select() keeps the named columns and drops all others, using the
tidyselect mini-language to describe column sets.
Design variables (weights, strata, PSU, FPC, replicate weights) are
always retained even when not explicitly selected — they are required
for variance estimation. After select(), print() shows only the columns
you selected; design variables remain in the object but are hidden from
display.
select() is irreversible: dropped columns are permanently removed from
the survey object and cannot be recovered within the same pipeline.
Usage
select(.data, ...)
# S3 method for class 'survey_base'
select(.data, ...)
# S3 method for class 'survey_result'
select(.data, ...)
# S3 method for class 'survey_collection'
select(.data, ..., .if_missing_var = NULL)Arguments
- .data
A
survey_baseobject, or asurvey_resultobject returned by a surveycore estimation function.- ...
<
tidy-select> One or more unquoted column names or tidy-select expressions.- .if_missing_var
Per-call override of
collection@if_missing_var. One of"error"or"skip", orNULL(the default) to inherit the collection's stored value. Seesurveycore::set_collection_if_missing_var().
Value
An object of the same type as .data with the following properties:
Rows are not modified.
Non-selected, non-design columns are permanently removed.
Design variables are always retained.
Survey design attributes are preserved.
Details
Survey collections
When applied to a survey_collection, select() is dispatched to each
member independently. Each member resolves its own tidyselect expression
against its own @data, so members may end up with different visible
columns when the selection is partial (e.g., any_of() against a
heterogeneous collection). Per-member design variables are always retained.
Before dispatching, select.survey_collection resolves the selection
against the first member's @data and raises
surveytidy_error_collection_select_group_removed if any column in
coll@groups would be removed. Group columns must remain in every member;
silently dropping them would violate the surveycore class validator (G1b).
Use ungroup() first if you intend to remove a group column.
relocate.survey_collection is not subject to this pre-flight —
dplyr::relocate only reorders columns and never drops them.
See also
relocate() to reorder columns, rename() to rename them,
mutate() to add new ones
Other selecting:
glimpse.survey_collection(),
pull.survey_collection(),
relocate
Examples
library(surveytidy)
library(surveycore)
# create a survey design from the pew_npors_2025 example dataset
d <- as_survey(pew_npors_2025, weights = weight, strata = stratum)
# select by name
select(d, gender, agecat)
#>
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#>
#> # A tibble: 5,022 × 2
#> gender agecat
#> <dbl> <dbl>
#> 1 2 4
#> 2 1 4
#> 3 2 4
#> 4 1 2
#> 5 1 4
#> 6 1 4
#> 7 1 3
#> 8 2 4
#> 9 2 3
#> 10 2 2
#> # ℹ 5,012 more rows
#>
#> ℹ Design variables preserved but hidden: weight and stratum.
#> ℹ Use `print(x, full = TRUE)` to show all variables.
# select by name pattern
select(d, tidyselect::starts_with("smuse_"))
#>
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#>
#> # A tibble: 5,022 × 11
#> smuse_fb smuse_yt smuse_x smuse_ig smuse_sc smuse_wa smuse_tt smuse_rd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 1 2 2 2 2 2 1
#> 2 1 1 2 1 2 2 1 2
#> 3 1 1 1 1 2 1 2 2
#> 4 2 1 2 2 2 2 2 2
#> 5 1 1 1 1 1 2 2 2
#> 6 NA NA NA NA NA NA NA NA
#> 7 1 1 2 1 2 1 2 2
#> 8 1 2 1 2 2 2 2 2
#> 9 1 1 2 2 1 2 2 2
#> 10 1 1 2 1 1 1 2 2
#> # ℹ 5,012 more rows
#> # ℹ 3 more variables: smuse_bsk <dbl>, smuse_th <dbl>, smuse_ts <dbl>
#>
#> ℹ Design variables preserved but hidden: weight and stratum.
#> ℹ Use `print(x, full = TRUE)` to show all variables.
# select by type
select(d, tidyselect::where(is.numeric))
#>
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#>
#> # A tibble: 5,022 × 63
#> respid mode language languageinitial stratum econ1mod econ1bmod comtype2
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1470 2 1 NA 10 4 2 3
#> 2 2374 2 1 NA 7 3 2 1
#> 3 1177 3 1 10 5 2 1 3
#> 4 15459 2 1 NA 10 3 3 3
#> 5 9849 1 1 9 9 2 1 2
#> 6 8178 3 1 9 10 2 2 1
#> 7 3682 1 1 9 4 3 2 1
#> 8 6999 2 1 NA 10 3 3 3
#> 9 9945 2 1 NA 10 3 2 3
#> 10 1901 1 1 9 10 1 1 2
#> # ℹ 5,012 more rows
#> # ℹ 55 more variables: unity <dbl>, crimesafe <dbl>, govprotct <dbl>,
#> # moregunimpact <dbl>, fin_sit <dbl>, vet1 <dbl>, vol12_cps <dbl>,
#> # eminuse <dbl>, intmob <dbl>, intfreq <dbl>, intfreq_collapsed <dbl>,
#> # home4nw2 <dbl>, bbhome <dbl>, smuse_fb <dbl>, smuse_yt <dbl>,
#> # smuse_x <dbl>, smuse_ig <dbl>, smuse_sc <dbl>, smuse_wa <dbl>,
#> # smuse_tt <dbl>, smuse_rd <dbl>, smuse_bsk <dbl>, smuse_th <dbl>, …
# drop columns with !
select(d, !tidyselect::starts_with("smuse_"))
#>
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#>
#> # A tibble: 5,022 × 54
#> respid mode language languageinitial stratum interview_start interview_end
#> <dbl> <dbl> <dbl> <dbl> <dbl> <date> <date>
#> 1 1470 2 1 NA 10 2025-05-27 2025-05-27
#> 2 2374 2 1 NA 7 2025-05-01 2025-05-01
#> 3 1177 3 1 10 5 2025-03-04 2025-03-04
#> 4 15459 2 1 NA 10 2025-05-05 2025-05-05
#> 5 9849 1 1 9 9 2025-02-22 2025-02-22
#> 6 8178 3 1 9 10 2025-03-10 2025-03-10
#> 7 3682 1 1 9 4 2025-02-27 2025-02-27
#> 8 6999 2 1 NA 10 2025-05-12 2025-05-12
#> 9 9945 2 1 NA 10 2025-05-09 2025-05-09
#> 10 1901 1 1 9 10 2025-03-01 2025-03-01
#> # ℹ 5,012 more rows
#> # ℹ 47 more variables: econ1mod <dbl>, econ1bmod <dbl>, comtype2 <dbl>,
#> # unity <dbl>, crimesafe <dbl>, govprotct <dbl>, moregunimpact <dbl>,
#> # fin_sit <dbl>, vet1 <dbl>, vol12_cps <dbl>, eminuse <dbl>, intmob <dbl>,
#> # intfreq <dbl>, intfreq_collapsed <dbl>, home4nw2 <dbl>, bbhome <dbl>,
#> # radio <dbl>, device1a <dbl>, smart2 <dbl>, nhisll <dbl>, relig <dbl>,
#> # religcat1 <dbl>, born <dbl>, attendper <dbl>, attendonline2 <dbl>, …