facet_wrap ?

[ertamaki]

Is there an equivalent for facet_wrap?

[tashapiro]

I was browsing for a similar answer earlier. I discovered you can use ggplot's facet_wrap function in combination with ggradar to produce facets:

ggradar()+
facet_wrap(vars(group))

[nickschurch]

How do you provide the grouping info though? When I try to provide a dataframe with a samples column and a group column, I get the error:

Error in FUN(X[[i]], ...) :
only defined on a data frame with all numeric-alike variables
6.
stop("only defined on a data frame with all numeric-alike variables")
5.
FUN(X[[i]], ...)
4.
lapply(args, function(x) {
x <- as.matrix(x)
if (!is.numeric(x) && !is.logical(x) && !is.complex(x))
stop("only defined on a data frame with all numeric-alike variables") ...
3.
Summary.data.frame(structure(list(na = c(4.58, 13.45, 8.35, 8.58,
7.76, 11.6, 3.91, 3.04, 4.1, 2.9, 5.17, 3.44, 6.93, 1.9, 10.35,
8.58, 9.72, 7.01, 1.5, 1.98, 2.11, 3.13, 1.47, 1.12, 0.98, 1.21,
1.04, 1.63, 1.49, 1.34, 2.51, 1.78, 2.81, 2.61, 2.12, 2.72, 3.14, ...
2.
ggradar(., grid.min = 0, grid.max = 100)
1.
data %>%
ggradar(grid.min = 0, grid.max = 100)

[williamlai2]

Was there an answer to this? I have a similar issue:

mtcars_radar <- mtcars %>% 
  tibble::as_tibble(rownames = "group") %>% 
  dplyr::mutate_at(vars(-group), scales::rescale) %>% 
  tail(4) %>% 
  dplyr::select(1:10)

ggradar::ggradar(mtcars_radar) +
  ggplot2::facet_wrap(vars(group))

Error in combine_vars():
! At least one layer must contain all faceting variables: group
x Plot is missing group
x Layer 1 is missing group
x Layer 2 is missing group
x Layer 3 is missing group
x Layer 4 is missing group
x Layer 5 is missing group
x Layer 6 is missing group
x Layer 7 is missing group
x Layer 8 is missing group
x Layer 9 is missing group
x Layer 10 is missing group
x Layer 11 is missing group
x Layer 12 is missing group
x Layer 13 is missing group
Run rlang::last_trace() to see where the error occurred.

Backtrace:
     x
  1. +-base (local) `<fn>`(x)
  2. \-ggplot2:::print.ggplot(x)
  3.   +-ggplot2::ggplot_build(x)
  4.   \-ggplot2:::ggplot_build.ggplot(x)
  5.     \-layout$setup(data, plot$data, plot$plot_env)
  6.       \-ggplot2 (local) setup(..., self = self)
  7.         \-self$facet$compute_layout(data, self$facet_params)
  8.           \-ggplot2 (local) compute_layout(..., self = self)
  9.             \-ggplot2::combine_vars(data, params$plot_env, vars, drop = params$drop)
 10.               \-cli::cli_abort(...)
 11.                 \-rlang::abort(...)

> packageVersion("ggradar")
[1] ‘0.2’
> packageVersion("ggplot2")
[1] ‘3.5.0’

[Iaguilaror]

Same error. Here is the code:

radar2 <- radar1 +
ggtitle( "Diferencias en las Evoluciones de Eevee" ) +
scale_color_manual( values = miscolores ) +
facet_wrap( ~ name )

radar2

`

radar2
Error in combine_vars():
! At least one layer must contain all faceting variables:
group
✖ Plot is missing group
✖ Layer 1 is missing group
✖ Layer 2 is missing group
✖ Layer 3 is missing group
✖ Layer 4 is missing group
✖ Layer 5 is missing group
✖ Layer 6 is missing group
✖ Layer 7 is missing group
✖ Layer 8 is missing group
✖ Layer 9 is missing group
✖ Layer 10 is missing group
✖ Layer 11 is missing group
✖ Layer 12 is missing group
✖ Layer 13 is missing group
Run rlang::last_trace() to see where the error occurred.
`

Session info:

