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Large tibble with very few non-missings shows 100% missing in miss_var_summary() #284

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jzadra opened this issue May 12, 2021 · 5 comments
Closed

Large tibble with very few non-missings shows 100% missing in miss_var_summary() #284

jzadra opened this issue May 12, 2021 · 5 comments
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maintenance Priority 1
Milestone
V1.1.0

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@jzadra
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jzadra commented May 12, 2021 •
edited
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I've been puzzling over why I was seeing a column with 100 percent missing data in a large tibble (~30,000 rows) made up of several tibbles combined with bind_rows() despite seeing that one of the individual tibbles does not show 100% missing for that column.

After a bunch of wild goose chases, I realized that the issue was that miss_var_summary() (and probably similar naniar functions) was rounding the pct_miss column up.

library(tidyverse)
library(naniar)

df <- tibble(x = rep(NA_real_, 30000)) %>% 
  add_row(x = 0)

df %>% miss_var_summary()
#> # A tibble: 1 x 3
#>   variable n_miss pct_miss
#>   <chr>     <int>    <dbl>
#> 1 x         30000     100.

df %>% filter(!is.na(x))
#> # A tibble: 1 x 1
#>       x
#>   <dbl>
#> 1     0

In this example, the percent missing is actually 99.9967%.

All that to say, I'd like to suggest that for the edge cases of near-zero and near-100 percent missings not be rounded to avoid this confusion.
@njtierney
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Thanks for the reprex! I can reproduce this.

This is actually an issue with how tibble prints - if we coerce to a data.frame we get the value back out.

library(tidyverse)
library(naniar)

df <- tibble(x = rep(NA_real_, 30000)) %>% 
  add_row(x = 0)

df %>% miss_var_summary() 
#> # A tibble: 1 x 3
#>   variable n_miss pct_miss
#>   <chr>     <int>    <dbl>
#> 1 x         30000     100.

df %>% miss_var_summary() %>% as.data.frame()
#>   variable n_miss pct_miss
#> 1        x  30000 99.99667

Created on 2021-05-13 by the reprex package (v2.0.0)

