAJOR, type: Surgery-related visits {NAMCS 2019 PUF} Collapse levels. variable PRIMCARE (whether physician patient’s primary care provider) levels Unknown Blank, among others. patient’s primary care provider? {NAMCS 2019 PUF} collapse Unknown Blank single level, type: patient’s primary care provider? {NAMCS 2019 PUF} Convert numeric factor. variable AGE numeric. Patient age years (raw - use caution) {NAMCS 2019 PUF} create new variable age categories based AGE, type: Age group {NAMCS 2019 PUF} var_cut() command, specify following information: name new categorical variable; name existing numeric variable; cut points – note intervals inclusive right; category labels. cognizant “special values” numeric variable might . data systems, negative values indicate unknowns, coded NA. ’s – value -Inf -0.1 gets coded missing (NA). Though particular data, unknowns “special values”. Check whether variable true. series logical variables, can check whether TRUE using var_any() command. physician visit considered “imaging services” visit number imaging services ordered provided. Imaging services indicated using logical variables, MRI XRAY. create Imaging services variable, type: Imaging services {NAMCS 2019 PUF} Interact variables. tab_cross() function creates table interaction two variables, save interacted variable. create interacted variable, use var_cross() command: Specify name new variable well names two variables interact. Copy variable. Create new variable copy another variable using var_copy(). can modify copy, original remains unchanged. example: Patient age recode {NAMCS 2019 PUF} Age group {NAMCS 2019 PUF} , AGER variable remains unchanged, Age group variable fewer categories.","code":"tab(\"MAJOR\") var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") var_case(\"Surgery-related visits\"   , \"MAJOR\"   , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") tab(\"PRIMCARE\") var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Unknown\", \"Blank\")) tab(\"PRIMCARE\") tab(\"AGE\") var_cut(\"Age group\"    , \"AGE\"    , c(-Inf, -0.1, 0, 4, 14, 64, Inf)    , c(NA, \"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") var_any(\"Imaging services\"   , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\"   , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") var_cross(\"Age x Sex\", \"AGER\", \"SEX\") var_copy(\"Age group\", \"AGER\") #> Warning in var_copy(\"Age group\", \"AGER\"): Age group: overwriting a variable #> that already exists. var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\")"},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"save-the-output","dir":"Articles","previous_headings":"","what":"Save the output","title":"Introduction to surveytable","text":"tab* total* functions argument called csv specifies name comma-separated values (CSV) file save output . Alternatively, can name default CSV output file using set_output() function. example, following directs surveytable send future output CSV file, create tables, turn sending output file: Type doctor (MD ) {NAMCS 2019 PUF} Type specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} Metropolitan Statistical Area Status physician location {NAMCS 2019 PUF} tabulation functions called within R Markdown notebook Quarto document, produce HTML LaTeX tables, appropriate. makes easy incorporate output surveytable package directly documents, presentations, “shiny” web apps, output types. Finally, tabulation functions return tables produce. advanced analysts can use functionality integrate surveytable programming tasks.","code":"set_output(csv = \"output.csv\") tab(\"MDDO\", \"SPECCAT\", \"MSA\") set_output(csv = \"\") #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Alex Strashny. Author, maintainer.","code":""},{"path":"https://cdcgov.github.io/surveytable/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Strashny (2023). surveytable: Formatted Survey Estimates. doi:10.32614/CRAN.package.surveytable, https://cdcgov.github.io/surveytable/.","code":"@Manual{,   title = {surveytable: Formatted Survey Estimates},   author = {Alex Strashny},   year = {2023},   url = {https://cdcgov.github.io/surveytable/},   doi = {10.32614/CRAN.package.surveytable}, }"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"survey-table-formatted-survey-estimates","dir":"","previous_headings":"","what":"Formatted Survey Estimates","title":"Formatted Survey Estimates","text":"surveytable R package conveniently tabulating estimates complex surveys. deal survey objects R (created survey::svydesign()), package . Works complex surveys (data systems involve survey design variables, like weights strata). Works unweighted data well. surveytable package provides short understandable commands generate tabulated, formatted, rounded survey estimates. surveytable, can tabulate estimated counts percentages, standard errors confidence intervals, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, perform hypothesis tests, tabulate rates, modify survey variables, save output. Optionally, tabulation functions can identify low-precision estimates using National Center Health Statistics (NCHS) algorithms (algorithms). surveytable code called R Markdown notebook Quarto document, automatically generates HTML LaTeX tables, appropriate. package reduces number commands users need execute, especially helpful users new R programming.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"Install CRAN: get development version GitHub:","code":"install.packages(\"surveytable\") install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"documentation","dir":"","previous_headings":"","what":"Documentation","title":"Formatted Survey Estimates","text":"Find documentation surveytable : https://cdcgov.github.io/surveytable/","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Formatted Survey Estimates","text":"basic example, get started. Load package: Specify survey wish analyze. surveytable comes survey called namcs2019sv, use examples. Survey info {NAMCS 2019 PUF} Specify variable analyze. NAMCS, AGER age category variable: Patient age recode {NAMCS 2019 PUF} table shows: Descriptive variable name Survey name Number observations Estimated count SE 95% CI Estimated percentage SE 95% CI Sample size Optionally, table can show whether low-precision estimates found","code":"library(surveytable) set_survey(namcs2019sv) #> * Mode: General. tab(\"AGER\")"},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"public-domain-standard-notice","dir":"","previous_headings":"","what":"Public Domain Standard Notice","title":"Formatted Survey Estimates","text":"repository constitutes work United States Government subject domestic copyright protection 17 USC § 105. repository public domain within United States, copyright related rights work worldwide waived CC0 1.0 Universal public domain dedication. contributions repository released CC0 dedication. submitting pull request agreeing comply waiver copyright interest.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"license-standard-notice","dir":"","previous_headings":"","what":"License Standard Notice","title":"Formatted Survey Estimates","text":"repository utilizes code licensed terms Apache Software License therefore licensed ASL v2 later. source code repository free: can redistribute /modify terms Apache Software License version 2, (option) later version. source code repository distributed hope useful, WITHOUT WARRANTY; without even implied warranty MERCHANTABILITY FITNESS PARTICULAR PURPOSE. See Apache Software License details. received copy Apache Software License along program. , see https://www.apache.org/licenses/LICENSE-2.0.html source code forked open source projects inherit license.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"privacy-standard-notice","dir":"","previous_headings":"","what":"Privacy Standard Notice","title":"Formatted Survey Estimates","text":"repository contains non-sensitive, publicly available data information. material community participation covered Disclaimer Code Conduct. information CDC’s privacy policy, please visit https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"contributing-standard-notice","dir":"","previous_headings":"","what":"Contributing Standard Notice","title":"Formatted Survey Estimates","text":"Anyone encouraged contribute repository forking submitting pull request. 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Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a codebook for the survey — codebook","title":"Create a codebook for the survey — codebook","text":"Create codebook survey","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a codebook for the survey — codebook","text":"","code":"codebook(all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a codebook for the survey — codebook","text":"tabulate variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a codebook for the survey — codebook","text":"list tables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/codebook.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a codebook for the survey — codebook","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  codebook() #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  #>                                     Codebook {NAMCS 2019 PUF}                                      #> ┌──────────┬─────────────┬───────────────────────┬─────────┬─────────────┬───────────────────────┐ #> │ Item no. │ Variable    │ Description           │ Class   │ Missing (%) │ Values                │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        1 │ CPSUM       │ Masked provider       │ numeric │           0 │ 100001 - 100398       │ #> │          │             │ marker                │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        2 │ CSTRATM     │ Masked sampling       │ numeric │           0 │ 10119101 - 10419115   │ #> │          │             │ stratum from which    │         │             │                       │ #> │          │             │ provider was selected │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        3 │ PATWT       │ Patient visit weight  │ numeric │           0 │ 7064.00718 -          │ #> │          │             │ used for national and │         │             │ 1120996.55599         │ #> │          │             │ subnational estimates │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        4 │ MDDO        │ Type of doctor (MD or │ factor  │           0 │ M.D. - Doctor of      │ #> │          │             │ DO)                   │         │             │ Medicine, D.O. -      │ #> │          │             │                       │         │             │ Doctor of Osteopathy  │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        5 │ SPECCAT     │ Type of specialty     │ factor  │           0 │ Primary care          │ #> │          │             │ (Primary, Medical,    │         │             │ specialty, Surgical   │ #> │          │             │ Surgical)             │         │             │ care specialty,       │ #> │          │             │                       │         │             │ Medical care          │ #> │          │             │                       │         │             │ specialty             │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        6 │ MSA         │ Metropolitan          │ factor  │           0 │ MSA (Metropolitan     │ #> │          │             │ Statistical Area      │         │             │ Statistical Area),    │ #> │          │             │ Status of physician   │         │             │ Non-MSA               │ #> │          │             │ location              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        7 │ AGER        │ Patient age recode    │ factor  │           0 │ Under 15 years, 15-24 │ #> │          │             │                       │         │             │ years, 25-44 years,   │ #> │          │             │                       │         │             │ 45-64 years, 65-74    │ #> │          │             │                       │         │             │ years, 75 years and   │ #> │          │             │                       │         │             │ over                  │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        8 │ SEX         │ Patient sex           │ factor  │           0 │ Female, Male          │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │        9 │ AGE         │ Patient age in years  │ numeric │           0 │ 0 - 94                │ #> │          │             │ (raw - use caution)   │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       10 │ NOPAY       │ Expected source of    │ factor  │           0 │ One or more           │ #> │          │             │ payment for visit: No │         │             │ categories marked, No │ #> │          │             │ answer to item        │         │             │ categories marked     │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       11 │ PAYPRIV     │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Private insurance     │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       12 │ PAYMCARE    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Medicare              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       13 │ PAYMCAID    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Medicaid or CHIP or   │         │             │                       │ #> │          │             │ other state-based     │         │             │                       │ #> │          │             │ program               │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       14 │ PAYWKCMP    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Workers Compensation  │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       15 │ PAYOTH      │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Other                 │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       16 │ PAYDK       │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Unknown               │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       17 │ PAYSELF     │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit:    │         │             │                       │ #> │          │             │ Self-pay              │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       18 │ PAYNOCHG    │ Expected source of    │ logical │           0 │                       │ #> │          │             │ payment for visit: No │         │             │                       │ #> │          │             │ Charge/Charity        │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       19 │ PRIMCARE    │ Are you the patient's │ factor  │           0 │ Blank, Unknown, Yes,  │ #> │          │             │ primary care          │         │             │ No                    │ #> │          │             │ provider?             │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       20 │ REFER       │ Was patient referred  │ factor  │           0 │ Blank, Unknown, Not   │ #> │          │             │ for visit?            │         │             │ applicable, Yes, No   │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       21 │ SENBEFOR    │ Has this patient been │ factor  │           0 │ Yes, established      │ #> │          │             │ seen in your practice │         │             │ patient, No, new      │ #> │          │             │ before?               │         │             │ patient               │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       22 │ MAJOR       │ Major reason for this │ factor  │           0 │ Blank, New problem    │ #> │          │             │ visit                 │         │             │ (less than 3 mos.     │ #> │          │             │                       │         │             │ onset), Chronic       │ #> │          │             │                       │         │             │ problem, routine,     │ #> │          │             │                       │         │             │ Chronic problem,      │ #> │          │             │                       │         │             │ flare-up,             │ #> │          │             │                       │         │             │ Pre-surgery,          │ #> │          │             │                       │         │             │ Post-surgery,         │ #> │          │             │                       │         │             │ Preventive care       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       23 │ NUMMED      │ Number of medications │ numeric │           0 │ 0 - 30                │ #> │          │             │ coded                 │         │             │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       24 │ ANYIMAGE    │ Any imaging           │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       25 │ BONEDENS    │ Bone mineral density  │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       26 │ CATSCAN     │ CT Scan               │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       27 │ ECHOCARD    │ Echocardiogram        │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       28 │ OTHULTRA    │ Ultrasound            │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       29 │ MAMMO       │ Mammography           │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       30 │ MRI         │ MRI                   │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       31 │ XRAY        │ X-ray                 │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       32 │ OTHIMAGE    │ Other imaging         │ logical │           0 │                       │ #> ├──────────┼─────────────┼───────────────────────┼─────────┼─────────────┼───────────────────────┤ #> │       33 │ SPECCAT.bad │ Type of specialty     │ factor  │          20 │ Primary care          │ #> │          │             │ (BAD - do not use)    │         │             │ specialty, Surgical   │ #> │          │             │                       │         │             │ care specialty,       │ #> │          │             │                       │         │             │ Medical care          │ #> │          │             │                       │         │             │ specialty             │ #> └──────────┴─────────────┴───────────────────────┴─────────┴─────────────┴───────────────────────┘ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":null,"dir":"Reference","previous_headings":"","what":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"Selected variables data system visits office-based physicians. Note unit observation visits, patients - distinction important since single patient can make multiple visits.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"","code":"namcs2019sv  namcs2019sv_df"},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"object class survey.design2 (inherits survey.design) 8250 rows 33 columns. object class data.frame 8250 rows 33 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/namcs2019_sas.zip Survey design variables: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/readme2019-sas.txt SAS formats: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/nam19for.txt Documentation: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2019-508.pdf National Summary Tables: https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/namcs2019sv.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) — namcs2019sv","text":"namcs2019sv_df data frame. namcs2019sv survey object created namcs2019sv_df using survey::svydesign().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Print surveytable tables — print.surveytable_table","title":"Print surveytable tables — print.surveytable_table","text":"Print surveytable tables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print surveytable tables — print.surveytable_table","text":"","code":"# S3 method for surveytable_table print(x, .output = NULL, ...)  # S3 method for surveytable_list print(x, .output = NULL, ...)"},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print surveytable tables — print.surveytable_table","text":"x object class surveytable_table surveytable_list. .output output type. NULL = auto-detect. ... ignored","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print surveytable tables — print.surveytable_table","text":"x invisibly.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print surveytable tables — print.surveytable_table","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  table1 = tab(\"AGER\") print(table1) #>                                     Patient age recode {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  table_many = tab(\"MDDO\", \"SPECCAT\", \"MSA\") print(table_many) #>                                  Type of doctor (MD or DO) {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ M.D. -      │ 7,498 │ 980,280,219 │ 48,387,921 │ 889,841,831 │ 1,079,910,2 │    94.6 │ 0.7 │ 93.1 │ 95.8 │ #> │ Doctor of   │       │             │            │             │          43 │         │     │      │      │ #> │ Medicine    │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ D.O. -      │   752 │  56,204,137 │  6,601,909 │  44,596,891 │  70,832,404 │     5.4 │ 0.7 │  4.2 │  6.9 │ #> │ Doctor of   │       │             │            │             │             │         │     │      │      │ #> │ Osteopathy  │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #>                       Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                        #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Primary     │ 2,993 │ 521,466,378 │ 31,136,212 │ 463,840,192 │ 586,251,877 │    50.3 │ 2.6 │ 45.1 │ 55.5 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Surgical    │ 3,050 │ 214,831,829 │ 31,110,335 │ 161,661,415 │ 285,489,984 │    20.7 │ 3   │ 15.1 │ 27.3 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Medical     │ 2,207 │ 300,186,150 │ 43,496,739 │ 225,806,019 │ 399,066,973 │    29   │ 3.6 │ 22.1 │ 36.6 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #>                 Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}                  #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ MSA         │ 7,496 │ 973,675,566 │ 50,514,928 │ 879,490,192 │ 1,077,947,3 │    93.9 │ 1.7 │ 89.6 │ 96.8 │ #> │ (Metropolit │       │             │            │             │          34 │         │     │      │      │ #> │ an          │       │             │            │             │             │         │     │      │      │ #> │ Statistical │       │             │            │             │             │         │     │      │      │ #> │ Area)       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Non-MSA     │   754 │  62,808,790 │ 17,549,184 │  36,248,698 │ 108,829,955 │     6.1 │ 1.7 │  3.2 │ 10.4 │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":null,"dir":"Reference","previous_headings":"","what":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"data system RCC residents.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"","code":"rccsu2018"},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"object class survey.design2 (inherits survey.design) 904 rows 81 columns.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/rccsu2018.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) — rccsu2018","text":"SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NPALS/final2018rcc_su_puf.sas7bdat Documentation: https://www.cdc.gov/nchs/npals/RCCresident-readme03152021vr.pdf Codebook: https://www.cdc.gov/nchs/data/npals/final2018rcc_su_puf_codebook.pdf","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":null,"dir":"Reference","previous_headings":"","what":"Rounding counts — set_count_1k","title":"Rounding counts — set_count_1k","text":"Determines counts rounded.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rounding counts — set_count_1k","text":"","code":"set_count_1k()  set_count_int()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rounding counts — set_count_1k","text":"(Nothing.)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rounding counts — set_count_1k","text":"set_count_1k(): round counts nearest 1,000. set_count_int(): round counts nearest integer.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_count_1k.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rounding counts — set_count_1k","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #>                        Total {NAMCS 2019 PUF}                        #> ┌───────┬───────────────┬────────────┬─────────────┬───────────────┐ #> │     n │        Number │         SE │          LL │            UL │ #> ├───────┼───────────────┼────────────┼─────────────┼───────────────┤ #> │ 8,250 │ 1,036,484,356 │ 48,836,217 │ 945,013,590 │ 1,136,808,860 │ #> └───────┴───────────────┴────────────┴─────────────┴───────────────┘ #>   N = 8250.                                                          #>   set_count_1k() #> * Rounding counts to the nearest 1,000. #> * ?set_count_1k for other options. total() #>                   Total {NAMCS 2019 PUF}                   #> ┌───────┬──────────────┬──────────┬──────────┬───────────┐ #> │     n │ Number (000) │ SE (000) │ LL (000) │  UL (000) │ #> ├───────┼──────────────┼──────────┼──────────┼───────────┤ #> │ 8,250 │    1,036,484 │   48,836 │  945,014 │ 1,136,809 │ #> └───────┴──────────────┴──────────┴──────────┴───────────┘ #>   N = 8250.                                                #>"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":null,"dir":"Reference","previous_headings":"","what":"Set output defaults — set_output","title":"Set output defaults — set_output","text":"show_output() shows current defaults.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set output defaults — set_output","text":"","code":"set_output(drop_na = NULL, max_levels = NULL, csv = NULL)  show_output()"},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set output defaults — set_output","text":"drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file \"\" turn CSV output","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set output defaults — set_output","text":"(Nothing.)","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_output.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set output defaults — set_output","text":"","code":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) tab(\"AGER\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                          #>  set_output(csv = \"\") # Turn off CSV output #> * Turning off CSV output. #> * ?set_output for other options."},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":null,"dir":"Reference","previous_headings":"","what":"Specify the survey to analyze — set_survey","title":"Specify the survey to analyze — set_survey","text":"must specify survey functions, tab(), work. convert data.frame survey object, see survey::svydesign() survey::svrepdesign().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(design, mode = \"default\", csv = getOption(\"surveytable.csv\"))  set_mode(mode = \"default\")"},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specify the survey to analyze — set_survey","text":"design either survey object (created survey::svydesign() survey::svrepdesign()); , unweighted survey, data.frame. mode set certain options. See . csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specify the survey to analyze — set_survey","text":"set_survey: info survey. set_mode: nothing.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Specify the survey to analyze — set_survey","text":"Optionally, survey can attribute called label, long name survey. Optionally, variable survey can attribute called label, variable's long name. sure mode , leave \"default\". mode : \"general\" \"default\": Round counts nearest integer -- see set_count_int(). look low-precision estimates. Percentage CI's: use standard Korn-Graubard CI's. \"nchs\": Round counts nearest 1,000 -- see set_count_1k(). Identify low-precision estimates. Percentage CI's: adjust Korn-Graubard CI's number degrees freedom, matching SUDAAN calculation.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/set_survey.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specify the survey to analyze — set_survey","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  set_mode(\"general\") #> * Mode: General."},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":null,"dir":"Reference","previous_headings":"","what":"Show package options — show_options","title":"Show package options — show_options","text":"See surveytable-options discussion options.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Show package options — show_options","text":"","code":"show_options(sw = \"surveytable\")"},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Show package options — show_options","text":"sw starting characters","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Show package options — show_options","text":"List options values.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/show_options.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Show package options — show_options","text":"","code":"show_options() #> $surveytable.adjust_svyciprop #> [1] FALSE #>  #> $surveytable.adjust_svyciprop.df_method #> [1] \"NHIS\" #>  #> $surveytable.csv #> [1] \"\" #>  #> $surveytable.drop_na #> [1] FALSE #>  #> $surveytable.find_lpe #> [1] FALSE #>  #> $surveytable.lpe_counts #> [1] \".lpe_counts\" #>  #> $surveytable.lpe_n #> [1] \".lpe_n\" #>  #> $surveytable.lpe_percents #> [1] \".lpe_percents\" #>  #> $surveytable.max_levels #> [1] 20 #>  #> $surveytable.names_count #> [1] \"n\"      \"Number\" \"SE\"     \"LL\"     \"UL\"     #>  #> $surveytable.names_prct #> [1] \"Percent\" \"SE\"      \"LL\"      \"UL\"      #>  #> $surveytable.p.adjust_method #> [1] \"bonferroni\" #>  #> $surveytable.rate_per #> [1] 100 #>  #> $surveytable.survey_label #> [1] \"NAMCS 2019 PUF\" #>  #> $surveytable.svychisq_statistic #> [1] \"F\" #>  #> $surveytable.tx_count #> [1] \".tx_count_int\" #>  #> $surveytable.tx_numeric #> [1] \".tx_numeric\" #>  #> $surveytable.tx_prct #> [1] \".tx_prct\" #>  #> $surveytable.tx_rate #> [1] \".tx_rate\" #>"},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":null,"dir":"Reference","previous_headings":"","what":"Package options — surveytable-options","title":"Package options — surveytable-options","text":"Run show_options() see available options. description notable options.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":"low-precision-estimates-","dir":"Reference","previous_headings":"","what":"Low-precision estimates.","title":"Package options — surveytable-options","text":"Optionally, tabulation functions can identify low-precision estimates. turn functionality, either set surveytable.find_lpe option TRUE, call set_survey() set_mode() argument mode = \"NCHS\". default, low-precision estimates identified using National Center Health Statistics (NCHS) algorithms. However, can changed, described . description options related identification low-precision estimates. surveytable.find_lpe: tabulation functions look low-precision estimates? can change directly options() mode argument set_survey() set_mode(). surveytable.lpe_n, surveytable.lpe_counts, surveytable.lpe_percents: names 3 functions. argument surveytable.lpe_n vector number observations level variable. argument surveytable.lpe_counts data frame count-related estimates. Specifically, data frame following variables: x: point estimates counts s: SE ll, ul: CI samp.size: effective sample size counts: actual sample size degf: degrees freedom argument surveytable.lpe_percents data frame percent-related estimates. Specifically, data frame following variables: Proportion: point estimates proportions (0 1) SE: SE LL, UL: CI n numerator: number observations variable TRUE n denominator: total number observations functions must return list following elements: id: name algorithm used, \"NCHS presentation standards\" flags: vector. level variable, short codes indicating presence low-precision estimates. .flag: vector short codes present flags. descriptions: named vector. names must short codes, values longer descriptions. example, variable 3 levels, flags might c(\"\", \"A1 A2\", \"\"). indicates first third level, nothing found, whereas second level, two different things found, indicated short codes A1 A2. case, .flag = c(\"A1\", \"A2\"), descriptions = c(A1 = \"A1: something\", A2 = \"A2: something else\").","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-options.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Package options — surveytable-options","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":null,"dir":"Reference","previous_headings":"","what":"surveytable: Formatted Survey Estimates — surveytable-package","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Short understandable commands generate tabulated, formatted, rounded survey estimates. Mostly wrapper 'survey' package (Lumley (2004) doi:10.18637/jss.v009.i08  https://CRAN.R-project.org/package=survey) identifies low-precision estimates using National Center Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) doi:10.15620/cdc:124368 ).","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/surveytable-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"surveytable: Formatted Survey Estimates — surveytable-package","text":"Maintainer: Alex Strashny AStrashny@cdc.gov (ORCID)","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset a survey, while preserving variable labels — survey_subset","title":"Subset a survey, while preserving variable labels — survey_subset","text":"Subset survey, preserving variable labels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"survey_subset(design, subset, label)"},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset a survey, while preserving variable labels — survey_subset","text":"design survey object subset expression specifying sub-population label survey label newly created survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset a survey, while preserving variable labels — survey_subset","text":"new survey object","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/survey_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset a survey, while preserving variable labels — survey_subset","text":"","code":"children = survey_subset(namcs2019sv, AGE < 18, \"Children < 18\") set_survey(children) #> * Mode: General. #>                          Survey info {Children < 18}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        1,066 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (139) clusters.                           │ #> │           │              │ survey_subset(namcs2019sv, AGE < 18, \"Children │ #> │           │              │ < 18\")                                         │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"AGER\") #>                                    Patient age recode {Children < 18}                                     #> ┌─────────────┬─────┬─────────────┬────────────┬────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │   n │      Number │         SE │         LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼─────┼─────────────┼────────────┼────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │ 887 │ 117,916,772 │ 14,097,315 │ 93,228,928 │ 149,142,177 │    86.1 │ 1.6 │ 82.5 │ 89.2 │ #> │ years       │     │             │            │            │             │         │     │      │      │ #> ├─────────────┼─────┼─────────────┼────────────┼────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │ 179 │  19,003,548 │  2,871,580 │ 14,050,905 │  25,701,891 │    13.9 │ 1.6 │ 10.8 │ 17.5 │ #> └─────────────┴─────┴─────────────┴────────────┴────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 1066.                                                                                               #>"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":null,"dir":"Reference","previous_headings":"","what":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"version survey::svyciprop() adjusts degrees freedom method = \"beta\".","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"svyciprop_adjusted(   formula,   design,   method = c(\"logit\", \"likelihood\", \"asin\", \"beta\", \"mean\", \"xlogit\"),   level = 0.95,   df_method,   ... )"},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"formula see survey::svyciprop(). design see survey::svyciprop(). method see survey::svyciprop(). level see survey::svyciprop(). df_method df calculated: \"default\" \"NHIS\". ... see survey::svyciprop().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"point estimate proportion, confidence interval attribute.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"Written Makram Talih 2019. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1. use function tabulations, call set_survey() set_mode() mode = \"NCHS\" argument, type: options(surveytable.adjust_svyciprop = TRUE).","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/svyciprop_adjusted.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Confidence intervals for proportions, adjusted for degrees of freedom — svyciprop_adjusted","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  set_mode(\"NCHS\") #> * Mode: NCHS. tab(\"AGER\") #>                                 Patient age recode {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬──────────┬──────────┬──────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │ SE (000) │ LL (000) │ UL (000) │ Percent │  SE │   LL │   UL │ #> │             │       │       (000) │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │     117,917 │   14,097 │   93,229 │  149,142 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │          │          │          │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │      64,856 │    7,018 │   52,387 │   80,292 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │     170,271 │   13,966 │  144,925 │  200,049 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │     309,506 │   23,290 │  266,994 │  358,787 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │     206,866 │   14,366 │  180,481 │  237,109 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼──────────┼──────────┼──────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │     167,069 │   15,179 │  139,746 │  199,735 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │          │          │          │         │     │      │      │ #> └─────────────┴───────┴─────────────┴──────────┴──────────┴──────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250. Checked NCHS presentation standards. Nothing to report.                                  #>  set_mode(\"general\") #> * Mode: General."},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate variables — tab","title":"Tabulate variables — tab","text":"Tabulate categorical (factor), logical, numeric variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate variables — tab","text":"","code":"tab(   ...,   test = FALSE,   alpha = 0.05,   p_adjust = FALSE,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate variables — tab","text":"... names variables (quotes) test perform hypothesis tests? alpha significance level tests p_adjust adjust p-values multiple comparisons? drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate variables — tab","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate variables — tab","text":"categorical logical variables, presents estimated counts, standard errors (SEs) confidence intervals (CIs), percentages, SEs CIs. Checks presentation guidelines counts percentages flags estimates , according guidelines, suppressed, footnoted, reviewed analyst. numeric variables, presents percentage observations known values, mean known values, standard error mean (SEM), standard deviation (SD). CIs calculated 95% confidence level. CIs count estimates log Student's t CIs, adaptations complex surveys. CIs percentage estimates Korn Graubard CIs.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate variables — tab","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"AGER\") #>                                     Patient age recode {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  tab(\"MDDO\", \"SPECCAT\", \"MSA\") #>                                  Type of doctor (MD or DO) {NAMCS 2019 PUF}                                  #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ M.D. -      │ 7,498 │ 980,280,219 │ 48,387,921 │ 889,841,831 │ 1,079,910,2 │    94.6 │ 0.7 │ 93.1 │ 95.8 │ #> │ Doctor of   │       │             │            │             │          43 │         │     │      │      │ #> │ Medicine    │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ D.O. -      │   752 │  56,204,137 │  6,601,909 │  44,596,891 │  70,832,404 │     5.4 │ 0.7 │  4.2 │  6.9 │ #> │ Doctor of   │       │             │            │             │             │         │     │      │      │ #> │ Osteopathy  │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #>                       Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF}                        #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Primary     │ 2,993 │ 521,466,378 │ 31,136,212 │ 463,840,192 │ 586,251,877 │    50.3 │ 2.6 │ 45.1 │ 55.5 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Surgical    │ 3,050 │ 214,831,829 │ 31,110,335 │ 161,661,415 │ 285,489,984 │    20.7 │ 3   │ 15.1 │ 27.3 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Medical     │ 2,207 │ 300,186,150 │ 43,496,739 │ 225,806,019 │ 399,066,973 │    29   │ 3.6 │ 22.1 │ 36.6 │ #> │ care        │       │             │            │             │             │         │     │      │      │ #> │ specialty   │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #>                 Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF}                  #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ MSA         │ 7,496 │ 973,675,566 │ 50,514,928 │ 879,490,192 │ 1,077,947,3 │    93.9 │ 1.7 │ 89.6 │ 96.8 │ #> │ (Metropolit │       │             │            │             │          34 │         │     │      │      │ #> │ an          │       │             │            │             │             │         │     │      │      │ #> │ Statistical │       │             │            │             │             │         │     │      │      │ #> │ Area)       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Non-MSA     │   754 │  62,808,790 │ 17,549,184 │  36,248,698 │ 108,829,955 │     6.1 │ 1.7 │  3.2 │ 10.4 │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>   # Numeric variables tab(\"NUMMED\") #> Number of medications coded {NAMCS 2019 PUF} #> ┌─────────┬──────┬───────┬──────┐ #> │ % known │ Mean │   SEM │   SD │ #> ├─────────┼──────┼───────┼──────┤ #> │     100 │ 3.46 │ 0.268 │ 4.43 │ #> └─────────┴──────┴───────┴──────┘ #>   # Hypothesis testing with categorical variables tab(\"AGER\", test = TRUE) #>                                     Patient age recode {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #> Comparison of all possible pairs of Patient age recode {NAMCS 2019 PUF} #> ┌────────────────┬───────────────────┬─────────┬──────┐ #> │ Level 1        │ Level 2           │ p-value │ Flag │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 15-24 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 25-44 years       │   0.012 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 75 years and over │   0.022 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 25-44 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 65-74 years       │   0.065 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 75 years and over │   0.878 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 65-74 years    │ 75 years and over │   0.019 │ *    │ #> └────────────────┴───────────────────┴─────────┴──────┘ #>   Design-based t-test. *: p <= 0.05                     #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates — tab_rate","title":"Calculate rates — tab_rate","text":"Calculate rates categorical (factor) logical variables.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates — tab_rate","text":"","code":"tab_rate(   vr,   pop,   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates — tab_rate","text":"vr variable tabulate pop either single number data.frame columns named Level Population. Level must exactly match levels vr. Population population level vr. per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates — tab_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates — tab_rate","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  # pop is a data frame tab_rate(\"MSA\", uspop2019$MSA) #> Metropolitan Statistical Area Status of physician location (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────────────────────┬───────┬───────┬──────┬───────┬───────┐ #> │ Level                 │     n │  Rate │   SE │    LL │    UL │ #> ├───────────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ MSA (Metropolitan     │ 7,496 │ 351.2 │ 18.2 │ 317.2 │ 388.8 │ #> │ Statistical Area)     │       │       │      │       │       │ #> ├───────────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ Non-MSA               │   754 │ 136.7 │ 38.2 │  78.9 │ 236.8 │ #> └───────────────────────┴───────┴───────┴──────┴───────┴───────┘ #>   N = 8250.                                                      #>   # pop is a single number tab_rate(\"MDDO\", uspop2019$total) #> * Rate based on the entire population. #> Type of doctor (MD or DO) (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────────────────────┬───────┬───────┬────┬───────┬───────┐ #> │ Level                 │     n │  Rate │ SE │    LL │    UL │ #> ├───────────────────────┼───────┼───────┼────┼───────┼───────┤ #> │ M.D. - Doctor of      │ 7,498 │ 303.3 │ 15 │ 275.3 │ 334.1 │ #> │ Medicine              │       │       │    │       │       │ #> ├───────────────────────┼───────┼───────┼────┼───────┼───────┤ #> │ D.O. - Doctor of      │   752 │  17.4 │  2 │  13.8 │  21.9 │ #> │ Osteopathy            │       │       │    │       │       │ #> └───────────────────────┴───────┴───────┴────┴───────┴───────┘ #>   N = 8250.                                                    #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":null,"dir":"Reference","previous_headings":"","what":"Tabulate subsets or interactions — tab_cross","title":"Tabulate subsets or interactions — tab_cross","text":"Create subsets survey using one variable, tabulate another variable within subsets. Interact two variables tabulate.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"tab_cross(   vr,   vrby,   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )  tab_subset(   vr,   vrby,   lvls = c(),   test = FALSE,   alpha = 0.05,   p_adjust = FALSE,   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tabulate subsets or interactions — tab_cross","text":"vr variable tabulate vrby use variable subset survey max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file lvls (optional) show levels vrby test perform hypothesis tests? alpha significance level tests p_adjust adjust p-values multiple comparisons? drop_na drop missing values (NA)? Categorical variables .","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tabulate subsets or interactions — tab_cross","text":"list tables single table.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Tabulate subsets or interactions — tab_cross","text":"tab_subset creates subsets using levels vrby, tabulates vr subset. Optionally, use lvls levels vrby. vr can categorical (factor), logical, numeric. tab_cross crosses interacts vr vrby tabulates new variable. Tables created using tab_subset tab_cross counts different percentages. tab_subset, percentages within subset add 100%. tab_cross, percentages across entire population add 100%. Also see var_cross(). test = TRUE performs test association two variables. Also performs t-tests possible pairs levels vr vrby.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tabulate subsets or interactions — tab_cross","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>   # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #>                          Patient age recode (Patient sex = Female) {NAMCS 2019 PUF}                          #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │  59,957,823 │  7,205,594 │  47,318,228 │  75,973,693 │     9.9 │ 1.2 │  7.6 │ 12.6 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   346 │  41,128,003 │  4,532,466 │  33,065,609 │  51,156,253 │     6.8 │ 0.7 │  5.4 │  8.4 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │   923 │ 113,708,461 │ 11,461,189 │  93,256,445 │ 138,645,797 │    18.8 │ 1.6 │ 15.8 │ 22.1 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 1,253 │ 175,978,133 │ 16,008,541 │ 147,152,826 │ 210,449,940 │    29.1 │ 1.7 │ 25.7 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │   891 │ 120,099,493 │ 11,066,146 │ 100,171,315 │ 143,992,203 │    19.8 │ 1.5 │ 17   │ 22.9 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │  94,173,155 │ 11,085,372 │  74,682,310 │ 118,750,789 │    15.6 │ 1.5 │ 12.8 │ 18.7 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 4609.                                                                                                  #>  #>                           Patient age recode (Patient sex = Male) {NAMCS 2019 PUF}                           #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │  57,958,950 │  7,727,594 │  44,569,688 │  75,370,504 │    13.4 │ 1.7 │ 10.3 │ 17.1 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   196 │  23,727,695 │  4,343,932 │  16,457,071 │  34,210,431 │     5.5 │ 0.8 │  4   │  7.4 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │   512 │  56,562,143 │  7,276,983 │  43,860,836 │  72,941,520 │    13.1 │ 1.3 │ 10.7 │ 15.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 1,030 │ 133,527,822 │ 12,956,239 │ 110,319,199 │ 161,619,006 │    30.9 │ 1.6 │ 27.8 │ 34.3 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │   770 │  86,766,489 │  6,766,876 │  74,409,284 │ 101,175,865 │    20.1 │ 1.5 │ 17.3 │ 23.1 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │  72,896,189 │  6,660,855 │  60,871,965 │  87,295,593 │    16.9 │ 1.5 │ 14   │ 20.2 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 3641.                                                                                                  #>   # Same counts as tab_subset(), but different percentages. tab_cross(\"AGER\", \"SEX\") #>                            (Patient age recode) x (Patient sex) {NAMCS 2019 PUF}                             #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │  59,957,823 │  7,205,594 │  47,318,228 │  75,973,693 │     5.8 │ 0.7 │  4.5 │  7.3 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   346 │  41,128,003 │  4,532,466 │  33,065,609 │  51,156,253 │     4   │ 0.4 │  3.2 │  4.9 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   923 │ 113,708,461 │ 11,461,189 │  93,256,445 │ 138,645,797 │    11   │ 1   │  9   │ 13.2 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,253 │ 175,978,133 │ 16,008,541 │ 147,152,826 │ 210,449,940 │    17   │ 1.1 │ 14.8 │ 19.3 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   891 │ 120,099,493 │ 11,066,146 │ 100,171,315 │ 143,992,203 │    11.6 │ 1   │  9.7 │ 13.7 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │  94,173,155 │ 11,085,372 │  74,682,310 │ 118,750,789 │     9.1 │ 0.9 │  7.3 │ 11.1 │ #> │ and over:   │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │  57,958,950 │  7,727,594 │  44,569,688 │  75,370,504 │     5.6 │ 0.7 │  4.3 │  7.2 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   196 │  23,727,695 │  4,343,932 │  16,457,071 │  34,210,431 │     2.3 │ 0.4 │  1.6 │  3.2 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   512 │  56,562,143 │  7,276,983 │  43,860,836 │  72,941,520 │     5.5 │ 0.6 │  4.3 │  6.8 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,030 │ 133,527,822 │ 12,956,239 │ 110,319,199 │ 161,619,006 │    12.9 │ 1   │ 10.9 │ 15.1 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   770 │  86,766,489 │  6,766,876 │  74,409,284 │ 101,175,865 │     8.4 │ 0.6 │  7.2 │  9.7 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │  72,896,189 │  6,660,855 │  60,871,965 │  87,295,593 │     7   │ 0.6 │  5.9 │  8.3 │ #> │ and over:   │       │             │            │             │             │         │     │      │      │ #> │ Male        │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>   # Numeric variables tab_subset(\"NUMMED\", \"AGER\") #> Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} #> ┌───────────────────┬─────────┬──────┬───────┬──────┐ #> │ Level             │ % known │ Mean │   SEM │   SD │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ Under 15 years    │     100 │ 1.58 │ 0.168 │ 1.75 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 15-24 years       │     100 │ 1.64 │ 0.112 │ 1.7  │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 25-44 years       │     100 │ 2.15 │ 0.225 │ 2.74 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 45-64 years       │     100 │ 3.49 │ 0.303 │ 4.49 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 65-74 years       │     100 │ 4.44 │ 0.431 │ 5.03 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 75 years and over │     100 │ 5.53 │ 0.494 │ 5.59 │ #> └───────────────────┴─────────┴──────┴───────┴──────┘ #>   # Hypothesis testing tab_subset(\"NUMMED\", \"AGER\", test = TRUE) #> Number of medications coded (for different levels of Patient age recode) {NAMCS 2019 PUF} #> ┌───────────────────┬─────────┬──────┬───────┬──────┐ #> │ Level             │ % known │ Mean │   SEM │   SD │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ Under 15 years    │     100 │ 1.58 │ 0.168 │ 1.75 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 15-24 years       │     100 │ 1.64 │ 0.112 │ 1.7  │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 25-44 years       │     100 │ 2.15 │ 0.225 │ 2.74 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 45-64 years       │     100 │ 3.49 │ 0.303 │ 4.49 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 65-74 years       │     100 │ 4.44 │ 0.431 │ 5.03 │ #> ├───────────────────┼─────────┼──────┼───────┼──────┤ #> │ 75 years and over │     100 │ 5.53 │ 0.494 │ 5.59 │ #> └───────────────────┴─────────┴──────┴───────┴──────┘ #>  #> Association between Number of medications coded and Patient age recode {NAMCS 2019 PUF} #> ┌──────────────┬──────────────┐ #> │      p-value │ Flag         │ #> ├──────────────┼──────────────┤ #> │            0 │ *            │ #> └──────────────┴──────────────┘ #>   Wald test. *: p <= 0.05       #>  #> Comparison of Number of medications coded across all possible pairs of Patient age recode {NAMCS 2019 PUF} #> ┌────────────────┬───────────────────┬─────────┬──────┐ #> │ Level 1        │ Level 2           │ p-value │ Flag │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 15-24 years       │   0.739 │      │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 25-44 years       │   0.043 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ Under 15 years │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 25-44 years       │   0.029 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 15-24 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 45-64 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 65-74 years       │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 25-44 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 65-74 years       │   0.007 │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 45-64 years    │ 75 years and over │   0     │ *    │ #> ├────────────────┼───────────────────┼─────────┼──────┤ #> │ 65-74 years    │ 75 years and over │   0.002 │ *    │ #> └────────────────┴───────────────────┴─────────┴──────┘ #>   Design-based t-test. *: p <= 0.05                     #>"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate rates for subsets — tab_subset_rate","title":"Calculate rates for subsets — tab_subset_rate","text":"Create subsets survey using one variable, tabulate rates another variable within subsets.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"tab_subset_rate(   vr,   vrby,   pop,   lvls = c(),   per = getOption(\"surveytable.rate_per\"),   drop_na = getOption(\"surveytable.drop_na\"),   max_levels = getOption(\"surveytable.max_levels\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate rates for subsets — tab_subset_rate","text":"vr variable tabulate vrby use variable subset survey pop data.frame columns named Level, Subset, Population. Level must exactly match levels vr. Subset must exactly match levels vrby. Population population level vr vrby. lvls (optional) show levels vrby per calculate rate per many items population drop_na drop missing values (NA)? max_levels categorical variable can many levels. Used avoid printing huge tables. csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate rates for subsets — tab_subset_rate","text":"list tables single table.","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/tab_subset_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate rates for subsets — tab_subset_rate","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`) #> Patient age recode (Patient sex = Female) (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────────────────┬───────┬───────┬──────┬───────┬───────┐ #> │ Level             │     n │  Rate │   SE │    LL │    UL │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ Under 15 years    │   434 │ 202.5 │ 24.3 │ 159.8 │ 256.6 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 15-24 years       │   346 │ 198.4 │ 21.9 │ 159.5 │ 246.8 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 25-44 years       │   923 │ 263.3 │ 26.5 │ 215.9 │ 321   │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 45-64 years       │ 1,253 │ 414   │ 37.7 │ 346.2 │ 495.1 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 65-74 years       │   891 │ 720.3 │ 66.4 │ 600.8 │ 863.6 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 75 years and over │   762 │ 758.1 │ 89.2 │ 601.2 │ 956   │ #> └───────────────────┴───────┴───────┴──────┴───────┴───────┘ #>   N = 4609.                                                  #>  #> Patient age recode (Patient sex = Male) (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────────────────┬───────┬───────┬──────┬───────┬───────┐ #> │ Level             │     n │  Rate │   SE │    LL │    UL │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ Under 15 years    │   453 │ 187.4 │ 25   │ 144.1 │ 243.7 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 15-24 years       │   196 │ 113.1 │ 20.7 │  78.4 │ 163   │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 25-44 years       │   512 │ 133.4 │ 17.2 │ 103.4 │ 172   │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 45-64 years       │ 1,030 │ 333.4 │ 32.3 │ 275.4 │ 403.5 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 65-74 years       │   770 │ 594.8 │ 46.4 │ 510.1 │ 693.6 │ #> ├───────────────────┼───────┼───────┼──────┼───────┼───────┤ #> │ 75 years and over │   680 │ 801.2 │ 73.2 │ 669.1 │ 959.5 │ #> └───────────────────┴───────┴───────┴──────┴───────┴───────┘ #>   N = 3641.                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":null,"dir":"Reference","previous_headings":"","what":"Total count — total","title":"Total count — total","text":"Total count","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Total count — total","text":"","code":"total(csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Total count — total","text":"csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Total count — total","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Total count — total","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  total() #>                        Total {NAMCS 2019 PUF}                        #> ┌───────┬───────────────┬────────────┬─────────────┬───────────────┐ #> │     n │        Number │         SE │          LL │            UL │ #> ├───────┼───────────────┼────────────┼─────────────┼───────────────┤ #> │ 8,250 │ 1,036,484,356 │ 48,836,217 │ 945,013,590 │ 1,136,808,860 │ #> └───────┴───────────────┴────────────┴─────────────┴───────────────┘ #>   N = 8250.                                                          #>"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Overall rate — total_rate","title":"Overall rate — total_rate","text":"Overall rate","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Overall rate — total_rate","text":"","code":"total_rate(   pop,   per = getOption(\"surveytable.rate_per\"),   csv = getOption(\"surveytable.csv\") )"},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Overall rate — total_rate","text":"pop population per calculate rate per many items population csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Overall rate — total_rate","text":"table","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/total_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Overall rate — total_rate","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  total_rate(uspop2019$total) #> Total (rate per 100 population) {NAMCS 2019 PUF} #> ┌───────┬───────┬──────┬───────┬───────┐ #> │     n │  Rate │   SE │    LL │    UL │ #> ├───────┼───────┼──────┼───────┼───────┤ #> │ 8,250 │ 320.7 │ 15.1 │ 292.4 │ 351.7 │ #> └───────┴───────┴──────┴───────┴───────┘ #>   N = 8250.                              #>"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":null,"dir":"Reference","previous_headings":"","what":"US Population in 2019 — uspop2019","title":"US Population in 2019 — uspop2019","text":"Population estimates civilian non-institutional population United States July 1, 2019. Used calculating rates. usage examples, see *_rate functions.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"US Population in 2019 — uspop2019","text":"","code":"uspop2019"},{"path":"https://cdcgov.github.io/surveytable/reference/uspop2019.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"US Population in 2019 — uspop2019","text":"object class list length 7.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":null,"dir":"Reference","previous_headings":"","what":"Are all the variables true? (Logical AND) — var_all","title":"Are all the variables true? (Logical AND) — var_all","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"var_all(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Are all the variables true? (Logical AND) — var_all","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Are all the variables true? (Logical AND) — var_all","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_all.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Are all the variables true? (Logical AND) — var_all","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_all(\"Medicare and Medicaid\", c(\"PAYMCARE\", \"PAYMCAID\")) tab(\"Medicare and Medicaid\") #>                                 Medicare and Medicaid {NAMCS 2019 PUF}                                 #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 8,126 │ 1,016,202,0 │ 47,395,074 │ 927,388,977 │ 1,113,520,4 │      98 │ 0.5 │ 96.9 │ 98.9 │ #> │       │       │          62 │            │             │          92 │         │     │      │      │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │   124 │  20,282,295 │  5,177,254 │  12,120,309 │  33,940,676 │       2 │ 0.5 │  1.1 │  3.1 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? (Logical OR) — var_any","title":"Is any variable true? (Logical OR) — var_any","text":"Create new variable true variables list variables true.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"var_any(newvr, vrs)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is any variable true? (Logical OR) — var_any","text":"newvr name new variable created vrs vector logical variables","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Is any variable true? (Logical OR) — var_any","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_any.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Is any variable true? (Logical OR) — var_any","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_any(\"Imaging services\" , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\" , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") #>                                   Imaging services {NAMCS 2019 PUF}                                    #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 7,148 │ 901,115,076 │ 43,298,146 │ 820,085,161 │ 990,151,291 │    86.9 │ 1.1 │ 84.6 │ 89.1 │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 1,102 │ 135,369,280 │ 13,573,736 │ 111,133,847 │ 164,889,838 │    13.1 │ 1.1 │ 10.9 │ 15.4 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert factor to logical — var_case","title":"Convert factor to logical — var_case","text":"Convert factor logical","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert factor to logical — var_case","text":"","code":"var_case(newvr, vr, cases, retain_na = TRUE)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert factor to logical — var_case","text":"newvr name new logical variable created vr factor variable cases one levels vr converted TRUE. levels converted FALSE. retain_na observations vr NA, newvr NA well?","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert factor to logical — var_case","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_case.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert factor to logical — var_case","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>   var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #>                                Preventive care visits {NAMCS 2019 PUF}                                 #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 6,682 │ 812,860,686 │ 45,220,483 │ 728,841,389 │ 906,565,549 │    78.4 │ 1.7 │ 74.9 │ 81.7 │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 1,568 │ 223,623,671 │ 18,519,789 │ 190,068,005 │ 263,103,441 │    21.6 │ 1.7 │ 18.3 │ 25.1 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                            #>   var_case(\"Surgery-related visits\" , \"MAJOR\" , c(\"Pre-surgery\", \"Post-surgery\")) tab(\"Surgery-related visits\") #>                                Surgery-related visits {NAMCS 2019 PUF}                                 #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 7,432 │ 969,450,753 │ 47,976,379 │ 879,792,684 │ 1,068,245,7 │    93.5 │ 0.8 │ 91.9 │ 94.9 │ #> │       │       │             │            │             │          12 │         │     │      │      │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │   818 │  67,033,604 │  7,810,237 │  53,273,079 │  84,348,494 │     6.5 │ 0.8 │  5.1 │  8.1 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                            #>   var_case(\"Non-primary\" , \"SPECCAT.bad\" , c(\"Surgical care specialty\", \"Medical care specialty\")) tab(\"Non-primary\") #>                                      Non-primary {NAMCS 2019 PUF}                                      #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 2,406 │ 422,806,843 │ 26,381,877 │ 374,098,520 │ 477,857,080 │    40.8 │ 2.2 │ 36.5 │ 45.2 │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 4,194 │ 406,215,659 │ 32,642,950 │ 346,937,333 │ 475,622,385 │    39.2 │ 2.1 │ 35   │ 43.5 │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │  │ 1,650 │ 207,461,854 │ 12,457,774 │ 184,377,795 │ 233,436,032 │    20   │ 0.8 │ 18.5 │ 21.6 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                            #>  tab(\"Non-primary\", drop_na = TRUE) #>                               Non-primary (knowns only) {NAMCS 2019 PUF}                               #> ┌───────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ FALSE │ 2,406 │ 422,806,843 │ 26,381,877 │ 374,098,520 │ 477,857,080 │      51 │ 2.6 │ 45.7 │ 56.3 │ #> ├───────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ TRUE  │ 4,194 │ 406,215,659 │ 32,642,950 │ 346,937,333 │ 475,622,385 │      49 │ 2.6 │ 43.7 │ 54.3 │ #> └───────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 6600.                                                                                            #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":null,"dir":"Reference","previous_headings":"","what":"Collapse factor levels — var_collapse","title":"Collapse factor levels — var_collapse","text":"Collapse two levels factor variable single level.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collapse factor levels — var_collapse","text":"","code":"var_collapse(vr, newlevel, oldlevels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collapse factor levels — var_collapse","text":"vr factor variable newlevel name new level oldlevels vector old levels","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collapse factor levels — var_collapse","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_collapse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Collapse factor levels — var_collapse","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  tab(\"PRIMCARE\") #>                      Are you the patient's primary care provider? {NAMCS 2019 PUF}                       #> ┌─────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level   │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Blank   │    16 │   1,150,066 │    478,377 │     440,081 │   3,005,475 │     0.1 │ 0   │  0   │  0.2 │ #> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Unknown │   300 │  39,518,576 │  9,507,422 │  24,519,903 │  63,691,845 │     3.8 │ 0.9 │  2.3 │  6   │ #> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Yes     │ 2,278 │ 383,480,893 │ 28,554,963 │ 331,361,656 │ 443,797,864 │    37   │ 2.6 │ 31.9 │ 42.3 │ #> ├─────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ No      │ 5,656 │ 612,334,822 │ 43,282,478 │ 533,049,777 │ 703,412,608 │    59.1 │ 2.5 │ 53.9 │ 64.1 │ #> └─────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                              #>  var_collapse(\"PRIMCARE\", \"Unknown if PCP\", c(\"Blank\", \"Unknown\")) tab(\"PRIMCARE\") #>                        Are you the patient's primary care provider? {NAMCS 2019 PUF}                         #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Unknown if  │   316 │  40,668,642 │  9,478,963 │  25,618,707 │  64,559,793 │     3.9 │ 0.9 │  2.4 │  6.1 │ #> │ PCP         │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Yes         │ 2,278 │ 383,480,893 │ 28,554,963 │ 331,361,656 │ 443,797,864 │    37   │ 2.6 │ 31.9 │ 42.3 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ No          │ 5,656 │ 612,334,822 │ 43,282,478 │ 533,049,777 │ 703,412,608 │    59.1 │ 2.5 │ 53.9 │ 64.1 │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":null,"dir":"Reference","previous_headings":"","what":"Copy a variable — var_copy","title":"Copy a variable — var_copy","text":"Create new variable copy another variable. can modify copy, original remains unchanged. See examples.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Copy a variable — var_copy","text":"","code":"var_copy(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Copy a variable — var_copy","text":"newvr name new variable created vr variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Copy a variable — var_copy","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_copy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Copy a variable — var_copy","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_copy(\"Age group\", \"AGER\") var_collapse(\"Age group\", \"65+\", c(\"65-74 years\", \"75 years and over\")) var_collapse(\"Age group\", \"25-64\", c(\"25-44 years\", \"45-64 years\")) tab(\"AGER\", \"Age group\") #>                                     Patient age recode {NAMCS 2019 PUF}                                      #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44 years │ 1,435 │ 170,270,604 │ 13,965,978 │ 144,924,545 │ 200,049,472 │    16.4 │ 1.1 │ 14.3 │ 18.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64 years │ 2,283 │ 309,505,956 │ 23,289,827 │ 266,994,092 │ 358,786,727 │    29.9 │ 1.4 │ 27.2 │ 32.6 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74 years │ 1,661 │ 206,865,982 │ 14,365,993 │ 180,480,708 │ 237,108,637 │    20   │ 1.2 │ 17.6 │ 22.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │ 1,442 │ 167,069,344 │ 15,179,082 │ 139,746,193 │ 199,734,713 │    16.1 │ 1.3 │ 13.7 │ 18.8 │ #> │ and over    │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>  #>                                          Age group {NAMCS 2019 PUF}                                          #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   887 │ 117,916,772 │ 14,097,315 │  93,228,928 │ 149,142,177 │    11.4 │ 1.3 │  8.9 │ 14.2 │ #> │ years       │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24 years │   542 │  64,855,698 │  7,018,359 │  52,386,950 │  80,292,164 │     6.3 │ 0.6 │  5.1 │  7.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-64       │ 3,718 │ 479,776,560 │ 32,174,693 │ 420,624,423 │ 547,247,222 │    46.3 │ 1.8 │ 42.7 │ 49.9 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65+         │ 3,103 │ 373,935,326 │ 24,522,516 │ 328,776,878 │ 425,296,417 │    36.1 │ 1.9 │ 32.3 │ 40   │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":null,"dir":"Reference","previous_headings":"","what":"Cross or interact two variables — var_cross","title":"Cross or interact two variables — var_cross","text":"Create new variable interaction two variables. Also see tab_cross().","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cross or interact two variables — var_cross","text":"","code":"var_cross(newvr, vr, vrby)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cross or interact two variables — var_cross","text":"newvr name new variable created vr first variable vrby second variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cross or interact two variables — var_cross","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cross.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cross or interact two variables — var_cross","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #>                                          Age x Sex {NAMCS 2019 PUF}                                          #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   434 │  59,957,823 │  7,205,594 │  47,318,228 │  75,973,693 │     5.8 │ 0.7 │  4.5 │  7.3 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   346 │  41,128,003 │  4,532,466 │  33,065,609 │  51,156,253 │     4   │ 0.4 │  3.2 │  4.9 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   923 │ 113,708,461 │ 11,461,189 │  93,256,445 │ 138,645,797 │    11   │ 1   │  9   │ 13.2 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,253 │ 175,978,133 │ 16,008,541 │ 147,152,826 │ 210,449,940 │    17   │ 1.1 │ 14.8 │ 19.3 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   891 │ 120,099,493 │ 11,066,146 │ 100,171,315 │ 143,992,203 │    11.6 │ 1   │  9.7 │ 13.7 │ #> │ years:      │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   762 │  94,173,155 │ 11,085,372 │  74,682,310 │ 118,750,789 │     9.1 │ 0.9 │  7.3 │ 11.1 │ #> │ and over:   │       │             │            │             │             │         │     │      │      │ #> │ Female      │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 15    │   453 │  57,958,950 │  7,727,594 │  44,569,688 │  75,370,504 │     5.6 │ 0.7 │  4.3 │  7.2 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-24       │   196 │  23,727,695 │  4,343,932 │  16,457,071 │  34,210,431 │     2.3 │ 0.4 │  1.6 │  3.2 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 25-44       │   512 │  56,562,143 │  7,276,983 │  43,860,836 │  72,941,520 │     5.5 │ 0.6 │  4.3 │  6.8 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 45-64       │ 1,030 │ 133,527,822 │ 12,956,239 │ 110,319,199 │ 161,619,006 │    12.9 │ 1   │ 10.9 │ 15.1 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65-74       │   770 │  86,766,489 │  6,766,876 │  74,409,284 │ 101,175,865 │     8.4 │ 0.6 │  7.2 │  9.7 │ #> │ years: Male │       │             │            │             │             │         │     │      │      │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 75 years    │   680 │  72,896,189 │  6,660,855 │  60,871,965 │  87,295,593 │     7   │ 0.6 │  5.9 │  8.3 │ #> │ and over:   │       │             │            │             │             │         │     │      │      │ #> │ Male        │       │             │            │             │             │         │     │      │      │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert numeric to factor — var_cut","title":"Convert numeric to factor — var_cut","text":"Create new categorical variable based numeric variable.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert numeric to factor — var_cut","text":"","code":"var_cut(newvr, vr, breaks, labels)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert numeric to factor — var_cut","text":"newvr name new factor variable created vr numeric variable breaks see cut() labels see cut()","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert numeric to factor — var_cut","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_cut.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert numeric to factor — var_cut","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  # In some data systems, variables might contain \"special values\". For example, # negative values might indicate unknowns (which should be coded as `NA`). # Though in this particular data, there are no unknowns. var_cut(\"Age group\"   , \"AGE\"   , c(-Inf, -0.1, 0, 4, 14, 64, Inf)   , c(NA, \"Under 1\", \"1-4\", \"5-14\", \"15-64\", \"65 and over\")) tab(\"Age group\") #>                                          Age group {NAMCS 2019 PUF}                                          #> ┌─────────────┬───────┬─────────────┬────────────┬─────────────┬─────────────┬─────────┬─────┬──────┬──────┐ #> │ Level       │     n │      Number │         SE │          LL │          UL │ Percent │  SE │   LL │   UL │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ Under 1     │   203 │  31,147,553 │  5,281,607 │  22,269,146 │  43,565,662 │     3   │ 0.5 │  2.1 │  4.1 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 1-4         │   281 │  38,240,087 │  5,443,933 │  28,863,791 │  50,662,237 │     3.7 │ 0.5 │  2.7 │  4.8 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 5-14        │   403 │  48,529,132 │  5,741,214 │  38,429,869 │  61,282,455 │     4.7 │ 0.5 │  3.7 │  5.9 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 15-64       │ 4,260 │ 544,632,258 │ 36,082,093 │ 478,254,001 │ 620,223,345 │    52.5 │ 2   │ 48.6 │ 56.5 │ #> ├─────────────┼───────┼─────────────┼────────────┼─────────────┼─────────────┼─────────┼─────┼──────┼──────┤ #> │ 65 and over │ 3,103 │ 373,935,326 │ 24,522,516 │ 328,776,878 │ 425,296,417 │    36.1 │ 1.9 │ 32.3 │ 40   │ #> └─────────────┴───────┴─────────────┴────────────┴─────────────┴─────────────┴─────────┴─────┴──────┴──────┘ #>   N = 8250.                                                                                                  #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List variables in a survey. — var_list","title":"List variables in a survey. — var_list","text":"List variables survey.","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List variables in a survey. — var_list","text":"","code":"var_list(sw = \"\", all = FALSE, csv = getOption(\"surveytable.csv\"))"},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List variables in a survey. — var_list","text":"sw starting characters variable name (case insensitive) print variables? csv name CSV file","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List variables in a survey. — var_list","text":"table","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List variables in a survey. — var_list","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_list(\"age\") #>          Variables beginning with 'age' {NAMCS 2019 PUF}          #> ┌──────────┬─────────┬──────────────────────────────────────────┐ #> │ Variable │ Class   │ Long name                                │ #> ├──────────┼─────────┼──────────────────────────────────────────┤ #> │ AGE      │ numeric │ Patient age in years (raw - use caution) │ #> ├──────────┼─────────┼──────────────────────────────────────────┤ #> │ AGER     │ factor  │ Patient age recode                       │ #> └──────────┴─────────┴──────────────────────────────────────────┘ #>"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":null,"dir":"Reference","previous_headings":"","what":"Logical NOT — var_not","title":"Logical NOT — var_not","text":"Logical ","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Logical NOT — var_not","text":"","code":"var_not(newvr, vr)"},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Logical NOT — var_not","text":"newvr name new variable created vr logical variable","code":""},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Logical NOT — var_not","text":"Survey object","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/reference/var_not.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Logical NOT — var_not","text":"","code":"set_survey(namcs2019sv) #> * Mode: General. #>                         Survey info {NAMCS 2019 PUF}                          #> ┌───────────┬──────────────┬────────────────────────────────────────────────┐ #> │ Variables │ Observations │ Design                                         │ #> ├───────────┼──────────────┼────────────────────────────────────────────────┤ #> │        33 │        8,250 │ Stratified 1 - level Cluster Sampling design   │ #> │           │              │ (with replacement)                             │ #> │           │              │ With (398) clusters.                           │ #> │           │              │ namcs2019sv = survey::svydesign(ids = ~CPSUM,  │ #> │           │              │ strata = ~CSTRATM, weights = ~PATWT            │ #> │           │              │ , data = namcs2019sv_df)                       │ #> └───────────┴──────────────┴────────────────────────────────────────────────┘ #>  var_not(\"Private insurance not used\", \"PAYPRIV\")"},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-development-version","dir":"Changelog","previous_headings":"","what":"surveytable (development version)","title":"surveytable (development version)","text":"rccsu2018 set_mode()","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-094","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.4","title":"surveytable 0.9.4","text":"CRAN release: 2024-05-20 Optionally adjust p-values multiple comparisons (p_adjust argument)","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-093","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.3","title":"surveytable 0.9.3","text":"codebook() Improved output. Allows unweighted survey data.frame. Can set certain options using argument. Tabulation functions show number observations. LaTeX printing.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-092","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.2","title":"surveytable 0.9.2","text":"CRAN release: 2024-01-18 Addressed CRAN comments.","code":""},{"path":"https://cdcgov.github.io/surveytable/news/index.html","id":"surveytable-091","dir":"Changelog","previous_headings":"","what":"surveytable 0.9.1","title":"surveytable 0.9.1","text":"Initial CRAN submission.","code":""}]
diff --git a/inst/CITATION b/inst/CITATION
new file mode 100644
index 0000000..cc9afc6
--- /dev/null
+++ b/inst/CITATION
@@ -0,0 +1,8 @@
+bibentry(
+  bibtype  = "Manual",
+  title    = "surveytable: Formatted Survey Estimates",
+  author   = "Alex Strashny",
+  year     = 2023,
+  url = "https://cdcgov.github.io/surveytable/",
+  doi      = "10.32614/CRAN.package.surveytable"
+)
diff --git a/man/set_survey.Rd b/man/set_survey.Rd
index e457db3..b4ae8ed 100644
--- a/man/set_survey.Rd
+++ b/man/set_survey.Rd
@@ -1,51 +1,55 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/set_survey.R
+% Please edit documentation in R/set_survey.R, R/set_mode.R
 \name{set_survey}
 \alias{set_survey}
+\alias{set_mode}
 \title{Specify the survey to analyze}
 \usage{
-set_survey(design, opts = "NCHS", csv = getOption("surveytable.csv"))
+set_survey(design, mode = "default", csv = getOption("surveytable.csv"))
+
+set_mode(mode = "default")
 }
 \arguments{
-\item{design}{either a survey object (\code{survey.design} or \code{svyrep.design}) or a
-\code{data.frame} for an unweighted survey.}
+\item{design}{either a survey object (created with \code{\link[survey:svydesign]{survey::svydesign()}} or
+\code{\link[survey:svrepdesign]{survey::svrepdesign()}}); or, for an unweighted survey, a \code{data.frame}.}
 
