`class_table()`: show a dataframe of the classes

njtierney commented 5 months ago

This is another quick workaround for showing class information, see #166

library(visdat)
class_table <- function(data){
  data %>% 
    dplyr::slice_head(n = 1) %>% 
    data_vis_dat() %>% 
    dplyr::select(-rows) %>% 
    dplyr::rename(
      class = valueType,
      first_value = value
    )
}

class_table(typical_data)
#> # A tibble: 9 × 3
#>   variable   class     first_value
#>   <chr>      <chr>     <chr>      
#> 1 Age        <NA>      0001       
#> 2 Died       logical   Black      
#> 3 Height(cm) numeric   <NA>       
#> 4 ID         character Male       
#> 5 IQ         numeric   175.9      
#> 6 Income     factor    110        
#> 7 Race       factor    FALSE      
#> 8 Sex        factor    4334.29    
#> 9 Smokes     logical   FALSE

^{Created on 2024-03-13 with reprex v2.1.0}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.3 (2024-02-29) #> os macOS Sonoma 14.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Australia/Hobart #> date 2024-03-13 #> pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.1) #> digest 0.6.34 2024-01-11 [1] CRAN (R 4.3.1) #> dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.3.1) #> evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.1) #> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.1) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0) #> fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0) #> glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.1) #> htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.1) #> knitr 1.45 2023-10-30 [1] CRAN (R 4.3.1) #> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.1) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0) #> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.0) #> R.cache 0.16.0 2022-07-21 [2] CRAN (R 4.3.0) #> R.methodsS3 1.8.2 2022-06-13 [2] CRAN (R 4.3.0) #> R.oo 1.26.0 2024-01-24 [2] CRAN (R 4.3.1) #> R.utils 2.12.3 2023-11-18 [2] CRAN (R 4.3.1) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0) #> reprex 2.1.0 2024-01-11 [2] CRAN (R 4.3.1) #> rlang 1.1.3 2024-01-10 [1] CRAN (R 4.3.1) #> rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.1) #> rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.3.0) #> sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.3.0) #> styler 1.10.2 2023-08-29 [2] CRAN (R 4.3.0) #> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.0) #> tidyr 1.3.1 2024-01-24 [1] CRAN (R 4.3.1) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0) #> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1) #> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.1) #> visdat * 0.6.0 2023-02-02 [2] CRAN (R 4.3.0) #> withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.1) #> xfun 0.42 2024-02-08 [1] CRAN (R 4.3.1) #> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.1) #> #> [1] /Users/nick/Library/R/arm64/4.3/library #> [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```

njtierney commented 5 months ago

This sometimes captures NA values, it might be best to use something like:


map_dfr(
  data,
  class
) %>% 
  pivot_longer(
    cols = everything(),
    names_to = "variable",
    values_to = "class"
  ) %>% 
  arrange(
    class, 
    variable
  )

Or to look more closely at the internals of fingerprint or something?

njtierney commented 5 months ago

Alright here we go:

library(visdat)

variable_first_value <- function(data){
  data %>% 
    dplyr::slice_head(n = 1) %>% 
    tidyr::pivot_longer(
      cols = tidyselect::everything(),
      names_to = "variable",
      values_to = "first_value",
      values_transform = as.character
    )
}

class_info <- function(data){
  data %>% 
    purrr::map_dfr(
      class
    ) %>% 
    tidyr::pivot_longer(
      cols = tidyselect::everything(),
      names_to = "variable",
      values_to = "class"
    ) %>% 
    dplyr::arrange(
      class, 
      variable
    )
}

class_table <- function(data){
  dplyr::left_join(
    x = class_info(data),
    y = variable_first_value(data),
    by = "variable"
  )
}

class_table(typical_data)
#> # A tibble: 9 × 3
#>   variable   class     first_value
#>   <chr>      <chr>     <chr>      
#> 1 Age        character <NA>       
#> 2 ID         character 0001       
#> 3 Income     factor    4334.29    
#> 4 Race       factor    Black      
#> 5 Sex        factor    Male       
#> 6 Died       logical   FALSE      
#> 7 Smokes     logical   FALSE      
#> 8 Height(cm) numeric   175.9      
#> 9 IQ         numeric   110

^{Created on 2024-03-13 with reprex v2.1.0}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.3 (2024-02-29) #> os macOS Sonoma 14.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Australia/Hobart #> date 2024-03-13 #> pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> cli 3.6.2 2023-12-11 [1] CRAN (R 4.3.1) #> digest 0.6.34 2024-01-11 [1] CRAN (R 4.3.1) #> dplyr 1.1.4 2023-11-17 [1] CRAN (R 4.3.1) #> evaluate 0.23 2023-11-01 [1] CRAN (R 4.3.1) #> fansi 1.0.6 2023-12-08 [1] CRAN (R 4.3.1) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0) #> fs 1.6.3 2023-07-20 [1] CRAN (R 4.3.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0) #> glue 1.7.0 2024-01-09 [1] CRAN (R 4.3.1) #> htmltools 0.5.7 2023-11-03 [1] CRAN (R 4.3.1) #> knitr 1.45 2023-10-30 [1] CRAN (R 4.3.1) #> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.1) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0) #> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.3.0) #> R.cache 0.16.0 2022-07-21 [2] CRAN (R 4.3.0) #> R.methodsS3 1.8.2 2022-06-13 [2] CRAN (R 4.3.0) #> R.oo 1.26.0 2024-01-24 [2] CRAN (R 4.3.1) #> R.utils 2.12.3 2023-11-18 [2] CRAN (R 4.3.1) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0) #> reprex 2.1.0 2024-01-11 [2] CRAN (R 4.3.1) #> rlang 1.1.3 2024-01-10 [1] CRAN (R 4.3.1) #> rmarkdown 2.25 2023-09-18 [1] CRAN (R 4.3.1) #> rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.3.0) #> sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.3.0) #> styler 1.10.2 2023-08-29 [2] CRAN (R 4.3.0) #> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.0) #> tidyr 1.3.1 2024-01-24 [1] CRAN (R 4.3.1) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0) #> utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1) #> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.1) #> visdat * 0.6.0 2023-02-02 [2] CRAN (R 4.3.0) #> withr 3.0.0 2024-01-16 [1] CRAN (R 4.3.1) #> xfun 0.42 2024-02-08 [1] CRAN (R 4.3.1) #> yaml 2.3.8 2023-12-11 [1] CRAN (R 4.3.1) #> #> [1] /Users/nick/Library/R/arm64/4.3/library #> [2] /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```

njtierney commented 5 months ago

Could maybe change first_value to typical_value to use the first non_missing value...or something?

njtierney commented 5 months ago

Haaaa this is what dplyr::glimpse() does. Ugh. I still maintain it is useful.

ropensci / visdat

`class_table()`: show a dataframe of the classes #167