Package {surveycore}

Title:	Core Survey Analysis Infrastructure
Version:	0.8.3
Description:	Provides 'S7'-based infrastructure for survey analysis. Supports Taylor series, replicate weight, and two-phase designs following the methods in 'Lumley' (2004) <doi:10.18637/jss.v009.i08>. Includes design-based estimators such as means, frequencies, and regression models, with weighted 'polychoric' and 'polyserial' correlation following 'Mannan' (2025) <doi:10.2139/ssrn.6580480>. A metadata system automatically preserves 'haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a 'tidyselect' interface for design specification.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.3.0)
Imports:	S7 (≥ 0.1.0), rlang (≥ 1.0.0), tidyselect (≥ 1.2.0), cli (≥ 3.6.0), tibble (≥ 3.1.0), dplyr (≥ 1.1.0), marginaleffects (≥ 0.18.0), pbivnorm (≥ 0.6.0), stats, graphics
Suggests:	testthat (≥ 3.0.0), withr (≥ 2.5.0), survey (≥ 4.0), survival, srvyr (≥ 1.0), haven (≥ 2.5.0), lifecycle (≥ 1.0.0), broom (≥ 1.0.0), polycor (≥ 0.8.0), jtools (≥ 2.2.0), covr, knitr, rmarkdown
VignetteBuilder:	knitr
Config/testthat/edition:	3
URL:	https://github.com/JDenn0514/surveycore, https://jdenn0514.github.io/surveycore/
BugReports:	https://github.com/JDenn0514/surveycore/issues
LazyData:	true
LazyDataCompression:	xz
NeedsCompilation:	no
Packaged:	2026-05-01 13:28:59 UTC; jacobdennen
Author:	Jacob Dennen [aut, cre, cph], Thomas Lumley [ctb, cph] (Author of variance estimation code vendored from the 'survey' package)
Maintainer:	Jacob Dennen <jdenn0514@gmail.com>
Repository:	CRAN
Date/Publication:	2026-05-05 15:12:03 UTC

Get design variable column names

Description

Returns a flat character vector of all design-variable column names (ids, weights, strata, fpc) for any survey design class. NULL entries are dropped; names are unique. Exported for use by extension packages (e.g., surveytidy); not intended for end users.

Usage

.get_design_vars_flat(design)

Arguments

Value

A character vector of column names.

Internal Domain Column Name Constant

Description

The name of the logical column added to ⁠@data⁠ by filter() (from surveytidy) to mark domain membership. Exposed here so that sibling packages (surveytidy, surveywts) can reference it without using :::.

Usage

SURVEYCORE_DOMAIN_COL

Format

An object of class character of length 1.

ACS PUMS 2022 1-Year: Wyoming Persons

Description

All person records from the 2022 American Community Survey (ACS) 1-Year Public Use Microdata Sample (PUMS) for Wyoming (state FIPS 56). Wyoming is the least-populous U.S. state, making this the smallest state-level PUMS file — ideal for fast tests and examples.

Usage

acs_pums_wy

Format

A data frame with 5,962 rows and 96 variables. Columns pwgtp1 through pwgtp80 are the 80 successive difference replicate weights for variance estimation; the remaining 16 variables are:

• puma: Public Use Microdata Area code. Use as the cluster ID (PSU) for variance estimation.

• st: State FIPS code (all 56 = Wyoming).

• pwgtp: Person weight. Represents the number of people in the Wyoming population that this record represents.

• agep: Age (0–99 years).

• sex: Sex (1 = male, 2 = female).

• rac1p: Recoded detailed race (1 = White alone, 2 = Black or African American alone, 3 = American Indian alone, 6 = Asian alone, 9 = Two or more races).

• hisp: Recoded Hispanic origin (01 = Not Spanish/Hispanic/Latino; 02–24 = specific Hispanic origin).

• schl: Educational attainment (24 categories: 01 = no schooling, 16 = regular high school diploma, 21 = bachelor's degree, 24 = doctorate degree).

• esr: Employment status recode (1 = civilian employed at work, 2 = civilian employed with job but not at work, 3 = unemployed, 4 = Armed Forces at work, 5 = Armed Forces not at work, 6 = Not in labor force).

• pincp: Total person income in the past 12 months (dollars, signed; negative values indicate a net loss). Multiply by adjinc / 1e6 to adjust to constant dollars.

• wagp: Wages or salary income in the past 12 months (dollars). NA if not applicable.

• hicov: Health insurance coverage (1 = with health insurance, 2 = without health insurance).

• dis: Disability recode (1 = with a disability, 2 = without a disability).

• povpip: Income-to-poverty ratio (0–501; 501 means 501% or more).

• wkhp: Usual hours worked per week in the past 12 months. NA if not in the labor force.

• adjinc: Adjustment factor for income and earnings. Divide by 1,000,000 and multiply income variables to convert to 2022 constant dollars.

Details

Survey design: Successive difference replication (SDR). Use as_survey_replicate() with all 80 replicate weights:

svy <- as_survey_replicate(
  acs_pums_wy,
  weights    = pwgtp,
  repweights = pwgtp1:pwgtp80,
  type       = "successive-difference"
)

Income adjustment: Income variables (pincp, wagp) are in survey-year dollars. Multiply by adjinc / 1e6 to convert to 2022 inflation-adjusted dollars before comparing across ACS years.

Metadata: The ACS PUMS source is a plain CSV with no embedded labels. Columns in acs_pums_wy carry no "label", "labels", or "question_preface" attributes. Variable descriptions are documented here in ?acs_pums_wy and in data-raw/README.md. Use set_var_label() and set_val_labels() to attach labels manually before analysis if needed.

Source

U.S. Census Bureau. 2022 ACS 1-Year PUMS. https://www.census.gov/programs-surveys/acs/microdata/access.html

Examples

# Wyoming population represented
sum(acs_pums_wy$pwgtp)

# Age distribution
hist(acs_pums_wy$agep, main = "Age distribution, Wyoming 2022",
     xlab = "Age")

# Confirm 80 replicate weights are present
sum(grepl("^pwgtp[0-9]", names(acs_pums_wy)))

Add Surveys to a survey_collection

Description

Appends one or more surveys to an existing collection and returns a new survey_collection. The original collection is unchanged. Surveys may be passed with explicit names or as bare symbols (auto-named, like as_survey_collection()). Duplicate names are repaired by appending ⁠_1⁠, ⁠_2⁠, … Existing names are never modified during repair.

Usage

add_survey(.collection, ...)

Arguments

Details

Calling add_survey(x) with no additional surveys returns x unchanged; no error is raised.

Value

A new survey_collection with the appended surveys.

See Also

as_survey_collection(), remove_survey()

Other collections: as_survey_collection(), remove_survey(), set_collection_id(), set_collection_if_missing_var(), survey_collection()

Examples

d1 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
d2 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
coll <- as_survey_collection(a = d1)
coll2 <- add_survey(coll, b = d2)
names(coll2)

ANES 2024: American National Election Studies Time Series

Description

A 19-variable extract from the 2024 American National Election Studies (ANES) Time Series Study, a landmark biennial pre- and post-election survey of the American electorate. Fielded via face-to-face interview and web (n = 5,521). This extract uses the FTF + Web combined design variables (v240103a–v240103d), the recommended set for most analyses.

Usage

anes_2024

Format

A data frame with 5,521 rows and 19 variables:

v240103a

Pre-election weight (FTF+Web combined). Use for variables asked before November 5, 2024.

v240103b

Post-election weight (FTF+Web combined). Use for variables asked after November 5, 2024.

v240103c

PSU (FTF+Web combined). Use as the cluster ID for variance estimation.

v240103d

Stratum (FTF+Web combined). Use as the stratification variable.

v240001

2024 Time Series Case ID. Unique respondent identifier.

v240003

Sample type: 1 = Panel, 2 = Fresh Web, 3 = Fresh FTF, 4 = GSS.

v240002c

Pre/Post interview completion: 1 = Pre-election only, 2 = Pre- and post-election.

v243002

State FIPS code.

v243007

Census region: 1 = Northeast, 2 = Midwest, 3 = South, 4 = West.

v241458x

Age on Election Day (summary). Top-coded at 80. -2 = missing.

v241550

Sex: 1 = male, 2 = female.

v241501x

Race/ethnicity (5-category summary): White non-Hispanic, Black non-Hispanic, Hispanic, Asian/NHPI non-Hispanic, Other/Multiracial non-Hispanic.

v241465x

Education (5-category summary): 1 = less than HS, 2 = HS diploma, 3 = some college, 4 = bachelor's degree, 5 = graduate degree.

v241566x

Household income (28 categories from < $5,000 to $250,000+).

v241177

Liberal-conservative self-placement (7-point scale): 1 = extremely liberal, 7 = extremely conservative. 99 = haven't thought about this.

v241222

Party identification strength: 1 = strong, 2 = not very strong.

v241223

Party identification lean (Independents): 1 = closer to Republican, 2 = neither, 3 = closer to Democrat.

v242066

Did respondent vote for President (POST): 1 = yes, 2 = no.

v242067

Presidential vote choice (POST): 1 = Harris, 2 = Trump, 3 = RFK Jr., 4 = West, 5 = Stein, 6 = Other.

Details

Survey design: Stratified cluster — use Taylor series linearization. Two weights are available depending on whether the analysis uses pre- or post-election variables:

# Pre-election analysis (party ID, ideology, candidate preference)
svy_pre <- as_survey(anes_2024,
  ids     = v240103c,
  strata  = v240103d,
  weights = v240103a,
  nest    = TRUE
)

# Post-election analysis (validated vote choice)
svy_post <- as_survey(anes_2024,
  ids     = v240103c,
  strata  = v240103d,
  weights = v240103b,
  nest    = TRUE
)

Missing value codes: The ANES uses negative integer codes for missing data throughout: -9 = Refused, -8 = Don't know, -4 = Technical error, -1 = Inapplicable, and others. These must be recoded to NA before analysis. Check attr(anes_2024$v241177, "labels") for the full set of codes for a given variable.

Metadata: All columns carry variable labels and value labels as R attributes from the original Stata file, automatically extracted into surveycore's metadata system when you call as_survey().

• Variable labels ("label" attribute): A human-readable description of each column. Example: attr(anes_2024$v241550, "label") returns "PRE: What is your sex?" (or similar ANES phrasing).

• Value labels ("labels" attribute): A named numeric vector mapping each code to its meaning, including all missing-value codes. Example: attr(anes_2024$v241550, "labels") returns a vector with entries for Male, Female, and the applicable negative missing codes.

Source

American National Election Studies. 2024 Time Series Study. Available at electionstudies.org (free account required to download raw data; the processed .rda is included in the package). Prepared by ⁠data-raw/prepare-anes-2024.R⁠.

Examples

# Variables in the dataset
names(anes_2024)

# Create pre-election design
svy <- as_survey(
  anes_2024,
  ids = v240103c,
  strata = v240103d,
  weights = v240103a,
  nest = TRUE
)

# Inspect variable label (ANES uses opaque V-codes; labels give context)
attr(anes_2024$v241177, "label")

# Inspect value labels, including missing-value codes
attr(anes_2024$v241177, "labels")

Create a Taylor Series Linearization Survey Design

Description

Creates a survey design object using Taylor series (linearization) for variance estimation. Supports simple random samples, stratified designs, single- and multi-stage cluster designs, and designs with finite population correction. Uses a tidy-select interface for all design variable arguments.

Usage

as_survey(
  data,
  ids = NULL,
  probs = NULL,
  weights = NULL,
  strata = NULL,
  fpc = NULL,
  nest = FALSE
)

Arguments

Value

A survey_taylor object.

Tidy-select

All design variable arguments (ids, probs, weights, strata, fpc) support tidy-select syntax: bare column names, c() to combine multiple columns (multi-stage ids = c(psu, ssu), multi-stage fpc), and tidyselect helpers like starts_with(). See the Examples section below for runnable demonstrations.

Simple random sample

When no ids or strata are specified, the result is a survey_taylor object with NULL ids and strata — i.e., a simple random sample (SRS). The Taylor variance machinery produces the same estimates as the classical SRS formula (1 - f) * s^2 / n. If weights and probs are also both omitted, uniform weights are assigned and a warning is issued.

Known limitations

as_survey() does not support probability-proportional-to-size (PPS) variance estimation. Taylor series linearization treats all designs as with-replacement, which overestimates (is conservative for) variance in PPS-without-replacement designs. The Yates-Grundy and Brewer/Overton estimators available in survey::svydesign() via its pps and variance arguments are not supported.

If your design requires PPS-specific variance estimation, create the design with survey::svydesign() and convert it with from_svydesign():

d_survey <- survey::svydesign(
  ids = ~psu, weights = ~wt, strata = ~stratum,
  pps = "brewer", data = mydata
)
d <- from_svydesign(d_survey)

References

Sarndal, C-E., Swensson, B. and Wretman, J. (1991) Model Assisted Survey Sampling. Springer.

Lumley, T. (2004) Analysis of complex survey samples. Journal of Statistical Software 9(1), 1–19.

Lumley, T. (2010) Complex Surveys: A Guide to Analysis Using R. John Wiley and Sons.

