tidyverse / tidyverse/ggplot2

use label attribute with facet strip labels

Open
#6,783 2 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

facets :gem:
Dominant language
R
Stars
7k
Forks
2.1k
Avg merge
59m
Merged PRs (30d)
2

Description

In situations when variables have label attributes, I would like to be able to use the label attributes in facet strip labels with labeller = label_both

For example, in the reprex below, I would like the facet strip labels to read "Iris Species: setosa", etc.:

library(tidyverse); library(labelled)
iris |> 
    mutate(Species = Species |> labelled::set_label_attribute("Iris Species")) |> 
    ggplot() + 
    aes(x = Sepal.Length, y = Sepal.Width) + 
    geom_point() + 
    facet_wrap(~Species, labeller = label_both)

Created on 2025-12-19 with reprex v2.1.1

Session info

 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.5.2 (2025-10-31)
#>  os       macOS Sequoia 15.7.2
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       America/Los_Angeles
#>  date     2025-12-19
#>  pandoc   3.6.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
#>  quarto   1.8.26 @ /usr/local/bin/quarto
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package      * version    date (UTC) lib source
#>  cli            3.6.5      2025-04-23 [1] CRAN (R 4.5.0)
#>  curl           7.0.0      2025-08-19 [1] CRAN (R 4.5.0)
#>  digest         0.6.39     2025-11-19 [1] CRAN (R 4.5.2)
#>  dplyr        * 1.1.4      2023-11-17 [1] CRAN (R 4.5.0)
#>  evaluate       1.0.5      2025-08-27 [1] CRAN (R 4.5.0)
#>  farver         2.1.2      2024-05-13 [1] CRAN (R 4.5.0)
#>  fastmap        1.2.0      2024-05-15 [1] CRAN (R 4.5.0)
#>  forcats      * 1.0.1      2025-09-25 [1] CRAN (R 4.5.0)
#>  fs             1.6.6      2025-04-12 [1] CRAN (R 4.5.0)
#>  generics       0.1.4      2025-05-09 [1] CRAN (R 4.5.0)
#>  ggplot2      * 4.0.1.9000 2025-12-13 [1] Github (tidyverse/ggplot2@60f5ed5)
#>  glue           1.8.0      2024-09-30 [1] CRAN (R 4.5.0)
#>  gtable         0.3.6      2024-10-25 [1] CRAN (R 4.5.0)
#>  haven          2.5.5      2025-05-30 [1] CRAN (R 4.5.0)
#>  hms            1.1.4      2025-10-17 [1] CRAN (R 4.5.0)
#>  htmltools      0.5.9      2025-12-04 [1] CRAN (R 4.5.2)
#>  knitr          1.50       2025-03-16 [1] CRAN (R 4.5.0)
#>  labeling       0.4.3      2023-08-29 [1] CRAN (R 4.5.0)
#>  labelled     * 2.16.0     2025-10-22 [1] CRAN (R 4.5.0)
#>  lifecycle      1.0.4      2023-11-07 [1] CRAN (R 4.5.0)
#>  lubridate    * 1.9.4      2024-12-08 [1] CRAN (R 4.5.0)
#>  magrittr       2.0.4      2025-09-12 [1] CRAN (R 4.5.0)
#>  pillar         1.11.1     2025-09-17 [1] CRAN (R 4.5.0)
#>  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.5.0)
#>  purrr        * 1.2.0      2025-11-04 [1] CRAN (R 4.5.0)
#>  R6             2.6.1      2025-02-15 [1] CRAN (R 4.5.0)
#>  RColorBrewer   1.1-3      2022-04-03 [1] CRAN (R 4.5.0)
#>  readr        * 2.1.6      2025-11-14 [1] CRAN (R 4.5.2)
#>  reprex         2.1.1      2024-07-06 [1] CRAN (R 4.5.0)
#>  rlang          1.1.6      2025-04-11 [1] CRAN (R 4.5.0)
#>  rmarkdown      2.30       2025-09-28 [1] CRAN (R 4.5.0)
#>  rstudioapi     0.17.1     2024-10-22 [1] CRAN (R 4.5.0)
#>  S7             0.2.1      2025-11-14 [1] CRAN (R 4.5.2)
#>  scales         1.4.0      2025-04-24 [1] CRAN (R 4.5.0)
#>  sessioninfo    1.2.3      2025-02-05 [1] CRAN (R 4.5.0)
#>  stringi        1.8.7      2025-03-27 [1] CRAN (R 4.5.0)
#>  stringr      * 1.6.0      2025-11-04 [1] CRAN (R 4.5.0)
#>  tibble       * 3.3.0      2025-06-08 [1] CRAN (R 4.5.0)
#>  tidyr        * 1.3.1      2024-01-24 [1] CRAN (R 4.5.0)
#>  tidyselect     1.2.1      2024-03-11 [1] CRAN (R 4.5.0)
#>  tidyverse    * 2.0.0      2023-02-22 [1] CRAN (R 4.5.0)
#>  timechange     0.3.0      2024-01-18 [1] CRAN (R 4.5.0)
#>  tzdb           0.5.0      2025-03-15 [1] CRAN (R 4.5.0)
#>  vctrs          0.6.5      2023-12-01 [1] CRAN (R 4.5.0)
#>  withr          3.0.2      2024-10-28 [1] CRAN (R 4.5.0)
#>  xfun           0.54       2025-10-30 [1] CRAN (R 4.5.0)
#>  xml2           1.5.1      2025-12-01 [1] CRAN (R 4.5.2)
#>  yaml           2.3.12     2025-12-10 [1] CRAN (R 4.5.2)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library
#>  * ── Packages attached to the search path.
#> 
#> ──────────────────────────────────────────────────────────────────────────────

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by running the supplied reprex using facet_wrap(~Species, labeller = label_both) and confirm the current facet labels. Trace the facet_wrap and label_both entry points to determine how the variable label attribute can affect the labels; done means the strips read "Iris Species: setosa", "Iris Species: versicolor", and "Iris Species: virginica".

Written by the indexing model from the issue text.

Assessment

Tech stack
r
Domain
data-visualization
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Clearly specified
Newbie friendliness
48/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.