intro

R/var_list.R

@@ -31,7 +31,7 @@ var_list = function(sw = "", all=FALSE
       Variable = ii
       , Class = paste(class(design$variables[,ii])
                       , collapse = ", ")
-      , Label = .getvarname(design, ii)
+      , `Long name` = .getvarname(design, ii)
     )
     ret %<>% rbind(r1)
   }

README.Rmd

@@ -18,11 +18,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 <!-- badges: end -->
 
-In R, the standard way of analyzing complex surveys is using the `survey` package. The major purpose of the `surveytable` package is to ease the use of the `survey` package in certain applications. `surveytable` improves the output of `survey` functions (by formatting and tabulating it); performs hypothesis tests; reduces the number of commands that users need to execute; and applies presentation standards to estimates, which is the usual practice at the National Center for Health Statistics (NCHS). 
-
-The `surveytable` package allows users to use simple commands and produces formatted tabulated output. One useful function, which operates on categorical and logical variables, tabulates estimated counts and percentages with their standard errors and confidence intervals. It applies presentation standards for counts and percentages, and flags estimates if they should be suppressed, footnoted, or reviewed. Other functions list the variables in a survey, estimate the total population, tabulate survey subsets and variable interactions, tabulate numeric variables, calculate rates, perform t-tests, and save the output. 
-
-The `surveytable` package is easier to use than using `survey` directly. With fewer commands, `surveytable` output has survey name, variable labels, formatted estimates, cleaner category values, both count and percentage estimates in a single table, and confidence intervals. The package also performs checking for presentation standards. 
+The `surveytable` package provides short and understandable commands that generate tabulated, formatted, and rounded survey estimates. One useful function, which operates on categorical and logical variables, tabulates estimated counts and percentages with their standard errors and confidence intervals. Other functions list the variables in a survey, estimate the total population, tabulate survey subsets and variable interactions, tabulate numeric variables, tabulate rates, create or modify survey variables, perform t-tests, and save the output. All of the tabulation functions check the NCHS presentation standards to flag low-precision estimates. If the `surveytable` code is called from an R Markdown notebook or a Quarto document, it generates HTML tables, which can be incorporated directly into documents.
 
 ## Installation
 

README.md

@@ -6,30 +6,18 @@
 <!-- badges: start -->
 <!-- badges: end -->
 
-In R, the standard way of analyzing complex surveys is using the
-`survey` package. The major purpose of the `surveytable` package is to
-ease the use of the `survey` package in certain applications.
-`surveytable` improves the output of `survey` functions (by formatting
-and tabulating it); performs hypothesis tests; reduces the number of
-commands that users need to execute; and applies presentation standards
-to estimates, which is the usual practice at the National Center for
-Health Statistics (NCHS).
-
-The `surveytable` package allows users to use simple commands and
-produces formatted tabulated output. One useful function, which operates
-on categorical and logical variables, tabulates estimated counts and
-percentages with their standard errors and confidence intervals. It
-applies presentation standards for counts and percentages, and flags
-estimates if they should be suppressed, footnoted, or reviewed. Other
-functions list the variables in a survey, estimate the total population,
-tabulate survey subsets and variable interactions, tabulate numeric
-variables, calculate rates, perform t-tests, and save the output.
-
-The `surveytable` package is easier to use than using `survey` directly.
-With fewer commands, `surveytable` output has survey name, variable
-labels, formatted estimates, cleaner category values, both count and
-percentage estimates in a single table, and confidence intervals. The
-package also performs checking for presentation standards.
+The `surveytable` package provides short and understandable commands
+that generate tabulated, formatted, and rounded survey estimates. One
+useful function, which operates on categorical and logical variables,
+tabulates estimated counts and percentages with their standard errors
+and confidence intervals. Other functions list the variables in a
+survey, estimate the total population, tabulate survey subsets and
+variable interactions, tabulate numeric variables, tabulate rates,
+create or modify survey variables, perform t-tests, and save the output.
+All of the tabulation functions check the NCHS presentation standards to
+flag low-precision estimates. If the `surveytable` code is called from
+an R Markdown notebook or a Quarto document, it generates HTML tables,
+which can be incorporated directly into documents.
 
 ## Installation
 

docs/pkgdown.yml

@@ -6,7 +6,7 @@ articles:
   a02-Example-Residential-Care-Community-RCC-Services-User-SU: a02-Example-Residential-Care-Community-RCC-Services-User-SU.html
   a03-Example-National-Ambulatory-Medical-Care-Survey-NAMCS-report: a03-Example-National-Ambulatory-Medical-Care-Survey-NAMCS-report.html
   surveytable: surveytable.html
-last_built: 2023-11-13T23:50Z
+last_built: 2023-11-16T22:48Z
 urls:
   reference: https://cdcgov.github.io/surveytable/reference
   article: https://cdcgov.github.io/surveytable/articles

vignettes/surveytable.Rmd

@@ -14,7 +14,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-`surveytable` provides easy commands that generate tabulated and formatted survey estimates.
+The `surveytable` package provides short and understandable commands that generate tabulated, formatted, and rounded survey estimates.
 
 The examples below use the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF). NAMCS is "an annual nationally representative sample survey of visits to nonfederal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists." Note that the unit of observation is visits, not patients – this distinction is important since a single patient can make multiple visits.