Title:	Streamlining Complex Survey Estimation and Reliability Assessment in R
Version:	0.9.9
Description:	Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) <doi:10.18637/jss.v009.i08> https://CRAN.R-project.org/package=survey) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) <doi:10.15620/cdc:124368>).
Date/Publication:	2025-08-25 19:20:02 UTC
License:	Apache License (≥ 2)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 2.10)
LazyData:	true
LazyDataCompression:	bzip2
Imports:	assertthat, magrittr, glue, survey, huxtable
Suggests:	gt, kableExtra, openxlsx2, mschart, knitr, rmarkdown
VignetteBuilder:	knitr
URL:	https://cdcgov.github.io/surveytable/, https://github.com/CDCgov/surveytable
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-08-25 18:47:06 UTC; kpr9
Author:	Alex Strashny [aut, cre]
Maintainer:	Alex Strashny <alex.strashny@gmail.com>
Repository:	CRAN

surveytable: Streamlining Complex Survey Estimation and Reliability Assessment in R

Description

Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the 'survey' package (Lumley (2004) doi:10.18637/jss.v009.i08 https://CRAN.R-project.org/package=survey) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf, Parker et al. (2023) doi:10.15620/cdc:124368).

Author(s)

Maintainer: Alex Strashny alex.strashny@gmail.com (ORCID)

See Also

Useful links:

• https://cdcgov.github.io/surveytable/

• https://github.com/CDCgov/surveytable

Coerce a surveytable table to a data frame

Description

If a tabulation function produces multiple tables, that group of tables is a list, with each element of the list being an individual table. To convert one of these tables to a data.frame, use [[. For example, in the following code, we generate 3 tables, and then convert the third table to a data.frame.

set_survey(namcs2019sv)
mytables = tab("MDDO", "SPECCAT", "MSA")
mydf = as.data.frame(mytables[[3]])

Usage

## S3 method for class 'surveytable_table'
as.data.frame(x, ...)

Arguments

Value

A data frame.

Examples

set_survey(namcs2019sv)
as.data.frame( tab("AGER") )

Create a codebook for the survey

Description

Create a codebook for the survey

Usage

codebook(all = FALSE)

Arguments

Value

A list of tables.

Examples

set_survey(namcs2019sv)
codebook()

Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF)

Description

Selected variables from a data system of visits to office-based physicians. Note that the unit of observation is visits, not patients - this distinction is important since a single patient can make multiple visits.

Usage

namcs2019sv

namcs2019sv_df

Format

An object of class survey.design2 (inherits from survey.design) with 8250 rows and 33 columns.

An object of class data.frame with 8250 rows and 33 columns.

Details

namcs2019sv_df is a data frame.

namcs2019sv is a survey object created from namcs2019sv_df using survey::svydesign().

Source

• SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/namcs2019_sas.zip

• Survey design variables: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/readme2019-sas.txt

• SAS formats: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/sas/nam19for.txt

• Documentation: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NAMCS/doc2019-508.pdf

• National Summary Tables: https://www.cdc.gov/nchs/data/ahcd/namcs_summary/2019-namcs-web-tables-508.pdf

Print surveytable tables

Description

If a tabulation function is called from the top level, it should print out its table(s) on its own. If that tabulation function is called not from the top level, such as from within a loop or another function, you need to call print() explicitly. For example:

set_survey(namcs2019sv)
for (vr in c("AGER", "SEX")) {
  print( tab_subset(vr, "MAJOR", "Preventive care") )
}

Usage

## S3 method for class 'surveytable_table'
print(x, ...)

## S3 method for class 'surveytable_list'
print(x, ...)

Arguments

Details

The package used to produce the tables can be changed – see the output argument of set_opts() for details. By default, the table-making package huxtable is used.

Value

Returns x invisibly.

Examples

set_survey(namcs2019sv)
table1 = tab("AGER")
print(table1)
table_many = tab("MDDO", "SPECCAT", "MSA")
print(table_many)

National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF)

Description

A data system of RCC residents.

Usage

rccsu2018

Format

An object of class survey.design2 (inherits from survey.design) with 904 rows and 81 columns.

Source

• SAS data: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NPALS/

• Documentation: https://www.cdc.gov/nchs/npals/RCCresident-readme03152021vr.pdf

• Codebook: https://www.cdc.gov/nchs/data/npals/final2018rcc_su_puf_codebook.pdf

Set certain options

Description

set_opts() sets certain package options. To view these options, use show_opts(). For more advanced control and detailed customization, experienced users can also employ options() and show_options() (refer to surveytable-options for further information).