See Also

as_survey_replicate() for replicate-weight designs, as_survey_twophase() for two-phase designs, set_var_label() to add variable labels

Other constructors: as_survey_nonprob(), as_survey_replicate(), as_survey_twophase(), survey_data(), survey_glm(), survey_glm_fit(), survey_nonprob(), survey_replicate(), survey_taylor(), survey_twophase()

Examples

# Full NHANES design: stratified cluster with PSU IDs nested within strata
d <- as_survey(
  nhanes_2017,
  ids     = sdmvpsu,
  weights = wtint2yr,
  strata  = sdmvstra,
  nest    = TRUE
)

# Stratified design without PSU cluster IDs
d_strat <- as_survey(nhanes_2017, weights = wtint2yr, strata = sdmvstra)

# Blood pressure analysis: filter to exam participants, use MEC weight
exam <- nhanes_2017[nhanes_2017$ridstatr == 2, ]
d_bp <- as_survey(exam, ids = sdmvpsu, weights = wtmec2yr,
                  strata = sdmvstra, nest = TRUE)

# c() to combine multiple columns — sketched on a synthetic two-stage frame
df <- data.frame(
  psu = rep(1:5, each = 4),
  ssu = 1:20,
  wt  = runif(20, 0.5, 2)
)
d_ms <- as_survey(df, ids = c(psu, ssu), weights = wt)

# Tidy-select helpers like starts_with() also work
d_h <- as_survey(
  gss_2024,
  ids = vpsu,
  strata = vstrat,
  weights = starts_with("wtssn"),
  nest = TRUE
)

Create a Collection of Survey Designs

Description

Builds a survey_collection from one or more survey design objects for comparative analysis across waves, cross-sections, or sub-populations. Each element is stored independently — designs are never combined, and variance estimation is never re-specified.

Usage

as_survey_collection(..., group, .id = ".survey", .if_missing_var = "error")

Arguments

Details

Arguments may be passed with explicit names ("wave1" = d1) or as bare symbols (d1, auto-named to "d1"). An unnamed argument that is not a bare symbol (e.g., an inline as_survey(...) call) raises surveycore_error_collection_unnamed_expr — name such arguments explicitly.

Duplicate names are repaired by appending ⁠_1⁠, ⁠_2⁠, … to subsequent occurrences (first occurrence preserved). When any rename occurs, a surveycore_warning_collection_duplicate_name_repaired warning is emitted showing the original -> repaired mapping.

Value

A survey_collection object containing the supplied surveys.

See Also

survey_collection, add_survey(), remove_survey()

Other collections: add_survey(), remove_survey(), set_collection_id(), set_collection_if_missing_var(), survey_collection()

Examples

d1 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
d2 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)

# Explicit names
coll <- as_survey_collection("2020" = d1, "2024" = d2)
names(coll)

# Bare-symbol auto-naming
coll2 <- as_survey_collection(d1, d2)
names(coll2)

# Uniform grouping across members
coll3 <- as_survey_collection(d1, d2, group = vstrat)
coll3@groups

Create a Calibrated / Non-Probability Survey Design

Description

Usage

as_survey_nonprob(data, weights, calibration = NULL)

Arguments

Details

Creates a survey design object for non-probability samples and post-hoc calibrated designs (e.g., raked online panels, post-stratified samples). Accepts pre-computed calibration weights and optionally stores calibration provenance from surveywts output for reproducibility.

Value

A survey_nonprob object.

Phase 2.5 skeleton

This constructor is a skeleton. The resulting survey_nonprob object supports estimation via a model-assisted SRS variance assumption — the same as calling as_survey() with weights only. Full bootstrap re-calibration variance (which re-applies the raking procedure on each replicate) will be implemented in Phase 2.5 alongside the surveywts package.

When to use

Use as_survey_nonprob() instead of as_survey() when:

• Your data comes from a non-probability sample (online panel, quota sample, MTurk/Prolific, etc.)

• You have calibration or raking weights but no probability sampling design structure (no PSU IDs, strata, etc.)

• You want to explicitly record the provenance of your calibration weights for reproducibility

If your data comes from a probability sample with known design structure, use as_survey(), as_survey_replicate(), or as_survey_twophase() instead.

Variance estimation note

Standard errors from a survey_nonprob object assume simple random sampling within the calibrated weights. This is consistent with common applied practice for raked non-probability samples, but is technically a model-assisted approximation rather than design-based variance. See vignette("creating-survey-objects") for details and limitations.

See Also

as_survey() for probability designs with Taylor variance, as_survey_replicate() for replicate-weight designs

Other constructors: as_survey(), as_survey_replicate(), as_survey_twophase(), survey_data(), survey_glm(), survey_glm_fit(), survey_nonprob(), survey_replicate(), survey_taylor(), survey_twophase()

Examples

# Minimal: pre-computed calibration weights from an external tool
df <- data.frame(
  y      = rnorm(200),
  age    = sample(c("18-34", "35-54", "55+"), 200, replace = TRUE),
  cal_wt = runif(200, 0.5, 2.5)
)
d <- as_survey_nonprob(df, weights = cal_wt)

Create a Replicate Weights Survey Design

Description

Creates a survey design object using replicate weights for variance estimation. Supports all common replicate methods: jackknife (JK1, JK2, JKn), balanced repeated replication (BRR, Fay), bootstrap, ACS, successive-difference, and user-defined types. Uses a tidy-select interface for weight and replicate-weight columns.

Usage

as_survey_replicate(
  data,
  weights,
  repweights,
  type = c("JK1", "JK2", "JKn", "BRR", "Fay", "bootstrap", "ACS",
    "successive-difference", "other"),
  scale = NULL,
  rscales = NULL,
  fpc = NULL,
  fpctype = c("fraction", "correction"),
  mse = TRUE
)

Arguments

Value

A survey_replicate object.

Tidy-select

Both weights and repweights support tidy-select syntax:

# Bare name for weights
as_survey_replicate(
  df, weights = wt, repweights = starts_with("repwt"), type = "BRR"
)
# c() for explicit replicate columns
as_survey_replicate(
  df, weights = wt, repweights = c(rep1, rep2, rep3), type = "JK1"
)

Replicate weight matrix

The replicate weight matrix is not stored in the object. Only the column names are stored in ⁠@variables$repweights⁠. Variance estimation computes the matrix on demand: as.matrix(design@data[, design@variables$repweights]).

Memory usage

Each call to an estimation function (e.g., get_means(), get_totals()) materialises the full replicate weight matrix from the data frame. For large designs (e.g., ACS PUMS with 500k+ rows × 80 replicates), this is roughly nrow * n_replicates * 8 bytes per call (~363 MB for ACS Wyoming × 80). If you are estimating many variables, this is repeated for each call. This behaviour matches the survey package reference implementation.

References

Judkins, D.R. (1990) Fay's method for variance estimation. Journal of the American Statistical Association 85(410), 895–904.

Canty, A.J. and Davison, A.C. (1999) Resampling-based variance estimation for labour force surveys. The Statistician 48(3), 379–391.

Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. Springer.

See Also

as_survey() for Taylor series designs, as_survey_twophase() for two-phase designs, set_var_label() to add variable labels

Other constructors: as_survey(), as_survey_nonprob(), as_survey_twophase(), survey_data(), survey_glm(), survey_glm_fit(), survey_nonprob(), survey_replicate(), survey_taylor(), survey_twophase()

Examples

# ACS PUMS Wyoming: 80 successive-difference replicate weights
d_acs <- as_survey_replicate(
  acs_pums_wy,
  weights    = pwgtp,
  repweights = pwgtp1:pwgtp80,
  type       = "successive-difference"
)

# Explicit replicate columns using c()
d_sub <- as_survey_replicate(
  acs_pums_wy,
  weights    = pwgtp,
  repweights = c(pwgtp1, pwgtp2, pwgtp3, pwgtp4),
  type       = "JK1"
)

Create a Two-Phase Survey Design

Description

Creates a two-phase (double) sampling design from an existing survey_taylor Phase 1 object. Phase 1 covers all rows; Phase 2 is a strict subset indicated by a logical column. Uses a tidy-select interface for all Phase 2 design variable arguments.

Usage

as_survey_twophase(
  phase1,
  ids2 = NULL,
  strata2 = NULL,
  probs2 = NULL,
  fpc2 = NULL,
  subset,
  method = c("full", "approx", "simple")
)

Arguments

Details

Variance methods

• "full" — Full two-phase variance formula. Accounts for variability in both phases. Requires Phase 2 design information (probs2, ids2, strata2) when Phase 2 is not a simple random subsample. If none of these are provided, a warning is issued and Phase 2 selection is treated as SRS within Phase 1 strata.

• "approx" — Approximation that ignores Phase 1 sampling variability. Faster but less accurate than "full" when the Phase 1 sampling fraction is non-negligible.

• "simple" — Treats Phase 2 as a single-phase design, ignoring Phase 1. Only valid when Phase 1 is a census (no sampling). Issues a warning when Phase 1 has PSU cluster variables, because this understates variance for clustered designs.

Value

A survey_twophase object.

References

Sarndal, C-E., Swensson, B. and Wretman, J. (1992) Model Assisted Survey Sampling. Springer.

Breslow, N.E. and Chatterjee, N. (1999) Design and analysis of two-phase studies with binary outcome applied to Wilms tumour prognosis. Applied Statistics 48, 457–468.

Breslow, N., Lumley, T., Ballantyne, C.M., Chambless, L.E. and Kulick, M. (2009) Improved Horvitz-Thompson estimation of model parameters from two-phase stratified samples: applications in epidemiology. Statistics in Biosciences. doi:10.1007/s12561-009-9001-6

See Also

as_survey() for Taylor series designs, as_survey_replicate() for replicate-weight designs

Other constructors: as_survey(), as_survey_nonprob(), as_survey_replicate(), survey_data(), survey_glm(), survey_glm_fit(), survey_nonprob(), survey_replicate(), survey_taylor(), survey_twophase()

Examples

# Minimal two-phase design: Phase 1 = full cohort, Phase 2 = random subset
df <- data.frame(
  id        = 1:20,
  wt        = rep(2, 20),
  in_phase2 = c(rep(TRUE, 10), rep(FALSE, 10)),
  y         = rnorm(20)
)
phase1 <- as_survey(df, ids = id, weights = wt)
d2 <- as_survey_twophase(phase1, subset = in_phase2)

# With Phase 2 stratification and inclusion probabilities
df2 <- data.frame(
  id          = 1:30,
  wt          = rep(3, 30),
  in_phase2   = c(rep(TRUE, 15), rep(FALSE, 15)),
  arm         = rep(c("A", "B", "C"), 10),
  subsamprate = rep(c(0.5, 0.7, 0.3), 10),
  y           = rnorm(30)
)
phase1b <- as_survey(df2, ids = id, weights = wt)
d2b <- as_survey_twophase(
  phase1b,
  strata2 = arm,
  probs2  = subsamprate,
  subset  = in_phase2,
  method  = "full"
)

Convert a surveycore Design Object to a survey Package Design

Description

Converts a survey_taylor, survey_replicate, or survey_twophase object to the corresponding survey package object: svydesign, svrepdesign, or twophase. Useful for accessing survey package estimation functions or for round-trip testing.

Usage

as_svydesign(x)

Arguments

Details

Metadata (variable labels, value labels) is NOT carried over — the survey package has no metadata system.

Value

A survey::svydesign, survey::svrepdesign, or survey::twophase object.

See Also

from_svydesign() to convert back from a survey design

Other conversion: as_tbl_svy(), from_svydesign(), from_tbl_svy()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
if (requireNamespace("survey", quietly = TRUE)) {
  sv <- as_svydesign(d)
  survey::svymean(~ridageyr, sv, na.rm = TRUE)
}

Convert a surveycore Design Object to an srvyr tbl_svy

Description

Converts a surveycore design object to an srvyr tbl_svy by first converting to a survey design via as_svydesign() and then wrapping with srvyr::as_survey(). Requires both survey and srvyr.

Usage

as_tbl_svy(x)

Arguments

Details

Metadata (variable labels, value labels) is NOT carried over.

Value

A srvyr::tbl_svy object.

See Also

from_tbl_svy() to convert back from a tbl_svy object

Other conversion: as_svydesign(), from_svydesign(), from_tbl_svy()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
if (requireNamespace("survey", quietly = TRUE) &&
    requireNamespace("srvyr",  quietly = TRUE)) {
  ts <- as_tbl_svy(d)
}

Classify Variable Question Types

Description

Groups variables by their shared question_preface metadata and classifies each group as one of "single", "sata", or "battery". This is the single source of truth used by downstream export functions to decide how to render each question.

Usage

classify_question_type(x, ..., variable = NULL)

Arguments

Details

The classification rules, applied per requested variable:

1. If the variable has no question_preface, or is the only requested variable sharing its preface, type = "single".

2. If a question_preface is shared by 2+ requested variables and at least one is flagged via set_sata(), all variables in that group get type = "sata".

3. Otherwise (shared preface, no SATA flag), all variables in the group get type = "battery".

Group numbers are assigned sequentially by first appearance in the input.

