Keep or drop rows using domain estimation

filter() and filter_out() mark rows as in or out of the survey domain without removing them. Unlike a standard data frame filter, all rows are always retained — only their domain status changes. Estimation functions restrict analysis to in-domain rows while using the full design for variance estimation.

filter() marks rows matching the condition as in-domain. filter_out() marks rows matching the condition as out-of-domain — it is the complement of filter(), and reads more naturally when the intent is exclusion.

Usage

# S3 method for class 'survey_base'
filter(.data, ..., .by = NULL, .preserve = FALSE)

# S3 method for class 'survey_result'
filter(.data, ...)

# S3 method for class 'survey_base'
filter_out(.data, ..., .by = NULL, .preserve = FALSE)

filter(.data, ..., .by = NULL, .preserve = FALSE)

Arguments

.data: A survey_base object, or a survey_result object returned by a surveycore estimation function.
...: <data-masking> Logical conditions evaluated against the survey data. Multiple conditions are combined with &. NA results are treated as FALSE.
.by: Not supported for survey objects. Use group_by() to add grouping.
.preserve: Ignored. Included for compatibility with the dplyr generic.

Value

An object of the same type as .data with the following properties:

All rows appear in the output.
Domain status of each row may be updated.
Columns are not modified.
Groups are not modified.
Survey design attributes are preserved.

Details

Chaining

Multiple calls accumulate via AND: a row must satisfy every condition to remain in-domain. These are equivalent:

filter(d, ridageyr >= 18, riagendr == 2)
filter(d, ridageyr >= 18) |> filter(riagendr == 2)

Missing values

Unlike base [, both functions treat NA as FALSE: rows where the condition evaluates to NA are treated as out-of-domain.

Useful filter functions

Comparisons: ==, >, >=, <, <=, !=
Logical: &, |, !, xor()
Missing values: is.na()
Range: dplyr::between(), dplyr::near()
Multi-column: dplyr::if_any(), dplyr::if_all()

Inspecting the domain

The domain status of each row is stored in the ..surveycore_domain.. column of @data. TRUE means in-domain; FALSE means out-of-domain.

Examples

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

# Keep adults 50 and older
filter(d, agecat >= 3)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …

# Multiple conditions are AND-ed together
filter(d, agecat >= 3, gender == 2)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …

# filter_out() excludes matching rows — complement of filter()
filter_out(d, agecat == 1)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …

# Chained calls accumulate (these are equivalent)
filter(d, agecat >= 3, gender == 2)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …
filter(d, agecat >= 3) |> filter(gender == 2)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …

# Multi-column conditions with if_any() and if_all()
filter(d, dplyr::if_any(c(smuse_fb, smuse_yt), ~ !is.na(.x)))
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …
filter(d, dplyr::if_all(c(smuse_fb, smuse_yt), ~ !is.na(.x)))
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 66
#>    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 59 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>, …