Usage

set_opts(
  reset = NULL,
  mode = NULL,
  adj = NULL,
  output = NULL,
  file = NULL,
  .file_temp = NULL,
  count = NULL,
  lpe = NULL,
  drop_na = NULL,
  max_levels = NULL
)

show_opts()

Arguments

Details

If you are not setting a particular option, leave it as NULL.

mode can be either "general" or "NCHS" and has the following meaning:

• "general":

  • Round counts to the nearest integer – same as count = "int".

  • Do not look for low-precision estimates – same as lpe = FALSE.

  • Percentage CI's: use standard Korn-Graubard CI's – same as adj = "none".

• "nchs":

  • Round counts to the nearest 1,000 – same as count = "1k".

  • Identify low-precision estimates – same as lpe = TRUE.

  • Percentage CI's: adjust Korn-Graubard CI's for the number of degrees of freedom, matching the SUDAAN calculation – same as adj = "nchs". This is appropriate for some, but not all, NCHS data systems. For some NCHS data systems, such as NHIS, you might need to set adj to one of the other values.

adj specifies the adjustment to the Korn and Graubard confidence intervals for proportions. See svyciprop_adjusted() for details.

output determines how the output is printed:

• "auto" (default): automatically select the table-making package, depending on the destination (such as screen, HTML, or PDF / LaTeX).

• "huxtable", "gt", or "kableExtra": use this table-making package. Be sure that this package is installed.

• "raw": unformatted / raw output. This is useful for getting lots of significant digits.

• "Excel": print to an Excel workbook. Please specify the name of an Excel file using the file argument. Before using Excel printing, please be sure to install these packages: openxlsx2 and mschart.

• "CSV": print to a comma-separated values (CSV) file. Please specify the name of a CSV file using the file argument.

Value

(Nothing.)

See Also

Other options: set_survey(), show_options(), surveytable-options

Examples

set_survey(namcs2019sv)

# Round counts to the nearest one thousand:
set_opts(count = "1k")
tab("AGER")
set_opts(count = "int")

show_opts()

Specify the survey to analyze

Description

You must specify a survey before the other functions, such as tab(), will work. To convert a data.frame or similar to a survey object, see survey::svydesign() or survey::svrepdesign().

Usage

set_survey(design, ...)

Arguments

Details

Optionally, the survey can have an attribute called label, which is the long name of the survey. Optionally, each variable in the survey can have an attribute called label, which is the variable's long name.

Value

info about the survey

See Also

Other options: set_opts(), show_options(), surveytable-options

Examples

set_survey(namcs2019sv)
set_survey(namcs2019sv, mode = "general")

Show package options

Description

See surveytable-options for a discussion of some of the options.

Usage

show_options(sw = "surveytable")

Arguments

Value

List of options and their values.

See Also

Other options: set_opts(), set_survey(), surveytable-options

Examples

show_options()

Subset a survey, while preserving variable labels

Description

Subset a survey, while preserving variable labels

Usage

survey_subset(design, subset, label)

Arguments

Value

a new survey object

Examples

children = survey_subset(namcs2019sv, AGE < 18, "Children < 18")
set_survey(children)
tab("AGER")

Package options

Description

This article describes certain package options and is intended for more advanced users. Typical users should see set_opts() and show_opts() to set and show certain options.

Details

To view all available options, use show_options(). Below is a description of some noteworthy options.

Changing the number of decimal places or significant digits

By default, all estimates are rounded in a certain way. The user can change how the rounding is performed.

The following options are the names of functions that control rounding: surveytable.tx_count (for estimates of counts), surveytable.tx_prct (for estimates of percentages), surveytable.tx_rate (for estimates of rates), and surveytable.tx_numeric (for estimates of numeric variables). To turn off all rounding, set each one of these options to ".tx_none".

Each function takes one argument, a data.frame with the following columns: x (point estimates), s (standard errors), ll and ul (CI's). Each function outputs a data.frame with the same column names. For examples of how this works, see the internal functions surveytable:::.tx_count_int (counts, rounded to the nearest integer), surveytable:::.tx_count_1k (counts, rounded to the nearest one thousand), surveytable:::.tx_prct (percentages), surveytable:::.tx_rate (rates), and surveytable:::.tx_numeric (numeric variables).

You can set the above options to your own custom functions. You might also want to adjust the following options, which are the names of columns in the printed tables: surveytable.names_count (by default, this changes when rounding counts to the nearest one thousand) and surveytable.names_prct.

