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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:
  1. 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)
  1. 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 20.0 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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