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.

Load the package:

library(surveytable)

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)

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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