Value

A tibble with columns:

• variable (character) — variable name

• question_preface (character) — the preface, or NA if none

• type (character) — one of "single", "sata", or "battery"

• group (integer) — group id; variables with the same non-NA preface share a group

See Also

set_sata(), extract_sata(), set_question_preface()

Other metadata: extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_question_preface(d, riagendr = "Demographics",
                             ridageyr = "Demographics")
d <- set_sata(d, riagendr, ridageyr)
classify_question_type(d, riagendr, ridageyr, bpxsy1)

Tidy a Survey GLM Fit

Description

Converts a survey_glm_fit object into a survey_glm_tidy result tibble with one row per model coefficient (plus optional reference rows for factor predictors), design-based standard errors, confidence intervals, and structured metadata.

Usage

clean(
  model,
  conf_level = 0.95,
  include_reference = TRUE,
  n = FALSE,
  statistic = TRUE,
  exponentiate = FALSE,
  interaction_sep = " * ",
  ...
)

Arguments

Value

A survey_glm_tidy object: a tibble with S3 class c("survey_glm_tidy", "survey_result", "tbl_df", "tbl", "data.frame"). Metadata is accessed via meta().

See Also

survey_glm() to fit the model, meta() to access metadata.

Other analysis: get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
fit <- survey_glm(d, age ~ sex)
clean(fit)
clean(fit, conf_level = 0.99, exponentiate = FALSE)

Extract All Metadata for Variables

Description

Returns a summary of all metadata fields for one or more variables in a survey design object or data frame. Useful for auditing metadata state or building codebooks.

Usage

extract_metadata(x, ..., fill = NULL)

Arguments

Value

A named list. Each entry is a named list with keys: variable_label, value_labels, question_preface, note, universe, missing_codes, transformations.

See Also

Other metadata: classify_question_type(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_universe(d, ridageyr = "All participants 0+")
extract_metadata(d, ridageyr)
extract_metadata(d, fill = "include")

Extract Missing Value Codes

Description

Returns missing value sentinel codes for one or more variables in a survey design object or data frame.

Usage

extract_missing_codes(x, ..., format = "list", fill = NULL)

Arguments

Value

• "list" (default): named list of atomic vectors. Empty: list().

• "data_frame": long-format tibble with columns variable, description (NA if codes vector is unnamed), code (coerced to character). Empty: zero-row tibble.

See Also

set_missing_codes() to set missing value codes

Other metadata: classify_question_type(), extract_metadata(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_missing_codes(d, ridageyr = c("Not applicable" = 999L))
extract_missing_codes(d, ridageyr)
extract_missing_codes(d, ridageyr, format = "data_frame")

Extract Question Prefaces

Description

Returns question preface text for one or more variables in a survey design object or data frame.

Usage

extract_question_preface(x, ..., format = "named_vector", fill = NULL)

Arguments

Value

• "named_vector" (default): named character vector. Empty: character(0).

• "list": named list of character scalars. Empty: list().

• "data_frame": tibble with columns variable and preface. Empty: zero-row tibble.

See Also

set_question_preface() to set a question preface

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
d <- set_question_preface(d, happy = "Taken all together...")
extract_question_preface(d, happy)

Extract SATA (Select-All-That-Apply) Flags

Description

Returns the SATA status for one or more variables in a survey design object or a data frame.

Usage

extract_sata(x, ..., format = "named_vector", fill = FALSE)

Arguments

Value

• "named_vector" (default): named logical vector. Empty: logical(0).

• "list": named list of logical scalars. Empty: list().

• "data_frame": tibble with columns variable (character) and sata (logical). Empty: zero-row tibble.

See Also

set_sata() to set SATA flags

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_sata(d, riagendr)
extract_sata(d, riagendr)
extract_sata(d, fill = NULL)

Extract Universe Descriptions

Description

Returns universe (eligibility) descriptions for one or more variables in a survey design object or data frame.

Usage

extract_universe(x, ..., format = "named_vector", fill = NULL)

Arguments

Value

• "named_vector" (default): named character vector. Empty: character(0).

• "list": named list of character scalars. Empty: list().

• "data_frame": tibble with columns variable and universe. Empty: zero-row tibble.

See Also

set_universe() to set a universe description

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_universe(d, ridageyr = "All participants 0+")
extract_universe(d)
extract_universe(d, ridageyr, format = "data_frame")

Extract Value Labels

Description

Returns value labels for one or more variables in a survey design object or data frame.

Usage

extract_val_labels(x, ..., format = "list", fill = NULL)

Arguments

Value

• "list" (default): named list of named vectors. Empty: list().

• "data_frame": long-format tibble with columns variable, label, value (codes coerced to character). Empty: zero-row tibble.

See Also

set_val_labels() to set value labels

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
extract_val_labels(d, riagendr)
extract_val_labels(d, riagendr, format = "data_frame")

Extract Variable Labels

Description

Returns variable labels for one or more variables in a survey design object or data frame.

Usage

extract_var_label(x, ..., format = "named_vector", fill = NULL)

Arguments

Value

• "named_vector" (default): named character vector. Empty: character(0).

• "list": named list of character scalars. Empty: list().

• "data_frame": tibble with columns variable and label. Empty: zero-row tibble.

See Also

set_var_label() to set a variable label

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
extract_var_label(d)
extract_var_label(d, riagendr, ridageyr)
extract_var_label(d, format = "data_frame")
extract_var_label(d, fill = NA_character_)

Extract Analyst Notes

Description

Returns analyst notes for one or more variables in a survey design object or data frame.

Usage

extract_var_note(x, ..., format = "named_vector", fill = NULL)

Arguments

Value

• "named_vector" (default): named character vector. Empty: character(0).

• "list": named list of character scalars. Empty: list().

• "data_frame": tibble with columns variable and note. Empty: zero-row tibble.

See Also

set_var_note() to set a note

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
d <- set_var_note(d, age = "Top-coded at 89")
extract_var_note(d, age)

Convert a survey Package Design to a surveycore Design Object

Description

Converts a survey package design object (svydesign, svrepdesign, or twophase) to the corresponding surveycore S7 object. The data, design variables, and replicate weights are preserved; metadata (variable labels, value labels) is not — the survey package has no metadata system.

Usage

from_svydesign(x)

Arguments

Details

Weight column names are recovered from the design call when available. When the call does not contain a formula (e.g., weights were passed as a vector), the weight column is identified by matching the stored weight values against columns in the data. If no match is found, a ..surveycore_wt.. column is added.

Value

A survey_taylor, survey_replicate, or survey_twophase object.

See Also

as_svydesign() to convert in the other direction

Other conversion: as_svydesign(), as_tbl_svy(), from_tbl_svy()

Examples

if (requireNamespace("survey", quietly = TRUE)) {
  sv <- survey::svydesign(
    ids = ~sdmvpsu, weights = ~wtint2yr, strata = ~sdmvstra,
    data = nhanes_2017, nest = TRUE
  )
  d <- from_svydesign(sv)
  survey_data(d)
}

Convert an srvyr tbl_svy to a surveycore Design Object

Description

Converts an srvyr tbl_svy to a surveycore design object by delegating to from_svydesign(). A tbl_svy IS a survey.design, so the conversion is structurally identical. Requires both survey and srvyr.

Usage

from_tbl_svy(x)

Arguments

Value

A survey_taylor, survey_replicate, or survey_twophase object.

See Also

as_tbl_svy() to convert in the other direction

Other conversion: as_svydesign(), as_tbl_svy(), from_svydesign()

Examples

if (requireNamespace("survey", quietly = TRUE) &&
    requireNamespace("srvyr",  quietly = TRUE)) {
  ts <- srvyr::as_survey(
    survey::svydesign(ids = ~sdmvpsu, weights = ~wtint2yr,
      strata = ~sdmvstra, data = nhanes_2017, nest = TRUE)
  )
  d <- from_tbl_svy(ts)
}

Design-Based Analysis of Variance for Survey GLM Fits

Description

Rao-Scott design-based ANOVA for survey_glm() fits. Accepts three input shapes on object:

Usage

get_anova(
  object,
  formula = NULL,
  response = NULL,
  predictors = NULL,
  ...,
  method = c("LRT", "Wald"),
  test = c("F", "Chisq"),
  null = NULL,
  tolerance = sqrt(.Machine$double.eps),
  decimals = NULL,
  label_vars = TRUE,
  name_style = "surveycore"
)

Arguments

Details

• A single survey_glm_fit — sequential mode, one row per term.

• A list of survey_glm_fit objects — chained pairwise comparison, producing length(object) - 1 rows.

• A survey design (any survey_base subclass) — fits the model internally via survey_glm() using formula (or response + predictors), then runs sequential anova on the fit.

Supports the four method x test combinations shared with survey::anova.svyglm(): Rao-Scott working-LRT with F or Chisq reference, and design-based Wald with F or Chisq reference.

Value

A survey_anova tibble with columns term, statistic, df, ddf, deff, p_value, stars and a .meta attribute.

See Also

Other analysis: clean(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

gss_cc <- gss_2024[
  stats::complete.cases(gss_2024[, c("age", "sex", "educ")]),
]
gss_design <- as_survey(
  gss_cc, ids = vpsu, weights = wtssps,
  strata = vstrat, nest = TRUE
)

# Single fit
fit <- survey_glm(gss_design, age ~ sex + educ)
get_anova(fit)

# Design + formula (fits internally)
get_anova(gss_design, age ~ sex + educ)

# List of fits (chained pairwise comparison)
fit_s <- survey_glm(gss_design, age ~ sex)
fit_b <- survey_glm(gss_design, age ~ sex + educ)
get_anova(list(fit_s, fit_b))

Survey-Weighted Correlation (Pearson, Polychoric, Polyserial)

Description

Compute pairwise correlations between two or more variables in a survey design, with design-based standard errors and confidence intervals. Returns results in long or wide format. The estimator is selected by method: "pearson" (default) for two numeric variables, "polychoric" for two ordinal variables under a bivariate-normal latent model (Olsson 1979), or "polyserial" for one ordinal + one continuous variable (Cox 1974). The survey-weighted polychoric and polyserial estimators (point estimates and design-based variance) are implemented from scratch following Mannan (2025); they are not derived from the survey package, which does not provide these estimators.

Usage

get_corr(
  design,
  x,
  group = NULL,
  format = c("long", "wide"),
  redundant = FALSE,
  diagonal = FALSE,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  method = "pearson",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Details

Polychoric / polyserial semantics. For method != "pearson", each pair is fit by a two-step MLE: weighted marginal thresholds (and, for polyserial, a weighted standardization of the continuous side) are estimated first, then rho is maximised over the weighted log-likelihood via stats::optimize() on ⁠(-1 + 1e-6, 1 - 1e-6)⁠. Confidence intervals are constructed on the Fisher-z scale (atanh(rho)) and back-transformed via tanh with truncation to ⁠[-1, 1]⁠. The Wald statistic zeta.hat / SE(zeta.hat) is referred to a standard normal distribution, so df = NA_integer_ — distinct from the Pearson case where df = n - 2 and the t-distribution is used. Column label attributes are method-neutral (e.g. "statistic", not "t-statistic" / "z-statistic"); check meta(result)$method to interpret the values.

Bivariate-normal assumption. The polychoric / polyserial MLEs assume the underlying latent variables are jointly bivariate-normal. This is an unverified assumption; no runtime diagnostic is performed.

Taylor-path cost. On a survey_taylor design, the variance path for method != "pearson" is O(n) re-optimisations per variable pair (a perturbation-based influence function). For large n and many pairs, passing a survey_replicate design (one re-fit per replicate, not per respondent) is substantially faster.

Replicate-type caveat. Mannan (2025) verifies the replicate-weight variance formula for jackknife and bootstrap replicates. BRR and Fay replicates are admitted mechanically via the design's stored scale / rscales coefficients, but the paper does not validate their behaviour for this non-linear pseudo-likelihood estimator.

Value

A survey_corr tibble (also inheriting survey_result).

When group is active, group variable columns are prepended before all other columns in both long and wide formats.

Long format columns:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• var1, var2 — variable names (or labels when label_vars = TRUE).

• r — Pearson correlation coefficient.

• Variance columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• p_value — two-tailed p-value.

• statistic — t-statistic.

• df — degrees of freedom for the t-test (n minus 2).

• n — pairwise unweighted count.

• n_weighted — pairwise sum of weights (only when requested).

Wide format columns:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• variable — row variable names (or labels).

• One column per focal variable, containing r values.

Use meta(result) to access design type, variable labels, and method ("pearson", "polychoric", or "polyserial"). For method != "pearson", meta(result)$bivariate_normal_cdf is "pbivnorm" (the bivariate-normal CDF used internally). When the replicate variance path observed one or more non-converged replicates, meta(result)$n_failed_replicates_total carries the scalar total.

References

Cox, N. R. (1974). Estimation of the correlation between a continuous and a discrete variable. Biometrics, 30(1), 171-178.

Mannan, H. (2025). SAS programs for estimation of weighted polychoric and weighted polyserial correlations in a complex survey. SSRN. doi:10.2139/ssrn.6580480

Olsson, U. (1979). Maximum likelihood estimation of the polychoric correlation coefficient. Psychometrika, 44(4), 443-460.

