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:

• surveycore is not a wrapper around survey. Its variance code is vendored from survey — so every estimate surveycore produces matches survey output numerically — but survey is not a runtime dependency.
• survey → srvyr added tidyverse syntax. surveycore rethinks the interface further: tidy-select constructors, dedicated analysis functions, automatic label handling from haven-imported data, and richer tibble output.

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)
#> Successive difference 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
#> Successive difference 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> (calibrated / 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)
svycor(~cand_favorability_trump + cand_favorability_biden, ns_corr_sv)

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  Inf

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     -0.486 -1.31     0.335     8
#> 2 B     -0.186 -0.683    0.312    15
#> 3 C      0.105 -0.0377   0.248   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

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7. Learning More

• vignette("getting-started") — full surveycore overview with worked examples
• vignette("creating-survey-objects") — all five constructors, including two-phase designs and the nest argument
• srvyr comparison vignette — the original side-by-side that this vignette is modeled on
• Lumley (2010) — the definitive reference on complex survey analysis in R

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.