set_survey ( "namcs2019sv" )
-#> _
-#> Survey name NAMCS 2019 PUF
-#> Number of variables 38
-#> Number of observations 8250
-#> Stratified 1 - level Cluster Sampling design (with replacement)
-#> With (398) clusters.
-#> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT,
-#> data = namcs2019sv_df)
+ set_survey ( namcs2019sv )
#> * To adjust how counts are rounded, see ?set_count_int
+#> _
+#> Survey name NAMCS 2019 PUF
+#> Number of variables 33
+#> Number of observations 8250
+#> Info1 Stratified 1 - level Cluster Sampling design (with replacement)
+#> Info2 With (398) clusters.
+#> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT,
+#> Info4 data = namcs2019sv_df)
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: Age group: overwriting a variable that already exists.
tab ( "Age group" )
#> Age group {NAMCS 2019 PUF}
#> +-------------------------------------------------------------------------------------------+
diff --git a/docs/reference/var_list.html b/docs/reference/var_list.html
index fd6bb6f..2f40897 100644
--- a/docs/reference/var_list.html
+++ b/docs/reference/var_list.html
@@ -10,7 +10,7 @@
surveytable
- 0.9.1
+ 0.9.2
@@ -60,12 +60,7 @@
Usage
-
var_list (
- sw = "" ,
- all = FALSE ,
- screen = getOption ( "surveytable.screen" ) ,
- csv = getOption ( "surveytable.csv" )
-)
+
var_list ( sw = "" , all = FALSE , csv = getOption ( "surveytable.csv" ) )
@@ -78,10 +73,6 @@
Examples
-
set_survey ( "namcs2019sv" )
-#> _
-#> Survey name NAMCS 2019 PUF
-#> Number of variables 38
-#> Number of observations 8250
-#> Stratified 1 - level Cluster Sampling design (with replacement)
-#> With (398) clusters.
-#> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT,
-#> data = namcs2019sv_df)
+ set_survey ( namcs2019sv )
#> * To adjust how counts are rounded, see ?set_count_int
+#> _
+#> Survey name NAMCS 2019 PUF
+#> Number of variables 33
+#> Number of observations 8250
+#> Info1 Stratified 1 - level Cluster Sampling design (with replacement)
+#> Info2 With (398) clusters.
+#> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT,
+#> Info4 data = namcs2019sv_df)
var_list ( "age" )
-#> Variables beginning with 'age' {NAMCS 2019 PUF}
-#> +------------------------------------------------------------+
-#> ¦ Variable ¦ Class ¦ Long name ¦
-#> +-----------+---------+--------------------------------------¦
-#> ¦ AGE ¦ numeric ¦ Patient age in years ¦
-#> +-----------+---------+--------------------------------------¦
-#> ¦ AGER ¦ factor ¦ Patient age recode ¦
-#> +-----------+---------+--------------------------------------¦
-#> ¦ Age group ¦ factor ¦ Age group ¦
-#> +-----------+---------+--------------------------------------¦
-#> ¦ Age x Sex ¦ factor ¦ (Patient age recode) x (Patient sex) ¦
-#> +------------------------------------------------------------+
+#> Variables beginning with 'age' {NAMCS 2019 PUF}
+#> +-------------------------------------------+
+#> ¦ Variable ¦ Class ¦ Long name ¦
+#> +----------+---------+----------------------¦
+#> ¦ AGE ¦ numeric ¦ Patient age in years ¦
+#> +----------+---------+----------------------¦
+#> ¦ AGER ¦ factor ¦ Patient age recode ¦
+#> +-------------------------------------------+
#>
diff --git a/docs/search.json b/docs/search.json
index 32c8b89..02d2810 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"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. Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) mysurvey = namcs2019sv set_survey(\"mysurvey\") ## _ ## Survey name NAMCS 2019 PUF ## Number of variables 33 ## Number of observations 8250 ## Stratified 1 - level Cluster Sampling design (with replacement) ## With (398) clusters. ## survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, ## data = namcs2019sv_df) ## * To adjust how counts are rounded, see ?set_count_int"},{"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} Recall survey design objects element called variables, data frame contains survey variables. Let’s examine levels MSA. calculate rates particular variable, need provide data frame variable called Level matches levels variable survey. Population gives population estimate. example, MSA, 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) levels(mysurvey$variables$MSA) ## [1] \"MSA (Metropolitan Statistical Area)\" \"Non-MSA\" 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$`AGER x SEX`)"},{"path":"https://cdcgov.github.io/surveytable/articles/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.html","id":"table-5","dir":"Articles","previous_headings":"","what":"Table 5","title":"Example: National Ambulatory Medical Care Survey (NAMCS) tables","text":"table gives expected sources payment. use PAY* variables create several new variables required table. Note PAY* variables logical (TRUE FALSE), simplifies syntax. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Recall survey design objects element called variables, data frame contains survey variables. Notes: ’re using within() function simplify syntax. nopwu = payment used, private, Medicare, etc. Self-pay (real) = self-pay used payment used. Expected source payment visit: Private insurance {NAMCS 2019 PUF} Expected source payment visit: Medicare {NAMCS 2019 PUF} Expected source payment visit: Medicaid CHIP state-based program {NAMCS 2019 PUF} Medicare Medicaid {NAMCS 2019 PUF} insurance {NAMCS 2019 PUF} Self-pay (real) {NAMCS 2019 PUF} charge (real) {NAMCS 2019 PUF} Expected source payment visit: Workers Compensation {NAMCS 2019 PUF} Expected source payment visit: {NAMCS 2019 PUF} Unknown blank {NAMCS 2019 PUF} Check presentation standards flags! NCHS presentation standards rules, estimates shown.","code":"mysurvey$variables = within(mysurvey$variables, { `Medicare and Medicaid` = PAYMCARE & PAYMCAID `Unknown or blank` = PAYDK | (NOPAY == \"No categories marked\") nopwu = !( PAYPRIV | PAYMCARE | PAYMCAID | PAYWKCMP | PAYOTH | PAYDK) `Self-pay (real)` = PAYSELF & nopwu `No charge (real)` = PAYNOCHG & nopwu `No insurance` = `Self-pay (real)` | `No charge (real)` }) tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\" , \"No insurance\", \"Self-pay (real)\", \"No charge (real)\" , \"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. variable Age group already created previous example. 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} (Patient age recode) x (Patient 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} (Patient age recode) x (Patient sex) (Major reason visit = Preventive care) {NAMCS 2019 PUF}","code":"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\")) { 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 many tables produced, output sent CSV file, printed screen. , 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 , screen output turned back . run code, tables stored CSV file. give idea tables look like, just one tables: Type specialty (Primary, Medical, Surgical) ((Major reason visit) x (Patient age recode) = Preventive care : 15 years) {NAMCS 2019 PUF} match percentage published table, see “Primary care specialty” row. sure check presentation standards flags.","code":"set_output(csv = \"out.csv\", screen = FALSE) for (vr in c(\"AGER\", \"Age group\", \"SEX\", \"Age x Sex\")) { var_cross(\"tmp\", \"MAJOR\", vr) for (lvl in levels(mysurvey$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 = \"\", screen = TRUE) ## * Turning off CSV output. ## * Sending output to the screen. ## * ?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(mysurvey$variables[,vr])[1] tab_subset(\"SPECCAT\", \"tmp\", paste0(\"Preventive care : \", lvl))"},{"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 identical namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years\" mysurvey = survey::svydesign(ids = ~ CPSUM , strata = ~ CSTRATM , weights = ~ PATWT , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" identical(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. Check survey label, survey design variables, number observations verify looks correct.","code":"set_survey(\"namcs2019sv\") #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int"},{"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 SEX 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 {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. 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. variables data frame. Recall survey objects element called variables, data frame contains survey variables. advanced users can create modify variables variables data frame directly.","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, 4, 14, 64, Inf) , c(\"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\") class(namcs2019sv$variables) #> [1] \"data.frame\""},{"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 also argument called screen, TRUE default, determines whether output sent screen. many tables produced, user might choose turn screen printing, output CSV file. tabulation functions called within R Markdown notebook, produce HTML tables. makes easy incorporate output surveytable package directly documents, presentations, “shiny” web apps, output types. Finally, tabulation functions return tables produce, either data frame, list data frames. advanced analysts can use functionality integrate surveytable programming tasks.","code":"set_output(csv = \"out.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. https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable.","code":"@Manual{, title = {surveytable: Formatted Survey Estimates}, author = {Alex Strashny}, year = {2023}, note = {https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/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 package provides short understandable commands generate tabulated, formatted, rounded survey estimates. One useful function, operates categorical logical variables, tabulates estimated counts percentages standard errors confidence intervals. functions list variables survey, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, tabulate rates, create modify survey variables, perform t-tests, save output. tabulation functions check National Center Health Statistics (NCHS) presentation standards flag low-precision estimates. surveytable code called R Markdown notebook Quarto document, generates HTML tables, can incorporated directly documents.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"can install surveytable like : surveytable available CRAN, able install like :","code":"install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\") install.packages(\"surveytable\")"},{"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. Patient age recode {NAMCS 2019 PUF}","code":"library(surveytable) set_survey(\"namcs2019sv\") #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int 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/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/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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #> Total {NAMCS 2019 PUF} #> +----------------------------------------------------------+ #> ¦ Number ¦ SE ¦ LL ¦ UL ¦ #> +---------------+------------+-------------+---------------¦ #> ¦ 1,036,484,356 ¦ 48,836,217 ¦ 945,013,590 ¦ 1,136,808,860 ¦ #> +----------------------------------------------------------+ #> (Checked 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} #> +---------------------------------------------------------------+ #> ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 1,036,484 ¦ 48,836 ¦ 945,014 ¦ 1,136,809 ¦ #> +---------------------------------------------------------------+ #> (Checked 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(csv = NULL, screen = NULL, max_levels = 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":"csv name CSV file \"\" turn CSV output screen print screen? max_levels categorical variable can many levels. Used avoid printing huge tables.","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":"if (FALSE) { set_output(csv = \"out.csv\") show_output() } 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(survey_name = \"\") show_survey()"},{"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":"survey_name name survey object (survey.design svyrep.design), quotation marks","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":"(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":"show_survey() shows survey specified. 