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value                       
#>  version  R version 4.0.5 (2021-03-31)
#>  os       macOS Big Sur 10.16         
#>  system   x86_64, darwin17.0          
#>  ui       X11                         
#>  language (EN)                        
#>  collate  en_AU.UTF-8                 
#>  ctype    en_AU.UTF-8                 
#>  tz       Australia/Perth             
#>  date     2021-05-13                  
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package     * version    date       lib source            
#>  assertthat    0.2.1      2019-03-21 [1] standard (@0.2.1) 
#>  backports     1.2.1      2020-12-09 [1] standard (@1.2.1) 
#>  broom         0.7.5      2021-02-19 [1] CRAN (R 4.0.2)    
#>  cellranger    1.1.0      2016-07-27 [1] standard (@1.1.0) 
#>  cli           2.5.0      2021-04-26 [1] CRAN (R 4.0.2)    
#>  colorspace    2.0-0      2020-11-11 [1] standard (@2.0-0) 
#>  crayon        1.4.1      2021-02-08 [1] CRAN (R 4.0.2)    
#>  DBI           1.1.1      2021-01-15 [1] CRAN (R 4.0.2)    
#>  dbplyr        2.1.0      2021-02-03 [1] CRAN (R 4.0.2)    
#>  digest        0.6.27     2020-10-24 [1] standard (@0.6.27)
#>  dplyr       * 1.0.6      2021-05-05 [1] CRAN (R 4.0.2)    
#>  ellipsis      0.3.1      2020-05-15 [1] standard (@0.3.1) 
#>  evaluate      0.14       2019-05-28 [1] standard (@0.14)  
#>  fansi         0.4.2      2021-01-15 [1] CRAN (R 4.0.2)    
#>  forcats     * 0.5.1      2021-01-27 [1] CRAN (R 4.0.2)    
#>  fs            1.5.0      2020-07-31 [1] standard (@1.5.0) 
#>  generics      0.1.0      2020-10-31 [1] standard (@0.1.0) 
#>  ggplot2     * 3.3.3      2020-12-30 [1] CRAN (R 4.0.2)    
#>  glue          1.4.2      2020-08-27 [1] standard (@1.4.2) 
#>  gtable        0.3.0      2019-03-25 [1] standard (@0.3.0) 
#>  haven         2.3.1      2020-06-01 [1] standard (@2.3.1) 
#>  highr         0.8        2019-03-20 [1] standard (@0.8)   
#>  hms           1.0.0      2021-01-13 [1] CRAN (R 4.0.2)    
#>  htmltools     0.5.1.1    2021-01-22 [1] CRAN (R 4.0.2)    
#>  httr          1.4.2      2020-07-20 [1] standard (@1.4.2) 
#>  jsonlite      1.7.2      2020-12-09 [1] standard (@1.7.2) 
#>  knitr         1.31       2021-01-27 [1] CRAN (R 4.0.2)    
#>  lifecycle     1.0.0      2021-02-15 [1] CRAN (R 4.0.2)    
#>  lubridate     1.7.10     2021-02-26 [1] CRAN (R 4.0.2)    
#>  magrittr      2.0.1      2020-11-17 [1] standard (@2.0.1) 
#>  modelr        0.1.8      2020-05-19 [1] standard (@0.1.8) 
#>  munsell       0.5.0      2018-06-12 [1] standard (@0.5.0) 
#>  naniar      * 0.6.0.9000 2020-12-23 [1] local             
#>  pillar        1.6.0      2021-04-13 [1] CRAN (R 4.0.2)    
#>  pkgconfig     2.0.3      2019-09-22 [1] standard (@2.0.3) 
#>  purrr       * 0.3.4      2020-04-17 [1] standard (@0.3.4) 
#>  R6            2.5.0      2020-10-28 [1] standard (@2.5.0) 
#>  Rcpp          1.0.6      2021-01-15 [1] CRAN (R 4.0.2)    
#>  readr       * 1.4.0      2020-10-05 [1] standard (@1.4.0) 
#>  readxl        1.3.1      2019-03-13 [1] standard (@1.3.1) 
#>  reprex        2.0.0      2021-04-02 [1] CRAN (R 4.0.2)    
#>  rlang         0.4.11     2021-04-30 [1] CRAN (R 4.0.2)    
#>  rmarkdown     2.7        2021-02-19 [1] CRAN (R 4.0.2)    
#>  rstudioapi    0.13       2020-11-12 [1] standard (@0.13)  
#>  rvest         1.0.0      2021-03-09 [1] CRAN (R 4.0.2)    
#>  scales        1.1.1      2020-05-11 [1] standard (@1.1.1) 
#>  sessioninfo   1.1.1      2018-11-05 [1] standard (@1.1.1) 
#>  stringi       1.5.3      2020-09-09 [1] standard (@1.5.3) 
#>  stringr     * 1.4.0      2019-02-10 [1] standard (@1.4.0) 
#>  styler        1.4.1      2021-03-30 [1] CRAN (R 4.0.2)    
#>  tibble      * 3.1.1      2021-04-18 [1] CRAN (R 4.0.3)    
#>  tidyr       * 1.1.3      2021-03-03 [1] CRAN (R 4.0.2)    
#>  tidyselect    1.1.0      2020-05-11 [1] standard (@1.1.0) 
#>  tidyverse   * 1.3.0      2019-11-21 [1] standard (@1.3.0) 
#>  utf8          1.2.1      2021-03-12 [1] CRAN (R 4.0.2)    
#>  vctrs         0.3.8      2021-04-29 [1] CRAN (R 4.0.2)    
#>  visdat        0.5.3      2019-02-15 [1] CRAN (R 4.0.2)    
#>  withr         2.4.2      2021-04-18 [1] CRAN (R 4.0.3)    
#>  xfun          0.22       2021-03-11 [1] CRAN (R 4.0.2)    
#>  xml2          1.3.2      2020-04-23 [1] standard (@1.3.2) 
#>  yaml          2.2.1      2020-02-01 [1] standard (@2.2.1) 
#> 
#> [1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library

I'll cross post this to tibble, this isn't ideal behaviour, thanks for reporting.