See Also

Other analysis: clean(), get_anova(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
get_corr(d, x = c(ridageyr, bpxsy1))

# Wide correlation matrix
get_corr(d, x = c(ridageyr, bpxsy1), format = "wide")

# AAPOR-compliant
get_corr(d, x = c(ridageyr, bpxsy1),
         variance = c("ci", "moe"), n_weighted = TRUE)

# Polychoric correlation between two ordinal variables
df <- data.frame(
  id = 1:200,
  wt = runif(200, 0.5, 2),
  o1 = factor(sample(1:4, 200, replace = TRUE), ordered = TRUE),
  o2 = factor(sample(1:4, 200, replace = TRUE), ordered = TRUE)
)
d_ord <- as_survey(df, weights = wt)
get_corr(d_ord, x = c(o1, o2), method = "polychoric")

Design-Based Population Covariance for a Survey Design

Description

Compute the design-based estimate of the finite-population Pearson covariance for every (unordered, by default) pair of numeric variables selected from x, with optional grouping, uncertainty quantification, and metadata-driven labelling. Matches the off-diagonal entries of survey::svyvar() (Kish n/(n-1) correction) on Taylor, replicate, twophase, and nonprob designs at numerical parity.

Usage

get_covariance(
  design,
  x,
  group = NULL,
  redundant = FALSE,
  diagonal = FALSE,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Details

Confidence intervals use the normal-Wald approximation on the SE of the covariance estimate: ci_low = covariance - z * se, ci_high = covariance + z * se, where z = qnorm((1 + conf_level) / 2). The bounds are not clamped. Covariance is unbounded — ci_low and ci_high may have opposite signs and may cross zero. Users who want clamped intervals can post-process. This behaviour matches survey::svyvar().

NA handling is pairwise-complete per pair: each ordered pair drops rows where either variable is NA. There is no na_handling argument; pairwise is the only policy. This matches survey::svyvar() off-diagonal pair-at-a-time semantics, not svyvar()'s default listwise deletion across a multi-variable formula. Numerical parity therefore only holds when oracle calls are made pair-at-a-time (survey::svyvar(~x + y, design) per pair).

Under diagonal = TRUE, the self-pair ⁠(x, x)⁠ returns the design-based Kish-corrected variance of x on the active domain — not 1 as in get_corr(). The covariance matrix diagonal is the variance vector, not the identity. The diagonal-parity gate guarantees that get_covariance(d, c(x, x), diagonal = TRUE)$covariance and ⁠$se⁠ equal get_variance(d, x)$variance and ⁠$se⁠ numerically (point at 1e-10, SE at 1e-8) when the active domains match.

Design effect (deff) uses the Goodnight / Mood-Graybill SRS reference SE_SRS(cov) = sqrt((Var(x) * Var(y) + cov^2) / (n - 1)). When both the design SE and SRS SE are zero (constant-variable pairs), deff is set to exactly 0 (0 / 0 guard).

Value

A survey_covariance tibble (also inheriting survey_result). Columns, in order:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• var1, var2 — factor columns identifying the pair (levels in x-supply order).

• covariance — design-based Pearson covariance estimate (Kish-corrected). NaN for degenerate cells; 0 for pairs where at least one variable is constant on the active domain.

• Uncertainty columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• n — pairwise unweighted count.

• n_weighted — pair's sum of weights (only when requested).

References

Mood, A. M., Graybill, F. A., & Boes, D. C. (1974). Introduction to the Theory of Statistics (3rd ed.). McGraw-Hill.

Lumley, T. (2010). Complex Surveys: A Guide to Analysis Using R. Wiley.

Cochran, W. G. (1977). Sampling Techniques (3rd ed.). Wiley.

Demnati, A., & Rao, J. N. K. (2004). Linearization variance estimators for survey data. Survey Methodology, 30, 17–26.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(
  nhanes_2017,
  ids = sdmvpsu,
  weights = wtint2yr,
  strata = sdmvstra,
  nest = TRUE
)
get_covariance(d, x = c(ridageyr, bpxsy1))

# Include the diagonal (self-pairs return Var(x), not 1)
get_covariance(d, x = c(ridageyr, bpxsy1), diagonal = TRUE)

# With grouping
get_covariance(d, x = c(ridageyr, bpxsy1), group = riagendr)

Treatment Effect Estimation for Survey Designs

Description

Estimates treatment effects (differences from a reference group) via survey-weighted regression. Supports bivariate and multivariate models, Gaussian and non-Gaussian families, and optional subgroup analysis.

Usage

get_diffs(
  design,
  x,
  treats,
  group = NULL,
  covariates = NULL,
  ref_level = NULL,
  pval_adj = NULL,
  show_means = TRUE,
  show_pct_change = FALSE,
  scale = c("ame", "link"),
  variance = "ci",
  conf_level = 0.95,
  min_cell_n = 30L,
  n_weighted = FALSE,
  decimals = NULL,
  na.rm = TRUE,
  label_values = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Details

Estimation Paths

get_diffs() uses two estimation paths:

• Clean path (bivariate Gaussian, no group): extracts coefficients directly from clean(). The intercept is the reference group mean; treatment coefficients are differences from reference.

• Marginaleffects path (covariates, non-Gaussian with scale = "ame", or group): uses avg_slopes() for estimates and avg_predictions() for means.

Link-Scale Suppression

When scale = "link" and the family is non-Gaussian, the mean and pct_change columns are suppressed (omitted entirely). Link-scale means are not substantively meaningful.

P-Value Adjustment

When group is active, p-value adjustment is applied independently within each group. For global adjustment across all comparisons, apply stats::p.adjust() to the result manually. Confidence intervals reflect the specified conf_level and are not affected by p-value adjustment.

Degrees of Freedom

All p-values and confidence intervals use the t-distribution with design-based residual degrees of freedom, regardless of estimation path.

Non-Gaussian Models

By default, non-Gaussian models report average marginal effects on the response scale. Set scale = "link" for coefficients on the link scale (e.g., log-odds for logistic regression).

Value

A survey_diffs tibble (also inheriting survey_result). Columns (in order): group columns (when active), treatment variable, estimate, pct_change (optional), mean (optional), n, n_weighted (optional), se (optional), ci_low (optional), ci_high (optional), p_value, stars. Use meta() to access design type, family, reference level, and other metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

library(marginaleffects)

# Create survey design with treatment groups
set.seed(42)
df <- data.frame(
  id = 1:200, wt = runif(200, 0.5, 2),
  dv = rnorm(200, 50, 10),
  arm = factor(sample(c("Control", "A", "B"), 200, TRUE))
)
d <- as_survey(df, weights = wt)

# Basic treatment effect
get_diffs(d, dv, arm)

# With percentage change and p-value adjustment
get_diffs(d, dv, arm, show_pct_change = TRUE, pval_adj = "BH")

Weighted Frequency Tables for Categorical Survey Variables

Description

Compute weighted proportions (percentages) for one or more categorical variables in a survey design, with optional grouping, uncertainty quantification, and metadata-driven labelling.

Usage

get_freqs(
  design,
  x,
  ...,
  group = NULL,
  names_to = "name",
  values_to = "value",
  variance = NULL,
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Details

Single-variable mode (when x resolves to exactly one variable): The focal variable name becomes the first column. Rows follow the factor level order (if the variable is a factor) or ascending sort order otherwise.

Multi-variable mode (when x resolves to two or more variables): Results are stacked in long format. The names_to column contains the variable label (when label_vars = TRUE) or the raw variable name as fallback. The values_to column contains the response values.

Domain estimation: Proportions use the ratio linearization approach, equivalent to survey::svymean() on a binary indicator within the active domain. The full design structure is used for variance estimation — rows are not physically removed for domain/group subsets.

na.rm = FALSE: NA is appended as the last level. All proportions (including non-NA levels) have their denominator inflated to include NA rows, so the pct column sums to 1.

Value

A survey_freqs tibble (also inheriting survey_result). Columns:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• ⁠[variable_name]⁠ (single) or ⁠[names_to]⁠ + ⁠[values_to]⁠ (multi).

• pct — weighted proportion (0–1).

• Variance columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• n — unweighted cell count (sample basis of each estimate).

• n_weighted — estimated population count (only when requested).

Use meta(result) to access design type, variable labels, value labels, and other metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

# NHANES exam weights are 0 for non-examined participants; filter first
nhanes_sub <- nhanes_2017[nhanes_2017$wtmec2yr > 0, ]
d <- as_survey(nhanes_sub, ids = sdmvpsu, weights = wtmec2yr,
               strata = sdmvstra, nest = TRUE)

# Single variable
get_freqs(d, riagendr)

# With confidence intervals
get_freqs(d, riagendr, variance = "ci")

# Grouped
get_freqs(d, riagendr, group = sdmvstra)

# Multi-variable (stacked)
get_freqs(d, c(riagendr, ridreth3), names_to = "item", values_to = "value")

Weighted Mean for a Survey Design

Description

Compute the weighted mean of a single numeric variable in a survey design, with optional grouping, uncertainty quantification, and metadata-driven labelling.

Usage

get_means(
  design,
  x,
  group = NULL,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_means tibble (also inheriting survey_result). Columns:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• mean — weighted mean estimate.

• Variance columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• n — unweighted count of non-NA observations used in the estimate.

• n_weighted — sum of weights (only when requested).

The variable name is stored in meta(result)$variable, not as a column. Use meta(result) to access design type, variable labels, and other metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
get_means(d, ridageyr)

# With grouped estimate
get_means(d, ridageyr, group = riagendr)

# AAPOR-compliant
get_means(d, ridageyr, variance = c("ci", "moe"), n_weighted = TRUE)

All-Pairs Pairwise T-Tests for Survey Designs

Description

Runs all k(k-1)/2 pairwise two-sample t-tests for a grouping variable with k levels and applies multiple-comparison p-value adjustment. Delegates pair-level computations to get_t_test().

Usage

get_pairwise(
  design,
  x,
  by,
  group = NULL,
  pval_adj = "holm",
  conf_level = 0.95,
  variance = "ci",
  na.rm = TRUE,
  min_cell_n = 30L,
  decimals = NULL,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_pairwise tibble (also inheriting survey_result). Columns: group columns (when active), level_a, level_b, estimate, mean_a, mean_b, n_a, n_b, se (optional), ci_low (optional), ci_high (optional), t_stat, df, p_value (adjusted), stars. Use meta() to access the adjustment method and other metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

gss_sub <- gss_2024[gss_2024$sex %in% c(1L, 2L) & !is.na(gss_2024$age), ]
gss_sub$sex <- factor(gss_sub$sex, levels = c(1, 2), labels = c("Male", "Female"))
gss_design <- as_survey(gss_sub,
  ids = vpsu, weights = wtssps, strata = vstrat, nest = TRUE)
get_pairwise(gss_design, age, by = sex)

Survey-Weighted Quantiles

Description

Compute survey-weighted quantiles (including the median) for a single numeric variable using the Woodruff (1952) confidence interval method. Supports optional grouping, domain estimation, and all five survey design classes.

Usage

get_quantiles(
  design,
  x,
  probs = c(0.25, 0.5, 0.75),
  group = NULL,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_quantiles tibble (also inheriting survey_result).

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• quantile — probability label: "p25", "p50", etc.

• estimate — weighted quantile estimate.

• Variance columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance. CIs are Woodruff intervals and are generally asymmetric around estimate. deff is always NA for quantile estimates: computing it requires a kernel density estimate at the quantile point (the Woodruff SRS approximation used by survey::svyquantile(deff = TRUE)), which is not implemented.

• n — unweighted count of non-NA observations used in the estimate.

• n_weighted — sum of weights (only when requested).

One row per (group combination × quantile probability). The variable name and probs vector are stored in meta(result).

References

Woodruff, R. S. (1952). Confidence intervals for medians and other position measures. Journal of the American Statistical Association, 47(260), 635–646.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_ratios(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)

# IQR + median (default)
get_quantiles(d, ridageyr)

# Median only with SE
get_quantiles(d, ridageyr, probs = 0.5, variance = c("ci", "se"))

# Grouped quartiles
get_quantiles(d, ridageyr, group = riagendr)

Survey-Weighted Ratio Estimation

Description

Estimate the ratio of two survey-weighted totals (numerator / denominator) for a survey design object. Uses the delta method (linearization) for variance estimation for Taylor, SRS, calibrated, and two-phase designs, and direct per-replicate computation for replicate-weight designs. Both approaches are equivalent to survey::svyratio() for their respective design types. Supports optional grouping, domain estimation, and all five survey design classes.

Usage

get_ratios(
  design,
  numerator,
  denominator,
  group = NULL,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_ratios tibble (also inheriting survey_result).

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• ratio — estimated ratio (weighted total of numerator / weighted total of denominator).

• Variance columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• n — unweighted count of rows where both numerator and denominator are non-NA.

• n_weighted — sum of weights (only when requested).

