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surveycore vs. survey and srvyr

If you’re coming from survey or srvyr, this vignette is a side-by-side reference showing how surveycore maps to the workflows you already know. Every section shows the same task three ways: survey, srvyr, and surveycore.

Two things to know upfront:

Constructor comparisons use the api dataset from the survey package — the same reference dataset as the srvyr comparison vignette, so cross-referencing is easy. Analysis comparisons use ns_wave1 (Nationscape Wave 1, Democracy Fund + UCLA) from surveycore’s bundled data.

1. Creating Survey Design Objects

1.1 Simple Random Sample

apisrs is a simple random sample of California schools.

survey

srs_sv <- svydesign(ids = ~1, fpc = ~fpc, weights = ~pw, data = apisrs)
srs_sv
#> Independent Sampling design
#> svydesign(ids = ~1, fpc = ~fpc, weights = ~pw, data = apisrs)

srvyr

srs_srvyr <- apisrs |> as_survey_design(ids = 1, fpc = fpc, weights = pw)
srs_srvyr
#> Independent Sampling design
#> Called via srvyr
#> Sampling variables:
#>   - ids: `1` 
#>   - fpc: fpc 
#>   - weights: pw 
#> Data variables: 
#>   - cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), dname (chr),
#>     dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), api00
#>     (int), api99 (int), target (int), growth (int), sch.wide (fct), comp.imp
#>     (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd (fct),
#>     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)

surveycore

srs_sc <- surveycore::as_survey(apisrs, weights = pw, fpc = fpc)
srs_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 200
#> 
#> # A tibble: 200 × 39
#>    cds       stype name  sname  snum dname  dnum cname  cnum  flag pcttest api00
#>    <chr>     <fct> <chr> <chr> <dbl> <chr> <int> <chr> <int> <int>   <int> <int>
#>  1 15739081… H     "McF… McFa…  1039 McFa…   432 Kern     14    NA      98   462
#>  2 19642126… E     "Sto… Stow…  1124 ABC …     1 Los …    18    NA     100   878
#>  3 30664493… H     "Bre… Brea…  2868 Brea…    79 Oran…    29    NA      98   734
#>  4 19644516… E     "Ala… Alam…  1273 Down…   187 Los …    18    NA      99   772
#>  5 40688096… E     "Sun… Sunn…  4926 San …   640 San …    39    NA      99   739
#>  6 19734456… E     "Los… Los …  2463 Haci…   284 Los …    18    NA      93   835
#>  7 19647336… M     "Nor… Nort…  2031 Los …   401 Los …    18    NA      98   456
#>  8 19647336… E     "Gla… Glas…  1736 Los …   401 Los …    18    NA      99   506
#>  9 19648166… E     "Max… Maxs…  2142 Moun…   470 Los …    18    NA     100   543
#> 10 38684786… E     "Tre… Trea…  4754 San …   632 San …    37    NA      90   649
#> # ℹ 190 more rows
#> # ℹ 27 more variables: api99 <int>, target <int>, growth <int>, sch.wide <fct>,
#> #   comp.imp <fct>, both <fct>, awards <fct>, meals <int>, ell <int>,
#> #   yr.rnd <fct>, 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>

ids = ~1 is survey’s idiom for “no clusters” — not immediately obvious to new users. as_survey() without ids or strata creates an SRS design directly, making the design type clear from context.

1.2 Stratified Design

apistrat is stratified by school type (stype: E = elementary, M = middle, H = high school).

survey

strat_sv <- svydesign(
  ids = ~1, strata = ~stype, weights = ~pw, fpc = ~fpc, data = apistrat
)
strat_sv
#> Stratified Independent Sampling design
#> svydesign(ids = ~1, strata = ~stype, weights = ~pw, fpc = ~fpc, 
#>     data = apistrat)

srvyr

strat_srvyr <- apistrat |>
  as_survey_design(strata = stype, weights = pw, fpc = fpc)
strat_srvyr
#> Stratified Independent Sampling design
#> Called via srvyr
#> Sampling variables:
#>   - ids: `1` 
#>   - strata: stype 
#>   - fpc: fpc 
#>   - weights: pw 
#> Data variables: 
#>   - cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), dname (chr),
#>     dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), api00
#>     (int), api99 (int), target (int), growth (int), sch.wide (fct), comp.imp
#>     (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd (fct),
#>     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)

surveycore

strat_sc <- surveycore::as_survey(apistrat, strata = stype, weights = pw, fpc = fpc)
strat_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 200
#> 
#> # A tibble: 200 × 39
#>    cds       stype name  sname  snum dname  dnum cname  cnum  flag pcttest api00
#>    <chr>     <fct> <chr> <chr> <dbl> <chr> <int> <chr> <int> <int>   <int> <int>
#>  1 19647336… E     Open… Open…  2077 Los …   401 Los …    18    NA      99   840
#>  2 19647336… E     Belv… Belv…  1622 Los …   401 Los …    18    NA     100   516
#>  3 19648816… E     Alta… Alta…  2236 Pasa…   541 Los …    18    NA      99   531
#>  4 19647336… E     Soto… Soto…  1921 Los …   401 Los …    18    NA     100   501
#>  5 56739406… E     Waln… Waln…  6140 Moor…   460 Vent…    55    NA     100   720
#>  6 56726036… E     Athe… Athe…  6077 Simi…   689 Vent…    55    NA     100   805
#>  7 56726036… E     Town… Town…  6071 Simi…   689 Vent…    55    NA      99   778
#>  8 15633216… E     Thor… Thor…   904 Bake…    41 Kern     14    NA      98   731
#>  9 37683956… E     Nico… Nico…  4637 Sout…   702 San …    36    NA     100   592
#> 10 37680236… E     Vall… Vall…  4311 Chul…   135 San …    36    NA     100   669
#> # ℹ 190 more rows
#> # ℹ 27 more variables: api99 <int>, target <int>, growth <int>, sch.wide <fct>,
#> #   comp.imp <fct>, both <fct>, awards <fct>, meals <int>, ell <int>,
#> #   yr.rnd <fct>, 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>