-\item{opts}{set certain options. See below.}
+\item{mode}{set certain options. See below.}
 
 \item{csv}{name of a CSV file}
 }
 \value{
-Info about the survey.
+\code{set_survey}: info about the survey. \code{set_mode}: nothing.
 }
 \description{
-You need to specify a survey before the other functions, such as \code{\link[=tab]{tab()}},
-will work.
+You must specify a survey before the other functions, such as \code{\link[=tab]{tab()}},
+will work. To convert a \code{data.frame} to a survey object, see \code{\link[survey:svydesign]{survey::svydesign()}}
+or \code{\link[survey:svrepdesign]{survey::svrepdesign()}}.
 }
 \details{
-\code{opts}:
+Optionally, the survey can have an attribute called \code{label}, which is the
+long name of the survey. Optionally, each variable in the survey can have an
+attribute called \code{label}, which is the variable's long name.
+
+If you are not sure what the \code{mode} should be, leave it as \code{"default"}. Here is
+what \code{mode} does:
 \itemize{
-\item \code{"nchs"}:
+\item \code{"general"} or \code{"default"}:
 \itemize{
-\item Round counts to the nearest 1,000 -- see \code{\link[=set_count_1k]{set_count_1k()}}.
-\item Identify low-precision estimates (\code{surveytable.find_lpe} option is \code{TRUE}).
-\item Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation (\code{surveytable.adjust_svyciprop} option is \code{TRUE}).
+\item Round counts to the nearest integer -- see \code{\link[=set_count_int]{set_count_int()}}.
+\item Do not look for low-precision estimates.
+\item Percentage CI's: use standard Korn-Graubard CI's.
 }
-\item \verb{"general":}
+\item \code{"nchs"}:
 \itemize{
-\item Round counts to the nearest integer -- see \code{\link[=set_count_int]{set_count_int()}}.
-\item Do not look for low-precision estimates (\code{surveytable.find_lpe} option is \code{FALSE}).
-\item Percentage CI's: use standard Korn-Graubard CI's (\code{surveytable.adjust_svyciprop} option is \code{FALSE}).
+\item Round counts to the nearest 1,000 -- see \code{\link[=set_count_1k]{set_count_1k()}}.
+\item Identify low-precision estimates.
+\item Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation.
 }
 }
-
-Optionally, the survey can have an attribute called \code{label}, which is the
-long name of the survey.
-
-Optionally, each variable in the survey can have an attribute called \code{label},
-which is the variable's long name.
 }
 \examples{
 set_survey(namcs2019sv)
+set_mode("general")
 }
 \seealso{
 Other options: 
diff --git a/man/surveytable-options.Rd b/man/surveytable-options.Rd
index 9b17a6f..ab2bf09 100644
--- a/man/surveytable-options.Rd
+++ b/man/surveytable-options.Rd
@@ -10,10 +10,21 @@ notable options.
 }
 \details{
 \subsection{Low-precision estimates.}{
+
+Optionally, all of the tabulation functions can identify low-precision estimates.
+To turn on this functionality, either set the \code{surveytable.find_lpe} option to \code{TRUE},
+or call \code{\link[=set_survey]{set_survey()}} or \code{\link[=set_mode]{set_mode()}} with the argument \code{mode = "NCHS"}.
+
+By default, low-precision estimates are identified using National Center for
+Health Statistics (NCHS) algorithms. However, this can be changed, as described
+below.
+
+Here is a description of the options related to the identification of low-precision
+estimates.
 \itemize{
 \item \code{surveytable.find_lpe}: should the tabulation functions look for low-precision
-estimates? You can change this directly with \code{options()} or with the \code{opts} argument
-to \code{set_survey()}.
+estimates? You can change this directly with \code{options()} or with the \code{mode} argument
+to \code{\link[=set_survey]{set_survey()}} or \code{\link[=set_mode]{set_mode()}}.
 \item \code{surveytable.lpe_n}, \code{surveytable.lpe_counts}, \code{surveytable.lpe_percents}: names
 of 3 functions.
 }
diff --git a/man/svyciprop_adjusted.Rd b/man/svyciprop_adjusted.Rd
index f786b1b..15026ea 100644
--- a/man/svyciprop_adjusted.Rd
+++ b/man/svyciprop_adjusted.Rd
@@ -30,17 +30,20 @@ svyciprop_adjusted(
 The point estimate of the proportion, with the confidence interval as an attribute.
 }
 \description{
-A version of \code{survey::svyciprop()} that adjusts for the degrees of freedom when \code{method = "beta"}.
+A version of \code{survey::svyciprop()} that adjusts for the degrees of freedom
+when \code{method = "beta"}.
 }
 \details{
 Written by Makram Talih in 2019.
 