Numerator and denominator variable names are stored in meta(result), not as output columns. Use meta(result)$numerator and meta(result)$denominator to access them.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_t_test(), get_totals(), get_variance(), meta()

Examples

d <- as_survey(pew_npors_2025, weights = weight, strata = stratum)

# Ratio of prayer frequency to in-person attendance frequency
get_ratios(d, numerator = pray, denominator = attendper)

# With grouped estimates
get_ratios(d, pray, attendper, group = gender)

# AAPOR-compliant output
get_ratios(d, pray, attendper, variance = c("ci", "moe"), n_weighted = TRUE)

Design-Based Two-Sample T-Test for Survey Designs

Description

Compares the weighted means of two groups using a design-based t-test. Follows the mathematical model of survey::svyttest() but uses surveycore's own variance machinery (survey_glm()). Supports all four survey design classes and optional subgroup analysis via group.

Usage

get_t_test(
  design,
  x,
  by,
  group = NULL,
  conf_level = 0.95,
  variance = "ci",
  na.rm = TRUE,
  min_cell_n = 30L,
  decimals = NULL,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_t_test tibble (also inheriting survey_result). Columns: group columns (when active), level_a, level_b, estimate, mean_a, mean_b, n_a, n_b, se (optional), ci_low (optional), ci_high (optional), t_stat, df, p_value, stars. Use meta() to access design type, conf_level, and variable metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_totals(), get_variance(), meta()

Examples

gss_sub <- gss_2024[gss_2024$sex %in% c(1L, 2L) & !is.na(gss_2024$age), ]
gss_sub$sex <- factor(gss_sub$sex, levels = c(1, 2), labels = c("Male", "Female"))
gss_design <- as_survey(gss_sub,
  ids = vpsu, weights = wtssps, strata = vstrat, nest = TRUE)
get_t_test(gss_design, age, by = sex)

Weighted Total for a Survey Design

Description

Compute the estimated population total of a numeric variable in a survey design, or the estimated population size when no variable is supplied. Supports optional grouping, uncertainty quantification, and metadata-driven labelling.

Usage

get_totals(
  design,
  x = NULL,
  group = NULL,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Value

A survey_totals tibble (also inheriting survey_result). Columns:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• total — the weighted sum estimate.

• Variance columns — only those requested via variance.

• n — unweighted count (omitted in no-variable mode).

• n_weighted — sum of weights (only when requested).

The variable name (or NULL for no-variable mode) is in meta(result)$variable. Use meta(result) for additional metadata.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_variance(), meta()

Examples

d <- as_survey_replicate(acs_pums_wy, weights = pwgtp,
                   repweights = pwgtp1:pwgtp80,
                   type = "successive-difference")

# Population size
get_totals(d)

# Total for a variable
get_totals(d, agep)

# Grouped
get_totals(d, agep, group = sex)

Design-Based Population Variance for a Survey Design

Description

Compute the design-based estimate of the finite-population variance for one or more numeric variables in a survey design, with optional grouping, uncertainty quantification, and metadata-driven labelling. Matches survey::svyvar() numerically (Kish n/(n-1) correction) on Taylor, replicate, twophase, and nonprob designs.

Usage

get_variance(
  design,
  x,
  group = NULL,
  variance = "ci",
  conf_level = 0.95,
  n_weighted = FALSE,
  decimals = NULL,
  min_cell_n = 30L,
  na.rm = TRUE,
  na_handling = c("pairwise", "listwise"),
  label_values = TRUE,
  label_vars = TRUE,
  name_style = "surveycore",
  ...,
  .id = NULL,
  .if_missing_var = NULL
)

Arguments

Details

Confidence intervals use the normal-Wald approximation on the SE of the variance estimate: ci_low = variance - z * se, ci_high = variance + z * se, where z = qnorm((1 + conf_level) / 2). The bounds are not clamped. When the true variance is near zero with wide SE, ci_low may be negative. Users who want non-negative lower bounds can clamp at 0 post-hoc. This behaviour matches survey::svyvar().

Under na_handling = "pairwise" (the default), each focal variable contributes its own per-variable complete-case count to n. Under na_handling = "listwise", every output row shares the intersection complete-case count — rows with NA in any selected variable are excluded from every variable's calculation.

Value

A survey_variance tibble (also inheriting survey_result). Columns, in order:

• ⁠[group_cols...]⁠ — group variable columns (when active), first.

• name — focal variable name (or its label when label_vars = TRUE).

• variance — design-based point estimate of the finite-population variance. NaN for degenerate cells; exact 0 for constant-in-domain variables.

• Uncertainty columns (se, var, cv, ci_low, ci_high, moe, deff) — only those requested via variance.

• n — unweighted count of non-NA observations used.

• n_weighted — sum of weights (only when n_weighted = TRUE).

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), meta()

Examples

d <- as_survey(
  nhanes_2017,
  ids = sdmvpsu,
  weights = wtint2yr,
  strata = sdmvstra,
  nest = TRUE
)
get_variance(d, ridageyr)

# Multiple variables
get_variance(d, c(ridageyr, bpxsy1))

# With grouping
get_variance(d, ridageyr, group = riagendr)

GSS 2024: General Social Survey

Description

A 27-variable extract from the 2024 General Social Survey (GSS), one of the longest-running sociological surveys in the United States (fielded annually or biennially since 1972). All 3,309 respondents from the 2024 cross-section are included.

Usage

gss_2024

Format

A data frame with 3,309 rows and 27 variables:

vpsu

Variance primary sampling unit. Use as the cluster ID for variance estimation.

vstrat

Variance stratum. Use as the stratification variable.

wtssps

Person post-stratification weight. Standard analysis weight.

wtssnrps

Person post-stratification weight adjusted for differential non-response. Preferred when non-response bias is a concern.

id

Respondent ID. Unique case identifier.

year

Survey year (all 2024 in this extract).

ballot

Ballot form (A, B, C, or D). The GSS uses a split-ballot design; not all questions appear on every ballot. Inapplicable items are coded -100.

age

Age in years (89 = 89 or older).

sex

Sex: 1 = male, 2 = female.

race

Race: 1 = white, 2 = black, 3 = other.

hispanic

Hispanic origin: 1 = not Hispanic; 2–50 = specific Hispanic origin.

educ

Highest year of school completed (0–20 years).

degree

Highest degree: 0 = less than HS, 1 = high school, 2 = associate, 3 = bachelor's, 4 = graduate.

income16

Total family income (26 categories from < $1,000 to $170,000+).

marital

Marital status: 1 = married, 2 = widowed, 3 = divorced, 4 = separated, 5 = never married.

wrkstat

Labor force status: 1 = full time, 2 = part time, 3 = temporarily not working, 4 = unemployed, 5 = retired, 6 = in school, 7 = keeping house, 8 = other.

hrs1

Hours worked last week (for employed respondents only).

adults

Number of adults in household (8 = 8 or more).

partyid

Party identification: 0 = strong Democrat, 3 = Independent, 6 = strong Republican, 7 = other party.

polviews

Political views: 1 = extremely liberal, 7 = extremely conservative.

happy

General happiness: 1 = very happy, 2 = pretty happy, 3 = not too happy.

health

Self-rated health: 1 = excellent, 2 = good, 3 = fair, 4 = poor.

trust

Social trust: 1 = most people can be trusted, 2 = can't be too careful, 3 = depends.

natfare

Government spending on welfare: 1 = too little, 2 = about right, 3 = too much.

abany

Abortion for any reason: 1 = yes, 2 = no.

attend

Religious service attendance: 0 = never, 8 = several times a week.

relig

Religious preference: 1 = Protestant, 2 = Catholic, 3 = Jewish, 4 = none, and others.

Details

Survey design: Stratified multi-stage cluster — use Taylor series linearization:

svy <- as_survey(gss_2024,
  ids     = vpsu,
  strata  = vstrat,
  weights = wtssps,      # or wtssnrps for non-response-adjusted weight
  nest    = TRUE
)

Missing value codes: The GSS uses a consistent system of negative integer codes for missing data across all variables:

These codes are stored as value labels on every column (check attr(gss_2024$happy, "labels")). Recode them to NA before analysis.

Split-ballot design: The ballot variable indicates which question module a respondent received. Variables asked only on some ballots will have -100 (Inapplicable) for respondents on other ballots.

Metadata: All columns carry variable labels and value labels as R attributes from the original SPSS file, automatically extracted into surveycore's metadata system when you call as_survey().

• Variable labels ("label" attribute): A human-readable description of each column. Example: attr(gss_2024$happy, "label") returns "GENERAL HAPPINESS".

• Value labels ("labels" attribute): A named numeric vector mapping each code to its meaning, including all missing-value codes. Example: attr(gss_2024$happy, "labels") returns entries for ⁠Very happy⁠, ⁠Pretty happy⁠, ⁠Not too happy⁠, and the negative missing codes.

Source

NORC at the University of Chicago. General Social Survey 2024. https://gss.norc.org (free account required to download raw data; the processed .rda is included in the package). Prepared by ⁠data-raw/prepare-gss-2024.R⁠.

Examples

# Variables in the dataset
names(gss_2024)

# Create survey design
svy <- as_survey(
  gss_2024,
  ids = vpsu,
  strata = vstrat,
  weights = wtssps,
  nest = TRUE
)

# Inspect variable label
attr(gss_2024$happy, "label")

# Inspect value labels (includes GSS missing-value codes)
attr(gss_2024$happy, "labels")

# Split-ballot: how many respondents per ballot form?
table(gss_2024$ballot)

Infer Question Prefaces from Variable Labels

Description

Scans variable labels in a survey design object or labelled data frame for groups of variables sharing a common preface (via separator or longest common prefix). Detected prefaces are written to question_preface in the metadata and the shared text is trimmed from each variable label, leaving only the unique suffix.

Usage

infer_question_prefaces(
  x,
  sep = c(" - ", "- ", " – ", ": ", " | "),
  min_vars = 2L,
  lcp_min = 20L,
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

Details

Detection algorithm (two passes):

1. Separator pass — for each separator in sep (tried in order):

   • Variables whose label contains the separator are grouped by their candidate preface (text before the first occurrence of the separator, trimmed).

   • Any group with \geq min_vars members is recorded; those variables are excluded from all subsequent passes.

2. LCP pass — for remaining labelled variables (\geq 2):

   • The character-level longest common prefix (LCP) of all remaining labels is computed and trimmed to the last word boundary.

   • If the trimmed LCP is \geq lcp_min characters, the group is recorded.

Apply step:

• Variables with an existing question_preface are skipped when overwrite = FALSE (default); a warning is emitted listing the count of skipped variables.

• Variables whose unique suffix would be empty after trimming are always skipped with a per-variable warning.

Data frame integration: When called on a data frame, the detected preface is written to attr(col, "question_preface"). Passing the result to as_survey() automatically picks up both the trimmed label and the preface via the internal haven metadata extraction step.

Value

The modified x, invisibly.

See Also

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), set_missing_codes(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

# Data frame with haven-style labels (Qualtrics / SPSS export pattern)
df <- data.frame(
  discrim_a = 1:5,
  discrim_b = 2:6,
  discrim_c = 3:7
)
attr(df$discrim_a, "label") <-
  "Please rate discrimination - Evangelical Christians"
attr(df$discrim_b, "label") <-
  "Please rate discrimination - Muslims"
attr(df$discrim_c, "label") <-
  "Please rate discrimination - Jews"

df <- infer_question_prefaces(df, verbose = FALSE)
attr(df$discrim_a, "label")            # "Evangelical Christians"
attr(df$discrim_a, "question_preface") # "Please rate discrimination"

Extract Metadata from a Survey Result

Description

Retrieves the structured metadata list attached to a survey result object returned by any ⁠get_*()⁠ analysis function.

Usage

meta(x, ...)

## S3 method for class 'survey_result'
meta(x, ...)

Arguments

Details

This is the only supported way to access result metadata — do not use attr(result, ".meta") directly.

Value

A named list. Common fields present on every result:

design_type

Character(1). Design class: "taylor", "replicate", "twophase", "srs", or "nonprob".

conf_level

Numeric(1). Confidence level used (e.g. 0.95).

call

Language. Matched call to the ⁠get_*()⁠ function.

n_respondents

Integer(1). Total rows in the design, regardless of groups, domain status, or weights.

group

Named list. One entry per grouping variable; empty list (list()) when no groups are active. Each entry is a named list with: variable_label (character or NULL), question_preface (character or NULL), value_labels (named vector or NULL).

x

Named list. One entry per focal variable. Length 1 for single-x functions (get_means, get_totals, get_quantiles); length N for multi-x functions (get_freqs, get_corr). Each entry has the same sub-structure as group entries. NULL for get_totals() when called without an x argument.

Function-specific additional fields:

probs

(get_quantiles only) Numeric vector of quantile probabilities.

method

(get_corr only) Character(1) correlation method.

numerator, denominator

(get_ratios only) Flat named lists with keys name, variable_label, question_preface, value_labels.