1.3 Cluster Design

apiclus1 is a one-stage cluster sample with school districts (dnum) as the primary sampling units.

survey

clus_sv <- svydesign(ids = ~dnum, fpc = ~fpc, weights = ~pw, data = apiclus1)
clus_sv
#> 1 - level Cluster Sampling design
#> With (15) clusters.
#> svydesign(ids = ~dnum, fpc = ~fpc, weights = ~pw, data = apiclus1)

srvyr

clus_srvyr <- apiclus1 |>
  as_survey_design(ids = dnum, fpc = fpc, weights = pw)
clus_srvyr
#> 1 - level Cluster Sampling design
#> With (15) clusters.
#> Called via srvyr
#> Sampling variables:
#>   - ids: dnum 
#>   - fpc: fpc 
#>   - weights: pw 
#> Data variables: 
#>   - cds (chr), stype (fct), name (chr), sname (chr), snum (dbl), dname (chr),
#>     dnum (int), cname (chr), cnum (int), flag (int), pcttest (int), api00
#>     (int), api99 (int), target (int), growth (int), sch.wide (fct), comp.imp
#>     (fct), both (fct), awards (fct), meals (int), ell (int), yr.rnd (fct),
#>     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), fpc
#>     (dbl), pw (dbl)

surveycore

clus_sc <- surveycore::as_survey(apiclus1, ids = dnum, fpc = fpc, weights = pw)
clus_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 183
#> 
#> # A tibble: 183 × 39
#>    cds       stype name  sname  snum dname  dnum cname  cnum  flag pcttest api00
#>    <chr>     <fct> <chr> <chr> <dbl> <chr> <int> <chr> <int> <int>   <int> <int>
#>  1 01612910… H     San … San …   236 San …   637 Alam…     1    NA      97   608
#>  2 01612916… E     Garf… Garf…   237 San …   637 Alam…     1    NA     100   684
#>  3 01612916… E     Jeff… Jeff…   238 San …   637 Alam…     1    NA     100   612
#>  4 01612916… E     Madi… Madi…   239 San …   637 Alam…     1    NA     100   710
#>  5 01612916… E     McKi… McKi…   240 San …   637 Alam…     1    NA      99   729
#>  6 01612916… E     Monr… Monr…   241 San …   637 Alam…     1    NA     100   714
#>  7 01612916… E     Roos… Roos…   242 San …   637 Alam…     1    NA      99   759
#>  8 01612916… E     Wash… Wash…   243 San …   637 Alam…     1    NA      99   585
#>  9 01612916… E     Wils… Wils…   244 San …   637 Alam…     1    NA     100   625
#> 10 01612916… M     Banc… Banc…   245 San …   637 Alam…     1    NA     100   664
#> # ℹ 173 more rows
#> # ℹ 27 more variables: api99 <int>, target <int>, growth <int>, sch.wide <fct>,
#> #   comp.imp <fct>, both <fct>, awards <fct>, meals <int>, ell <int>,
#> #   yr.rnd <fct>, 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>, fpc <dbl>, pw <dbl>

1.4 Replicate Weights

Replicate weights are common in government surveys like the ACS PUMS (80 successive-difference replicates) and Pew’s Jewish Americans Study (100 JK1 replicates). Both datasets are bundled with surveycore.

The key interface difference: survey selects replicate columns with a raw regex string; surveycore uses tidyselect — the same composable selection language used throughout the tidyverse.

