rowwise() enables row-by-row computation in mutate(). Each row is
treated as an independent group, so expressions like
mutate(d, row_max = max(c_across(starts_with("y")))) compute the maximum
across columns for each row independently.
Use ungroup() or group_by() to exit rowwise mode.
Arguments
- data
A
survey_baseobject.- ...
<
tidy-select> Optional id columns that identify each row (used withdplyr::c_across()). Commonly omitted.
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.
See also
Other grouping:
group_by,
is_grouped(),
is_rowwise()
Examples
library(surveytidy)
library(surveycore)
library(dplyr)
d <- as_survey(pew_npors_2025, weights = weight, strata = stratum)
# Row-wise max across several columns
d |>
rowwise() |>
mutate(row_max = max(c_across(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>, …