library(tidyverse)
library(labelled)

字符串
创建一个data.frame，其变量标签与您的描述相似。

same <-
  '1.5 Which actions present the greatest opportunity to advance this scenario\'s goal? Select up to 3 of your previously selected actions, drag them into the box, and rank them in order of their importance for this goal (1=most important). Scenario Context & Assumptions Imagine yourself as a manager responsible for planning all street tree management on a hypothetical street in your region for the next 20 years. The street has a sidewalk and low-rise development, with buildings that are approximately 1-3 stories tall. In this mostly perfect world, you have full control over the trees adjacent to the sidewalk. Within these scenarios, assume the following will be true: Each action you select is in comparison to not doing the actions at all Actions will be performed according to industry standard" Trees will not be removed by events such as vandalism, new construction, extreme storms, or pest invasions Newly planted trees will be watered through establishment and as needed for survival Urban form will not change (street, pavement, buildings will remain the same with reasonable maintenance) Species planted will be adapted for any change in hardiness zone projected in 20 years due to climate change - Ranks - Opportunities to achieve this scenario\'s goal (rank 3) - '

var_labels <- paste(same, "plant native species", 1:10)

var_names <- paste("var", 1:10, sep = "_")

df <-
  var_names |>
  lapply(\(x) tibble(a = 1:10) |> set_names(x)) |>
  bind_cols() |>
  set_variable_labels(.labels = var_labels)

型
我们可以使用labelled::get_variable_labels()获取所有变量标签，然后使用正则表达式（RegEx）使用stringr::str_remove()修改它们，正如user 20650在其评论中所建议的那样。正则表达式.*-指示str_remove()删除所有字符，直到它遇到最后一个破折号/连字符。如果所有变量标签都用破折号分隔最后一位，则这应该适用于所有标签。

short_var_labels <-
  df |>
  get_variable_labels() |>
  str_remove(".*-") |>
  trimws()

short_var_labels
#>  [1] "plant native species 1"  "plant native species 2" 
#>  [3] "plant native species 3"  "plant native species 4" 
#>  [5] "plant native species 5"  "plant native species 6" 
#>  [7] "plant native species 7"  "plant native species 8" 
#>  [9] "plant native species 9"  "plant native species 10"

型
现在我们可以将短变量标签应用于data.frame。

df <-
  df |>
  set_variable_labels(.labels = short_var_labels)

get_variable_labels(df)
#> $var_1
#> [1] "plant native species 1"
#> 
#> $var_2
#> [1] "plant native species 2"
#> 
#> $var_3
#> [1] "plant native species 3"
#> 
#> $var_4
#> [1] "plant native species 4"
#> 
#> $var_5
#> [1] "plant native species 5"
#> 
#> $var_6
#> [1] "plant native species 6"
#> 
#> $var_7
#> [1] "plant native species 7"
#> 
#> $var_8
#> [1] "plant native species 8"
#> 
#> $var_9
#> [1] "plant native species 9"
#> 
#> $var_10
#> [1] "plant native species 10"

型