ACS PUMS Wyoming — successive-difference replicates

acs_sv <- svrepdesign(
  data             = acs_pums_wy,
  weights          = ~pwgtp,
  repweights       = "pwgtp[0-9]+",   # regex string
  type             = "successive-difference",
  combined.weights = TRUE
)
acs_sv
#> Call: svrepdesign.default(data = acs_pums_wy, weights = ~pwgtp, repweights = "pwgtp[0-9]+", 
#>     type = "successive-difference", combined.weights = TRUE)
#> with 80 replicates.
acs_srvyr <- acs_pums_wy |>
  as_survey_rep(
    weights          = pwgtp,
    repweights       = matches("^pwgtp[0-9]+$"), # tidyselect
    type             = "successive-difference",
    combined_weights = TRUE
  )
acs_srvyr
#> Call: Called via srvyr
#> with 80 replicates.
#> Sampling variables:
#>   - repweights: `pwgtp1 + pwgtp2 + pwgtp3 + pwgtp4 + pwgtp5 + pwgtp6 + pwgtp7 +
#>     pwgtp8 + pwgtp9 + pwgtp10 + pwgtp11 + pwgtp12 + pwgtp13 + pwgtp14 + pwgtp15
#>     + pwgtp16 + pwgtp17 + pwgtp18 + pwgtp19 + pwgtp20 + pwgtp21 + pwgtp22 +
#>     pwgtp23 + pwgtp24 + pwgtp25 + pwgtp26 + pwgtp27 + pwgtp28 + pwgtp29 +
#>     pwgtp30 + pwgtp31 + pwgtp32 + pwgtp33 + pwgtp34 + pwgtp35 + pwgtp36 +
#>     pwgtp37 + pwgtp38 + pwgtp39 + pwgtp40 + pwgtp41 + pwgtp42 + pwgtp43 +
#>     pwgtp44 + pwgtp45 + pwgtp46 + pwgtp47 + pwgtp48 + pwgtp49 + pwgtp50 +
#>     pwgtp51 + pwgtp52 + pwgtp53 + pwgtp54 + pwgtp55 + pwgtp56 + pwgtp57 +
#>     pwgtp58 + pwgtp59 + pwgtp60 + pwgtp61 + pwgtp62 + pwgtp63 + pwgtp64 +
#>     pwgtp65 + pwgtp66 + pwgtp67 + pwgtp68 + pwgtp69 + pwgtp70 + pwgtp71 +
#>     pwgtp72 + pwgtp73 + pwgtp74 + pwgtp75 + pwgtp76 + pwgtp77 + pwgtp78 +
#>     pwgtp79 + pwgtp80` 
#>   - weights: pwgtp 
#> Data variables: 
#>   - puma (int), st (int), pwgtp (int), pwgtp1 (int), pwgtp2 (int), pwgtp3
#>     (int), pwgtp4 (int), pwgtp5 (int), pwgtp6 (int), pwgtp7 (int), pwgtp8
#>     (int), pwgtp9 (int), pwgtp10 (int), pwgtp11 (int), pwgtp12 (int), pwgtp13
#>     (int), pwgtp14 (int), pwgtp15 (int), pwgtp16 (int), pwgtp17 (int), pwgtp18
#>     (int), pwgtp19 (int), pwgtp20 (int), pwgtp21 (int), pwgtp22 (int), pwgtp23
#>     (int), pwgtp24 (int), pwgtp25 (int), pwgtp26 (int), pwgtp27 (int), pwgtp28
#>     (int), pwgtp29 (int), pwgtp30 (int), pwgtp31 (int), pwgtp32 (int), pwgtp33
#>     (int), pwgtp34 (int), pwgtp35 (int), pwgtp36 (int), pwgtp37 (int), pwgtp38
#>     (int), pwgtp39 (int), pwgtp40 (int), pwgtp41 (int), pwgtp42 (int), pwgtp43
#>     (int), pwgtp44 (int), pwgtp45 (int), pwgtp46 (int), pwgtp47 (int), pwgtp48
#>     (int), pwgtp49 (int), pwgtp50 (int), pwgtp51 (int), pwgtp52 (int), pwgtp53
#>     (int), pwgtp54 (int), pwgtp55 (int), pwgtp56 (int), pwgtp57 (int), pwgtp58
#>     (int), pwgtp59 (int), pwgtp60 (int), pwgtp61 (int), pwgtp62 (int), pwgtp63
#>     (int), pwgtp64 (int), pwgtp65 (int), pwgtp66 (int), pwgtp67 (int), pwgtp68
#>     (int), pwgtp69 (int), pwgtp70 (int), pwgtp71 (int), pwgtp72 (int), pwgtp73
#>     (int), pwgtp74 (int), pwgtp75 (int), pwgtp76 (int), pwgtp77 (int), pwgtp78
#>     (int), pwgtp79 (int), pwgtp80 (int), agep (int), sex (int), rac1p (int),
#>     hisp (int), schl (int), esr (int), pincp (int), wagp (int), hicov (int),
#>     dis (int), povpip (int), wkhp (int), adjinc (int)
acs_sc <- as_survey_replicate(
  acs_pums_wy,
  weights    = pwgtp,
  repweights = tidyselect::matches("^pwgtp[0-9]+$"), # tidyselect
  type       = "successive-difference"
)
acs_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_replicate> (SUCCESSIVE-DIFFERENCE, 80 replicates)
#> Sample size: 5962
#> 
#> # A tibble: 5,962 × 96
#>     puma    st pwgtp pwgtp1 pwgtp2 pwgtp3 pwgtp4 pwgtp5 pwgtp6 pwgtp7 pwgtp8
#>    <int> <int> <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>  <int>
#>  1   500    56    25     24     28     20     24     27     26     27     25
#>  2   400    56   128    158    145    133    141    133    128    124    116
#>  3   200    56   121    104     93    121     97     94    146    169    147
#>  4   300    56    24      0     22     41      0      5     43     24     20
#>  5   500    56    26     31     33     28     32     29     26     28     27
#>  6   300    56    25     26      0     24      0     25     24      0     22
#>  7   300    56    91     85     93     80     80     99    100     97     96
#>  8   500    56    20     21     19     36     23     32     16     20     43
#>  9   500    56   132    138    143    138    143    151    150    134    144
#> 10   100    56    89    113     83    146     71     76    141    117     10
#> # ℹ 5,952 more rows
#> # ℹ 85 more variables: pwgtp9 <int>, pwgtp10 <int>, pwgtp11 <int>,
#> #   pwgtp12 <int>, pwgtp13 <int>, pwgtp14 <int>, pwgtp15 <int>, pwgtp16 <int>,
#> #   pwgtp17 <int>, pwgtp18 <int>, pwgtp19 <int>, pwgtp20 <int>, pwgtp21 <int>,
#> #   pwgtp22 <int>, pwgtp23 <int>, pwgtp24 <int>, pwgtp25 <int>, pwgtp26 <int>,
#> #   pwgtp27 <int>, pwgtp28 <int>, pwgtp29 <int>, pwgtp30 <int>, pwgtp31 <int>,
#> #   pwgtp32 <int>, pwgtp33 <int>, pwgtp34 <int>, pwgtp35 <int>, …

