Skip to contents

surveycore

surveycore is the foundation of the surveyverse ecosystem, a modern tidyverse-compatible replacement for the survey and srvyr packages in R.

It provides S7-based survey design objects with:

  • A tidy-select interface (ids = c(psu, ssu), no formula syntax)
  • Automatic preservation of haven-style variable labels and value labels
  • Exact variance estimation (Taylor linearization, replicate weights, two-phase designs)
  • Seamless conversion to and from survey::svydesign and srvyr::tbl_svy

For a side-by-side comparison with survey and srvyr, see vignette("surveycore-vs-survey").

Installation 

# From CRAN:
install.packages("surveycore")

# Development version from GitHub:
# install.packages("pak")
pak::pak("JDenn0514/surveycore")

What surveycore provides

Who is this for?

surveycore is intended for:

  • Survey researchers and methodologists who analyse complex probability samples and need design-consistent variance estimates (stratified, clustered, replicate-weight, and two-phase designs).
  • Social scientists, epidemiologists, and public health researchers working with population surveys such as NHANES, ACS, GSS, or custom organizational surveys.
  • R users who want a tidyverse-compatible interface for the survey analysis workflows currently served by survey and srvyr.

The software is designed to analyse rectangular survey microdata: one row per respondent, numeric or categorical outcome variables, and either explicit survey weights or a design specification (ids, strata, FPC). It supports:

  • Data frames, tibbles, and data.table objects as input.
  • Variables with haven-style variable labels and value labels (e.g. from .xpt or .sav files read with haven).
  • Grouped analyses (via surveytidy::group_by()).

Thirteen analysis functions cover means, totals, frequencies, correlations, ratios, differences, t-tests, pairwise comparisons, ANOVA, variance, covariance, quantiles, and effective sample size. survey_glm() fits survey-weighted generalized linear models.

Basic usage 

library(surveycore)

# Simple random sample: 2000 California API schools
d <- as_survey(ca_api_2000, weights = pw, fpc = fpc)
d
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 200
#> 
#> # A tibble: 200 × 38
#>    cds       stype name  sname  snum dname  dnum cname  cnum pcttest api00 api99
#>    <chr>     <int> <chr> <chr> <dbl> <chr> <int> <chr> <int>   <int> <int> <int>
#>  1 15739081…     2 "McF… McFa…  1039 McFa…   432 Kern     14      98   462   448
#>  2 19642126…     1 "Sto… Stow…  1124 ABC …     1 Los …    18     100   878   831
#>  3 30664493…     2 "Bre… Brea…  2868 Brea…    79 Oran…    29      98   734   742
#>  4 19644516…     1 "Ala… Alam…  1273 Down…   187 Los …    18      99   772   657
#>  5 40688096…     1 "Sun… Sunn…  4926 San …   640 San …    39      99   739   719
#>  6 19734456…     1 "Los… Los …  2463 Haci…   284 Los …    18      93   835   822
#>  7 19647336…     3 "Nor… Nort…  2031 Los …   401 Los …    18      98   456   472
#>  8 19647336…     1 "Gla… Glas…  1736 Los …   401 Los …    18      99   506   474
#>  9 19648166…     1 "Max… Maxs…  2142 Moun…   470 Los …    18     100   543   458
#> 10 38684786…     1 "Tre… Trea…  4754 San …   632 San …    37      90   649   604
#> # ℹ 190 more rows
#> # ℹ 26 more variables: target <int>, growth <int>, sch_wide <int>,
#> #   comp_imp <int>, both <int>, awards <int>, meals <int>, ell <int>,
#> #   yr_rnd <int>, mobility <int>, acs_k3 <int>, acs_46 <int>, acs_core <int>,
#> #   pct_resp <int>, not_hsg <int>, hsg <int>, some_col <int>, col_grad <int>,
#> #   grad_sch <int>, avg_ed <dbl>, full <int>, emer <int>, enroll <int>,
#> #   api_stu <int>, pw <dbl>, fpc <dbl>