 \code{df_method}: for \code{"default"}, \code{df = degf(design)}; for \code{"NHIS"}, \code{df = nrow(design) - 1}.
 
-To use this function in tabulations, call \code{\link[=set_survey]{set_survey()}} with the \code{opts = "NCHS"} argument,
-or type: \code{options(surveytable.adjust_svyciprop = TRUE)}.
+To use this function in tabulations, call \code{\link[=set_survey]{set_survey()}} or \code{\link[=set_mode]{set_mode()}} with the
+\code{mode = "NCHS"} argument, or type: \code{options(surveytable.adjust_svyciprop = TRUE)}.
 }
 \examples{
-set_survey(namcs2019sv, opts = "NCHS")
+set_survey(namcs2019sv)
+set_mode("NCHS")
 tab("AGER")
+set_mode("general")
 }
diff --git a/vignettes/Advanced-topics.Rmd b/vignettes/Advanced-topics.Rmd
index 335f1c2..8b58c5f 100644
--- a/vignettes/Advanced-topics.Rmd
+++ b/vignettes/Advanced-topics.Rmd
@@ -54,7 +54,7 @@ Be sure to check the table title to verify that you are tabulating the new surve
 First, let's review what I call "advanced variable editing".
 
 * `surveytable` provides a number of functions to create or modify survey variables. 
-* Some examples include `var_collapse()` and `var_cut()`.
+* Some examples include [var_collapse()] and [var_cut()].
 * Occasionally, you might need to do advanced variable editing. Here's how:
 