Pew Jewish Americans 2020 — JK1 jackknife replicates

pew_sc <- as_survey_replicate(
  pew_jewish_2020,
  weights    = extweight,
  repweights = extweight1:extweight100,
  type       = "JK1"
)
pew_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_replicate> (JK1, 100 replicates)
#> Sample size: 5881
#> 
#> # A tibble: 5,881 × 130
#>    extweight extweight1 extweight2 extweight3 extweight4 extweight5 extweight6
#>        <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
#>  1      271.       267.       272.       271.       272.       269.       265.
#>  2      186.       183.       236.       186.       189.       185.       182.
#>  3      182.       181.       185.       188.       184.       181.       189.
#>  4      308.       307.       312.       324.       308.       305.       320.
#>  5      165.       165.       167.       170.       166.       163.       164.
#>  6      173.       170.       175.       173.       174.       173.       168.
#>  7      352.       347.       353.       351.       358.       353.       338.
#>  8      314.       312.       318.       316.       314.       314.       309.
#>  9      395.       394.       395.       394.       392.       392.       392.
#> 10      176.       177.       178.       181.       177.       175.       172.
#> # ℹ 5,871 more rows
#> # ℹ 123 more variables: extweight7 <dbl>, extweight8 <dbl>, extweight9 <dbl>,
#> #   extweight10 <dbl>, extweight11 <dbl>, extweight12 <dbl>, extweight13 <dbl>,
#> #   extweight14 <dbl>, extweight15 <dbl>, extweight16 <dbl>, extweight17 <dbl>,
#> #   extweight18 <dbl>, extweight19 <dbl>, extweight20 <dbl>, extweight21 <dbl>,
#> #   extweight22 <dbl>, extweight23 <dbl>, extweight24 <dbl>, extweight25 <dbl>,
#> #   extweight26 <dbl>, extweight27 <dbl>, extweight28 <dbl>, …

1.5 Calibrated / Non-Probability Samples

ns_wave1 is the Nationscape Wave 1 survey — a non-probability quota panel with raking weights calibrated to ACS demographics and 2016 vote.

survey and srvyr have no dedicated constructor for calibrated or non-probability designs. The design intent is lost in the code:

# No way to signal this is calibrated or non-probability
ns_sv <- svydesign(ids = ~1, weights = ~weight, data = ns_wave1)
ns_srvyr <- ns_wave1 |> as_survey_design(weights = weight)
# as_survey_nonprob() makes the design type explicit
ns_sc <- as_survey_nonprob(ns_wave1, weights = weight)
ns_sc
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_nonprob> (non-probability) [experimental]
#> Sample size: 6422
#> 
#> # A tibble: 6,422 × 171
#>    response_id start_date          right_track economy_better interest
#>    <chr>       <dttm>                    <dbl>          <dbl>    <dbl>
#>  1 00100002    2019-07-18 08:11:41           2              2        2
#>  2 00100003    2019-07-18 08:12:31           1              3        1
#>  3 00100004    2019-07-18 08:12:04           2              3        2
#>  4 00100005    2019-07-18 08:12:05           2              2        2
#>  5 00100007    2019-07-18 08:11:43           1              1        1
#>  6 00100008    2019-07-18 08:12:24           2              2        2
#>  7 00100009    2019-07-18 08:13:15           2              2        4
#>  8 00100010    2019-07-18 08:13:06           1              1        1
#>  9 00100011    2019-07-18 08:11:47           2              2        3
#> 10 00100012    2019-07-18 08:12:25           2              3        2
#> # ℹ 6,412 more rows
#> # ℹ 166 more variables: registration <dbl>, news_sources_facebook <dbl>,
#> #   news_sources_cnn <dbl>, news_sources_msnbc <dbl>, news_sources_fox <dbl>,
#> #   news_sources_network <dbl>, news_sources_localtv <dbl>,
#> #   news_sources_telemundo <dbl>, news_sources_npr <dbl>,
#> #   news_sources_amtalk <dbl>, news_sources_new_york_times <dbl>,
#> #   news_sources_local_newspaper <dbl>, news_sources_other <dbl>, …

as_survey_nonprob() preserves the distinction in code, output, and documentation. Standard errors are approximate — they assume the calibration weights produce approximately correct variance estimates (Elliott and Valliant 2017).

1.6 Two-Phase Designs

Two-phase designs are uncommon. surveycore’s as_survey_twophase() matches survey::twophase() for the Breslow-Cain variance estimator (Breslow and Cain 1988). For a full worked example using survival::nwtco, see vignette("creating-survey-objects").

1.7 Constructor Summary

Design survey srvyr surveycore
SRS svydesign(ids=~1, ...) as_survey_design(ids=1, ...) as_survey(...) (no ids/strata)
Stratified svydesign(strata=~s, ...) as_survey_design(strata=s, ...) as_survey(..., strata=s)
Cluster svydesign(ids=~d, ...) as_survey_design(ids=d, ...) as_survey(..., ids=d)
Replicate wts svrepdesign(repweights="regex") as_survey_rep(repweights=matches(...)) as_survey_replicate(repweights=matches(...))
Calibrated/NPS svydesign(ids=~1, weights=~w) ⚠ as_survey_design(weights=w) ⚠ as_survey_nonprob(...)
Two-phase twophase(...) as_survey_twophase(...) as_survey_twophase(...)

⚠ No dedicated non-probability constructor — design intent is not preserved.

2. Summary Statistics

The sections below use ns_sc (already created above) alongside the equivalent survey and srvyr designs. The label contrast — raw integer codes in survey/srvyr vs. human-readable labels in surveycore — is the recurring theme. ns_wave1 was imported with haven labels intact; surveycore resolves them automatically.