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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int show_survey() #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int"},{"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":"Show package 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.check_present #> [1] TRUE #> #> $surveytable.csv #> [1] \"\" #> #> $surveytable.drop_na #> [1] FALSE #> #> $surveytable.max_levels #> [1] 20 #> #> $surveytable.names_count #> [1] \"Number (000)\" \"SE (000)\" \"LL (000)\" \"UL (000)\" #> #> $surveytable.names_prct #> [1] \"Percent\" \"SE\" \"LL\" \"UL\" #> #> $surveytable.present_count #> [1] \".present_count\" #> #> $surveytable.present_prop #> [1] \".present_prop\" #> #> $surveytable.present_restricted #> [1] \".present_restricted\" #> #> $surveytable.rate_per #> [1] 100 #> #> $surveytable.screen #> [1] TRUE #> #> $surveytable.survey #> [1] \"namcs2019sv\" #> #> $surveytable.survey_envir #>
#> #> $surveytable.survey_label #> [1] \"NAMCS 2019 PUF\" #> #> $surveytable.svychisq_statistic #> [1] \"F\" #> #> $surveytable.tx_count #> [1] \".tx_count_1k\" #> #> $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.","code":""},{"path":[]},{"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.","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 design object subset expression specifying sub-population label survey label newly created survey design 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 design 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":"if (FALSE) { children = survey_subset(namcs2019sv, AGE < 18, \"Children\") set_survey(\"children\") tab(\"AGER\") }"},{"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\"), 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. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1.","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. use function tabulations, 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\") #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int options(surveytable.adjust_svyciprop = TRUE) tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> options(surveytable.adjust_svyciprop = FALSE) tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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, drop_na = getOption(\"surveytable.drop_na\"), max_levels = getOption(\"surveytable.max_levels\"), screen = getOption(\"surveytable.screen\"), 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 drop_na drop missing values (NA)? Categorical variables . max_levels categorical variable can many levels. Used avoid printing huge tables. screen print screen? 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 data.frame tables single data.frame 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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> tab(\"MDDO\", \"SPECCAT\", \"MSA\") #> Type of doctor (MD or DO) {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦ 980,280 ¦ 48,388 ¦ 889,842 ¦ 1,079,910 ¦ 94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦ 56,204 ¦ 6,602 ¦ 44,597 ¦ 70,832 ¦ 5.4 ¦ 0.7 ¦ 4.2 ¦ 6.9 ¦ #> ¦ of Osteopathy ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care ¦ 521,466 ¦ 31,136 ¦ 463,840 ¦ 586,252 ¦ 50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦ 214,832 ¦ 31,110 ¦ 161,661 ¦ 285,490 ¦ 20.7 ¦ 3 ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care ¦ 300,186 ¦ 43,497 ¦ 225,806 ¦ 399,067 ¦ 29 ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA ¦ 973,676 ¦ 50,515 ¦ 879,490 ¦ 1,077,947 ¦ 93.9 ¦ 1.7 ¦ 89.6 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Statistical ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Area) ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA ¦ 62,809 ¦ 17,549 ¦ 36,249 ¦ 108,830 ¦ 6.1 ¦ 1.7 ¦ 3.2 ¦ 10.4 ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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-value <= 0.05 #> # Integrate the output into other programming tasks my_table = tab(\"AGER\", screen = FALSE) my_table = within(my_table, {RSE = `SE (000)` / `Number (000)`})"},{"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\"), screen = getOption(\"surveytable.screen\"), 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. screen print screen? 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 data.frame tables single data.frame 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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int # 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 ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ MSA (Metropolitan ¦ 351.2 ¦ 18.2 ¦ 317.2 ¦ 388.8 ¦ #> ¦ Statistical Area) ¦ ¦ ¦ ¦ ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ Non-MSA ¦ 136.7 ¦ 38.2 ¦ 78.9 ¦ 236.8 ¦ #> +-----------------------------------------------------------+ #> (Checked 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 ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ M.D. - Doctor of Medicine ¦ 303.3 ¦ 15 ¦ 275.3 ¦ 334.1 ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ D.O. - Doctor of ¦ 17.4 ¦ 2 ¦ 13.8 ¦ 21.9 ¦ #> ¦ Osteopathy ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------+ #> (Checked 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\"), screen = getOption(\"surveytable.screen\"), csv = getOption(\"surveytable.csv\") ) tab_subset( vr, vrby, lvls = c(), test = FALSE, alpha = 0.05, drop_na = getOption(\"surveytable.drop_na\"), max_levels = getOption(\"surveytable.max_levels\"), screen = getOption(\"surveytable.screen\"), 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. screen print screen? csv name CSV file lvls (optional) show levels vrby test perform hypothesis tests? alpha significance level tests 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":"tab_subset: list data.frame tables single data.frame table. tab_cross: data.frame 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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #> Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 9.9 ¦ 1.2 ¦ 7.6 ¦ 12.6 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 6.8 ¦ 0.7 ¦ 5.4 ¦ 8.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 18.8 ¦ 1.6 ¦ 15.8 ¦ 22.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 29.1 ¦ 1.7 ¦ 25.7 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 19.8 ¦ 1.5 ¦ 17 ¦ 22.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 15.6 ¦ 1.5 ¦ 12.8 ¦ 18.7 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 13.4 ¦ 1.7 ¦ 10.3 ¦ 17.1 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 5.5 ¦ 0.8 ¦ 4 ¦ 7.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 13.1 ¦ 1.3 ¦ 10.7 ¦ 15.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 30.9 ¦ 1.6 ¦ 27.8 ¦ 34.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 20.1 ¦ 1.5 ¦ 17.3 ¦ 23.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 16.9 ¦ 1.5 ¦ 14 ¦ 20.2 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 5.8 ¦ 0.7 ¦ 4.5 ¦ 7.3 ¦ #> ¦ years : ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 4 ¦ 0.4 ¦ 3.2 ¦ 4.9 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 11 ¦ 1 ¦ 9 ¦ 13.2 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 17 ¦ 1.1 ¦ 14.8 ¦ 19.3 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 11.6 ¦ 1 ¦ 9.7 ¦ 13.7 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 9.1 ¦ 0.9 ¦ 7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 5.6 ¦ 0.7 ¦ 4.3 ¦ 7.2 ¦ #> ¦ years : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 2.3 ¦ 0.4 ¦ 1.6 ¦ 3.2 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 5.5 ¦ 0.6 ¦ 4.3 ¦ 6.8 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 12.9 ¦ 1 ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 8.4 ¦ 0.6 ¦ 7.2 ¦ 9.7 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 7 ¦ 0.6 ¦ 5.9 ¦ 8.3 ¦ #> ¦ over : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> # Hypothesis testing tab_subset(\"AGER\", \"SEX\", test = TRUE) #> Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 9.9 ¦ 1.2 ¦ 7.6 ¦ 12.6 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 6.8 ¦ 0.7 ¦ 5.4 ¦ 8.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 18.8 ¦ 1.6 ¦ 15.8 ¦ 22.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 29.1 ¦ 1.7 ¦ 25.7 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 19.8 ¦ 1.5 ¦ 17 ¦ 22.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 15.6 ¦ 1.5 ¦ 12.8 ¦ 18.7 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 13.4 ¦ 1.7 ¦ 10.3 ¦ 17.1 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 5.5 ¦ 0.8 ¦ 4 ¦ 7.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 13.1 ¦ 1.3 ¦ 10.7 ¦ 15.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 30.9 ¦ 1.6 ¦ 27.8 ¦ 34.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 20.1 ¦ 1.5 ¦ 17.3 ¦ 23.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 16.9 ¦ 1.5 ¦ 14 ¦ 20.2 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Association between Patient age recode and Patient sex {NAMCS 2019 PUF} #> +---------------------------------------------------------------+ #> ¦ p-value ¦ Flag ¦ #> +-------------------------------+-------------------------------¦ #> ¦ 0.003 ¦ * ¦ #> +---------------------------------------------------------------+ #> Pearson's X^2: Rao & Scott adjustment. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient age recode (Patient sex = #> Female) {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years ¦ 0.01 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦ 0.007 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 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 ¦ 0.677 ¦ ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years ¦ 75 years and over ¦ 0.201 ¦ ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years ¦ 65-74 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years ¦ 75 years and over ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years ¦ 75 years and over ¦ 0.034 ¦ * ¦ #> +-----------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient age recode (Patient sex = #> Male) {NAMCS 2019 PUF} #> +-----------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 15-24 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 25-44 years ¦ 0.894 ¦ ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 45-64 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 65-74 years ¦ 0.008 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ Under 15 years ¦ 75 years and over ¦ 0.166 ¦ ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 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 ¦ 0.001 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 25-44 years ¦ 75 years and over ¦ 0.113 ¦ ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years ¦ 65-74 years ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 45-64 years ¦ 75 years and over ¦ 0 ¦ * ¦ #> +----------------+-------------------+---------+------¦ #> ¦ 65-74 years ¦ 75 years and over ¦ 0.105 ¦ ¦ #> +-----------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> Under 15 years) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0.69 ¦ ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> 15-24 years) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0.003 ¦ * ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> 25-44 years) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0 ¦ * ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> 45-64 years) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0.014 ¦ * ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> 65-74 years) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0.002 ¦ * ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> #> Comparison of all possible pairs of Patient sex (Patient age recode = #> 75 years and over) {NAMCS 2019 PUF} #> +---------------------------------------------------+ #> ¦ Level 1 ¦ Level 2 ¦ p-value ¦ Flag ¦ #> +------------+------------+------------+------------¦ #> ¦ Female ¦ Male ¦ 0.024 ¦ * ¦ #> +---------------------------------------------------+ #> Design-based t-test. *: p-value <= 0.05 #> # 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 ¦ #> +---------------------------------------------------+ #>"},{"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\"), screen = getOption(\"surveytable.screen\"), 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. screen print screen? 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 data.frame tables single data.frame 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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int tab_subset_rate(\"AGER\", \"SEX\", uspop2019$`AGER x SEX`) #> Patient age recode (Patient sex = Female) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years ¦ 202.5 ¦ 24.3 ¦ 159.8 ¦ 256.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years ¦ 198.4 ¦ 21.9 ¦ 159.5 ¦ 246.8 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years ¦ 263.3 ¦ 26.5 ¦ 215.9 ¦ 321 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years ¦ 414 ¦ 37.7 ¦ 346.2 ¦ 495.1 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years ¦ 720.3 ¦ 66.4 ¦ 600.8 ¦ 863.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦ 758.1 ¦ 89.2 ¦ 601.2 ¦ 956 ¦ #> +---------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Patient age recode (Patient sex = Male) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years ¦ 187.4 ¦ 25 ¦ 144.1 ¦ 243.7 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years ¦ 113.1 ¦ 20.7 ¦ 78.4 ¦ 163 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years ¦ 133.4 ¦ 17.2 ¦ 103.4 ¦ 172 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years ¦ 333.4 ¦ 32.3 ¦ 275.4 ¦ 403.5 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years ¦ 594.8 ¦ 46.4 ¦ 510.1 ¦ 693.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦ 801.2 ¦ 73.2 ¦ 669.1 ¦ 959.5 ¦ #> +---------------------------------------------------------------------------+ #> (Checked 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( screen = getOption(\"surveytable.screen\"), 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":"screen print screen? 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":"data.frame","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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int total() #> Total {NAMCS 2019 PUF} #> +---------------------------------------------------------------+ #> ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 1,036,484 ¦ 48,836 ¦ 945,014 ¦ 1,136,809 ¦ #> +---------------------------------------------------------------+ #> (Checked 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\"), screen = getOption(\"surveytable.screen\"), 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 screen print screen? 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":"data.frame","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 name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int total_rate(uspop2019$total) #> Total (rate per 100 population) {NAMCS 2019 PUF} #> +---------------------------------------------------------------+ #> ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 320.7 ¦ 15.1 ¦ 292.4 ¦ 351.7 ¦ #> +---------------------------------------------------------------+ #> (Checked 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_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? — var_any","title":"Is any variable true? — 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? — 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? — 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? — var_any","text":"(Nothing.)","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? — var_any","text":"","code":"set_survey(\"namcs2019sv\") #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int var_any(\"Imaging services\" , c(\"ANYIMAGE\", \"BONEDENS\", \"CATSCAN\", \"ECHOCARD\", \"OTHULTRA\" , \"MAMMO\", \"MRI\", \"XRAY\", \"OTHIMAGE\")) tab(\"Imaging services\") #> Imaging services {NAMCS 2019 PUF} #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 901,115 ¦ 43,298 ¦ 820,085 ¦ 990,151 ¦ 86.9 ¦ 1.1 ¦ 84.6 ¦ 89.1 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 135,369 ¦ 13,574 ¦ 111,134 ¦ 164,890 ¦ 13.1 ¦ 1.1 ¦ 10.9 ¦ 15.4 ¦ #> +-------------------------------------------------------------------------------------+ #> (Checked 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)"},{"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.","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":"(Nothing.)","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 name NAMCS 2019 PUF #> Number of variables 34 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #> Preventive care visits {NAMCS 2019 PUF} #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 812,861 ¦ 45,220 ¦ 728,841 ¦ 906,566 ¦ 78.4 ¦ 1.7 ¦ 74.9 ¦ 81.7 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 223,624 ¦ 18,520 ¦ 190,068 ¦ 263,103 ¦ 21.6 ¦ 1.7 ¦ 18.3 ¦ 25.1 ¦ #> +-------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 969,451 ¦ 47,976 ¦ 879,793 ¦ 1,068,246 ¦ 93.5 ¦ 0.8 ¦ 91.9 ¦ 94.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 67,034 ¦ 7,810 ¦ 53,273 ¦ 84,348 ¦ 6.5 ¦ 0.8 ¦ 5.1 ¦ 8.1 ¦ #> +--------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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 name NAMCS 2019 PUF #> Number of variables 36 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int tab(\"PRIMCARE\") #> Are you the patient's primary care provider? {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ Flags ¦ #> ¦ ¦ (000) ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Blank ¦ 1,150 ¦ 478 ¦ 440 ¦ 3,005 ¦ 0.1 ¦ 0 ¦ 0 ¦ 0.2 ¦ Cx ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Unknown ¦ 39,519 ¦ 9,507 ¦ 24,520 ¦ 63,692 ¦ 3.8 ¦ 0.9 ¦ 2.3 ¦ 6 ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Yes ¦ 383,481 ¦ 28,555 ¦ 331,362 ¦ 443,798 ¦ 37 ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ No ¦ 612,335 ¦ 43,282 ¦ 533,050 ¦ 703,413 ¦ 59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ ¦ #> +----------------------------------------------------------------------------------------------+ #> 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Unknown if ¦ 40,669 ¦ 9,479 ¦ 25,619 ¦ 64,560 ¦ 3.9 ¦ 0.9 ¦ 2.4 ¦ 6.1 ¦ #> ¦ PCP ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Yes ¦ 383,481 ¦ 28,555 ¦ 331,362 ¦ 443,798 ¦ 37 ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ No ¦ 612,335 ¦ 43,282 ¦ 533,050 ¦ 703,413 ¦ 59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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 name NAMCS 2019 PUF #> Number of variables 36 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Age group {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-64 ¦ 479,777 ¦ 32,175 ¦ 420,624 ¦ 547,247 ¦ 46.3 ¦ 1.8 ¦ 42.7 ¦ 49.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65+ ¦ 373,935 ¦ 24,523 ¦ 328,777 ¦ 425,296 ¦ 36.1 ¦ 1.9 ¦ 32.3 ¦ 40 ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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 name NAMCS 2019 PUF #> Number of variables 37 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #> (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 5.8 ¦ 0.7 ¦ 4.5 ¦ 7.3 ¦ #> ¦ years : ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 4 ¦ 0.4 ¦ 3.2 ¦ 4.9 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 11 ¦ 1 ¦ 9 ¦ 13.2 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 17 ¦ 1.1 ¦ 14.8 ¦ 19.3 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 11.6 ¦ 1 ¦ 9.7 ¦ 13.7 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 9.1 ¦ 0.9 ¦ 7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 5.6 ¦ 0.7 ¦ 4.3 ¦ 7.2 ¦ #> ¦ years : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 2.3 ¦ 0.4 ¦ 1.6 ¦ 3.2 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 5.5 ¦ 0.6 ¦ 4.3 ¦ 6.8 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 12.9 ¦ 1 ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 8.4 ¦ 0.6 ¦ 7.2 ¦ 9.7 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 7 ¦ 0.6 ¦ 5.9 ¦ 8.3 ¦ #> ¦ over : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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 name NAMCS 2019 PUF #> Number of variables 38 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int 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: Age group: overwriting a variable that already exists. tab(\"Age group\") #> Age group {NAMCS 2019 PUF} #> +-------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 1 ¦ 31,148 ¦ 5,282 ¦ 22,269 ¦ 43,566 ¦ 3 ¦ 0.5 ¦ 2.1 ¦ 4.1 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 1-4 ¦ 38,240 ¦ 5,444 ¦ 28,864 ¦ 50,662 ¦ 3.7 ¦ 0.5 ¦ 2.7 ¦ 4.8 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 5-14 ¦ 48,529 ¦ 5,741 ¦ 38,430 ¦ 61,282 ¦ 4.7 ¦ 0.5 ¦ 3.7 ¦ 5.9 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-64 ¦ 544,632 ¦ 36,082 ¦ 478,254 ¦ 620,223 ¦ 52.5 ¦ 2 ¦ 48.6 ¦ 56.5 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65 and over ¦ 373,935 ¦ 24,523 ¦ 328,777 ¦ 425,296 ¦ 36.1 ¦ 1.9 ¦ 32.3 ¦ 40 ¦ #> +-------------------------------------------------------------------------------------------+ #> (Checked 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, screen = getOption(\"surveytable.screen\"), 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? screen print screen? 