See Also

Other analysis: clean(), get_anova(), get_corr(), get_covariance(), get_diffs(), get_freqs(), get_means(), get_pairwise(), get_quantiles(), get_ratios(), get_t_test(), get_totals(), get_variance()

Examples

# Construct a minimal survey_result to illustrate meta():
result <- structure(
  tibble::tibble(mean = 42.0, se = 1.5, n = 100L),
  .meta = list(
    design_type   = "taylor",
    conf_level    = 0.95,
    call          = quote(get_means(d, x)),
    n_respondents = 100L,
    group         = list(),
    x             = list(
      x = list(variable_label = NULL, question_preface = NULL,
               value_labels = NULL)
    )
  ),
  class = c("survey_means", "survey_result", "tbl_df", "tbl", "data.frame")
)
meta(result)$design_type    # "taylor"
meta(result)$n_respondents  # 100L
meta(result)$conf_level     # 0.95

NHANES 2017-2018: Demographics and Blood Pressure

Description

A merged dataset from the National Health and Nutrition Examination Survey (NHANES) 2017-2018 cycle, combining demographic characteristics with blood pressure measurements. Covers all 9,254 sampled participants; blood pressure variables are NA for the 550 interview-only participants (ridstatr == 1).

Usage

nhanes_2017

Format

A data frame with 9,254 rows and 14 variables:

seqn

Respondent sequence number (unique identifier, join key).

sdmvpsu

Masked variance pseudo-PSU. Use as the cluster ID for variance estimation. See Details.

sdmvstra

Masked variance pseudo-stratum. Use as the stratification variable for variance estimation. See Details.

wtmec2yr

Full-sample 2-year MEC examination weight. Use for any analysis involving examination measurements (e.g., blood pressure).

wtint2yr

Full-sample 2-year interview weight. Use for analyses based on interview data only.

ridstatr

Interview/examination status: 1 = interview only, 2 = both interview and MEC examination.

riagendr

Gender: 1 = male, 2 = female.

ridageyr

Age in years at screening, top-coded at 80.

ridreth3

Race/Hispanic origin (6 categories): 1 = Mexican American, 2 = Other Hispanic, 3 = Non-Hispanic White, 4 = Non-Hispanic Black, 6 = Non-Hispanic Asian, 7 = Other/Multiracial.

indfmpir

Ratio of family income to the federal poverty level (continuous, 0–5; values >5 are top-coded at 5).

dmdeduc2

Education level for adults 20+: 1 = Less than 9th grade, 2 = 9th–11th grade, 3 = High school graduate/GED, 4 = Some college/AA, 5 = College graduate or above.

bpxsy1

Systolic blood pressure, 1st reading (mm Hg). NA if not examined.

bpxdi1

Diastolic blood pressure, 1st reading (mm Hg). NA if not examined.

bpxpls

60-second pulse rate (beats per minute). NA if not examined.

Details

Survey design: Taylor series linearization. When creating a survey design object, use sdmvpsu as the cluster ID, sdmvstra as the stratum, and wtmec2yr as the weight for examination-based analyses:

svy <- as_survey(nhanes_2017,
  ids     = sdmvpsu,
  strata  = sdmvstra,
  weights = wtmec2yr
)

Use wtint2yr instead of wtmec2yr for interview-only variables (e.g., income, education).

Metadata: All columns carry variable labels and value labels as R attributes, automatically extracted into surveycore's metadata system when you call as_survey().

• Variable labels ("label" attribute): A human-readable description of each column. Example: attr(nhanes_2017$riagendr, "label") returns "Gender".

• Value labels ("labels" attribute): A named numeric vector mapping each code to its meaning. Example: attr(nhanes_2017$riagendr, "labels") returns c(Male = 1, Female = 2).

Source files: DEMO_J.xpt (demographics) merged with BPX_J.xpt (blood pressure) on seqn. Prepared by data-raw/download-nhanes.R.

Source

National Center for Health Statistics, CDC. NHANES 2017-2018 Continuous Survey. https://www.cdc.gov/nchs/nhanes/

Examples

# All 9,254 participants (interview + exam)
head(nhanes_2017)

# Restrict to exam participants for blood pressure analysis
exam_only <- nhanes_2017[nhanes_2017$ridstatr == 2, ]

# Inspect variable label
attr(nhanes_2017$riagendr, "label")

# Inspect value labels
attr(nhanes_2017$riagendr, "labels")

# Inspect value labels for race/ethnicity
attr(nhanes_2017$ridreth3, "labels")

Nationscape Wave 1: July 18, 2019

Description

The first weekly wave of the Democracy Fund + UCLA Nationscape survey, fielded July 18–24, 2019. Approximately 6,250 completed online interviews drawn from the Lucid respondent exchange platform using a non-probability quota design, with raking weights calibrated to ACS demographic targets and 2016 presidential vote choice.

Usage

ns_wave1

Format

A data frame with approximately 6,250 rows and 171 variables (170 survey variables plus wave_id added by the prepare script).

response_id

Unique respondent ID (integer).

start_date

Interview date (character, "YYYY-MM-DD" format).

wave_id

Wave identifier: "ns20190718" for all rows in this dataset.

weight

Raking weight calibrated to ACS demographic targets and 2016 presidential vote choice. Use for all population-level estimates.

right_track

Country direction: 1 = Right direction, 2 = Wrong track, 3 = Not sure.

economy_better

Economy outlook: 1 = Better, 2 = Worse, 3 = Same, 4 = Not sure.

interest

Political interest (4-pt): 1 = Very interested, 4 = Not at all interested.

registration

Voter registration: 1 = Registered, 2 = Not registered, 3 = Not eligible.

pres_approval

Trump presidential approval: 1 = Strongly approve, 2 = Somewhat approve, 3 = Somewhat disapprove, 4 = Strongly disapprove.

vote_intention

2020 vote intention: 1 = Trump, 2 = Democratic candidate, 3 = Other, 4 = Don't plan to vote, 5 = Not sure.

vote_2016

2016 presidential vote. See labels.

vote_2016_other_text

Write-in for vote_2016 "other" choice.

consider_trump

Would consider voting for Trump: 1 = Yes, 2 = No.

not_trump

Reason for not considering Trump (open text).

primary_party

Primary vote party: 1 = Democratic, 2 = Republican, 3 = Other.

dem_vote_intent

Democratic primary vote intention. See labels.

dem_vote_intent_TEXT

Write-in for dem_vote_intent "other".

rank_dems_1

Top-ranked Democratic presidential candidate. See labels.

rank_dems_2

Second-ranked Democratic candidate. See labels.

rank_dems_3

Third-ranked Democratic candidate. See labels.

replace_trump

Wants non-Trump Republican nominee: 1 = Yes, 2 = No, 3 = Not sure.

house_intent

U.S. House vote intention: 1 = Democrat, 2 = Republican, 3 = Other, 4 = Won't vote, 5 = Not sure.

senate_intent

U.S. Senate vote intention. Same codes as house_intent.

governor_intent

Governor vote intention. Same codes as house_intent.

news_sources_facebook

Used social media for political news in past week: 1 = Selected, 2 = Not selected. See "question_preface" attribute for shared question stem. Same coding for all ⁠news_sources_*⁠ variables.

news_sources_cnn

Used CNN for political news.

news_sources_msnbc

Used MSNBC for political news.

news_sources_fox

Used Fox News for political news.

news_sources_network

Used network news (ABC/CBS/NBC/PBS).

news_sources_localtv

Used local TV news.

news_sources_telemundo

Used Telemundo or Univision.

news_sources_npr

Used NPR.

news_sources_amtalk

Used AM talk radio.

news_sources_new_york_times

Used a national newspaper.

news_sources_local_newspaper

Used a local newspaper.

news_sources_other

Used another news source: 1 = Selected, 2 = Not selected.

news_sources_other_TEXT

Write-in for news_sources_other.

group_favorability_whites

Favorability toward Whites: 1 = Very favorable, 2 = Somewhat favorable, 3 = Somewhat unfavorable, 4 = Very unfavorable, 5 = Not sure. Same coding for all ⁠group_favorability_*⁠ variables.

group_favorability_blacks

Favorability toward Blacks.

group_favorability_latinos

Favorability toward Latinos.

group_favorability_asians

Favorability toward Asians.

group_favorability_christians

Favorability toward Christians.

group_favorability_socialists

Favorability toward Socialists.

group_favorability_muslims

Favorability toward Muslims.

group_favorability_labor_unions

Favorability toward labor unions.

group_favorability_the_police

Favorability toward the police.

group_favorability_undocumented

Favorability toward undocumented immigrants.

group_favorability_lgbt

Favorability toward gays and lesbians.

group_favorability_republicans

Favorability toward Republicans.

group_favorability_democrats

Favorability toward Democrats.

cand_favorability_trump

Favorability toward Donald Trump. Same 5-point scale as ⁠group_favorability_*⁠ variables.

cand_favorability_obama

Favorability toward Barack Obama.

cand_favorability_cortez

Favorability toward Alexandria Ocasio-Cortez.

cand_favorability_biden

Favorability toward Joe Biden.

cand_favorability_harris

Favorability toward Kamala Harris.

cand_favorability_buttigieg

Favorability toward Pete Buttigieg.

cand_favorability_warren

Favorability toward Elizabeth Warren.

cand_favorability_sanders

Favorability toward Bernie Sanders.

cand_favorability_pence

Favorability toward Mike Pence.

trump_biden

Trump vs. Biden head-to-head: 1 = Trump, 2 = Biden, 3 = Not sure. Same coding for all ⁠trump_*⁠ matchup variables.

trump_sanders

Trump vs. Sanders.

trump_harris

Trump vs. Harris.

trump_warren

Trump vs. Warren.

trump_buttigieg

Trump vs. Buttigieg.

trump_booker

Trump vs. Cory Booker.

trump_castro

Trump vs. Julian Castro.

trump_gabbard

Trump vs. Tulsi Gabbard.

trump_gillibrand

Trump vs. Kirsten Gillibrand.

trump_orourke

Trump vs. Beto O'Rourke.

pence_biden

Pence vs. Biden head-to-head: 1 = Pence, 2 = Biden, 3 = Not sure. Same coding for all ⁠pence_*⁠ matchup variables.

pence_buttigieg

Pence vs. Buttigieg.

pence_harris

Pence vs. Harris.

pence_sanders

Pence vs. Sanders.

pence_warren

Pence vs. Warren.

cand_truth_donald_trump

Whether Donald Trump cares about telling the truth: 1 = Yes, 2 = No, 3 = Not sure. Same coding for all ⁠cand_truth_*⁠ variables.

cand_truth_elizabeth_warren

Whether Elizabeth Warren cares about the truth.

cand_truth_joe_biden

Whether Joe Biden cares about the truth.

cand_truth_bernie_sanders

Whether Bernie Sanders cares about the truth.

cand_truth_pete_buttigieg

Whether Pete Buttigieg cares about the truth.

cand_truth_kamala_harris

Whether Kamala Harris cares about the truth.

cand_facts_donald_trump

Whether Donald Trump relies on facts vs. hunches: 1 = Facts and evidence, 2 = Hunches, 3 = Not sure. Same coding for all ⁠cand_facts_*⁠ variables.

cand_facts_elizabeth_warren

Whether Elizabeth Warren relies on facts.

cand_facts_joe_biden

Whether Joe Biden relies on facts.

cand_facts_bernie_sanders

Whether Bernie Sanders relies on facts.

cand_facts_pete_buttigieg

Whether Pete Buttigieg relies on facts.

cand_facts_kamala_harris

Whether Kamala Harris relies on facts.

racial_attitudes_tryhard

Agree/disagree: minorities should work their way up without special favors. 1 = Strongly agree, 2 = Agree, 3 = Neither, 4 = Disagree, 5 = Strongly disagree. Same scale for all ⁠racial_attitudes_*⁠ and ⁠gender_attitudes_*⁠ variables.

racial_attitudes_generations

Agree/disagree: generations of slavery make it difficult for Blacks to work out of the lower class.

racial_attitudes_marry

Agree/disagree: I prefer close relatives marry someone from the same race.

racial_attitudes_date

Agree/disagree: it's alright for Blacks and Whites to date.

gender_attitudes_maleboss

Agree/disagree: more comfortable with a male boss than female boss.

gender_attitudes_logical

Agree/disagree: women are just as capable of thinking logically as men.

gender_attitudes_opportunity

Agree/disagree: increased opportunities for women have improved quality of life.

gender_attitudes_complain

Agree/disagree: women who complain about harassment cause more problems than they solve.

discrimination_blacks

Perceived discrimination against Blacks: 1 = A great deal, 2 = A lot, 3 = A little, 4 = None at all, 5 = Not sure. Same scale for all ⁠discrimination_*⁠ variables.

discrimination_whites

Perceived discrimination against Whites.

discrimination_muslims

Perceived discrimination against Muslims.

discrimination_christians

Perceived discrimination against Christians.

discrimination_women

Perceived discrimination against Women.

discrimination_men

Perceived discrimination against Men.

sen_knowledge

U.S. Senate knowledge question. See labels.

sc_knowledge

U.S. Supreme Court knowledge question. See labels.

pid3

3-category party ID: 1 = Democrat, 2 = Republican, 3 = Independent, 4 = Something else.

pid7_legacy

7-point party ID (legacy coding). See labels.

strength_democrat

Strength of Democratic ID (conditional on pid3 == 1). See labels.

strength_republican

Strength of Republican ID (conditional on pid3 == 2). See labels.

lean_independent

Partisan lean of Independents (conditional on pid3 == 3). See labels.

ideo5

5-point ideological self-placement: 1 = Very liberal, 5 = Very conservative.