2.1 Weighted Means (Grouped)

Estimated discrimination experienced by Black Americans, broken out by party identification (pid3).

survey — group values appear as raw codes (1, 2, 3, 4)

svyby(~discrimination_blacks, ~pid3, ns_sv, svymean, na.rm = TRUE)
#>   pid3 discrimination_blacks         se
#> 1    1              1.827663 0.03845797
#> 2    2              3.044733 0.04709251
#> 3    3              2.517407 0.05141302
#> 4    4              2.360898 0.09929886

srvyr — also raw codes unless pid3 is manually factored first

ns_srvyr |>
  group_by(pid3) |>
  summarise(m = survey_mean(discrimination_blacks, vartype = "ci", na.rm = TRUE))
#> # A tibble: 5 × 4
#>    pid3     m m_low m_upp
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     1  1.83 1.75   1.90
#> 2     2  3.04 2.95   3.14
#> 3     3  2.52 2.42   2.62
#> 4     4  2.36 2.17   2.56
#> 5    NA  1.17 0.814  1.53

surveycore — “Democrat”, “Republican”, “Independent”, “Something else” from the haven labels, automatically

get_means(ns_sc, discrimination_blacks, group = pid3)
#> # A tibble: 4 × 5
#>   pid3            mean ci_low ci_high     n
#>   <fct>          <dbl>  <dbl>   <dbl> <int>
#> 1 Democrat        1.83   1.75    1.90  2272
#> 2 Republican      3.04   2.95    3.14  1805
#> 3 Independent     2.52   2.42    2.62  1856
#> 4 Something else  2.36   2.17    2.56   427

2.2 Proportions / Frequency Tables

Distribution of willingness to consider voting for Trump (consider_trump).

survey — svymean() on a factor produces column names like consider_trump1, consider_trump2, consider_trump999

svymean(~factor(consider_trump), ns_sv, na.rm = TRUE)
#>                              mean     SE
#> factor(consider_trump)1   0.32052 0.0102
#> factor(consider_trump)2   0.55475 0.0110
#> factor(consider_trump)999 0.12473 0.0075

srvyr

ns_srvyr |>
  group_by(consider_trump) |>
  summarise(pct = survey_mean(na.rm = TRUE))
#> Warning: There was 1 warning in `dplyr::summarise()`.
#> ℹ In argument: `pct = survey_mean(na.rm = TRUE)`.
#> ℹ In group 1: `consider_trump = 1`.
#> Caused by warning:
#> ! na.rm argument has no effect on survey_mean when calculating grouped proportions. 
#> This warning is displayed once per session.
#> # A tibble: 4 × 3
#>   consider_trump     pct  pct_se
#>            <dbl>   <dbl>   <dbl>
#> 1              1 0.320   0.0102 
#> 2              2 0.553   0.0110 
#> 3            999 0.124   0.00744
#> 4             NA 0.00276 0.00134

surveycore — consider_trump column shows “Yes”, “No”, “Don’t know”

get_freqs(ns_sc, consider_trump)
#> # A tibble: 3 × 3
#>   consider_trump   pct     n
#>   <fct>          <dbl> <int>
#> 1 Yes            0.321  2087
#> 2 No             0.555  3615
#> 3 Don't know     0.125   705

2.3 Population Totals

ns_wave1 uses calibration weights scaled to the sample size (weights sum to 6,422 — the number of respondents). get_totals() with no variable argument returns the estimated population size — here, it confirms the calibration:

survey — svytotal(~1, design) is not supported; the sum of weights gives the estimated N, and svytotal() requires a real variable

sum(weights(ns_sv))                         # estimated population N
#> [1] 6422
svytotal(~age, ns_sv, na.rm = TRUE)         # total of a continuous variable
#>      total     SE
#> age 302835 6025.5

srvyr — survey_total(1) computes estimated N

ns_srvyr |> summarise(n_pop = survey_total(1))       # estimated N
#> # A tibble: 1 × 2
#>   n_pop n_pop_se
#>   <dbl>    <dbl>
#> 1 6422.     117.
ns_srvyr |> summarise(age_total = survey_total(age, na.rm = TRUE))
#> # A tibble: 1 × 2
#>   age_total age_total_se
#>       <dbl>        <dbl>
#> 1   302835.        6025.

surveycore

get_totals(ns_sc)           # estimated N (no x argument)
#> # A tibble: 1 × 3
#>   total ci_low ci_high
#>   <dbl>  <dbl>   <dbl>
#> 1 6422.  6192.   6652.
get_totals(ns_sc, age)      # total of a continuous variable
#> # A tibble: 1 × 4
#>     total  ci_low ci_high     n
#>     <dbl>   <dbl>   <dbl> <int>
#> 1 302835. 291026. 314645.  6422

For a design with probability weights that sum to the actual population (like the Pew Jewish Americans study), get_totals() returns the estimated population count in millions:

get_totals(pew_sc)
#> # A tibble: 1 × 3
#>      total   ci_low  ci_high
#>      <dbl>    <dbl>    <dbl>
#> 1 9971358. 9971322. 9971394.

2.4 Quantiles

Weighted age distribution of Nationscape respondents.

survey

svyquantile(~age, ns_sv, quantiles = c(0.25, 0.5, 0.75), na.rm = TRUE)
#> $age
#>      quantile ci.2.5 ci.97.5        se
#> 0.25       32     31      34 0.7651759
#> 0.5        47     46      49 0.7651759
#> 0.75       62     62      63 0.2550586
#> 
#> attr(,"hasci")
#> [1] TRUE
#> attr(,"class")
#> [1] "newsvyquantile"

srvyr

ns_srvyr |>
  summarise(q = survey_quantile(age, c(0.25, 0.5, 0.75), na.rm = TRUE))
#> # A tibble: 1 × 6
#>   q_q25 q_q50 q_q75 q_q25_se q_q50_se q_q75_se
#>   <dbl> <dbl> <dbl>    <dbl>    <dbl>    <dbl>
#> 1    32    47    62    0.765    0.765    0.255

surveycore — Woodruff (1952) confidence intervals, guaranteed to respect the data range

get_quantiles(ns_sc, age)
#> # A tibble: 3 × 5
#>   quantile estimate ci_low ci_high     n
#>   <chr>       <dbl>  <dbl>   <dbl> <int>
#> 1 p25            32     31      34  6422
#> 2 p50            47     46      49  6422
#> 3 p75            62     62      63  6422

2.5 Ratios

api00 / api99 is a natural ratio: Academic Performance Index in 2000 relative to 1999. We use apisrs here because it provides a clear probability design where the ratio estimator is unambiguous.