# Weighted mean API score and total enrollment
get_means(d, api00)
#> # A tibble: 1 × 4
#>    mean ci_low ci_high     n
#>   <dbl>  <dbl>   <dbl> <int>
#> 1  657.   638.    675.   200
get_totals(d, enroll)
#> # A tibble: 1 × 4
#>      total   ci_low  ci_high     n
#>      <dbl>    <dbl>    <dbl> <int>
#> 1 3621074. 3288822. 3953327.   200

Complex survey designs 

# ── Replicate weights (BRR) ───────────────────────────────────────────────────
df_rep <- data.frame(
  y = rnorm(20),
  wt = runif(20, 1, 3),
  rep1 = runif(20, 0.5, 2),
  rep2 = runif(20, 0.5, 2),
  rep3 = runif(20, 0.5, 2),
  rep4 = runif(20, 0.5, 2)
)

d_rep <- as_survey_replicate(
  df_rep,
  weights = wt,
  repweights = starts_with("rep"),
  type = "BRR"
)
d_rep
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_replicate> (BRR, 4 replicates)
#> Sample size: 20
#> 
#> # A tibble: 20 × 6
#>          y    wt  rep1  rep2  rep3  rep4
#>      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 -0.634   1.10 0.653 0.690 1.09  0.968
#>  2  0.642   1.05 1.48  1.14  1.97  0.688
#>  3  0.0802  2.96 1.18  0.582 1.92  1.22 
#>  4  0.270   2.68 1.44  1.79  0.947 1.31 
#>  5 -0.251   2.24 0.665 1.29  1.95  0.546
#>  6 -0.131   2.69 0.632 0.773 0.508 1.03 
#>  7 -1.64    2.69 1.41  1.58  1.56  1.78 
#>  8 -0.919   1.41 0.656 1.18  1.52  0.818
#>  9 -0.325   1.08 1.91  0.998 1.13  0.954
#> 10  0.285   2.09 1.82  1.68  1.70  1.09 
#> # ℹ 10 more rows

survey_collection groups multiple designs for comparative analysis across waves or design variants. All analysis functions dispatch across members and return a combined result:

coll <- as_survey_collection(wave1 = d_wave1, wave2 = d_wave2)
get_means(coll, api00)

Variable labels

surveycore preserves haven-style labels automatically when reading .xpt or .sav files. You can also set labels manually:

d2 <- set_var_label(d, api00 = "Academic Performance Index score (2000)")
d2 <- set_var_label(d2, enroll = "Number of students enrolled")

extract_var_label(d2, api00)
#>                                     api00 
#> "Academic Performance Index score (2000)"
extract_var_label(d2, enroll)
#>                        enroll 
#> "Number of students enrolled"

Conversion to/from survey and srvyr 

# To survey::svydesign
svy <- as_svydesign(d)
class(svy)
#> [1] "survey.design2" "survey.design"

# Back to surveycore
d_rt <- from_svydesign(svy)
d_rt

The surveyverse ecosystem

surveycore is the foundation of the surveyverse, a family of packages built around it:

  • surveytidy: dplyr verbs (filter(), select(), mutate(), group_by()) that respect survey design structure, so grouped summaries and subsetting always propagate weights and strata correctly.
  • surveywts: weight adjustment utilities for survey data. Calibration-adjusted variance is already available in surveycore via as_caldata(); additional weight adjustment methods are in development.

Development status

The package API is stable (v1.0.0). All classes, constructors, metadata functions, and analysis functions are not expected to change in breaking ways. New analysis functions and utilities may be added in future releases. See NEWS.md for the full changelog.

Code of Conduct

Please note that the surveycore project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

GPL-3. Variance estimation code vendored from the survey package (Thomas Lumley, GPL-2/GPL-3); see VENDORED.md for full attribution.

References

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.