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":"data.frame","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 name NAMCS 2019 PUF #> Number of variables 38 #> Number of observations 8250 #> Stratified 1 - level Cluster Sampling design (with replacement) #> With (398) clusters. #> survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> data = namcs2019sv_df) #> * To adjust how counts are rounded, see ?set_count_int var_list(\"age\") #> Variables beginning with 'age' {NAMCS 2019 PUF} #> +------------------------------------------------------------+ #> ¦ Variable ¦ Class ¦ Long name ¦ #> +-----------+---------+--------------------------------------¦ #> ¦ AGE ¦ numeric ¦ Patient age in years ¦ #> +-----------+---------+--------------------------------------¦ #> ¦ AGER ¦ factor ¦ Patient age recode ¦ #> +-----------+---------+--------------------------------------¦ #> ¦ Age group ¦ factor ¦ Age group ¦ #> +-----------+---------+--------------------------------------¦ #> ¦ Age x Sex ¦ factor ¦ (Patient age recode) x (Patient sex) ¦ #> +------------------------------------------------------------+ #>"},{"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/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. Check survey name, survey design variables, number observations verify looks correct.","code":"library(surveytable) mysurvey = namcs2019sv set_survey(mysurvey) ## * To adjust how counts are rounded, see ?set_count_int ## _ ## Survey name NAMCS 2019 PUF ## Number of variables 33 ## Number of observations 8250 ## Info1 Stratified 1 - level Cluster Sampling design (with replacement) ## Info2 With (398) clusters. ## Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, ## Info4 data = namcs2019sv_df)"},{"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} Recall survey design objects element called variables, data frame contains survey variables. Let’s examine levels MSA. calculate rates particular variable, need provide data frame variable called Level matches levels variable survey. Population gives population estimate. example, MSA, 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$total) levels(mysurvey$variables$MSA) ## [1] \"MSA (Metropolitan Statistical Area)\" \"Non-MSA\" uspop2019$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 syntax. (survey imported R using importsurvey package, automatically detects binary variables imports logical variables.) Recall survey design objects element called variables, data frame contains survey variables. Notes: ’re using within() function simplify syntax. nopwu = payment used, private, Medicare, etc. Self-pay (real) = self-pay used payment used. making changes mysurvey, need call set_survey() . 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 (real) {NAMCS 2019 PUF} charge (real) {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":"mysurvey$variables = within(mysurvey$variables, { `Medicare and Medicaid` = PAYMCARE & PAYMCAID `Unknown or blank` = PAYDK | (NOPAY == \"No categories marked\") nopwu = !( PAYPRIV | PAYMCARE | PAYMCAID | PAYWKCMP | PAYOTH | PAYDK) `Self-pay (real)` = PAYSELF & nopwu `No charge (real)` = PAYNOCHG & nopwu `No insurance` = `Self-pay (real)` | `No charge (real)` }) set_survey(mysurvey) ## * To adjust how counts are rounded, see ?set_count_int ## _ ## Survey name NAMCS 2019 PUF ## Number of variables 39 ## Number of observations 8250 ## Info1 Stratified 1 - level Cluster Sampling design (with replacement) ## Info2 With (398) clusters. ## Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, ## Info4 data = namcs2019sv_df) tab(\"PAYPRIV\", \"PAYMCARE\", \"PAYMCAID\", \"Medicare and Medicaid\" , \"No insurance\", \"Self-pay (real)\", \"No charge (real)\" , \"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} (Patient age recode) x (Patient 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} (Patient age recode) x (Patient 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\") ) 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) ((Major reason visit) x (Patient age recode) = 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":[]},{"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$AGE) #> [1] \"numeric\""},{"path":"https://cdcgov.github.io/surveytable/articles/surveytable.html","id":"create-a-survey-object","dir":"Articles","previous_headings":"Preliminaries","what":"Create a survey object","title":"Introduction to surveytable","text":"seen , tables produced surveytable clearer either variable names descriptive, variables \"label\" attribute descriptive. namcs2019sv_df, variables already \"label\" attribute set. example, variable name AGE descriptive, variable descriptive \"label\" attribute: Documentation NAMCS survey provides names survey design variables. Specifically, NAMCS, cluster ID’s, also known primary sampling units (PSU’s), given CPSUM; strata given CSTRATM; sampling weights given PATWT. Thus, namcs2019sv_df data frame can turned survey object follows: Tables produced surveytable clearer either name survey object descriptive, object \"label\" attribute descriptive. Let’s set attribute mysurvey: mysurvey object now identical namcs2019sv. Let’s verify : just successfully created survey object data frame.","code":"attr(namcs2019sv_df$AGE, \"label\") #> [1] \"Patient age in years\" mysurvey = survey::svydesign(ids = ~ CPSUM , strata = ~ CSTRATM , weights = ~ PATWT , data = namcs2019sv_df) attr(mysurvey, \"label\") = \"NAMCS 2019 PUF\" identical(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. Check survey label, survey design variables, number observations verify looks correct.","code":"set_survey(namcs2019sv) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df)"},{"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 {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. 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. variables data frame. Recall survey objects element called variables, data frame contains survey variables. advanced users can create modify variables variables data frame directly. modify variables, must call set_survey() . example: Medicare Medicaid {NAMCS 2019 PUF} Note, however, var_*() functions modify survey object specified set_survey() directly. Rather, modify variables inside following data frame: surveytable:::env$survey$variables. use var_*() functions need access modified / created variables, ’s look. example:","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, 4, 14, 64, Inf) , c(\"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\") class(namcs2019sv$variables) #> [1] \"data.frame\" namcs2019sv$variables$`Medicare and Medicaid` = ( namcs2019sv$variables$PAYMCARE & namcs2019sv$variables$PAYMCAID) set_survey(namcs2019sv) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 34 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) tab(\"Medicare and Medicaid\") var_cross(\"newvar\", \"MAJOR\", \"AGER\") # This should give NULL. The new variable does not exist here: namcs2019sv$variables$newvar #> NULL # Rather, the new variable is here: str(surveytable:::env$survey$variables$newvar) #> Factor w/ 42 levels \"Blank : Under 15 years\",..: 17 31 24 38 38 24 30 31 24 38 ... #> - attr(*, \"label\")= chr \"(Major reason for this visit) x (Patient age recode)\""},{"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, produce HTML tables. 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":"tmp_file = tempfile(fileext = \".csv\") suppressMessages( set_output(csv = tmp_file) ) 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. https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable.","code":"@Manual{, title = {surveytable: Formatted Survey Estimates}, author = {Alex Strashny}, year = {2023}, note = {https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/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 package provides short understandable commands generate tabulated, formatted, rounded survey estimates. One useful function, operates categorical logical variables, tabulates estimated counts percentages standard errors confidence intervals. functions list variables survey, estimate total population, tabulate survey subsets variable interactions, tabulate numeric variables, tabulate rates, create modify survey variables, perform hypothesis tests, save output. tabulation functions check National Center Health Statistics (NCHS) presentation standards flag low-precision estimates. surveytable code called R Markdown notebook Quarto document, generates HTML tables, can incorporated directly documents.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Formatted Survey Estimates","text":"can install surveytable like : surveytable available CRAN, able install like :","code":"install.packages(c(\"remotes\", \"git2r\")) remotes::install_github(\"CDCgov/surveytable\", upgrade = \"never\") install.packages(\"surveytable\")"},{"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. Patient age recode {NAMCS 2019 PUF}","code":"library(surveytable) set_survey(namcs2019sv) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) 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. (new GitHub, might start basic tutorial.) contributing project, grant world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license users terms Apache Software License v2 later. comments, messages, pull requests, submissions received CDC including GitHub page may subject applicable federal law, including limited Federal Records Act, may archived. Learn https://www.cdc.gov//privacy.html.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"records-management-standard-notice","dir":"","previous_headings":"","what":"Records Management Standard Notice","title":"Formatted Survey Estimates","text":"repository source government records, copy increase collaboration collaborative potential. government records published CDC web site.","code":""},{"path":"https://cdcgov.github.io/surveytable/index.html","id":"additional-standard-notices","dir":"","previous_headings":"","what":"Additional Standard Notices","title":"Formatted Survey Estimates","text":"Please refer CDC’s Template Repository information contributing repository, public domain notices disclaimers, code conduct.","code":""},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"https://cdcgov.github.io/surveytable/LICENSE.html","id":"1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. 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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/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, ...) # S3 method for surveytable_list print(x, ...)"},{"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. ... 