survey — positional argument order requires knowing which formula is numerator vs. denominator

svyratio(~api00, ~api99, srs_sv)
#> Ratio estimator: svyratio.survey.design2(~api00, ~api99, srs_sv)
#> Ratios=
#>          api99
#> api00 1.051066
#> SEs=
#>             api99
#> api00 0.003603991

srvyr

srs_srvyr |> summarise(ratio = survey_ratio(api00, api99))
#> # A tibble: 1 × 2
#>   ratio ratio_se
#>   <dbl>    <dbl>
#> 1  1.05  0.00360

surveycore — named arguments make direction self-documenting

get_ratios(srs_sc, numerator = api00, denominator = api99)
#> # A tibble: 1 × 4
#>   ratio ci_low ci_high     n
#>   <dbl>  <dbl>   <dbl> <int>
#> 1  1.05   1.04    1.06   200

numerator = / denominator = remove the ambiguity present in svyratio(~y, ~x, design).

2.6 Correlations

Pearson correlation between Trump and Biden favorability (cand_favorability_* is a 1–4 scale; 999 codes respondents who haven’t heard enough — filtered below).

# Pre-filter non-substantive responses before creating the design
ns_corr <- ns_wave1[
  !is.na(ns_wave1$cand_favorability_trump) &
    ns_wave1$cand_favorability_trump != 999 &
    !is.na(ns_wave1$cand_favorability_biden) &
    ns_wave1$cand_favorability_biden != 999,
]
ns_corr_sc <- as_survey_nonprob(ns_corr, weights = weight)

survey — matrix output, no confidence intervals

ns_corr_sv <- svydesign(ids = ~1, weights = ~weight, data = ns_corr)
jtools::svycor(~cand_favorability_trump + cand_favorability_biden, ns_corr_sv)
#>                         cand_favorability_trump cand_favorability_biden
#> cand_favorability_trump                    1.00                   -0.49
#> cand_favorability_biden                   -0.49                    1.00

srvyr — no dedicated survey_corr() verb; users must fall back to survey

surveycore — long tibble with Fisher-Z confidence intervals (bounds guaranteed in [−1, 1])

get_corr(ns_corr_sc, c(cand_favorability_trump, cand_favorability_biden))
#> # A tibble: 1 × 9
#>   var1         var2           r ci_low ci_high p_value statistic    df     n
#>   <fct>        <fct>      <dbl>  <dbl>   <dbl>   <dbl>     <dbl> <int> <int>
#> 1 Donald Trump Joe Biden -0.495 -0.524  -0.464       0     -41.3  5276  5278

svycor() returns a matrix with no CIs. get_corr() returns a tidy tibble with Fisher-Z confidence intervals. srvyr has no survey_corr() verb at all — users fall back to survey directly.

3. Controlling Uncertainty Output

All surveycore analysis functions share a variance argument that controls which uncertainty columns appear. In survey, you call a separate function per metric. In srvyr, you repeat the summarise() call for each type.

survey — separate call per uncertainty type

m <- svymean(~age, ns_sv, na.rm = TRUE)
m                      # SE only in the estimate
#>       mean     SE
#> age 47.156 0.3956
confint(m)             # CI — separate call
#>        2.5 %   97.5 %
#> age 46.38062 47.93123
cv(m)                  # CV — separate call
#>             age
#> age 0.008388587
svymean(~age, ns_sv, deff = TRUE, na.rm = TRUE) # DEFF — different return structure
#>         mean       SE       DEff
#> age 47.15593  0.39557 1.7637e+15

srvyr — one call per type; the variable is estimated multiple times

ns_srvyr |>
  summarise(
    m_se   = survey_mean(age, vartype = "se",   na.rm = TRUE),
    m_ci   = survey_mean(age, vartype = "ci",   na.rm = TRUE),
    m_cv   = survey_mean(age, vartype = "cv",   na.rm = TRUE),
    m_deff = survey_mean(age, deff = TRUE,      na.rm = TRUE)
  )
#> # A tibble: 1 × 10
#>    m_se m_se_se  m_ci m_ci_low m_ci_upp  m_cv m_cv_cv m_deff m_deff_se
#>   <dbl>   <dbl> <dbl>    <dbl>    <dbl> <dbl>   <dbl>  <dbl>     <dbl>
#> 1  47.2   0.396  47.2     46.4     47.9  47.2 0.00839   47.2     0.396
#> # ℹ 1 more variable: m_deff_deff <dbl>

surveycore — one call, any combination of metrics

get_means(ns_sc, age, variance = c("se", "ci", "cv", "deff"))
#> # A tibble: 1 × 7
#>    mean    se      cv ci_low ci_high  deff     n
#>   <dbl> <dbl>   <dbl>  <dbl>   <dbl> <dbl> <int>
#> 1  47.2 0.396 0.00839   46.4    47.9  3.47  6422

Set variance = NULL to return point estimates and sample counts only:

get_means(ns_sc, age, variance = NULL)
#> # A tibble: 1 × 2
#>    mean     n
#>   <dbl> <int>
#> 1  47.2  6422

Available variance codes:

Code What it returns
"se" Standard error
"ci" Confidence interval: ci_low, ci_high
"var" Variance (SE²)
"cv" Coefficient of variation (SE / estimate)
"moe" Margin of error at conf_level
"deff" Design effect (complex / SRS variance)

The conf_level argument controls the level for "ci" and "moe". Default is 0.95; for a 90% interval: get_means(ns_sc, age, conf_level = 0.9).