@njtierney njtierney mentioned this issue May 13, 2021
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@krlmlr
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krlmlr commented Aug 1, 2021

Black hat: more power

library(tidyverse)
library(naniar)

N <- 30000000

df <- tibble(x = rep(NA_real_, N)) %>%
  add_row(x = 0)

df %>% miss_var_summary()
#> # A tibble: 1 × 3
#>   variable   n_miss pct_miss
#>   <chr>       <int>    <dbl>
#> 1 x        30000000     100.

df %>%
  miss_var_summary() %>%
  as.data.frame()
#>   variable   n_miss pct_miss
#> 1        x 30000000      100

df %>%
  miss_var_summary() %>%
  mutate(pct_miss = num(pct_miss, digits = trunc(log10(N) + 2)))
#> # A tibble: 1 × 3
#>   variable   n_miss     pct_miss
#>   <chr>       <int>    <num:.9!>
#> 1 x        30000000 99.999996667

df %>%
  miss_var_summary() %>%
  mutate(pct_miss = num(pct_miss, digits = trunc(log10(N) + 2))) %>%
  as.data.frame()
#>   variable   n_miss     pct_miss
#> 1        x 30000000 99.999996667

Created on 2021-08-01 by the reprex package (v2.0.0.9000)

pillar::num() (reexported as tibble::num()) allow specifying arbitrary digits or significant figures in this specific example.

@krlmlr
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krlmlr commented Aug 1, 2021

You could also store pct_miss as a value between 0 and 1 with num(scale = 100) .

@njtierney
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Owner

Oh that's awesome! Thanks so much, @krlmlr - I'll add this feature in the next release.

@njtierney njtierney reopened this Aug 2, 2021
@njtierney njtierney added this to the V0.7.0 milestone Oct 14, 2022
@njtierney
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Owner

OK so here is the old way

library(tidyverse)
library(naniar)

N <- 30000000

df <- tibble(x = rep(NA_real_, N)) %>%
  add_row(x = 0)

df %>% miss_var_summary()
#> # A tibble: 1 × 3
#>   variable   n_miss pct_miss
#>   <chr>       <int>    <dbl>
#> 1 x        30000000     100.

df %>%
  miss_var_summary() %>%
  as.data.frame()
#>   variable   n_miss pct_miss
#> 1        x 30000000      100