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) table1 = tab(\"AGER\") print(table1) #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> table_many = tab(\"MDDO\", \"SPECCAT\", \"MSA\") print(table_many) #> Type of doctor (MD or DO) {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦ 980,280 ¦ 48,388 ¦ 889,842 ¦ 1,079,910 ¦ 94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦ 56,204 ¦ 6,602 ¦ 44,597 ¦ 70,832 ¦ 5.4 ¦ 0.7 ¦ 4.2 ¦ 6.9 ¦ #> ¦ of Osteopathy ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care ¦ 521,466 ¦ 31,136 ¦ 463,840 ¦ 586,252 ¦ 50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦ 214,832 ¦ 31,110 ¦ 161,661 ¦ 285,490 ¦ 20.7 ¦ 3 ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care ¦ 300,186 ¦ 43,497 ¦ 225,806 ¦ 399,067 ¦ 29 ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA ¦ 973,676 ¦ 50,515 ¦ 879,490 ¦ 1,077,947 ¦ 93.9 ¦ 1.7 ¦ 89.6 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Statistical ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Area) ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA ¦ 62,809 ¦ 17,549 ¦ 36,249 ¦ 108,830 ¦ 6.1 ¦ 1.7 ¦ 3.2 ¦ 10.4 ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #>"},{"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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) set_count_int() #> * Rounding counts to the nearest integer. #> * ?set_count_int for other options. total() #> Total {NAMCS 2019 PUF} #> +----------------------------------------------------------+ #> ¦ Number ¦ SE ¦ LL ¦ UL ¦ #> +---------------+------------+-------------+---------------¦ #> ¦ 1,036,484,356 ¦ 48,836,217 ¦ 945,013,590 ¦ 1,136,808,860 ¦ #> +----------------------------------------------------------+ #> (Checked 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} #> +---------------------------------------------------------------+ #> ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 1,036,484 ¦ 48,836 ¦ 945,014 ¦ 1,136,809 ¦ #> +---------------------------------------------------------------+ #> (Checked 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(csv = NULL, max_levels = 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":"csv name CSV file \"\" turn CSV output max_levels categorical variable can many levels. Used avoid printing huge tables.","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) ) 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)"},{"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 survey object (survey.design svyrep.design)","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":"Object 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":"Optionally, survey can attribute called label, long name survey. Optionally, variable survey can attribute called label, variable's long name.","code":""},{"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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 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":"Show package 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.check_present #> [1] TRUE #> #> $surveytable.csv #> [1] \"\" #> #> $surveytable.drop_na #> [1] FALSE #> #> $surveytable.max_levels #> [1] 20 #> #> $surveytable.names_count #> [1] \"Number (000)\" \"SE (000)\" \"LL (000)\" \"UL (000)\" #> #> $surveytable.names_prct #> [1] \"Percent\" \"SE\" \"LL\" \"UL\" #> #> $surveytable.present_count #> [1] \".present_count\" #> #> $surveytable.present_prop #> [1] \".present_prop\" #> #> $surveytable.present_restricted #> [1] \".present_restricted\" #> #> $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_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.","code":""},{"path":[]},{"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) implements 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 design object subset expression specifying sub-population label survey label newly created survey design 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 design 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name Children < 18 #> Number of variables 33 #> Number of observations 1066 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (139) clusters. #> Info3 NULL tab(\"AGER\") #> Patient age recode {Children < 18} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 86.1 ¦ 1.6 ¦ 82.5 ¦ 89.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 19,004 ¦ 2,872 ¦ 14,051 ¦ 25,702 ¦ 13.9 ¦ 1.6 ¦ 10.8 ¦ 17.5 ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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\"), 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. df_method: \"default\", df = degf(design); \"NHIS\", df = nrow(design) - 1.","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. use function tabulations, 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) options(surveytable.adjust_svyciprop = TRUE) tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> options(surveytable.adjust_svyciprop = FALSE) tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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, 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 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 data.frame tables single data.frame 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) tab(\"AGER\") #> Patient age recode {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> tab(\"MDDO\", \"SPECCAT\", \"MSA\") #> Type of doctor (MD or DO) {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ M.D. - Doctor ¦ 980,280 ¦ 48,388 ¦ 889,842 ¦ 1,079,910 ¦ 94.6 ¦ 0.7 ¦ 93.1 ¦ 95.8 ¦ #> ¦ of Medicine ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ D.O. - Doctor ¦ 56,204 ¦ 6,602 ¦ 44,597 ¦ 70,832 ¦ 5.4 ¦ 0.7 ¦ 4.2 ¦ 6.9 ¦ #> ¦ of Osteopathy ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Type of specialty (Primary, Medical, Surgical) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Primary care ¦ 521,466 ¦ 31,136 ¦ 463,840 ¦ 586,252 ¦ 50.3 ¦ 2.6 ¦ 45.1 ¦ 55.5 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Surgical care ¦ 214,832 ¦ 31,110 ¦ 161,661 ¦ 285,490 ¦ 20.7 ¦ 3 ¦ 15.1 ¦ 27.3 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Medical care ¦ 300,186 ¦ 43,497 ¦ 225,806 ¦ 399,067 ¦ 29 ¦ 3.6 ¦ 22.1 ¦ 36.6 ¦ #> ¦ specialty ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Metropolitan Statistical Area Status of physician location {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ MSA ¦ 973,676 ¦ 50,515 ¦ 879,490 ¦ 1,077,947 ¦ 93.9 ¦ 1.7 ¦ 89.6 ¦ 96.8 ¦ #> ¦ (Metropolitan ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Statistical ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Area) ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ Non-MSA ¦ 62,809 ¦ 17,549 ¦ 36,249 ¦ 108,830 ¦ 6.1 ¦ 1.7 ¦ 3.2 ¦ 10.4 ¦ #> +----------------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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-value <= 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 data.frame tables single data.frame 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 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 ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ MSA (Metropolitan ¦ 351.2 ¦ 18.2 ¦ 317.2 ¦ 388.8 ¦ #> ¦ Statistical Area) ¦ ¦ ¦ ¦ ¦ #> +----------------------------+-------+------+-------+-------¦ #> ¦ Non-MSA ¦ 136.7 ¦ 38.2 ¦ 78.9 ¦ 236.8 ¦ #> +-----------------------------------------------------------+ #> (Checked 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 ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ M.D. - Doctor of Medicine ¦ 303.3 ¦ 15 ¦ 275.3 ¦ 334.1 ¦ #> +----------------------------+-------+----+-------+-------¦ #> ¦ D.O. - Doctor of ¦ 17.4 ¦ 2 ¦ 13.8 ¦ 21.9 ¦ #> ¦ Osteopathy ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------+ #> (Checked 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, 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 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":"tab_subset: list data.frame tables single data.frame table. tab_cross: data.frame 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) # For each SEX, tabulate AGER tab_subset(\"AGER\", \"SEX\") #> Patient age recode (Patient sex = Female) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 9.9 ¦ 1.2 ¦ 7.6 ¦ 12.6 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 6.8 ¦ 0.7 ¦ 5.4 ¦ 8.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 18.8 ¦ 1.6 ¦ 15.8 ¦ 22.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 29.1 ¦ 1.7 ¦ 25.7 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 19.8 ¦ 1.5 ¦ 17 ¦ 22.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 15.6 ¦ 1.5 ¦ 12.8 ¦ 18.7 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Patient age recode (Patient sex = Male) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 13.4 ¦ 1.7 ¦ 10.3 ¦ 17.1 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 5.5 ¦ 0.8 ¦ 4 ¦ 7.4 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 13.1 ¦ 1.3 ¦ 10.7 ¦ 15.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 30.9 ¦ 1.6 ¦ 27.8 ¦ 34.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 20.1 ¦ 1.5 ¦ 17.3 ¦ 23.1 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 16.9 ¦ 1.5 ¦ 14 ¦ 20.2 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 5.8 ¦ 0.7 ¦ 4.5 ¦ 7.3 ¦ #> ¦ years : ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 4 ¦ 0.4 ¦ 3.2 ¦ 4.9 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 11 ¦ 1 ¦ 9 ¦ 13.2 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 17 ¦ 1.1 ¦ 14.8 ¦ 19.3 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 11.6 ¦ 1 ¦ 9.7 ¦ 13.7 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 9.1 ¦ 0.9 ¦ 7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 5.6 ¦ 0.7 ¦ 4.3 ¦ 7.2 ¦ #> ¦ years : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 2.3 ¦ 0.4 ¦ 1.6 ¦ 3.2 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 5.5 ¦ 0.6 ¦ 4.3 ¦ 6.8 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 12.9 ¦ 1 ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 8.4 ¦ 0.6 ¦ 7.2 ¦ 9.7 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 7 ¦ 0.6 ¦ 5.9 ¦ 8.3 ¦ #> ¦ over : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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-value <= 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-value <= 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 data.frame tables single data.frame 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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 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 ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years ¦ 202.5 ¦ 24.3 ¦ 159.8 ¦ 256.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years ¦ 198.4 ¦ 21.9 ¦ 159.5 ¦ 246.8 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years ¦ 263.3 ¦ 26.5 ¦ 215.9 ¦ 321 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years ¦ 414 ¦ 37.7 ¦ 346.2 ¦ 495.1 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years ¦ 720.3 ¦ 66.4 ¦ 600.8 ¦ 863.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦ 758.1 ¦ 89.2 ¦ 601.2 ¦ 956 ¦ #> +---------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Patient age recode (Patient sex = Male) (rate per 100 population) #> {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------+ #> ¦ Level ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ Under 15 years ¦ 187.4 ¦ 25 ¦ 144.1 ¦ 243.7 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 15-24 years ¦ 113.1 ¦ 20.7 ¦ 78.4 ¦ 163 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 25-44 years ¦ 133.4 ¦ 17.2 ¦ 103.4 ¦ 172 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 45-64 years ¦ 333.4 ¦ 32.3 ¦ 275.4 ¦ 403.5 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 65-74 years ¦ 594.8 ¦ 46.4 ¦ 510.1 ¦ 693.6 ¦ #> +-------------------+-------------+-------------+-------------+-------------¦ #> ¦ 75 years and over ¦ 801.2 ¦ 73.2 ¦ 669.1 ¦ 959.5 ¦ #> +---------------------------------------------------------------------------+ #> (Checked 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":"data.frame","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) total() #> Total {NAMCS 2019 PUF} #> +---------------------------------------------------------------+ #> ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 1,036,484 ¦ 48,836 ¦ 945,014 ¦ 1,136,809 ¦ #> +---------------------------------------------------------------+ #> (Checked 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":"data.frame","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) total_rate(uspop2019$total) #> Total (rate per 100 population) {NAMCS 2019 PUF} #> +---------------------------------------------------------------+ #> ¦ Rate ¦ SE ¦ LL ¦ UL ¦ #> +---------------+---------------+---------------+---------------¦ #> ¦ 320.7 ¦ 15.1 ¦ 292.4 ¦ 351.7 ¦ #> +---------------------------------------------------------------+ #> (Checked 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_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Is any variable true? — var_any","title":"Is any variable true? — 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? — 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? — 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? — var_any","text":"(Nothing.)","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? — var_any","text":"","code":"set_survey(namcs2019sv) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 901,115 ¦ 43,298 ¦ 820,085 ¦ 990,151 ¦ 86.9 ¦ 1.1 ¦ 84.6 ¦ 89.1 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 135,369 ¦ 13,574 ¦ 111,134 ¦ 164,890 ¦ 13.1 ¦ 1.1 ¦ 10.9 ¦ 15.4 ¦ #> +-------------------------------------------------------------------------------------+ #> (Checked 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)"},{"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.","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":"(Nothing.)","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) var_case(\"Preventive care visits\", \"MAJOR\", \"Preventive care\") tab(\"Preventive care visits\") #> Preventive care visits {NAMCS 2019 PUF} #> +-------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 812,861 ¦ 45,220 ¦ 728,841 ¦ 906,566 ¦ 78.4 ¦ 1.7 ¦ 74.9 ¦ 81.7 ¦ #> +-------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 223,624 ¦ 18,520 ¦ 190,068 ¦ 263,103 ¦ 21.6 ¦ 1.7 ¦ 18.3 ¦ 25.1 ¦ #> +-------------------------------------------------------------------------------------+ #> (Checked 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ FALSE ¦ 969,451 ¦ 47,976 ¦ 879,793 ¦ 1,068,246 ¦ 93.5 ¦ 0.8 ¦ 91.9 ¦ 94.9 ¦ #> +-------+--------------+----------+----------+-----------+---------+-----+------+------¦ #> ¦ TRUE ¦ 67,034 ¦ 7,810 ¦ 53,273 ¦ 84,348 ¦ 6.5 ¦ 0.8 ¦ 5.1 ¦ 8.1 ¦ #> +--------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) tab(\"PRIMCARE\") #> Are you the patient's primary care provider? {NAMCS 2019 PUF} #> +----------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ Flags ¦ #> ¦ ¦ (000) ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Blank ¦ 1,150 ¦ 478 ¦ 440 ¦ 3,005 ¦ 0.1 ¦ 0 ¦ 0 ¦ 0.2 ¦ Cx ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Unknown ¦ 39,519 ¦ 9,507 ¦ 24,520 ¦ 63,692 ¦ 3.8 ¦ 0.9 ¦ 2.3 ¦ 6 ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ Yes ¦ 383,481 ¦ 28,555 ¦ 331,362 ¦ 443,798 ¦ 37 ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ ¦ #> +---------+-------------+----------+----------+----------+---------+-----+------+------+-------¦ #> ¦ No ¦ 612,335 ¦ 43,282 ¦ 533,050 ¦ 703,413 ¦ 59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ ¦ #> +----------------------------------------------------------------------------------------------+ #> 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Unknown if ¦ 40,669 ¦ 9,479 ¦ 25,619 ¦ 64,560 ¦ 3.9 ¦ 0.9 ¦ 2.4 ¦ 6.1 ¦ #> ¦ PCP ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Yes ¦ 383,481 ¦ 28,555 ¦ 331,362 ¦ 443,798 ¦ 37 ¦ 2.6 ¦ 31.9 ¦ 42.3 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ No ¦ 612,335 ¦ 43,282 ¦ 533,050 ¦ 703,413 ¦ 59.1 ¦ 2.5 ¦ 53.9 ¦ 64.1 ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 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 ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years ¦ 170,271 ¦ 13,966 ¦ 144,925 ¦ 200,049 ¦ 16.4 ¦ 1.1 ¦ 14.3 ¦ 18.8 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years ¦ 309,506 ¦ 23,290 ¦ 266,994 ¦ 358,787 ¦ 29.9 ¦ 1.4 ¦ 27.2 ¦ 32.6 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years ¦ 206,866 ¦ 14,366 ¦ 180,481 ¦ 237,109 ¦ 20 ¦ 1.2 ¦ 17.6 ¦ 22.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 167,069 ¦ 15,179 ¦ 139,746 ¦ 199,735 ¦ 16.1 ¦ 1.3 ¦ 13.7 ¦ 18.8 ¦ #> ¦ over ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked presentation standards. Nothing to report.) #> #> Age group {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 117,917 ¦ 14,097 ¦ 93,229 ¦ 149,142 ¦ 11.4 ¦ 1.3 ¦ 8.9 ¦ 14.2 ¦ #> ¦ years ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years ¦ 64,856 ¦ 7,018 ¦ 52,387 ¦ 80,292 ¦ 6.3 ¦ 0.6 ¦ 5.1 ¦ 7.5 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-64 ¦ 479,777 ¦ 32,175 ¦ 420,624 ¦ 547,247 ¦ 46.3 ¦ 1.8 ¦ 42.7 ¦ 49.9 ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65+ ¦ 373,935 ¦ 24,523 ¦ 328,777 ¦ 425,296 ¦ 36.1 ¦ 1.9 ¦ 32.3 ¦ 40 ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) var_cross(\"Age x Sex\", \"AGER\", \"SEX\") tab(\"Age x Sex\") #> (Patient age recode) x (Patient sex) {NAMCS 2019 PUF} #> +---------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 59,958 ¦ 7,206 ¦ 47,318 ¦ 75,974 ¦ 5.8 ¦ 0.7 ¦ 4.5 ¦ 7.3 ¦ #> ¦ years : ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 41,128 ¦ 4,532 ¦ 33,066 ¦ 51,156 ¦ 4 ¦ 0.4 ¦ 3.2 ¦ 4.9 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 113,708 ¦ 11,461 ¦ 93,256 ¦ 138,646 ¦ 11 ¦ 1 ¦ 9 ¦ 13.2 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 175,978 ¦ 16,009 ¦ 147,153 ¦ 210,450 ¦ 17 ¦ 1.1 ¦ 14.8 ¦ 19.3 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 120,099 ¦ 11,066 ¦ 100,171 ¦ 143,992 ¦ 11.6 ¦ 1 ¦ 9.7 ¦ 13.7 ¦ #> ¦ Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 94,173 ¦ 11,085 ¦ 74,682 ¦ 118,751 ¦ 9.1 ¦ 0.9 ¦ 7.3 ¦ 11.1 ¦ #> ¦ over : Female ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 15 ¦ 57,959 ¦ 7,728 ¦ 44,570 ¦ 75,371 ¦ 5.6 ¦ 0.7 ¦ 4.3 ¦ 7.2 ¦ #> ¦ years : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-24 years : ¦ 23,728 ¦ 4,344 ¦ 16,457 ¦ 34,210 ¦ 2.3 ¦ 0.4 ¦ 1.6 ¦ 3.2 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 25-44 years : ¦ 56,562 ¦ 7,277 ¦ 43,861 ¦ 72,942 ¦ 5.5 ¦ 0.6 ¦ 4.3 ¦ 6.8 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 45-64 years : ¦ 133,528 ¦ 12,956 ¦ 110,319 ¦ 161,619 ¦ 12.9 ¦ 1 ¦ 10.9 ¦ 15.1 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65-74 years : ¦ 86,766 ¦ 6,767 ¦ 74,409 ¦ 101,176 ¦ 8.4 ¦ 0.6 ¦ 7.2 ¦ 9.7 ¦ #> ¦ Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 75 years and ¦ 72,896 ¦ 6,661 ¦ 60,872 ¦ 87,296 ¦ 7 ¦ 0.6 ¦ 5.9 ¦ 8.3 ¦ #> ¦ over : Male ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ ¦ #> +---------------------------------------------------------------------------------------------+ #> (Checked 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":"(Nothing.)","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) 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(\"Age group\") #> Age group {NAMCS 2019 PUF} #> +-------------------------------------------------------------------------------------------+ #> ¦ Level ¦ Number (000) ¦ SE (000) ¦ LL (000) ¦ UL (000) ¦ Percent ¦ SE ¦ LL ¦ UL ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ Under 1 ¦ 31,148 ¦ 5,282 ¦ 22,269 ¦ 43,566 ¦ 3 ¦ 0.5 ¦ 2.1 ¦ 4.1 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 1-4 ¦ 38,240 ¦ 5,444 ¦ 28,864 ¦ 50,662 ¦ 3.7 ¦ 0.5 ¦ 2.7 ¦ 4.8 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 5-14 ¦ 48,529 ¦ 5,741 ¦ 38,430 ¦ 61,282 ¦ 4.7 ¦ 0.5 ¦ 3.7 ¦ 5.9 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 15-64 ¦ 544,632 ¦ 36,082 ¦ 478,254 ¦ 620,223 ¦ 52.5 ¦ 2 ¦ 48.6 ¦ 56.5 ¦ #> +-------------+--------------+----------+----------+----------+---------+-----+------+------¦ #> ¦ 65 and over ¦ 373,935 ¦ 24,523 ¦ 328,777 ¦ 425,296 ¦ 36.1 ¦ 1.9 ¦ 32.3 ¦ 40 ¦ #> +-------------------------------------------------------------------------------------------+ #> (Checked 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":"data.frame","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) #> * To adjust how counts are rounded, see ?set_count_int #> _ #> Survey name NAMCS 2019 PUF #> Number of variables 33 #> Number of observations 8250 #> Info1 Stratified 1 - level Cluster Sampling design (with replacement) #> Info2 With (398) clusters. #> Info3 survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT, #> Info4 data = namcs2019sv_df) var_list(\"age\") #> Variables beginning with 'age' {NAMCS 2019 PUF} #> +-------------------------------------------+ #> ¦ Variable ¦ Class ¦ Long name ¦ #> +----------+---------+----------------------¦ #> ¦ AGE ¦ numeric ¦ Patient age in years ¦ #> +----------+---------+----------------------¦ #> ¦ AGER ¦ factor ¦ Patient age recode ¦ #> +-------------------------------------------+ #>"},{"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":"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/docs/sitemap.xml b/docs/sitemap.xml
index 9c219b2..aa74820 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -30,6 +30,9 @@
https://cdcgov.github.io/surveytable/reference/namcs2019sv.html
+
+ https://cdcgov.github.io/surveytable/reference/print.surveytable_table.html
+
https://cdcgov.github.io/surveytable/reference/set_count_1k.html
diff --git a/man/print.surveytable_table.Rd b/man/print.surveytable_table.Rd
new file mode 100644
index 0000000..354e8a1
--- /dev/null
+++ b/man/print.surveytable_table.Rd
@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print.surveytable_table.R
+\name{print.surveytable_table}
+\alias{print.surveytable_table}
+\alias{print.surveytable_list}
+\title{Print surveytable tables}
+\usage{
+\method{print}{surveytable_table}(x, ...)
+
+\method{print}{surveytable_list}(x, ...)
+}
+\arguments{
+\item{x}{an object of class \code{surveytable_table} or \code{surveytable_list}.}
+
+\item{...}{ignored}
+}
+\value{
+\code{x} invisibly.
+}
+\description{
+Print surveytable tables
+}
+\examples{
+set_survey(namcs2019sv)
+table1 = tab("AGER")
+print(table1)
+table_many = tab("MDDO", "SPECCAT", "MSA")
+print(table_many)
+}
diff --git a/man/set_count_1k.Rd b/man/set_count_1k.Rd
index 9a37aa4..58c7fe2 100644
--- a/man/set_count_1k.Rd
+++ b/man/set_count_1k.Rd
@@ -22,7 +22,7 @@ Determines how counts should be rounded.
}
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
set_count_int()
total()
@@ -32,7 +32,6 @@ total()
\seealso{
Other options:
\code{\link{set_output}()},
-\code{\link{set_survey}()},
\code{\link{show_options}()},
\code{\link{surveytable-options}}
}
diff --git a/man/set_output.Rd b/man/set_output.Rd
index 77bfa5f..12739e2 100644
--- a/man/set_output.Rd
+++ b/man/set_output.Rd
@@ -5,15 +5,13 @@
\alias{show_output}
\title{Set output defaults}
\usage{
-set_output(csv = NULL, screen = NULL, max_levels = NULL)
+set_output(csv = NULL, max_levels = NULL)
show_output()
}
\arguments{
\item{csv}{name of a CSV file or "" to turn off CSV output}
-\item{screen}{print to the screen?}
-
\item{max_levels}{a categorical variable can have at most this many levels. Used to avoid printing huge tables.}
}
\value{
@@ -23,16 +21,13 @@ show_output()
\code{show_output()} shows the current defaults.
}
\examples{
-\dontrun{
-set_output(csv = "out.csv")
-show_output()
-}
+tmp_file = tempfile(fileext = ".csv")
+suppressMessages( set_output(csv = tmp_file) )
set_output(csv = "") # Turn off CSV output
}
\seealso{
Other options:
\code{\link{set_count_1k}()},
-\code{\link{set_survey}()},
\code{\link{show_options}()},
\code{\link{surveytable-options}}
}
diff --git a/man/set_survey.Rd b/man/set_survey.Rd
index d1cab62..c7d76b4 100644
--- a/man/set_survey.Rd
+++ b/man/set_survey.Rd
@@ -2,26 +2,21 @@
% Please edit documentation in R/set_survey.R
\name{set_survey}
\alias{set_survey}
-\alias{show_survey}
\title{Specify the survey to analyze}
\usage{
-set_survey(survey_name = "")
-
-show_survey()
+set_survey(design)
}
\arguments{
-\item{survey_name}{the name of a survey object (\code{survey.design} or \code{svyrep.design}), in quotation marks}
+\item{design}{a survey object (\code{survey.design} or \code{svyrep.design})}
}
\value{
-(Nothing.)