4. Features With No survey / srvyr Equivalent

4.1 Automatic Value Labels

ns_wave1 was imported with haven labels intact. surveycore resolves them automatically — no manual recoding required.

survey / srvyr — group column values are raw integer codes

# pid3 values: 1, 2, 3, 4 — the reader must consult the codebook
svyby(~discrimination_blacks, ~pid3, ns_sv, svymean, na.rm = TRUE)
#>   pid3 discrimination_blacks         se
#> 1    1              1.827663 0.03845797
#> 2    2              3.044733 0.04709251
#> 3    3              2.517407 0.05141302
#> 4    4              2.360898 0.09929886

surveycore — “Democrat”, “Republican”, “Independent”, “Something else”

get_means(ns_sc, discrimination_blacks, group = pid3)
#> # A tibble: 4 × 5
#>   pid3            mean ci_low ci_high     n
#>   <fct>          <dbl>  <dbl>   <dbl> <int>
#> 1 Democrat        1.83   1.75    1.90  2272
#> 2 Republican      3.04   2.95    3.14  1805
#> 3 Independent     2.52   2.42    2.62  1856
#> 4 Something else  2.36   2.17    2.56   427

Opt out with label_values = FALSE to see raw codes:

get_means(ns_sc, discrimination_blacks, group = pid3, label_values = FALSE)
#> # A tibble: 4 × 5
#>    pid3  mean ci_low ci_high     n
#>   <dbl> <dbl>  <dbl>   <dbl> <int>
#> 1     1  1.83   1.75    1.90  2272
#> 2     2  3.04   2.95    3.14  1805
#> 3     3  2.52   2.42    2.62  1856
#> 4     4  2.36   2.17    2.56   427

4.2 Multiple Variables in One Call

ns_wave1 includes a battery of 13 news source items (news_sources_facebook, news_sources_cnn, …, news_sources_other). Analyzing all at once requires a loop in survey and srvyr; surveycore stacks them in a single call.

survey / srvyr — must loop; output is a list that the user binds manually

news_vars <- c(
  "news_sources_facebook", "news_sources_cnn", "news_sources_fox",
  "news_sources_npr", "news_sources_new_york_times"
)
results_sv <- lapply(news_vars, function(v) {
  f <- as.formula(paste0("~", v))
  svymean(f, ns_sv, na.rm = TRUE)
})
# Results are a list — user must bind rows and add a name column manually
do.call(rbind, lapply(seq_along(results_sv), function(i) {
  data.frame(name = news_vars[[i]], coef(results_sv[[i]]))
}))
#>                                                    name coef.results_sv..i...
#> news_sources_facebook             news_sources_facebook              1.384971
#> news_sources_cnn                       news_sources_cnn              1.599659
#> news_sources_fox                       news_sources_fox              1.639275
#> news_sources_npr                       news_sources_npr              1.862283
#> news_sources_new_york_times news_sources_new_york_times              1.727273

surveycore — one call; a name column identifies each item; variable labels are applied automatically

get_freqs(
  ns_sc,
  c(news_sources_facebook:news_sources_other)
)
#> # A tibble: 24 × 4
#>    name                                   value   pct     n
#>    <fct>                                  <chr> <dbl> <int>
#>  1 Social media (e.g., Facebook, Twitter) Yes   0.615  4187
#>  2 Social media (e.g., Facebook, Twitter) No    0.385  2235
#>  3 CNN                                    Yes   0.400  2532
#>  4 CNN                                    No    0.600  3890
#>  5 MSNBC                                  Yes   0.266  1667
#>  6 MSNBC                                  No    0.734  4755
#>  7 Fox News (cable)                       Yes   0.361  2360
#>  8 Fox News (cable)                       No    0.639  4062
#>  9 Network news (ABC, CBS, NBC) or PBS    Yes   0.580  3711
#> 10 Network news (ABC, CBS, NBC) or PBS    No    0.420  2711
#> # ℹ 14 more rows

4.3 Minimum Cell Size Warnings

survey and srvyr return estimates for tiny cells silently — the user may not notice that a group has only 8 respondents. surveycore warns when any unweighted cell count falls below min_cell_n (default: 30).

# Construct a design with deliberately small cells
small_df <- data.frame(
  group = rep(c("A", "B", "C"), c(8, 15, 200)),
  x     = rnorm(223),
  w     = 1
)
small_svy <- surveycore::as_survey(small_df, weights = w)

get_means(small_svy, x, group = group)
#> Warning: ! 2 cells have fewer than 30 unweighted observations. Estimates in these cells
#>   may be unreliable for public reporting (AAPOR guidance).
#> # A tibble: 3 × 5
#>   group    mean ci_low ci_high     n
#>   <chr>   <dbl>  <dbl>   <dbl> <int>
#> 1 A      0.122  -0.626  0.869      8
#> 2 B     -0.227  -0.508  0.0528    15
#> 3 C     -0.0823 -0.230  0.0653   200

Suppress the warning when small cells are expected:

get_means(small_svy, x, group = group, min_cell_n = 0L)

4.4 Weighted Sample Size

In survey and srvyr, getting both the unweighted and estimated population count for each cell requires a separate svytotal(~1, ...) call. surveycore adds it with one argument:

survey — extra call for weighted N

# Proportions by group (unweighted n not shown in output)
svyby(~factor(consider_trump), ~pid3, ns_sv, svymean, na.rm = TRUE)
#>   pid3 factor(consider_trump)1 factor(consider_trump)2
#> 1    1               0.0500996               0.8873073
#> 2    2               0.7749952               0.1281248
#> 3    3               0.2384136               0.5662499
#> 4    4               0.2090438               0.5703057
#>   factor(consider_trump)999 se.factor(consider_trump)1
#> 1                0.06259312                0.007599112
#> 2                0.09688000                0.017249294
#> 3                0.19533655                0.017201021
#> 4                0.22065055                0.033161961
#>   se.factor(consider_trump)2 se.factor(consider_trump)999
#> 1                 0.01183081                  0.009518794
#> 2                 0.01417166                  0.011797172
#> 3                 0.02051542                  0.016804745
#> 4                 0.03924281                  0.033059895
# Estimated weighted N per group — requires a separate call
svyby(~as.numeric(!is.na(consider_trump)), ~pid3, ns_sv, svytotal, na.rm = TRUE)
#>   pid3 as.numeric(!is.na(consider_trump))       se
#> 1    1                           2198.163 78.10298
#> 2    2                           1784.745 69.80846
#> 3    3                           1874.555 74.13607
#> 4    4                            538.104 41.74667