Printing using various table-making packages

The tabulation functions return objects of class surveytable_table (for a single table) or surveytable_list (for multiple tables, which is just a list of surveytable_table objects). A surveytable_table object is just a data.frame with the following attributes: title, footer, and num, which is the index of columns that should be formatted as a number.

Naturally, these objects can be printed using a variety of packages. surveytable ships with the ability to use huxtable, gt, or kableExtra. See the output argument of set_opts().

You can supply custom code to use another table-making package or to use one of these table-making packages, but in a different way. The surveytable.print option is the name of a function with the following arguments: x and ..., where x is either a surveytable_table or a surveytable_list object. The function prints this object. For an example of this, see the internal function surveytable:::.print_huxtable().

Low-precision estimates

Optionally, all of the tabulation functions can identify low-precision estimates. Turn on this functionality using any of the following: set_opts(lpe = TRUE), set_opts(mode = "nchs"), set_survey(*, mode = "nchs"), or options(surveytable.find_lpe = TRUE).

By default, low-precision estimates are identified using National Center for Health Statistics (NCHS) algorithms. However, this can be changed, as described below.

Here is a description of the options related to the identification of low-precision estimates.

• surveytable.find_lpe: should the tabulation functions look for low-precision estimates? You can change this directly with options() or with either set_opts() or set_survey().

• surveytable.lpe_n, surveytable.lpe_counts, surveytable.lpe_percents: names of 3 functions.

The argument for surveytable.lpe_n is a vector of the number of observations for each level of the variable.

The argument for surveytable.lpe_counts is a data frame with count-related estimates. Specifically, the data frame has the following variables:

• x: point estimates of counts

• s: SE

• ll, ul: CI

• samp.size: effective sample size

• counts: actual sample size

• degf: degrees of freedom

The argument for surveytable.lpe_percents is a data frame with percent-related estimates. Specifically, the data frame has the following variables:

• Proportion: point estimates of proportions (between 0 and 1)

• SE: SE

• LL, UL: CI

• ⁠n numerator⁠: the number of observations for which the variable is TRUE

• ⁠n denominator⁠: the total number of observations

Each of these functions must return a list with the following elements:

• id: the name of the algorithm used, such as "NCHS presentation standards"

• flags: a vector. For each level of the variable, short codes indicating the presence of low-precision estimates.

• has.flag: a vector of short codes that are present in flags.

• descriptions: a named vector. The names must be the short codes, the values are the longer descriptions.

For example, if a variable has 3 levels, flags might be c("", "A1 A2", ""). This indicates that for the first and third level, nothing was found, whereas for the second level, two different things were found, indicated by short codes A1 and A2. In this case, has.flag = c("A1", "A2"), descriptions = c(A1 = "A1: something", A2 = "A2: something else").

Author(s)

Maintainer: Alex Strashny alex.strashny@gmail.com (ORCID)

See Also

Useful links:

• https://cdcgov.github.io/surveytable/

• https://github.com/CDCgov/surveytable

Other options: set_opts(), set_survey(), show_options()

Korn and Graubard confidence intervals for proportions, adjusted for degrees of freedom

Description

A version of survey::svyciprop( method = "beta" ) that adjusts for the degrees of freedom.

Usage

svyciprop_adjusted(formula, design, level = 0.95, adj = "none", ...)

Arguments

Details

adj specifies the adjustment to the Korn and Graubard confidence intervals.

• "none": No adjustment is performed. Produces standard Korn and Graubard confidence intervals, same as survey::svyciprop( method = "beta" ).

• "NCHS": Adjustment that might be required by some (though not all) NCHS data systems. With this adjustment, the degrees of freedom is set to degf(design). Consult the documentation for the data system that you are analyzing to determine if this is the appropriate adjustment.

• "NHIS": Adjustment that might be required by NHIS. With this adjustment, the degrees of freedom is set to nrow(design) - 1. Consult the documentation for the data system that you are analyzing to determine if this is the appropriate adjustment.

To use these adjustments in surveytable tabulations, call set_survey() or set_opts() with the appropriate mode or adj argument.

Originally written by Makram Talih in 2019.

Value

The point estimate of the proportion, with the confidence interval as an attribute.

Examples

set_survey(namcs2019sv)
set_opts(adj = "NCHS")
tab("AGER")
set_opts(adj = "none")

Tabulate variables

Description

Tabulate categorical (factor or character), logical, or numeric variables.