 * Every survey object has an element called `variables`
diff --git a/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd b/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
index cb7df90..f0123ae 100644
--- a/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
+++ b/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
@@ -34,6 +34,12 @@ set_survey(namcs2019sv)
 
 Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.
 
+For this example, we do want to turn on certain NCHS-specific options, such as identifying low-precision estimates. If you do not care about identifying low-precision estimates, you can skip this command. To turn on the NCHS-specific options:
+
+```{r, results='asis'}
+set_mode("NCHS")
+```
+
 # Table 1
 
 ## Counts and percentages
diff --git a/vignettes/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.Rmd b/vignettes/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.Rmd
index e89ab14..09c1f22 100644
--- a/vignettes/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.Rmd
+++ b/vignettes/Example-Residential-Care-Community-Services-User-NSLTCP-RCC-SU-report.Rmd
@@ -34,6 +34,18 @@ set_survey(rccsu2018)
 
 Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.
 
+For this example, we do want to turn on certain NCHS-specific options, such as identifying low-precision estimates. If you do not care about identifying low-precision estimates, you can skip this command. To turn on the NCHS-specific options:
+
+```{r, results='asis'}
+set_mode("NCHS")
+```
+
+Alternatively, you can combine these two commands into a single command, like so:
+
+```{r, results='asis'}
+set_survey(rccsu2018, mode = "NCHS")
+```
+
 # Figure 1
 
 This figure shows the percentage of residents by sex, race / ethnicity, and age group.
@@ -145,7 +157,7 @@ for (vr in c("hbp", "alz", "depress", "arth", "diabetes", "heartdise", "osteo"
   idx = which(rccsu2018$variables[,vr])
   rccsu2018$variables$num_cc[idx] = rccsu2018$variables$num_cc[idx] + 1
 }
-set_survey(rccsu2018)
+set_survey(rccsu2018, mode = "NCHS")
 ```
 
 `num_cc` is a numeric variable with the number of chronic conditions. The published figure uses a categorical variable which is based on this numeric variable. Use `var_cut()`, which converts numeric variables to categorical (`factor`) variables.
@@ -202,7 +214,7 @@ for (vr in c("bathhlp", "walkhlp", "dreshlp", "transhlp", "toilhlp", "eathlp"))
       , "BOTH"))
   rccsu2018$variables$num_adl[idx] = rccsu2018$variables$num_adl[idx] + 1
 }
-set_survey(rccsu2018)
+set_survey(rccsu2018, mode = "NCHS")
 ```
 
 For generating the figure, create a categorical variable based on `num_adl`, which is numeric. 
diff --git a/vignettes/surveytable.Rmd b/vignettes/surveytable.Rmd
index 7405394..e2d5338 100644
--- a/vignettes/surveytable.Rmd
+++ b/vignettes/surveytable.Rmd
@@ -121,6 +121,12 @@ set_survey(namcs2019sv)
 
 Check the survey label, survey design variables, and the number of observations to verify that it all looks correct.
 
+For this example, we do want to turn on certain NCHS-specific options, such as identifying low-precision estimates. If you do not care about identifying low-precision estimates, you can skip this command. To turn on the NCHS-specific options:
+
+```{r, results='asis'}
+set_mode("NCHS")
+```
+
 ## List variables
 
 The `var_list()` function lists the variables in the survey. To avoid unintentionally listing all the variables in a survey, which can be many, the starting characters of variable names are specified. For example, to list the variables that start with the letters `age`, type: