Make use of the newly-introduced sort_by() function in base R.

R/as_jackknife_design.R

@@ -332,10 +332,9 @@ as_random_group_jackknife_design.survey.design <- function(
   ) |> as.numeric()
 
   # Order the data by varstrat, then stratum, then sort variable, then by PSU
-  design_vars <- design_vars[order(design_vars[['RAND_PSU_ID']]),,drop=FALSE]
-  design_vars <- design_vars[order(design_vars[['SORT_VAR']]),,drop=FALSE]
-  design_vars <- design_vars[order(design_vars[['STRATUM']]),,drop=FALSE]
-  design_vars <- design_vars[order(design_vars[['VAR_STRAT']]),,drop=FALSE]
+  design_vars <- design_vars |> sort_by(
+    ~ VAR_STRAT + STRATUM + SORT_VAR + RAND_PSU_ID
+  )
 
   # Create random groups separately by VAR_STRAT
   design_vars[['RANDOM_GROUP_VAR_UNIT']] <- NA_real_

R/fays_generalized_replication.R

@@ -387,9 +387,8 @@ make_fays_gen_rep_factors <- function(
 #'   data('library_stsys_sample', package = 'svrep')
 #'
 #'   ## First, ensure data are sorted in same order as was used in sampling
-#'   library_stsys_sample <- library_stsys_sample[
-#'     order(library_stsys_sample$SAMPLING_SORT_ORDER),
-#'   ]
+#'   library_stsys_sample <- library_stsys_sample |>
+#'     sort_by(~ SAMPLING_SORT_ORDER)
 #'
 #'   ## Create a survey design object
 #'   design_obj <- svydesign(

R/generalized_bootstrap.R

@@ -462,9 +462,8 @@ make_gen_boot_factors <- function(Sigma, num_replicates, tau = "auto", exact_vco
 #'    data('library_stsys_sample', package = 'svrep')
 #'
 #'    ## First, ensure data are sorted in same order as was used in sampling
-#'    library_stsys_sample <- library_stsys_sample[
-#'      order(library_stsys_sample$SAMPLING_SORT_ORDER),
-#'    ]
+#'    library_stsys_sample <- library_stsys_sample |>
+#'      sort_by(~ SAMPLING_SORT_ORDER)
 #'
 #'    ## Create a survey design object
 #'    design_obj <- svydesign(