Usage

tab(
  ...,
  test = FALSE,
  alpha = 0.05,
  p_adjust = FALSE,
  drop_na = getOption("surveytable.drop_na"),
  max_levels = getOption("surveytable.max_levels")
)

Arguments

Details

For categorical and logical variables, for each category, this function presents the following:

• the number of observations (n);

• the estimated count (Number), with its standard error (SE) and confidence interval (LL and UL); and

• the estimated percentage (Percent), with its standard error (SE) and confidence interval (LL and UL).

Optionally, this function identifies low-precision estimates and flags them if, according to the guidelines (such as the NCHS presentation standards), they should be suppressed, footnoted, or reviewed by an analyst. To enable this functionality, see set_opts() with arguments lpe = TRUE or mode = "NCHS".

For numeric variables, this function presents the following:

• percentage of observations with known values (⁠% known⁠);

• the mean of known values (Mean), with its standard error (SEM) and confidence interval (LL and UL); and

• the standard deviation (SD).

Confidence intervals (CIs) are calculated at the 95% confidence level. CIs for count estimates are the log Student's t CIs, with adaptations for complex surveys. CIs for percentage estimates are the Korn and Graubard CIs, with optional adjustments. See set_opts() argument adj. CIs for estimates of means are the Wald CIs.

Value

A list of tables or a single table.

See Also

Other tables: tab_cross(), tab_rate(), tab_subset_rate(), total(), total_rate()

Examples

set_survey(namcs2019sv)
tab("AGER")
tab("MDDO", "SPECCAT", "MSA")

# Numeric variables
tab("NUMMED")

# Hypothesis testing with categorical variables
tab("AGER", test = TRUE)

Tabulate subsets or interactions

Description

Create subsets of the survey using one variable, and tabulate another variable within each of the subsets. Interact two variables and tabulate.

Usage

tab_cross(vr, vrby, max_levels = getOption("surveytable.max_levels"))

tab_subset(
  vr,
  vrby,
  lvls = c(),
  test = FALSE,
  alpha = 0.05,
  p_adjust = FALSE,
  drop_na = getOption("surveytable.drop_na"),
  max_levels = getOption("surveytable.max_levels")
)

Arguments

Details

tab_subset() creates subsets using the levels of vrby, and tabulates vr in each subset. Optionally, only use the lvls levels of vrby. vr can be categorical (factor or character), logical, or numeric.

tab_cross() crosses or interacts vr and vrby and tabulates the new variable. Tables created using tab_subset() and tab_cross() have the same counts but different percentages. With tab_subset(), percentages within each subset add up to 100%. With tab_cross(), percentages across the entire population add up to 100%. Also see var_cross().

test = TRUE performs a test of association between the two variables. Also performs t-tests for all pairs of levels of vr and vrby.

test = "{LEVEL}", where {LEVEL} is a level of vr, performs a conditional independence test to compare the proportion of vr = "{LEVEL}" for different values of vrby.

Value

A list of tables or a single table.

See Also

Other tables: tab(), tab_rate(), tab_subset_rate(), total(), total_rate()

Examples

set_survey(namcs2019sv)

# For each SEX, tabulate AGER
tab_subset("AGER", "SEX")

# Same counts as tab_subset(), but different percentages.
tab_cross("AGER", "SEX")

# Numeric variables
tab_subset("NUMMED", "AGER")

# Hypothesis testing
tab_subset("NUMMED", "AGER", test = TRUE)

Calculate rates

Description

Calculate the rates for categorical (factor) or logical variables.

Usage

tab_rate(
  vr,
  pop,
  per = getOption("surveytable.rate_per"),
  drop_na = getOption("surveytable.drop_na"),
  max_levels = getOption("surveytable.max_levels")
)

Arguments

Value

A list of tables or a single table.

See Also

Other tables: tab(), tab_cross(), tab_subset_rate(), total(), total_rate()

Examples

set_survey(namcs2019sv)
# pop is a data frame
tab_rate("MSA", uspop2019$MSA)

# pop is a single number
tab_rate("MDDO", uspop2019$total)

Calculate rates for subsets

Description

Create subsets of the survey using one variable, and tabulate the rates of another variable within each of the subsets.

Usage

tab_subset_rate(
  vr,
  vrby,
  pop,
  lvls = c(),
  per = getOption("surveytable.rate_per"),
  drop_na = getOption("surveytable.drop_na"),
  max_levels = getOption("surveytable.max_levels")
)

Arguments

Value

A list of tables or a single table.