`

session_info()
─ Session info ───────────────────────────────────────────────────
setting value
version R version 4.3.2 (2023-10-31 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RStudio
language (EN)
collate Spanish_Mexico.utf8
ctype Spanish_Mexico.utf8
tz America/Mexico_City
date 2024-07-17
rstudio 2023.12.1+402 Ocean Storm (desktop)
pandoc NA

Packages ───────────────────────────────────────────────────────
package * version date (UTC) lib source
bit 4.0.5 2022-11-15 [1] CRAN (R 4.3.2)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.3.2)
cachem 1.0.8 2023-05-01 [1] CRAN (R 4.3.2)
callr 3.7.6 2024-03-25 [1] CRAN (R 4.3.3)
cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.2)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.2)
crayon 1.5.3 2024-06-20 [1] CRAN (R 4.3.3)
curl 5.2.0 2023-12-08 [1] CRAN (R 4.3.2)
desc 1.4.3 2023-12-10 [1] CRAN (R 4.3.2)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.3.3)
digest 0.6.36 2024-06-23 [1] CRAN (R 4.3.3)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.2)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.3.2)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.2)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.3.3)
fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.2)
forcats 1.0.0 2023-01-29 [1] CRAN (R 4.3.3)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.3.3)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.2)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.3.3)
ggradar * 0.2 2024-07-18 [1] Github (f99517a)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.2)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.3.3)
htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.2)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.3.3)
httpuv 1.6.14 2024-01-26 [1] CRAN (R 4.3.2)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.3.1)
later 1.3.2 2023-12-06 [1] CRAN (R 4.3.2)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.2)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.2)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.3.2)
mime 0.12 2021-09-28 [1] CRAN (R 4.3.1)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.3.3)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.3.3)
pacman * 0.5.1 2019-03-11 [1] CRAN (R 4.3.3)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.2)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.3.3)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.2)
pkgload 1.4.0 2024-06-28 [1] CRAN (R 4.3.3)
processx 3.8.4 2024-03-16 [1] CRAN (R 4.3.3)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.3.3)
promises 1.2.1 2023-08-10 [1] CRAN (R 4.3.2)
ps 1.7.7 2024-07-02 [1] CRAN (R 4.3.3)
purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.2)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.2)
ragg 1.3.2 2024-05-15 [1] CRAN (R 4.3.3)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.3.1)
Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.3.2)
remotes 2.4.2.1 2023-07-18 [1] CRAN (R 4.3.2)
rlang 1.1.4 2024-06-04 [1] CRAN (R 4.3.3)
rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.3.2)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.3.2)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.3)
shiny 1.8.0 2023-11-17 [1] CRAN (R 4.3.2)
stringi 1.8.3 2023-12-11 [1] CRAN (R 4.3.2)
stringr 1.5.1 2023-11-14 [1] CRAN (R 4.3.2)
systemfonts 1.1.0 2024-05-15 [1] CRAN (R 4.3.3)
textshaping 0.3.7 2023-10-09 [1] CRAN (R 4.3.3)
tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.2)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.3)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.2)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.3.3)
usethis * 2.2.3 2024-02-19 [1] CRAN (R 4.3.3)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.2)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.2)
vroom * 1.6.5 2023-12-05 [1] CRAN (R 4.3.2)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.2)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.3.2)

[1] C:/Users/iaguilar/AppData/Local/R/win-library/4.3
[2] C:/Program Files/R/R-4.3.2/library
`

Thanks for your time.

[jczimm]

@williamlai2 From inspecting the returned gg object, I found a workaround (I only switched away from mutate_at because it was superceded):

mtcars_radar <- mtcars %>% 
  tibble::as_tibble(rownames = "group") %>% 
  dplyr::mutate(across(-group, scales::rescale)) %>% 
  tail(4) %>%
  dplyr::select(1:10)
ggradar::ggradar(mtcars_radar) +
  ggplot2::facet_wrap(~`Group.1`)

The key is that from ggplot's perspective, the grouping variable is actually named "Group.1" (source).

> R.version.string
[1] "R version 4.2.3 (2023-03-15 ucrt)"
> packageVersion("tibble")
[1] ‘3.2.1’
> packageVersion("dplyr")
[1] ‘1.1.1’
> packageVersion("ggradar")
[1] ‘0.2’