Change column order in a survey design object

relocate() moves columns to a new position using the same tidyselect mini-language as select(). Design variables (weights, strata, PSUs) are not moved — only analysis columns change position.

Usage

relocate(.data, ..., .before = NULL, .after = NULL)

# S3 method for class 'survey_base'
relocate(.data, ..., .before = NULL, .after = NULL)

Arguments

.data: A survey_base object.
...: <tidy-select> Columns to move.
.before, .after: <tidy-select> A destination column. Columns in ... are placed immediately before or after it. Specify at most one of .before and .after.

Value

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

Rows are not modified.
All columns are present; only their order changes.
Design variables are not moved.
Groups and survey design attributes are preserved.

Details

Design variable positions

Design variables are always preserved at their current position in the underlying data. When you call relocate(), only non-design columns are affected by the reordering.

After `select()`

When select() has been called, relocate() reorders the visible columns (those shown when you print the object). This has no effect on the physical column order in the underlying data.

Examples

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

# Move agecat before gender
relocate(d, agecat, .before = gender)
#> 
#> ── 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>, …

# Move all social media columns to the front
relocate(d, dplyr::starts_with("smuse_"))
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 65
#>    smuse_fb smuse_yt smuse_x smuse_ig smuse_sc smuse_wa smuse_tt smuse_rd
#>       <dbl>    <dbl>   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
#>  1        2        1       2        2        2        2        2        1
#>  2        1        1       2        1        2        2        1        2
#>  3        1        1       1        1        2        1        2        2
#>  4        2        1       2        2        2        2        2        2
#>  5        1        1       1        1        1        2        2        2
#>  6       NA       NA      NA       NA       NA       NA       NA       NA
#>  7        1        1       2        1        2        1        2        2
#>  8        1        2       1        2        2        2        2        2
#>  9        1        1       2        2        1        2        2        2
#> 10        1        1       2        1        1        1        2        2
#> # ℹ 5,012 more rows
#> # ℹ 57 more variables: smuse_bsk <dbl>, smuse_th <dbl>, smuse_ts <dbl>,
#> #   respid <dbl>, mode <dbl>, language <dbl>, languageinitial <dbl>,
#> #   stratum <dbl>, interview_start <date>, interview_end <date>,
#> #   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>, …

# After select(), relocate reorders the visible columns
d |>
  select(gender, agecat, partysum) |>
  relocate(partysum, .before = gender)
#> 
#> ── Survey Design ───────────────────────────────────────────────────────────────
#> <survey_taylor> (Taylor series linearization)
#> Sample size: 5022
#> 
#> # A tibble: 5,022 × 3
#>    partysum gender agecat
#>       <dbl>  <dbl>  <dbl>
#>  1        2      2      4
#>  2        2      1      4
#>  3        1      2      4
#>  4        1      1      2
#>  5        1      1      4
#>  6        1      1      4
#>  7        2      1      3
#>  8        1      2      4
#>  9        1      2      3
#> 10        2      2      2
#> # ℹ 5,012 more rows
#> 
#> ℹ Design variables preserved but hidden: weight and stratum.
#> ℹ Use `print(x, full = TRUE)` to show all variables.