Survey table: formatted survey estimates

surveytable is an R package for conveniently tabulating estimates from complex surveys.

• If you deal with survey objects in R (created with survey::svydesign()), then this package is for you.

• Works with complex surveys (data systems that involve survey design variables, like weights and strata).

• Works with unweighted data as well.

• The surveytable package provides short and understandable commands that generate tabulated, formatted, and rounded survey estimates.

• With surveytable, you can

  • tabulate estimated counts and percentages, with their standard errors and confidence intervals,
  • estimate the total population,
  • tabulate survey subsets and variable interactions,
  • tabulate numeric variables,
  • perform hypothesis tests,
  • tabulate rates,
  • modify survey variables, and
  • save the output.

• Optionally, all of the tabulation functions can identify low-precision estimates using the National Center for Health Statistics (NCHS) algorithms (or other algorithms).

• If the surveytable code is called from an R Markdown notebook or a Quarto document, it automatically generates HTML or PDF tables, as appropriate.

• The package reduces the number of commands that users need to execute, which is especially helpful for users new to R or to programming.

Installation

Install from CRAN:

install.packages("surveytable")

or get the development version from GitHub:

install.packages(c("remotes", "git2r"))
remotes::install_github("CDCgov/surveytable", upgrade = "never")

Documentation

Find the documentation for surveytable here: https://cdcgov.github.io/surveytable/

Example

Here is a basic example, to get you started.

1. Load the package:

library(surveytable)

2. Specify the survey that you wish you analyze. surveytable comes with a survey called namcs2019sv, for use in examples.

set_survey(namcs2019sv)

3. Specify the variable to analyze. In NAMCS, AGER is the age category variable:

tab("AGER")

The table shows:

• Descriptive variable name
• Survey name
• For each level of the variable:
  • Number of observations
  • Estimated count with its SE and 95% CI
  • Estimated percentage with its SE and 95% CI
• Sample size
• Optionally, the table can show whether any low-precision estimates were found

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