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 LaTeX tables, as appropriate.

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

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)

Survey info {NAMCS 2019 PUF}

Variables	Observations	Design
33	8,250	Stratified 1 - level Cluster Sampling design (with replacement) With (398) clusters. namcs2019sv = survey::svydesign(ids = ~CPSUM, strata = ~CSTRATM, weights = ~PATWT , data = namcs2019sv_df)

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

tab("AGER")

Patient age recode {NAMCS 2019 PUF}

Level	n	Number	SE	LL	UL	Percent	SE	LL	UL
Under 15 years	887	117,916,772	14,097,315	93,228,928	149,142,177	11.4	1.3	8.9	14.2
15-24 years	542	64,855,698	7,018,359	52,386,950	80,292,164	6.3	0.6	5.1	7.5
25-44 years	1,435	170,270,604	13,965,978	144,924,545	200,049,472	16.4	1.1	14.3	18.8
45-64 years	2,283	309,505,956	23,289,827	266,994,092	358,786,727	29.9	1.4	27.2	32.6
65-74 years	1,661	206,865,982	14,365,993	180,480,708	237,108,637	20	1.2	17.6	22.5
75 years and over	1,442	167,069,344	15,179,082	139,746,193	199,734,713	16.1	1.3	13.7	18.8
N = 8250.

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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