Physically remove rows from a survey design object

subset() physically removes rows from a survey_base object where condition evaluates to FALSE. This changes the survey design. Unless the design was explicitly built for the subset population, variance estimates will be incorrect.

For subpopulation analyses, use filter() instead. filter() marks rows as in or out of the domain without removing them, leaving the full design intact for variance estimation.

subset() always emits a surveycore_warning_physical_subset warning as a reminder of the statistical implications.

Usage

# S3 method for class 'survey_base'
subset(x, condition, ...)

Arguments

x

A survey_base object.

condition

A logical expression evaluated against the survey data. Rows where condition is FALSE or NA are removed.

...

Ignored. Included for compatibility with the base subset() generic.

Value

An object of the same type as x with only matching rows retained. Always issues surveycore_warning_physical_subset.

See also

filter() for domain-aware row marking (preferred for subpopulation analyses)

Examples

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

# Physical row removal — always issues a warning
subset(d, agecat >= 3)
#> Warning: ! `subset()` physically removes rows from the survey data.
#> ℹ This is different from `filter()`, which preserves all rows for correct
#>   variance estimation.
#> ✔ Use `filter()` for subpopulation analyses instead.
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 3143
#> 
#> # A tibble: 3,143 × 65
#>    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   9849     1        1               9       9 2025-02-22      2025-02-22   
#>  5   8178     3        1               9      10 2025-03-10      2025-03-10   
#>  6   3682     1        1               9       4 2025-02-27      2025-02-27   
#>  7   6999     2        1              NA      10 2025-05-12      2025-05-12   
#>  8   9945     2        1              NA      10 2025-05-09      2025-05-09   
#>  9  18470     2        1              NA      10 2025-05-08      2025-05-08   
#> 10  18634     2        1              NA      10 2025-05-02      2025-05-02   
#> # ℹ 3,133 more rows
#> # ℹ 58 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>,
#> #   smuse_fb <dbl>, smuse_yt <dbl>, smuse_x <dbl>, smuse_ig <dbl>,
#> #   smuse_sc <dbl>, smuse_wa <dbl>, smuse_tt <dbl>, smuse_rd <dbl>, …