Created on 2023-04-10 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.3 (2023-03-15)
#>  os       macOS Ventura 13.2
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Australia/Hobart
#>  date     2023-04-10
#>  pandoc   2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package       * version    date (UTC) lib source
#>  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.0)
#>  backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)
#>  broom           1.0.3      2023-01-25 [1] CRAN (R 4.2.0)
#>  cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
#>  cli             3.6.0      2023-01-09 [1] CRAN (R 4.2.0)
#>  colorspace      2.1-0      2023-01-23 [1] CRAN (R 4.2.0)
#>  crayon          1.5.2      2022-09-29 [1] CRAN (R 4.2.0)
#>  DBI             1.1.3      2022-06-18 [1] CRAN (R 4.2.0)
#>  dbplyr          2.3.0      2023-01-16 [1] CRAN (R 4.2.0)
#>  digest          0.6.31     2022-12-11 [1] CRAN (R 4.2.0)
#>  dplyr         * 1.1.1      2023-03-22 [1] CRAN (R 4.2.0)
#>  ellipsis        0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
#>  evaluate        0.20       2023-01-17 [1] CRAN (R 4.2.0)
#>  fansi           1.0.4      2023-01-22 [1] CRAN (R 4.2.0)
#>  fastmap         1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
#>  forcats       * 1.0.0      2023-01-29 [1] CRAN (R 4.2.0)
#>  fs              1.6.1      2023-02-06 [1] CRAN (R 4.2.0)
#>  gargle          1.3.0      2023-01-30 [1] CRAN (R 4.2.0)
#>  generics        0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
#>  ggplot2       * 3.4.1      2023-02-10 [1] CRAN (R 4.2.0)
#>  glue            1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
#>  googledrive     2.0.0      2021-07-08 [1] CRAN (R 4.2.0)
#>  googlesheets4   1.0.1      2022-08-13 [1] CRAN (R 4.2.0)
#>  gtable          0.3.1      2022-09-01 [1] CRAN (R 4.2.0)
#>  haven           2.5.1      2022-08-22 [1] CRAN (R 4.2.0)
#>  hms             1.1.2      2022-08-19 [1] CRAN (R 4.2.0)
#>  htmltools       0.5.4      2022-12-07 [1] CRAN (R 4.2.0)
#>  httr            1.4.4      2022-08-17 [1] CRAN (R 4.2.0)
#>  jsonlite        1.8.4      2022-12-06 [1] CRAN (R 4.2.0)
#>  knitr           1.42       2023-01-25 [1] CRAN (R 4.2.0)
#>  lifecycle       1.0.3      2022-10-07 [1] CRAN (R 4.2.0)
#>  lubridate       1.9.1      2023-01-24 [1] CRAN (R 4.2.0)
#>  magrittr        2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
#>  modelr          0.1.10     2022-11-11 [1] CRAN (R 4.2.0)
#>  munsell         0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
#>  naniar        * 1.0.0.9000 2023-04-10 [1] local
#>  pillar          1.8.1      2022-08-19 [1] CRAN (R 4.2.0)
#>  pkgconfig       2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
#>  purrr         * 1.0.1      2023-01-10 [1] CRAN (R 4.2.0)
#>  R.cache         0.16.0     2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3     1.8.2      2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo            1.25.0     2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils         2.12.2     2022-11-11 [1] CRAN (R 4.2.0)
#>  R6              2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
#>  readr         * 2.1.3      2022-10-01 [1] CRAN (R 4.2.0)
#>  readxl          1.4.1      2022-08-17 [1] CRAN (R 4.2.0)
#>  reprex          2.0.2      2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang           1.1.0      2023-03-14 [1] CRAN (R 4.2.0)
#>  rmarkdown       2.20       2023-01-19 [1] CRAN (R 4.2.0)
#>  rstudioapi      0.14       2022-08-22 [1] CRAN (R 4.2.0)
#>  rvest           1.0.3      2022-08-19 [1] CRAN (R 4.2.0)
#>  scales          1.2.1      2022-08-20 [1] CRAN (R 4.2.0)
#>  sessioninfo     1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
#>  stringi         1.7.12     2023-01-11 [1] CRAN (R 4.2.0)
#>  stringr       * 1.5.0      2022-12-02 [1] CRAN (R 4.2.0)
#>  styler          1.9.0      2023-01-15 [1] CRAN (R 4.2.0)
#>  tibble        * 3.2.1      2023-03-20 [1] CRAN (R 4.2.0)
#>  tidyr         * 1.3.0      2023-01-24 [1] CRAN (R 4.2.0)
#>  tidyselect      1.2.0      2022-10-10 [1] CRAN (R 4.2.0)
#>  tidyverse     * 1.3.2      2022-07-18 [1] CRAN (R 4.2.0)
#>  timechange      0.2.0      2023-01-11 [1] CRAN (R 4.2.0)
#>  tzdb            0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
#>  utf8            1.2.3      2023-01-31 [1] CRAN (R 4.2.0)
#>  vctrs           0.6.1      2023-03-22 [1] CRAN (R 4.2.0)
#>  visdat          0.6.0      2023-02-02 [1] local
#>  withr           2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
#>  xfun            0.37       2023-01-31 [1] CRAN (R 4.2.0)
#>  xml2            1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
#>  yaml            2.3.7      2023-01-23 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