surveycore — one argument

get_freqs(ns_sc, consider_trump, group = pid3, n_weighted = TRUE)
#> # A tibble: 12 × 5
#>    pid3           consider_trump    pct     n n_weighted
#>    <fct>          <fct>           <dbl> <int>      <dbl>
#>  1 Democrat       Yes            0.0501   136       110.
#>  2 Democrat       No             0.887   2042      1950.
#>  3 Democrat       Don't know     0.0626   111       138.
#>  4 Republican     Yes            0.775   1403      1383.
#>  5 Republican     No             0.128    227       229.
#>  6 Republican     Don't know     0.0969   183       173.
#>  7 Independent    Yes            0.238    475       447.
#>  8 Independent    No             0.566   1071      1061.
#>  9 Independent    Don't know     0.195    316       366.
#> 10 Something else Yes            0.209     73       112.
#> 11 Something else No             0.570    272       307.
#> 12 Something else Don't know     0.221     91       119.

The n_weighted column is the sum of weights within each cell — the estimated population size that cell represents.

4.5 Metadata-Rich Results (.meta)

surveycore attaches a .meta attribute to every result tibble. It contains the variable label, value labels, and question preface for each focal and grouping variable — everything needed to build a publication-ready table without consulting the codebook separately.

result <- get_means(ns_sc, discrimination_blacks, group = pid3)

# Variable label for the focal variable
attr(result, ".meta")$x$discrimination_blacks$variable_label
#> [1] "Blacks"
# Value labels for the grouping variable
attr(result, ".meta")$group$pid3$value_labels
#>       Democrat     Republican    Independent Something else 
#>              1              2              3              4

In survey and srvyr, metadata is not attached to results — label information is lost after estimation.

5. Notable Differences

survey srvyr surveycore
Output format S3 svystat / matrix Tibble with _se/_low/_upp suffix columns S3 tibble subclass with CI columns by default
Interface ~formula throughout Mixed: tidy constructor, formula in summarise() Bare names throughout (tidy-select)
Value labels Not applied Not applied Applied automatically from haven attributes
Multiple variables Loop required Loop required c(x, y, z) in one call
Min-cell warning None None Default min_cell_n = 30L
Weighted N Separate call Separate call n_weighted = TRUE
Correlation CIs None (svycor()) No verb Fisher-Z CIs via get_corr()
Non-probability design No dedicated constructor No dedicated constructor as_survey_nonprob()
Manipulation Pre/post construction Bundled via pipe surveytidy (companion package)
Runtime survey dep. Is survey Wraps survey Vendored — survey not required

6. Function Reference Table

Task survey srvyr surveycore
SRS design svydesign(ids=~1, ...) as_survey_design(ids=1, ...) as_survey(...) (no ids/strata)
Stratified design svydesign(strata=~s, ...) as_survey_design(strata=s, ...) as_survey(..., strata=s)
Cluster design svydesign(ids=~d, ...) as_survey_design(ids=d, ...) as_survey(..., ids=d)
Replicate weights svrepdesign(repweights="regex") as_survey_rep(repweights=matches(...)) as_survey_replicate(repweights=matches(...))
Calibrated/NPS svydesign(weights=~w) ⚠ as_survey_design(weights=w) ⚠ as_survey_nonprob(...)
Two-phase twophase(...) as_survey_twophase(...) as_survey_twophase(...)
Weighted mean svymean(~x, d) summarise(survey_mean(x)) get_means(d, x)
Grouped mean svyby(~x, ~g, d, svymean) group_by(g) \|> summarise(...) get_means(d, x, group=g)
Proportions svymean(~factor(x), d) group_by(x) \|> summarise(survey_mean()) get_freqs(d, x)
Total svytotal(~x, d) summarise(survey_total(x)) get_totals(d, x)
Population N svytotal(~1, d) summarise(survey_total(1)) get_totals(d)
Quantiles svyquantile(~x, d, q) summarise(survey_quantile(x, q)) get_quantiles(d, x, probs=q)
Ratio svyratio(~y, ~x, d) summarise(survey_ratio(y, x)) get_ratios(d, numerator=y, denominator=x)
Correlation svycor(~x+y, d) ⚠ no CI ✗ no verb get_corr(d, c(x, y)) with CI
Multiple variables Loop + bind Loop + bind get_means(d, c(x, y, z))
Value labels Manual recode Manual recode label_values = TRUE (default)
Min-cell warning ✗ ✗ min_cell_n = 30L (default)
Weighted N Separate call Separate call n_weighted = TRUE
Domain filter subset(d, cond) filter(cond) filter(cond) (surveytidy)
Mutate Modify df, recreate mutate(...) mutate(...) (surveytidy)
Group by svyby(...) group_by(...) group_by(...) (surveytidy) or group= arg

⚠ = partial / workaround; ✗ = no equivalent

7. Learning More

Breslow, Norman E., and Kevin C. Cain. 1988. “Logistic Regression for Two-Stage Case-Control Data.” Biometrika 75 (1): 11–20. https://doi.org/10.1093/biomet/75.1.11.
Elliott, Michael R., and Richard Valliant. 2017. “Inference for Nonprobability Samples.” Statistical Science 32 (2): 249–64. https://doi.org/10.1214/16-STS598.
Lumley, Thomas. 2010. Complex Surveys: A Guide to Analysis Using R. John Wiley & Sons. https://doi.org/10.1002/9780470580066.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.