See Also

Other tables: tab(), tab_cross(), tab_rate(), total(), total_rate()

Examples

set_survey(namcs2019sv)
tab_subset_rate("AGER", "SEX", uspop2019$`AGER x SEX`)

Total count

Description

Total count

Usage

total()

Value

A table

See Also

Other tables: tab(), tab_cross(), tab_rate(), tab_subset_rate(), total_rate()

Examples

set_survey(namcs2019sv)
total()

Overall rate

Description

Overall rate

Usage

total_rate(pop, per = getOption("surveytable.rate_per"))

Arguments

Value

A table

See Also

Other tables: tab(), tab_cross(), tab_rate(), tab_subset_rate(), total()

Examples

set_survey(namcs2019sv)
total_rate(uspop2019$total)

US Population in 2019

Description

Population estimates of the civilian non-institutional population of the United States as of July 1, 2019. Used for calculating rates. For usage examples, see the ⁠*_rate⁠ functions.

Usage

uspop2019

Format

An object of class list of length 7.

Are all the variables true? (Logical AND)

Description

Create a new variable which is true if all of the variables in a list of variables are true.

Usage

var_all(newvr, vrs)

Arguments

Value

Survey object

See Also

Other variables: var_any(), var_case(), var_collapse(), var_copy(), var_cross(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)
var_all("Medicare and Medicaid", c("PAYMCARE", "PAYMCAID"))
tab("Medicare and Medicaid")

Is any variable true? (Logical OR)

Description

Create a new variable which is true if any of the variables in a list of variables are true.

Usage

var_any(newvr, vrs)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_case(), var_collapse(), var_copy(), var_cross(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)
var_any("Imaging services"
, c("ANYIMAGE", "BONEDENS", "CATSCAN", "ECHOCARD", "OTHULTRA"
, "MAMMO", "MRI", "XRAY", "OTHIMAGE"))
tab("Imaging services")

Convert factor to logical

Description

Convert factor to logical

Usage

var_case(newvr, vr, cases, retain_na = TRUE)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_collapse(), var_copy(), var_cross(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)

var_case("Preventive care visits", "MAJOR", "Preventive care")
tab("Preventive care visits")

var_case("Surgery-related visits"
, "MAJOR"
, c("Pre-surgery", "Post-surgery"))
tab("Surgery-related visits")

var_case("Non-primary"
, "SPECCAT.bad"
, c("Surgical care specialty", "Medical care specialty"))
tab("Non-primary")
tab("Non-primary", drop_na = TRUE)

Collapse factor levels

Description

Collapse two or more levels of a factor variable into a single level.

Usage

var_collapse(vr, newlevel, oldlevels)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_case(), var_copy(), var_cross(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)
tab("PRIMCARE")
var_collapse("PRIMCARE", "Unknown if PCP", c("Blank", "Unknown"))
tab("PRIMCARE")

Copy a variable

Description

Create a new variable that is a copy of another variable. You can modify the copy, while the original remains unchanged. See examples.

Usage

var_copy(newvr, vr)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_case(), var_collapse(), var_cross(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)
var_copy("Age group", "AGER")
var_collapse("Age group", "65+", c("65-74 years", "75 years and over"))
var_collapse("Age group", "25-64", c("25-44 years", "45-64 years"))
tab("AGER", "Age group")

Cross or interact two variables

Description

Create a new variable which is an interaction of two other variables. Also see tab_cross().

Usage

var_cross(newvr, vr, vrby)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_case(), var_collapse(), var_copy(), var_cut(), var_not()

Examples

set_survey(namcs2019sv)
var_cross("Age x Sex", "AGER", "SEX")
tab("Age x Sex")

Convert numeric to factor

Description

Create a new categorical variable based on a numeric variable.

Usage

var_cut(newvr, vr, breaks, labels)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_case(), var_collapse(), var_copy(), var_cross(), var_not()

Examples

set_survey(namcs2019sv)
# In some data systems, variables might contain "special values". For example,
# negative values might indicate unknowns (which should be coded as `NA`).
# Though in this particular data, there are no unknowns.
var_cut("Age group"
  , "AGE"
  , c(-Inf, -0.1, 0, 4, 14, 64, Inf)
  , c(NA, "Under 1", "1-4", "5-14", "15-64", "65 and over"))
tab("Age group")

List variables in a survey.

Description

List variables in a survey.

Usage

var_list(sw = "", all = FALSE)

Arguments

Value

A table

Examples

set_survey(namcs2019sv)
var_list("age")

Logical NOT

Description

Logical NOT

Usage

var_not(newvr, vr)

Arguments

Value

Survey object

See Also

Other variables: var_all(), var_any(), var_case(), var_collapse(), var_copy(), var_cross(), var_cut()

Examples

set_survey(namcs2019sv)
var_not("Private insurance not used", "PAYPRIV")