And the new way

library(tidyverse)
library(naniar)

N <- 30000000

df <- tibble(x = rep(NA_real_, N)) %>%
  add_row(x = 0)

df %>% miss_var_summary()
#> # A tibble: 1 × 3
#>   variable   n_miss pct_miss
#>   <chr>       <int>    <num>
#> 1 x        30000000     100.
df %>% miss_var_summary(digits = 6)
#> # A tibble: 1 × 3
#>   variable   n_miss  pct_miss
#>   <chr>       <int> <num:.6!>
#> 1 x        30000000 99.999997

Created on 2023-04-10 with reprex v2.0.2

Session info 
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.2.3 (2023-03-15)
#>  os       macOS Ventura 13.2
#>  system   aarch64, darwin20
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Australia/Hobart
#>  date     2023-04-10
#>  pandoc   2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  package       * version    date (UTC) lib source
#>  assertthat      0.2.1      2019-03-21 [1] CRAN (R 4.2.0)
#>  backports       1.4.1      2021-12-13 [1] CRAN (R 4.2.0)
#>  broom           1.0.3      2023-01-25 [1] CRAN (R 4.2.0)
#>  cellranger      1.1.0      2016-07-27 [1] CRAN (R 4.2.0)
#>  cli             3.6.0      2023-01-09 [1] CRAN (R 4.2.0)
#>  colorspace      2.1-0      2023-01-23 [1] CRAN (R 4.2.0)
#>  crayon          1.5.2      2022-09-29 [1] CRAN (R 4.2.0)
#>  DBI             1.1.3      2022-06-18 [1] CRAN (R 4.2.0)
#>  dbplyr          2.3.0      2023-01-16 [1] CRAN (R 4.2.0)
#>  digest          0.6.31     2022-12-11 [1] CRAN (R 4.2.0)
#>  dplyr         * 1.1.1      2023-03-22 [1] CRAN (R 4.2.0)
#>  ellipsis        0.3.2      2021-04-29 [1] CRAN (R 4.2.0)
#>  evaluate        0.20       2023-01-17 [1] CRAN (R 4.2.0)
#>  fansi           1.0.4      2023-01-22 [1] CRAN (R 4.2.0)
#>  fastmap         1.1.0      2021-01-25 [1] CRAN (R 4.2.0)
#>  forcats       * 1.0.0      2023-01-29 [1] CRAN (R 4.2.0)
#>  fs              1.6.1      2023-02-06 [1] CRAN (R 4.2.0)
#>  gargle          1.3.0      2023-01-30 [1] CRAN (R 4.2.0)
#>  generics        0.1.3      2022-07-05 [1] CRAN (R 4.2.0)
#>  ggplot2       * 3.4.1      2023-02-10 [1] CRAN (R 4.2.0)
#>  glue            1.6.2      2022-02-24 [1] CRAN (R 4.2.0)
#>  googledrive     2.0.0      2021-07-08 [1] CRAN (R 4.2.0)
#>  googlesheets4   1.0.1      2022-08-13 [1] CRAN (R 4.2.0)
#>  gtable          0.3.1      2022-09-01 [1] CRAN (R 4.2.0)
#>  haven           2.5.1      2022-08-22 [1] CRAN (R 4.2.0)
#>  hms             1.1.2      2022-08-19 [1] CRAN (R 4.2.0)
#>  htmltools       0.5.4      2022-12-07 [1] CRAN (R 4.2.0)
#>  httr            1.4.4      2022-08-17 [1] CRAN (R 4.2.0)
#>  jsonlite        1.8.4      2022-12-06 [1] CRAN (R 4.2.0)
#>  knitr           1.42       2023-01-25 [1] CRAN (R 4.2.0)
