R-datasets.R

ld <- .getNamespaceInfo(asNamespace("datasets"), "lazydata")

library(dplyr)
library(purrr)

df <- tibble::tibble(
  name = names(ld),
  data = map(name, ~ ld[[.x]]),
  is_df = map_lgl(data, inherits, "data.frame")
  ) %>%
  filter(is_df) %>%
  select(-is_df) %>%
  mutate(types = map(data, ~ map(.x, ~ if (inherits(.x, "factor")) "factor" else typeof(.x))))

print(df, n = Inf)
#> # A tibble: 44 x 3
#>    name             data                           types            
#>    <chr>            <list>                         <list>           
#>  1 OrchardSprays    <df[,4] [64 × 4]>              <named list [4]> 
#>  2 BOD              <df[,2] [6 × 2]>               <named list [2]> 
#>  3 iris             <df[,5] [150 × 5]>             <named list [5]> 
#>  4 faithful         <df[,2] [272 × 2]>             <named list [2]> 
#>  5 npk              <df[,5] [24 × 5]>              <named list [5]> 
#>  6 women            <df[,2] [15 × 2]>              <named list [2]> 
#>  7 Loblolly         <nfnGroupedData[,3] [84 × 3]>  <named list [3]> 
#>  8 airquality       <df[,6] [153 × 6]>             <named list [6]> 
#>  9 chickwts         <df[,2] [71 × 2]>              <named list [2]> 
#> 10 anscombe         <df[,8] [11 × 8]>              <named list [8]> 
#> 11 stackloss        <df[,4] [21 × 4]>              <named list [4]> 
#> 12 beaver1          <df[,4] [114 × 4]>             <named list [4]> 
#> 13 beaver2          <df[,4] [100 × 4]>             <named list [4]> 
#> 14 esoph            <df[,5] [88 × 5]>              <named list [5]> 
#> 15 Formaldehyde     <df[,2] [6 × 2]>               <named list [2]> 
#> 16 trees            <df[,3] [31 × 3]>              <named list [3]> 
#> 17 attitude         <df[,7] [30 × 7]>              <named list [7]> 
#> 18 attenu           <df[,5] [182 × 5]>             <named list [5]> 
#> 19 Orange           <nfnGroupedData[,3] [35 × 3]>  <named list [3]> 
#> 20 ToothGrowth      <df[,3] [60 × 3]>              <named list [3]> 
#> 21 rock             <df[,4] [48 × 4]>              <named list [4]> 
#> 22 Puromycin        <df[,3] [23 × 3]>              <named list [3]> 
#> 23 Indometh         <nfnGroupedData[,3] [66 × 3]>  <named list [3]> 
#> 24 mtcars           <df[,11] [32 × 11]>            <named list [11]>
#> 25 swiss            <df[,6] [47 × 6]>              <named list [6]> 
#> 26 ChickWeight      <nfnGroupedData[,4] [578 × 4]> <named list [4]> 
#> 27 longley          <df[,7] [16 × 7]>              <named list [7]> 
#> 28 LifeCycleSavings <df[,5] [50 × 5]>              <named list [5]> 
#> 29 USArrests        <df[,4] [50 × 4]>              <named list [4]> 
#> 30 warpbreaks       <df[,3] [54 × 3]>              <named list [3]> 
#> 31 USJudgeRatings   <df[,12] [43 × 12]>            <named list [12]>
#> 32 freeny           <df[,5] [39 × 5]>              <named list [5]> 
#> 33 InsectSprays     <df[,2] [72 × 2]>              <named list [2]> 
#> 34 DNase            <nfnGroupedData[,3] [176 × 3]> <named list [3]> 
#> 35 Theoph           <nfnGroupedData[,5] [132 × 5]> <named list [5]> 
#> 36 cars             <df[,2] [50 × 2]>              <named list [2]> 
#> 37 infert           <df[,8] [248 × 8]>             <named list [8]> 
#> 38 randu            <df[,3] [400 × 3]>             <named list [3]> 
#> 39 sleep            <df[,3] [20 × 3]>              <named list [3]> 
#> 40 morley           <df[,3] [100 × 3]>             <named list [3]> 
#> 41 PlantGrowth      <df[,2] [30 × 2]>              <named list [2]> 
#> 42 pressure         <df[,2] [19 × 2]>              <named list [2]> 
#> 43 CO2              <nfnGroupedData[,5] [84 × 5]>  <named list [5]> 
#> 44 quakes           <df[,5] [1,000 × 5]>           <named list [5]>