Order rows using column values

arrange() orders the rows of a survey_base object by the values of selected columns.

Unlike most other verbs, arrange() largely ignores grouping — use .by_group = TRUE to sort by grouping variables first.

Usage

# S3 method for class 'survey_base'
arrange(.data, ..., .by_group = FALSE, .locale = NULL)

# S3 method for class 'survey_result'
arrange(.data, ..., .by_group = FALSE)

# S3 method for class 'survey_collection'
arrange(.data, ..., .by_group = FALSE, .locale = NULL, .if_missing_var = NULL)

arrange(.data, ..., .by_group = FALSE)

Arguments

.data: A survey_base object, or a survey_result object returned by a surveycore estimation function.
...: <data-masking> Variables, or functions of variables. Use desc() to sort a variable in descending order.
.by_group: If TRUE, sorts first by the grouping variables set by group_by().
.locale: The locale to use for ordering strings. If NULL, uses the "C" locale. See stringi::locale() for available locales.
.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

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

All rows appear in the output, usually in a different position.
Columns are not modified.
Groups are not modified.
Survey design attributes are preserved.

Details

Missing values

Unlike base sort(), NA values are always sorted to the end, even when using desc().

Domain column

The domain column moves with the rows — row reordering does not affect which rows are in or out of the survey domain.

Survey collections

When applied to a survey_collection, arrange() is dispatched to each member independently. Each member's rows are sorted in place; per-member domain columns travel with the sorted rows. The output survey_collection preserves the input's @id, @if_missing_var, and @groups. Use .if_missing_var to override the collection's stored missing-variable behavior for this call.

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)

# sort by age category ascending
arrange(d, agecat)
#> 
#> ── 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  13673     1        1               9      10 2025-03-02      2025-03-02   
#>  2   9247     1        1               9       7 2025-03-03      2025-03-03   
#>  3  14402     1        1               9      10 2025-03-09      2025-03-09   
#>  4   2820     1        1               9       9 2025-02-24      2025-02-24   
#>  5  12802     1        1               9       6 2025-02-24      2025-02-24   
#>  6   7003     1        1               9       7 2025-02-25      2025-02-25   
#>  7   8391     1        2              10       7 2025-03-14      2025-03-14   
#>  8   2499     1        1               9      10 2025-02-25      2025-02-25   
#>  9     80     1        1               9       1 2025-03-12      2025-03-12   
#> 10   5357     1        1               9      10 2025-03-15      2025-03-15   
#> # 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>, …

# sort by age category descending
arrange(d, dplyr::desc(agecat))
#> 
#> ── 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  17184     1        1               9      10 2025-02-24      2025-02-24   
#>  2   3144     1        1               9      10 2025-02-26      2025-02-26   
#>  3  10038     1        1               9      10 2025-02-24      2025-02-24   
#>  4  17831     1        1               9      10 2025-02-27      2025-02-27   
#>  5   3251     1        1               9       2 2025-03-10      2025-03-10   
#>  6  17144     1        2               9       3 2025-02-27      2025-02-27   
#>  7   2383     1        1               9      10 2025-02-25      2025-02-25   
#>  8   7856     1        1               9      10 2025-03-18      2025-03-18   
#>  9   2301     3        1              10       6 2025-03-25      2025-03-25   
#> 10   2206     1        1               9      10 2025-04-01      2025-04-01   
#> # 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>, …

# sort by multiple variables
arrange(d, gender, dplyr::desc(agecat))
#> 
#> ── 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  10038     1        1               9      10 2025-02-24      2025-02-24   
#>  2  17831     1        1               9      10 2025-02-27      2025-02-27   
#>  3   2301     3        1              10       6 2025-03-25      2025-03-25   
#>  4   2206     1        1               9      10 2025-04-01      2025-04-01   
#>  5  15100     1        1               9      10 2025-02-22      2025-02-22   
#>  6  17489     1        1               9      10 2025-03-03      2025-03-03   
#>  7  18269     1        1               9       9 2025-03-09      2025-03-09   
#>  8   8270     1        1               9       8 2025-03-21      2025-03-21   
#>  9  12606     1        1               9      10 2025-03-05      2025-03-05   
#> 10  13052     1        1               9      10 2025-03-28      2025-03-28   
#> # 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>, …

# sort by grouping variables first
d_grouped <- group_by(d, gender)
arrange(d_grouped, .by_group = TRUE, agecat)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> Groups: gender
#> 
#> # A tibble: 5,022 × 65
#>    respid  mode language languageinitial stratum interview_start interview_end
#>     <dbl> <dbl>    <dbl>           <dbl>   <dbl> <date>          <date>       
#>  1  13673     1        1               9      10 2025-03-02      2025-03-02   
#>  2   9247     1        1               9       7 2025-03-03      2025-03-03   
#>  3   2499     1        1               9      10 2025-02-25      2025-02-25   
#>  4  10841     1        1               9       5 2025-03-25      2025-03-25   
#>  5   5576     1        1               9      10 2025-02-27      2025-02-27   
#>  6  11753     2        1              NA       5 2025-05-01      2025-05-01   
#>  7  16776     2        1              NA      10 2025-05-02      2025-05-02   
#>  8  11751     1        1               9       9 2025-02-28      2025-02-28   
#>  9  14386     1        1               9       7 2025-02-24      2025-02-24   
#> 10   8965     2        1              NA       2 2025-05-20      2025-05-20   
#> # 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>, …