+Object with info about the survey.
}
\description{
You need to specify a survey before the other functions, such as \code{\link[=tab]{tab()}},
will work.
}
\details{
-\code{show_survey()} shows the survey that you've specified.
-
Optionally, the survey can have an attribute called \code{label}, which is the
long name of the survey.
@@ -29,14 +24,5 @@ Optionally, each variable in the survey can have an attribute called \code{label
which is the variable's long name.
}
\examples{
-set_survey("namcs2019sv")
-show_survey()
-}
-\seealso{
-Other options:
-\code{\link{set_count_1k}()},
-\code{\link{set_output}()},
-\code{\link{show_options}()},
-\code{\link{surveytable-options}}
+set_survey(namcs2019sv)
}
-\concept{options}
diff --git a/man/show_options.Rd b/man/show_options.Rd
index 2007ef9..9753fe7 100644
--- a/man/show_options.Rd
+++ b/man/show_options.Rd
@@ -22,7 +22,6 @@ show_options()
Other options:
\code{\link{set_count_1k}()},
\code{\link{set_output}()},
-\code{\link{set_survey}()},
\code{\link{surveytable-options}}
}
\concept{options}
diff --git a/man/survey_subset.Rd b/man/survey_subset.Rd
index 436d4ed..f143311 100644
--- a/man/survey_subset.Rd
+++ b/man/survey_subset.Rd
@@ -20,9 +20,7 @@ a new survey design object
Subset a survey, while preserving variable labels
}
\examples{
-\dontrun{
-children = survey_subset(namcs2019sv, AGE < 18, "Children")
-set_survey("children")
+children = survey_subset(namcs2019sv, AGE < 18, "Children < 18")
+set_survey(children)
tab("AGER")
}
-}
diff --git a/man/surveytable-options.Rd b/man/surveytable-options.Rd
index e5a72ba..7521b93 100644
--- a/man/surveytable-options.Rd
+++ b/man/surveytable-options.Rd
@@ -11,7 +11,6 @@ Run \code{\link[=show_options]{show_options()}} to see available options.
Other options:
\code{\link{set_count_1k}()},
\code{\link{set_output}()},
-\code{\link{set_survey}()},
\code{\link{show_options}()}
}
\concept{options}
diff --git a/man/surveytable-package.Rd b/man/surveytable-package.Rd
index 64560a5..ed590ce 100644
--- a/man/surveytable-package.Rd
+++ b/man/surveytable-package.Rd
@@ -6,7 +6,7 @@
\alias{surveytable-package}
\title{surveytable: Formatted Survey Estimates}
\description{
-Short and understandable commands that generate tabulated, formatted, and rounded survey estimates.
+Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) \doi{10.18637/jss.v009.i08} \url{https://CRAN.R-project.org/package=survey}) that implements the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) \url{https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf}, Parker et al. (2023) \doi{10.15620/cdc:124368}).
}
\seealso{
Useful links:
diff --git a/man/svyciprop_adjusted.Rd b/man/svyciprop_adjusted.Rd
index 231060e..f85b33a 100644
--- a/man/svyciprop_adjusted.Rd
+++ b/man/svyciprop_adjusted.Rd
@@ -40,7 +40,7 @@ Written by Makram Talih in 2019.
To use this function in tabulations, type: \code{options(surveytable.adjust_svyciprop = TRUE)}.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
options(surveytable.adjust_svyciprop = TRUE)
tab("AGER")
options(surveytable.adjust_svyciprop = FALSE)
diff --git a/man/tab.Rd b/man/tab.Rd
index d90fcc3..9d3725f 100644
--- a/man/tab.Rd
+++ b/man/tab.Rd
@@ -10,7 +10,6 @@ tab(
alpha = 0.05,
drop_na = getOption("surveytable.drop_na"),
max_levels = getOption("surveytable.max_levels"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
}
@@ -25,8 +24,6 @@ tab(
\item{max_levels}{a categorical variable can have at most this many levels. Used to avoid printing huge tables.}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -53,7 +50,7 @@ for complex surveys. CIs for percentage estimates are
the Korn and Graubard CIs.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
tab("AGER")
tab("MDDO", "SPECCAT", "MSA")
@@ -62,10 +59,6 @@ tab("NUMMED")
# Hypothesis testing with categorical variables
tab("AGER", test = TRUE)
-
-# Integrate the output into other programming tasks
-my_table = tab("AGER", screen = FALSE)
-my_table = within(my_table, {RSE = `SE (000)` / `Number (000)`})
}
\seealso{
Other tables:
diff --git a/man/tab_rate.Rd b/man/tab_rate.Rd
index 1eb238f..72f68a5 100644
--- a/man/tab_rate.Rd
+++ b/man/tab_rate.Rd
@@ -10,7 +10,6 @@ tab_rate(
per = getOption("surveytable.rate_per"),
drop_na = getOption("surveytable.drop_na"),
max_levels = getOption("surveytable.max_levels"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
}
@@ -28,8 +27,6 @@ level of \code{vr}.}
\item{max_levels}{a categorical variable can have at most this many levels. Used to avoid printing huge tables.}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -39,7 +36,7 @@ A list of \code{data.frame} tables or a single \code{data.frame} table.
Calculate the rates for categorical (factor) or logical variables.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
# pop is a data frame
tab_rate("MSA", uspop2019$MSA)
diff --git a/man/tab_subset.Rd b/man/tab_subset.Rd
index aa470c0..15aef85 100644
--- a/man/tab_subset.Rd
+++ b/man/tab_subset.Rd
@@ -9,7 +9,6 @@ tab_cross(
vr,
vrby,
max_levels = getOption("surveytable.max_levels"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
@@ -21,7 +20,6 @@ tab_subset(
alpha = 0.05,
drop_na = getOption("surveytable.drop_na"),
max_levels = getOption("surveytable.max_levels"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
}
@@ -32,8 +30,6 @@ tab_subset(
\item{max_levels}{a categorical variable can have at most this many levels. Used to avoid printing huge tables.}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
\item{lvls}{(optional) only show these levels of \code{vrby}}
@@ -69,7 +65,7 @@ population add up to 100\%. Also see \code{\link[=var_cross]{var_cross()}}.
performs t-tests for all possible pairs of levels of \code{vr} and \code{vrby}.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
# For each SEX, tabulate AGER
tab_subset("AGER", "SEX")
@@ -77,11 +73,11 @@ tab_subset("AGER", "SEX")
# Same counts as tab_subset(), but different percentages.
tab_cross("AGER", "SEX")
-# Hypothesis testing
-tab_subset("AGER", "SEX", test = TRUE)
-
# Numeric variables
tab_subset("NUMMED", "AGER")
+
+# Hypothesis testing
+tab_subset("NUMMED", "AGER", test = TRUE)
}
\seealso{
Other tables:
diff --git a/man/tab_subset_rate.Rd b/man/tab_subset_rate.Rd
index 3c4b771..600b3dd 100644
--- a/man/tab_subset_rate.Rd
+++ b/man/tab_subset_rate.Rd
@@ -12,7 +12,6 @@ tab_subset_rate(
per = getOption("surveytable.rate_per"),
drop_na = getOption("surveytable.drop_na"),
max_levels = getOption("surveytable.max_levels"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
}
@@ -33,8 +32,6 @@ exactly match the levels of \code{vr}. \code{Subset} must exactly match the leve
\item{max_levels}{a categorical variable can have at most this many levels. Used to avoid printing huge tables.}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -45,7 +42,7 @@ Create subsets of the survey using one variable, and tabulate
the rates of another variable within each of the subsets.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)
}
\seealso{
diff --git a/man/total.Rd b/man/total.Rd
index 7bfcdfc..436fb8a 100644
--- a/man/total.Rd
+++ b/man/total.Rd
@@ -4,14 +4,9 @@
\alias{total}
\title{Total count}
\usage{
-total(
- screen = getOption("surveytable.screen"),
- csv = getOption("surveytable.csv")
-)
+total(csv = getOption("surveytable.csv"))
}
\arguments{
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -21,7 +16,7 @@ total(
Total count
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
total()
}
\seealso{
diff --git a/man/total_rate.Rd b/man/total_rate.Rd
index dbf6bda..8291124 100644
--- a/man/total_rate.Rd
+++ b/man/total_rate.Rd
@@ -7,7 +7,6 @@
total_rate(
pop,
per = getOption("surveytable.rate_per"),
- screen = getOption("surveytable.screen"),
csv = getOption("surveytable.csv")
)
}
@@ -16,8 +15,6 @@ total_rate(
\item{per}{calculate rate per this many items in the population}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -27,7 +24,7 @@ total_rate(
Overall rate
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
total_rate(uspop2019$total)
}
\seealso{
diff --git a/man/var_any.Rd b/man/var_any.Rd
index c85ff9b..d6e282c 100644
--- a/man/var_any.Rd
+++ b/man/var_any.Rd
@@ -19,7 +19,7 @@ Create a new variable which is true if any of the variables in a list of
variables are true.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
var_any("Imaging services"
, c("ANYIMAGE", "BONEDENS", "CATSCAN", "ECHOCARD", "OTHULTRA"
, "MAMMO", "MRI", "XRAY", "OTHIMAGE"))
diff --git a/man/var_case.Rd b/man/var_case.Rd
index 5c40d70..62c2657 100644
--- a/man/var_case.Rd
+++ b/man/var_case.Rd
@@ -20,7 +20,7 @@ var_case(newvr, vr, cases)
Convert factor to logical
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
var_case("Preventive care visits", "MAJOR", "Preventive care")
tab("Preventive care visits")
var_case("Surgery-related visits"
diff --git a/man/var_collapse.Rd b/man/var_collapse.Rd
index aa03baf..747b661 100644
--- a/man/var_collapse.Rd
+++ b/man/var_collapse.Rd
@@ -20,7 +20,7 @@ var_collapse(vr, newlevel, oldlevels)
Collapse two or more levels of a factor variable into a single level.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
tab("PRIMCARE")
var_collapse("PRIMCARE", "Unknown if PCP", c("Blank", "Unknown"))
tab("PRIMCARE")
diff --git a/man/var_copy.Rd b/man/var_copy.Rd
index 829c468..7facee2 100644
--- a/man/var_copy.Rd
+++ b/man/var_copy.Rd
@@ -19,7 +19,7 @@ Create a new variable that is a copy of another variable. You can modify the cop
while the original remains unchanged. See examples.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
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"))
diff --git a/man/var_cross.Rd b/man/var_cross.Rd
index acc7425..6c7cd57 100644
--- a/man/var_cross.Rd
+++ b/man/var_cross.Rd
@@ -21,7 +21,7 @@ Create a new variable which is an interaction of two other variables. Also
see \code{\link[=tab_cross]{tab_cross()}}.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
var_cross("Age x Sex", "AGER", "SEX")
tab("Age x Sex")
}
diff --git a/man/var_cut.Rd b/man/var_cut.Rd
index c746be0..0577b2b 100644
--- a/man/var_cut.Rd
+++ b/man/var_cut.Rd
@@ -22,7 +22,7 @@ var_cut(newvr, vr, breaks, labels)
Create a new categorical variable based on a numeric variable.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
var_cut("Age group"
, "AGE"
, c(-Inf, 0, 4, 14, 64, Inf)
diff --git a/man/var_list.Rd b/man/var_list.Rd
index ad01e26..f2160aa 100644
--- a/man/var_list.Rd
+++ b/man/var_list.Rd
@@ -4,20 +4,13 @@
\alias{var_list}
\title{List variables in a survey.}
\usage{
-var_list(
- sw = "",
- all = FALSE,
- screen = getOption("surveytable.screen"),
- csv = getOption("surveytable.csv")
-)
+var_list(sw = "", all = FALSE, csv = getOption("surveytable.csv"))
}
\arguments{
\item{sw}{starting characters in variable name (case insensitive)}
\item{all}{print all variables?}
-\item{screen}{print to the screen?}
-
\item{csv}{name of a CSV file}
}
\value{
@@ -27,6 +20,6 @@ var_list(
List variables in a survey.