employment

Employment status (selected choice). See labels.

employment_other_text

Write-in for employment "other".

foreign_born

Born outside the U.S.: 1 = Yes, 2 = No.

language

Primary language at home. See labels.

religion

Religious affiliation (selected choice). See labels.

religion_other_text

Write-in for religion "other".

is_evangelical

Born-again or evangelical Christian: 1 = Yes, 2 = No.

orientation_group

Sexual orientation. See labels.

in_union

Labor union membership: 1 = Yes, 2 = No, 3 = Non-union household, 4 = Not sure.

household_gun_owner

Household gun ownership: 1 = Yes, 2 = No, 3 = Not sure.

wall

Support building a wall on the southern U.S. border: 1 = Strongly support, 2 = Somewhat support, 3 = Somewhat oppose, 4 = Strongly oppose, 5 = Not sure. Same scale for all policy items through limit_magazines. See "question_preface" attribute on each variable for the exact shared question stem.

cap_carbon

Support capping carbon emissions.

environment

Support large-scale government investment in environmental technology.

guns_bg

Support requiring background checks for all gun purchases.

mctaxes

Support cutting taxes for families making < $100K/year.

estate_tax

Support eliminating the estate tax.

raise_upper_tax

Support raising taxes on families making > $600K.

college

Support ensuring all students can graduate from state colleges debt-free.

abortion_waiting

Support requiring a waiting period and ultrasound before an abortion.

abortion_never

Support never permitting abortion.

abortion_conditions

Support permitting abortion in cases other than rape/incest/life at risk.

late_term_abortion

Support permitting late-term abortion.

abortion_insurance

Support allowing employers to decline abortion coverage.

guaranteed_jobs

Support guaranteeing jobs for all Americans.

green_new_deal

Support enacting a Green New Deal.

gun_registry

Support creating a public registry of gun ownership.

immigration_separation

Support separating children from parents prosecuted for illegal border crossing.

immigration_system

Support shifting to a merit-based immigration system.

immigration_wire

Support requiring proof of citizenship to wire money internationally.

impeach_trump

Support impeaching President Trump.

israel

Support withdrawing military support for Israel.

marijuana

Support legalizing marijuana.

maternityleave

Support requiring 12 weeks of paid maternity leave.

medicare_for_all

Support Medicare-for-All.

military_size

Support reducing the size of the U.S. military.

minwage

Support raising the minimum wage to $15/hour.

muslimban

Support banning people from predominantly Muslim countries.

oil_and_gas

Support removing barriers to domestic oil and gas drilling.

reparations

Support granting reparations to descendants of slaves.

right_to_work

Support allowing people to work in unionized workplaces without paying union dues.

ten_commandments

Support displaying the Ten Commandments in public schools and courthouses.

trade

Support limiting trade with other countries.

trans_military

Support allowing transgender people to serve in the military.

uctaxes2

Support raising taxes on families making > $250K.

vouchers

Support providing tax-funded vouchers for private or religious schools.

gov_insurance

Support providing government-run health insurance to all Americans.

public_option

Support providing the option to purchase government-run insurance.

health_subsidies

Support subsidizing health insurance for lower income people not on Medicaid.

path_to_citizenship

Support creating a path to citizenship for all undocumented immigrants.

dreamers

Support a path to citizenship for DREAMers.

deportation

Support deporting all undocumented immigrants.

ban_guns

Support banning all guns.

ban_assault_rifles

Support banning assault rifles.

limit_magazines

Support limiting gun magazines to 10 bullets.

age

Respondent age in years.

gender

Gender: 1 = Male, 2 = Female, 3 = Other.

census_region

Census region: 1 = Northeast, 2 = Midwest, 3 = South, 4 = West.

hispanic

Hispanic or Latino origin: 1 = Yes, 2 = No.

race_ethnicity

Race/ethnicity (6 categories). See labels.

household_income

Household income (7 brackets). See labels.

education

Educational attainment (6 categories). See labels.

state

U.S. state of residence (2-letter abbreviation).

congress_district

Congressional district.

Details

This dataset is the first of 77 weekly waves collected from July 2019 through January 2021. The full survey ran in three phases:

Only Wave 1 is bundled in the package because 77 waves × ~6,250 rows would be prohibitively large. To obtain the full dataset by phase, use the prepare scripts in ⁠data-raw/⁠ (see the Source section).

Survey design: The Nationscape is a calibrated non-probability sample (quota design with raking weights). Use as_survey_nonprob() — it is designed specifically for this use case and will gain bootstrap re-calibration variance in Phase 2.5:

svy <- as_survey_nonprob(ns_wave1, weights = weight)

Metadata: All substantive columns carry variable labels ("label" attribute) set during data preparation. Battery items additionally carry a "question_preface" attribute with the shared question stem. Value labels ("labels" attribute) are present for all coded response items.

Battery structure: Most multi-item question groups follow a ⁠{battery}_{item}⁠ naming convention. All items within a battery share an identical "question_preface" attribute:

Three policy batteries share the same Agree/Disagree/Neither scale: wall, cap_carbon, environment, guns_bg, mctaxes, estate_tax, raise_upper_tax, college, abortion_waiting, abortion_never, abortion_conditions, late_term_abortion, abortion_insurance, guaranteed_jobs, green_new_deal, gun_registry, immigration_separation, immigration_system, immigration_wire, impeach_trump, israel, marijuana, maternityleave, medicare_for_all, military_size, minwage, muslimban, oil_and_gas, reparations, right_to_work, ten_commandments, trade, trans_military, uctaxes2, vouchers, gov_insurance, public_option, health_subsidies, path_to_citizenship, dreamers, deportation, ban_guns, ban_assault_rifles, limit_magazines.

Source

Democracy Fund Voter Study Group / UCLA. Nationscape Data Set, version December 2021. https://www.voterstudygroup.org/data/nationscape (free download; academic research use). Prepared by data-raw/prepare-nationscape-phase1.R.

For full methodology, see the Nationscape User Guide and the Representative Assessment report in ⁠data-raw/nationscape/Nationscape-User-Guide-2021Dec.pdf⁠.

References

Tausanovitch, Chris and Lynn Vavreck. 2021. Democracy Fund + UCLA Nationscape, October 10–17, 2019 (version 20210301). Retrieved from voterstudygroup.org/data/nationscape.

Rivers, Douglas and Delia Bailey. 2009. "Inference from matched samples in the 2008 U.S. national elections." Proceedings of the Joint Statistical Meetings, Social Statistics Section.

Examples

# Design variables
head(ns_wave1[, c("response_id", "weight", "age", "gender")])

# Inspect a battery item's metadata
attr(ns_wave1$group_favorability_blacks, "label")
attr(ns_wave1$group_favorability_blacks, "question_preface")
attr(ns_wave1$news_sources_cnn, "labels")

# Create a calibrated survey design (correct approach for raked
# non-prob samples)
svy <- as_survey_nonprob(ns_wave1, weights = weight)
get_freqs(svy, pres_approval)

# Party identification distribution
table(ns_wave1$pid3)

Pew Jewish Americans 2020

Description

The extended survey dataset from Pew Research Center's 2019-2020 Survey of U.S. Jews, fielded November 19, 2019 – June 3, 2020 (n = 5,881). Respondents were drawn from a national, stratified random sample of residential mailing addresses with oversampling of households likely to contain Jewish respondents. The dataset carries 100 jackknife replicate weights alongside the main weight.

Usage

pew_jewish_2020

Format

A data frame with 5,881 rows and 130 variables. Variables extweight1–extweight100 are jackknife replicate weights; the remaining 30 variables are:

extweight

Full-sample base weight. Use for all estimates.

extweight1

Jackknife replicate weight 1 of 100.

extweight2

Jackknife replicate weight 2 of 100.

extweight3

Jackknife replicate weight 3 of 100.

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Jackknife replicate weight 4 of 100.

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extweight100

Jackknife replicate weight 100 of 100.

qkey

Unique respondent identifier.

jewishcat

Jewish identity category: 1 = Jews By Religion, 2 = Jews Of No Religion, 3 = Jewish Background, 4 = Jewish Affinity, 5 = Respondent Not Jewish In Any Way.

finalmode

Collection mode: 1 = Screener And Extended Survey Via Cawi, 2 = Screener And Extended Survey Via Teleform, 3 = Screener Via Cawi, Extended Survey Via Teleform.

region

Census region: 1 = Northeast, 2 = Midwest, 3 = South, 4 = West.

sexask

Sex: 1 = Male, 2 = Female, 99 = Not Answered.

age4cat

Age: 1 = 18-29, 2 = 30-49, 3 = 50-64, 4 = 65+; 999 = No Answer.

educ4cat

Education: 1 = High School Or Less, 2 = Some College, 3 = College Graduate, 4 = Postgrad Degree; 99 = No Answer.

religmod

Current religion (24 categories including Jewish subgroups and combinations).

hisp

Hispanic origin: 1 = Yes, 2 = No, 99 = Not Answered.

racecmb

Race (5 categories).

racethn

Race-ethnicity (4 categories).

presapp

Presidential approval (Trump): 1 = Strongly Approve, 2 = Somewhat Approve, 3 = Somewhat Disapprove, 4 = Strongly Disapprove, 99 = Not Answered.

track

Right track/wrong track: 1 = Generally Headed In The Right Direction, 2 = Off On The Wrong Track, 99 = Not Answered.

satisfpersmod

Personal life satisfaction: 1 = Excellent, 2 = Good, 3 = Only Fair, 4 = Poor, 99 = Not Answered.

localrating

Community as a place to live: 1 = Excellent, 2 = Good, 3 = Only Fair, 4 = Poor, 99 = Not Answered.

relconsider_a

Jewish. Battery 1: religious identity (select-all-that-apply). See Details for question text.

relconsider_b

Catholic. Battery 1: religious identity.

relconsider_c

Mormon. Battery 1: religious identity.

relconsider_d

Muslim. Battery 1: religious identity.

relraised_a

Jewish. Battery 2: religious background (select-all-that-apply). See Details for question text.

relraised_b

Catholic. Battery 2: religious background.

relraised_c

Mormon. Battery 2: religious background.

relraised_d

Muslim. Battery 2: religious background.

discrim_a

Evangelical Christians. Battery 3: discrimination perceptions (rating scale). See Details for question text.

discrim_b

Muslims. Battery 3: discrimination perceptions.

discrim_c

Jews. Battery 3: discrimination perceptions.

discrim_d

Blacks. Battery 3: discrimination perceptions.

discrim_e

Hispanics. Battery 3: discrimination perceptions.

discrim_f

Gays and lesbians. Battery 3: discrimination perceptions.

Details

Survey design: Jackknife replication — use as_survey_replicate() with all 100 replicate weights:

svy <- as_survey_replicate(
  pew_jewish_2020,
  weights    = extweight,
  repweights = extweight1:extweight100,
  type       = "JK1"
)

Jewish identity classification: The jewishcat variable classifies respondents into five mutually exclusive categories used in the published Pew report. Use jewishcat rather than constructing your own classification from the raw religion variables.

Battery question stems:

• Battery 1 (relconsider_a–relconsider_d): "ASIDE from religion, do you consider yourself to be any of the following in any way (for example ethnically, culturally or because of your family's background)?" Values: 1 = Yes, Consider Myself This, 2 = No, Do Not Consider Myself This, 99 = Refused.

• Battery 2 (relraised_a–relraised_d): "Please indicate whether you were raised in any of the following traditions or had a parent from any of the following backgrounds." Values: 1 = Yes, Was Raised In This Tradition Or Had A Parent From This Background, 2 = No, Was Not Raised In This Tradition And Did Not Have A Parent From This Background, 99 = Refused.

• Battery 3 (discrim_a–discrim_f): "Please tell us how much discrimination there is against each of these groups in our society today." Values: 1 = A Lot, 2 = Some, 3 = Not Much, 4 = None At All, 99 = Not Answered.

Metadata: All columns carry variable labels and value labels as R attributes from the original Stata file. The three battery variable groups additionally carry a "question_preface" attribute with the shared question stem. All three attribute types are automatically extracted into surveycore's metadata system when you call as_survey_replicate().

• Variable labels ("label" attribute): A human-readable description of each column — for battery items this is the unique item text (e.g., "Jewish"). Example: attr(pew_jewish_2020$relconsider_a, "label") returns "Jewish".

• Value labels ("labels" attribute): A named numeric vector mapping each code to its meaning. Example: attr(pew_jewish_2020$relconsider_a, "labels") returns c("Yes, Consider Myself This" = 1, "No, Do Not Consider Myself This" = 2, Refused = 99).

• Question preface ("question_preface" attribute): The shared question stem for each battery group. Example: attr(pew_jewish_2020$discrim_a, "question_preface") returns "Please tell us how much discrimination there is against each of these groups in our society today.".

Source

Pew Research Center. Jewish Americans in 2020 (Extended Dataset). https://www.pewresearch.org/datasets/ (free account required to download raw data; the processed .rda is included in the package). Prepared by ⁠data-raw/prepare-pew-jewish-2020.R⁠.