#>  lifecycle       1.0.3      2022-10-07 [1] CRAN (R 4.2.0)
#>  lubridate       1.9.1      2023-01-24 [1] CRAN (R 4.2.0)
#>  magrittr        2.0.3      2022-03-30 [1] CRAN (R 4.2.0)
#>  modelr          0.1.10     2022-11-11 [1] CRAN (R 4.2.0)
#>  munsell         0.5.0      2018-06-12 [1] CRAN (R 4.2.0)
#>  naniar        * 1.0.0.9000 2023-04-10 [1] local
#>  pillar          1.8.1      2022-08-19 [1] CRAN (R 4.2.0)
#>  pkgconfig       2.0.3      2019-09-22 [1] CRAN (R 4.2.0)
#>  purrr         * 1.0.1      2023-01-10 [1] CRAN (R 4.2.0)
#>  R.cache         0.16.0     2022-07-21 [1] CRAN (R 4.2.0)
#>  R.methodsS3     1.8.2      2022-06-13 [1] CRAN (R 4.2.0)
#>  R.oo            1.25.0     2022-06-12 [1] CRAN (R 4.2.0)
#>  R.utils         2.12.2     2022-11-11 [1] CRAN (R 4.2.0)
#>  R6              2.5.1      2021-08-19 [1] CRAN (R 4.2.0)
#>  readr         * 2.1.3      2022-10-01 [1] CRAN (R 4.2.0)
#>  readxl          1.4.1      2022-08-17 [1] CRAN (R 4.2.0)
#>  reprex          2.0.2      2022-08-17 [1] CRAN (R 4.2.0)
#>  rlang           1.1.0      2023-03-14 [1] CRAN (R 4.2.0)
#>  rmarkdown       2.20       2023-01-19 [1] CRAN (R 4.2.0)
#>  rstudioapi      0.14       2022-08-22 [1] CRAN (R 4.2.0)
#>  rvest           1.0.3      2022-08-19 [1] CRAN (R 4.2.0)
#>  scales          1.2.1      2022-08-20 [1] CRAN (R 4.2.0)
#>  sessioninfo     1.2.2      2021-12-06 [1] CRAN (R 4.2.0)
#>  stringi         1.7.12     2023-01-11 [1] CRAN (R 4.2.0)
#>  stringr       * 1.5.0      2022-12-02 [1] CRAN (R 4.2.0)
#>  styler          1.9.0      2023-01-15 [1] CRAN (R 4.2.0)
#>  tibble        * 3.2.1      2023-03-20 [1] CRAN (R 4.2.0)
#>  tidyr         * 1.3.0      2023-01-24 [1] CRAN (R 4.2.0)
#>  tidyselect      1.2.0      2022-10-10 [1] CRAN (R 4.2.0)
#>  tidyverse     * 1.3.2      2022-07-18 [1] CRAN (R 4.2.0)
#>  timechange      0.2.0      2023-01-11 [1] CRAN (R 4.2.0)
#>  tzdb            0.3.0      2022-03-28 [1] CRAN (R 4.2.0)
#>  utf8            1.2.3      2023-01-31 [1] CRAN (R 4.2.0)
#>  vctrs           0.6.1      2023-03-22 [1] CRAN (R 4.2.0)
#>  visdat          0.6.0      2023-02-02 [1] local
#>  withr           2.5.0      2022-03-03 [1] CRAN (R 4.2.0)
#>  xfun            0.37       2023-01-31 [1] CRAN (R 4.2.0)
#>  xml2            1.3.3      2021-11-30 [1] CRAN (R 4.2.0)
#>  yaml            2.3.7      2023-01-23 [1] CRAN (R 4.2.0)
#> 
#>  [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#> 
#> ──────────────────────────────────────────────────────────────────────────────

@njtierney njtierney mentioned this issue Apr 10, 2023
Merged
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