Compute row-wise on a survey design object

rowwise() enables row-by-row computation in mutate(). Each row is treated as an independent group, so expressions like mutate(d, row_max = max(dplyr::c_across(tidyselect::starts_with("y")))) compute the maximum across columns for each row independently.

Use ungroup() or group_by() to exit rowwise mode.

Usage

rowwise(data, ...)

# S3 method for class 'survey_base'
rowwise(data, ...)

# S3 method for class 'survey_collection'
rowwise(data, ..., .if_missing_var = NULL)

Arguments

data: A survey_base object.
...: <tidy-select> Optional id columns that identify each row (used with dplyr::c_across()). Commonly omitted.
.if_missing_var: Per-call override of collection@if_missing_var. One of "error" or "skip", or NULL (the default) to inherit the collection's stored value. See surveycore::set_collection_if_missing_var().

Value

data with @variables$rowwise = TRUE and @variables$rowwise_id_cols set. All other properties are unchanged.

Details

Storage

Rowwise mode is stored in @variables$rowwise (logical TRUE) and @variables$rowwise_id_cols (character vector of id column names). @groups is not modified — rowwise mode is independent of grouping.

Exiting rowwise mode

ungroup(d) — exits rowwise mode and removes all groups.
group_by(d, ...) — exits rowwise mode and sets new groups.
group_by(d, ..., .add = TRUE) — promotes id columns to groups, then appends the new groups, then exits rowwise mode.

mutate() behaviour

mutate() detects rowwise mode and routes internally through dplyr::rowwise(@data) before calling dplyr::mutate(). The rowwise_df class is stripped from @data after mutation so subsequent operations are not accidentally rowwise.

Survey collections

When applied to a survey_collection, rowwise() is dispatched to each member independently — every member receives @variables$rowwise = TRUE and the same @variables$rowwise_id_cols. The collection has no rowwise marker; rowwise state lives entirely per-member. @groups, @id, and @if_missing_var on the collection are unchanged.

Construction-time uniformity is by-construction: every member is rowwise after the call. Mixed rowwise state across members is detected later by mutate() (see §IV.5 of the survey-collection spec) and warned about rather than blocked.

Examples

# create a survey object from the bundled NPORS dataset
d <- surveycore::as_survey(
  surveycore::pew_npors_2025,
  weights = weight,
  strata = stratum
)

# row-wise max across several columns
d |>
  rowwise() |>
  mutate(
    row_max = max(dplyr::c_across(tidyselect::starts_with("econ")), na.rm = TRUE)
  )
#> 
#> ── 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>, …

# exit rowwise mode
d |>
  rowwise() |>
  ungroup()
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 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  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 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>, …