Examples

# Design variables
head(pew_jewish_2020[, c("qkey", "extweight", "jewishcat")])

# Confirm 100 replicate weights are present
sum(grepl("^extweight[0-9]", names(pew_jewish_2020)))

# Inspect variable label (unique item text for battery variable)
attr(pew_jewish_2020$discrim_a, "label")

# Inspect value labels
attr(pew_jewish_2020$discrim_a, "labels")

# Inspect question preface (shared stem across the battery)
attr(pew_jewish_2020$discrim_a, "question_preface")

# Jewish identity distribution (use jewishcat, not raw religion vars)
table(pew_jewish_2020$jewishcat)

Pew NPORS 2025: National Public Opinion Reference Survey

Description

The 2025 National Public Opinion Reference Survey (NPORS), conducted February 5 – June 18, 2025, by Pew Research Center (n = 5,022). An address-based sample (ABS) drawn from the USPS Computerized Delivery Sequence File, with respondents completing the survey online, by paper, or by telephone in English or Spanish. All 65 columns from the public release file are retained.

Usage

pew_npors_2025

Format

A data frame with 5,022 rows and 65 variables. The 11 ⁠smuse_*⁠ variables form a battery asking about social media platform use and share a "question_preface" attribute. All other variables are documented individually below:

respid

Case ID. Unique respondent identifier.

stratum

Sampling stratum (10 levels, defined by census block group demographics).

basewt

Base weight — inverse probability of selection, with adaptive mode adjustment.

weight

Final weight — basewt after raking to Census population targets. Use for all population-level estimates.

mode

Data collection mode: 1 = Online, 2 = Paper, 3 = Phone.

language

Language interview completed in: 1 = English, 2 = Spanish.

languageinitial

Language interview started in.

interview_start

Interview start timestamp.

interview_end

Interview end timestamp.

econ1mod

Economic conditions in your community today (Excellent / Good / Fair / Poor).

econ1bmod

Economic conditions one year from now (Better / Worse / Same).

comtype2

Community type: Urban / Suburban / Rural.

unity

Americans united vs. divided on values.

crimesafe

Area safety in terms of crime (Extremely safe – Not at all safe).

govprotct

Government's role in protecting people from themselves.

moregunimpact

Impact of more gun ownership on crime.

fin_sit

Household financial situation (Comfortable – Can't meet basics).

vet1

Military service in household.

vol12_cps

Volunteered for any organization in past 12 months.

eminuse

Uses internet or email at least occasionally.

intmob

Accesses internet on a mobile device.

intfreq

Internet use frequency (6 categories).

intfreq_collapsed

Internet use frequency (4 categories, derived).

home4nw2

Subscribes to home internet service.

bbhome

Home internet type (dial-up, broadband, etc.).

smuse_fb

Facebook. Part of social media use battery (see Details).

smuse_yt

YouTube. Part of social media use battery (see Details).

smuse_x

X (formerly Twitter). Part of social media use battery.

smuse_ig

Instagram. Part of social media use battery.

smuse_sc

Snapchat. Part of social media use battery.

smuse_wa

WhatsApp. Part of social media use battery.

smuse_tt

TikTok. Part of social media use battery.

smuse_rd

Reddit. Part of social media use battery.

smuse_bsk

Bluesky. Part of social media use battery.

smuse_th

Threads. Part of social media use battery.

smuse_ts

Truth Social. Part of social media use battery.

radio

Listens to radio.

device1a

Has a cell phone.

smart2

Cell phone is a smartphone.

nhisll

Has a working landline telephone at home.

relig

Current religion (12 categories).

religcat1

Religion (4 categories: Protestant, Catholic, Unaffiliated, Other).

born

Born-again or evangelical Christian.

attendper

In-person religious service attendance (6 categories).

attendonline2

Online/TV religious service participation (6 categories).

relimp

Importance of religion in life (Very – Not at all).

pray

Prayer frequency outside of services (7 categories).

educcat

Education level (categorical).

hisp

Hispanic origin.

racecmb

Race (5 categories).

racethn

Race-ethnicity (5 categories including Asian non-Hispanic).

agegrp

Age in 13 five-year groups.

agecat

Age (4 categories: 18-29, 30-49, 50-64, 65+).

birthplace

U.S. born vs. foreign born.

gender

Gender (man / woman / other).

adults

Number of adults in household.

inc_sdt1

Total family income (8 categories from < $30,000 to $150,000+).

cregion

Census region (NE / MW / S / W).

metro

Metropolitan area indicator.

registration

Registered to vote at current address.

party

Party affiliation (Rep / Dem / Ind / Other).

partyln

Party lean for Independents (Rep / Dem).

partysum

Party summary (Rep+Lean Rep / Dem+Lean Dem / No lean).

voted2024

Voted in the 2024 presidential election.

votegen_post

2024 presidential vote choice (Trump / Harris / Other).

Details

Survey design: Stratified address-based sample with raking post-stratification — use Taylor series linearization. NPORS has no PSU (each address is its own unit, effectively a stratified SRS):

svy <- as_survey(pew_npors_2025,
  strata  = stratum,
  weights = weight
)

Use basewt instead of weight for sensitivity analyses comparing pre- and post-raking estimates.

Social media battery: All 11 ⁠smuse_*⁠ variables share the question stem "Please indicate whether or not you ever use the following websites or apps." Values: 1 = Selected, 2 = Not selected, 99 = Refused. Each variable additionally carries a "question_preface" attribute with this shared stem.

Metadata: All columns carry variable labels and value labels as R attributes from the original SPSS file. The 11 ⁠smuse_*⁠ battery variables additionally carry a "question_preface" attribute with the shared question stem. All three attribute types are automatically extracted into surveycore's metadata system when you call as_survey().

• Variable labels ("label" attribute): A human-readable description of each column — for ⁠smuse_*⁠ variables this is just the platform name (e.g., "Facebook"). Example: attr(pew_npors_2025$smuse_fb, "label") returns "Facebook".

• Value labels ("labels" attribute): A named numeric vector mapping each code to its meaning. Example: attr(pew_npors_2025$smuse_fb, "labels") returns c(Selected = 1, "Not selected" = 2, Refused = 99).

• Question preface ("question_preface" attribute): The shared question stem for battery items, set on all ⁠smuse_*⁠ columns. Example: attr(pew_npors_2025$smuse_fb, "question_preface") returns "Please indicate whether or not you ever use the following websites or apps.".

Source

Pew Research Center. 2025 National Public Opinion Reference Survey. https://www.pewresearch.org/datasets/ (free account required to download raw data; the processed .rda is included in the package). Prepared by ⁠data-raw/prepare-pew-npors-2025.R⁠.

Examples

# Variables in the dataset
names(pew_npors_2025)

# Create survey design (no PSU for ABS design)
svy <- as_survey(
  pew_npors_2025,
  strata = stratum,
  weights = weight
)

# Inspect variable label
attr(pew_npors_2025$smuse_fb, "label")

# Inspect value labels
attr(pew_npors_2025$smuse_fb, "labels")

# Inspect question preface (shared stem for all smuse_* battery items)
attr(pew_npors_2025$smuse_fb, "question_preface")

Print a Survey Diffs Result

Description

Prints a structured header showing design type, family, dependent variable, treatment variable with reference level, and estimation method, then delegates to the tibble print method for the body.

Usage

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

Arguments

Value

x, invisibly.

Print a Survey Result Object

Description

Prints a labelled header showing the specific result class and dimensions, then delegates to the tibble print method for the tabular content.

Usage

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

Arguments

Value

x, invisibly.

Examples

result <- structure(
  tibble::tibble(mean = 42.0, se = 1.5, n = 100L),
  .meta = list(
    design_type = "taylor", conf_level = 0.95,
    call = quote(get_means(d, x)), n_respondents = 100L,
    group = list(),
    x = list(x = list(variable_label = NULL, question_preface = NULL,
                       value_labels = NULL))
  ),
  class = c("survey_means", "survey_result", "tbl_df", "tbl", "data.frame")
)
print(result)

Remove Surveys from a survey_collection

Description

Drops one or more named surveys from a collection and returns a new survey_collection. Errors if any requested name is not present.

Usage

remove_survey(x, name)

Arguments

Value

A new survey_collection without the dropped surveys. Errors surveycore_error_collection_empty if removing would leave the collection empty.

See Also

as_survey_collection(), add_survey()

Other collections: add_survey(), as_survey_collection(), set_collection_id(), set_collection_if_missing_var(), survey_collection()

Examples

d1 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
d2 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
coll <- as_survey_collection(a = d1, b = d2)
coll2 <- remove_survey(coll, "a")
names(coll2)

Set the Identifier Column on a survey_collection

Description

Updates the ⁠@id⁠ property of a survey_collection. The new value is the column name .dispatch_over_collection() injects when an analysis function (get_means(), get_freqs(), etc.) is dispatched across the collection without an explicit per-call .id.

Usage

set_collection_id(x, id)

Arguments

Details

Setting the same value as the existing ⁠@id⁠ returns the collection unchanged (no error, no warning). All other invariants on the collection (⁠@surveys⁠, ⁠@groups⁠, ⁠@if_missing_var⁠) are preserved.

Pipes naturally with the rest of the collection API:

coll |> set_collection_id("wave") |> get_means(y1)

Value

The modified survey_collection, invisibly.

See Also

Other collections: add_survey(), as_survey_collection(), remove_survey(), set_collection_if_missing_var(), survey_collection()

Examples

d1 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
coll <- as_survey_collection(a = d1)
coll <- set_collection_id(coll, "wave")
coll@id

Set the Missing-Variable Behaviour on a survey_collection

Description

Updates the ⁠@if_missing_var⁠ property of a survey_collection. The new value is the per-call default .dispatch_over_collection() uses when an analysis function (get_means(), get_freqs(), etc.) is dispatched across the collection without an explicit per-call .if_missing_var.

Usage

set_collection_if_missing_var(x, if_missing_var)

Arguments

Details

Setting the same value as the existing ⁠@if_missing_var⁠ returns the collection unchanged (no error, no warning). All other invariants on the collection (⁠@surveys⁠, ⁠@groups⁠, ⁠@id⁠) are preserved.

Pipes naturally with the rest of the collection API:

coll |> set_collection_if_missing_var("skip") |> get_means(y1)

Value

The modified survey_collection, invisibly.

See Also

Other collections: add_survey(), as_survey_collection(), remove_survey(), set_collection_id(), survey_collection()

Examples

d1 <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
                strata = vstrat, nest = TRUE)
coll <- as_survey_collection(a = d1)
coll <- set_collection_if_missing_var(coll, "skip")
coll@if_missing_var

Set Missing Code(s)

Description

Sets missing-value codes for one or more variables. Missing codes are atomic vectors documenting which data values represent missing data (e.g., c(Refused = -2L, DontKnow = -1L)).

Usage

set_missing_codes(x, ..., variable = NULL, codes = NULL)

Arguments

Details

Supports Conventions 1, 2, and 3 — see set_var_label() for details on the calling conventions. For Convention 3 with a single variable, a bare named atomic vector is accepted in addition to a list.

Value

The modified object, invisibly.

See Also

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_question_preface(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
d <- set_missing_codes(d, happy = c(Refused = -1L, DK = -2L))
extract_missing_codes(d, happy)

Set Question Preface(s)

Description

Sets the question preface string for one or more variables. Question prefaces are the shared introductory text for a battery of related questions.

Usage

set_question_preface(x, ..., variable = NULL, preface = NULL)

Arguments

Details

Supports Conventions 1, 2, and 3 — see set_var_label() for details.

Value

The modified object, invisibly.

See Also

extract_question_preface() to retrieve a preface

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_sata(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(gss_2024, ids = vpsu, weights = wtssps,
               strata = vstrat, nest = TRUE)
d <- set_question_preface(d, happy = "Taken all together...")
extract_question_preface(d, happy)

Set SATA (Select-All-That-Apply) Flag

Description

Marks one or more variables as select-all-that-apply (SATA) in a survey design object or a data frame. Unlike the other unified setters (which map variable names to heterogeneous content), set_sata() applies a single logical flag to all listed variables, so it uses a simplified two-convention pattern.

Usage

set_sata(x, ..., variable = NULL, sata = TRUE)

Arguments

Details

Convention A (tidy-select ...) — recommended:

design |> set_sata(news_tv, news_online, news_radio)
design |> set_sata(starts_with("news_"))

Convention B (variable = character vector) — programmatic:

sata_vars <- c("news_tv", "news_online", "news_radio")
design |> set_sata(variable = sata_vars)

Setting sata = FALSE unmarks the listed variables.

Value

The modified object, invisibly.

See Also

extract_sata() to retrieve SATA flags

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_sata(d, riagendr, ridageyr)
d <- set_sata(d, riagendr, sata = FALSE)

Set Universe Description(s)

Description

Sets the universe description for one or more variables. The universe describes the population to which a variable applies (e.g., "Adults 18+").

Usage

set_universe(x, ..., variable = NULL, universe = NULL)

Arguments

Details

Supports Conventions 1, 2, and 3 — see set_var_label() for details.

Value

The modified object, invisibly.

See Also

Other metadata: classify_question_type(), extract_metadata(), extract_missing_codes(), extract_question_preface(), extract_sata(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_sata(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()