}
\examples{
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
var_list("age")
}
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 b1bb2b9..526f766 100644
--- a/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
+++ b/vignettes/Example-National-Ambulatory-Medical-Care-Survey-NAMCS-tables.Rmd
@@ -30,7 +30,7 @@ Now, specify the survey that you'd like to analyze.
```{r}
mysurvey = namcs2019sv
-set_survey("mysurvey")
+set_survey(mysurvey)
```
Check the survey name, survey design variables, and the number of observations to verify that it all looks correct.
@@ -151,6 +151,7 @@ mysurvey$variables = within(mysurvey$variables, {
`No charge (real)` = PAYNOCHG & nopwu
`No insurance` = `Self-pay (real)` | `No charge (real)`
})
+set_survey(mysurvey)
```
Notes:
@@ -158,6 +159,7 @@ Notes:
* We're using the `within()` function to simplify the syntax.
* `nopwu` = No other payment was used, such as private, Medicare, etc.
* `Self-pay (real)` = self-pay was used AND no other payment was used.
+* After making changes to `mysurvey`, we need to call `set_survey()` again.
```{r, results='asis'}
tab("PAYPRIV", "PAYMCARE", "PAYMCAID", "Medicare and Medicaid"
@@ -195,9 +197,12 @@ tab_subset("REFER", "SENBEFOR")
This table shows the same information as Table 3, but only for preventive care visits. That is, estimates for each age group, as well as for each age group by sex, but only for preventive care visits.
-The variable `Age group` has already been created in a previous example. Cross `AGER` and `SEX` to create a variable called `Age x Sex`:
+Let's create `Age group` from `AGE` and cross `AGER` and `SEX` to create a variable called `Age x Sex`:
```{r}
+var_cut("Age group", "AGE"
+ , c(-Inf, 0, 4, 14, 64, Inf)
+ , c("Under 1", "1-4", "5-14", "15-64", "65 and over") )
var_cross("Age x Sex", "AGER", "SEX")
```
@@ -220,47 +225,47 @@ As each of the above commands is similar, and differs only in the first variable
```{r, results='asis'}
for (vr in c("AGER", "Age group", "SEX", "Age x Sex")) {
- tab_subset(vr, "MAJOR", "Preventive care")
+ print( tab_subset(vr, "MAJOR", "Preventive care") )
}
```
+Note that when called from inside a `for` loop, the `print()` function needs to be called explicitly.
+
## More advanced coding
In addition, for each age-sex category, the published table shows the percentage of preventive care visits made to primary care physicians.
To calculate these percentages, a slightly more involved `for` loop is needed. Below is the code, followed by an explanation:
-```{r, results=FALSE, message=FALSE, warning=FALSE}
-set_output(csv = "out.csv", screen = FALSE)
+```{r}
+tmp_file = tempfile(fileext = ".csv")
+suppressMessages( set_output(csv = tmp_file) )
```
```{r}
for (vr in c("AGER", "Age group", "SEX", "Age x Sex")) {
var_cross("tmp", "MAJOR", vr)
- for (lvl in levels(mysurvey$variables[,vr])) {
+ for (lvl in levels(surveytable:::env$survey$variables[,vr])) {
tab_subset("SPECCAT", "tmp", paste0("Preventive care : ", lvl))
}
}
-set_output(csv = "", screen = TRUE)
-```
-
-```{r, echo=FALSE}
-unlink("out.csv")
+set_output(csv = "")
```
-* Since so many tables are being produced, the output is sent to a CSV file, and is not printed to the screen.
+* Since `tab_subset()` is called from within a `for` loop, if we wanted to print to the screen, we would need to use `print( tab_subset(*) )`. Since we don't want to print to the screen, a call to `print()` is omitted.
+* Since so many tables are being produced, the output is being sent to a CSV file.
* As before, the loop goes through the age, sex, and age / sex interaction variables, calling each of these variables `vr`.
* MAJOR and `vr` are crossed, with the result stored in a variable called `tmp`.
* Next, the inner loop goes through all levels of `vr`, calling each of these levels `lvl`.
* The code tabulates `SPECCAT` (Type of specialty – Primary, Medical, Surgical) on a subset in which `tmp` (which is `MAJOR` crossed with `vr`) is restricted to “Preventive care:” followed by `lvl`, which is some level of `vr`, such as “Under 15 years” for `AGER`.
-* Finally, CSV output is turned off, and screen output is turned back on.
+* Finally, CSV output is turned off.
If you run this code, all of the tables should be stored in the CSV file. To give you an idea of what the tables should look like, here is just one of the tables:
```{r, results='asis'}
vr = "AGER"
var_cross("tmp", "MAJOR", vr)
-lvl = levels(mysurvey$variables[,vr])[1]
+lvl = levels(surveytable:::env$survey$variables[,vr])[1]
tab_subset("SPECCAT", "tmp", paste0("Preventive care : ", lvl))
```
diff --git a/vignettes/surveytable.Rmd b/vignettes/surveytable.Rmd
index 4ffc1c2..e60a117 100644
--- a/vignettes/surveytable.Rmd
+++ b/vignettes/surveytable.Rmd
@@ -112,7 +112,7 @@ We have just successfully created a survey object from a data frame.
First, specify the survey object that you'd like to analyze.
```{r}
-set_survey("namcs2019sv")
+set_survey(namcs2019sv)
```
Check the survey label, survey design variables, and the number of observations to verify that it all looks correct.
@@ -232,7 +232,7 @@ The `tab_subset()` function makes it easy to perform hypothesis testing by using
## Categorical variables
-Consider the relationship between `SEX` an `SPECCAT`:
+Consider the relationship between `AGER` an `SPECCAT`:
```{r, results='asis'}
tab_subset("AGER", "SPECCAT", test = TRUE)
@@ -427,27 +427,45 @@ Here, the `AGER` variable remains unchanged, while the `Age group` variable has
class(namcs2019sv$variables)
```
-More advanced users can create or modify variables in the `variables` data frame directly.
+More advanced users can create or modify variables in the `variables` data frame directly. After they modify these variables, they must call `set_survey()` again. For example:
+
+```{r}
+namcs2019sv$variables$`Medicare and Medicaid` = (
+ namcs2019sv$variables$PAYMCARE & namcs2019sv$variables$PAYMCAID)
+set_survey(namcs2019sv)
+```
+
+```{r, results='asis'}
+tab("Medicare and Medicaid")
+```
+
+Note, however, that the `var_*()` functions do not modify the survey object specified in `set_survey()` directly. Rather, they modify variables inside the following data frame: `surveytable:::env$survey$variables`. If you use the `var_*()` functions and then need access to the modified / created variables, that's where you should look. For example:
+
+```{r}
+var_cross("newvar", "MAJOR", "AGER")
+# This should give NULL. The new variable does not exist here:
+namcs2019sv$variables$newvar
+# Rather, the new variable is here:
+str(surveytable:::env$survey$variables$newvar)
+```
# Save the output
The `tab*` and `total*` functions have an argument called `csv` that specifies the name of a comma-separated values (CSV) file to save the output to. Alternatively, you can name the default CSV output file using the `set_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:
-```{r, results=FALSE, message=FALSE, warning=FALSE}
-set_output(csv = "out.csv")
+```{r}
+tmp_file = tempfile(fileext = ".csv")
+suppressMessages( set_output(csv = tmp_file) )
```
```{r, results='asis'}
tab("MDDO", "SPECCAT", "MSA")
-set_output(csv = "")
```
-```{r, echo=FALSE}
-unlink("out.csv")
+```{r}
+set_output(csv = "")
```
-Tabulation functions also have an argument called `screen`, `TRUE` by default, which determines whether the output is sent to the screen. If many tables are being produced, a user might choose to turn off screen printing, and only output to a CSV file.
-
If the tabulation functions are called from within an R Markdown notebook, they produce HTML tables. This makes it easy to incorporate the output of the `surveytable` package directly into documents, presentations, “shiny” web apps, and other output types.
-Finally, the tabulation functions return the tables that they produce, either as a data frame, or as a list of data frames. More advanced analysts can use this functionality to integrate `surveytable` into other programming tasks.
+Finally, the tabulation functions return the tables that they produce. More advanced analysts can use this functionality to integrate `surveytable` into other programming tasks.