The goal of varSummary is to create a named variable summary for dataframes
You can install the development version of varSummary like so:
# install.packages("devtools")
devtools::install_github("L-Groeninger/varSummary")This is a basic example which shows you how to solve a common problem:
library(varSummary)
## basic example codedatafile <- system.file("penguins.RDS", package = "varSummary")
penguins <- readRDS(datafile)
tbl(penguins, starts_with("bill"))
#> $bill_length_mm
#> # A tibble: 165 x 5
#> bill_length_mm n percent valid_percent cum_percent
#> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 41.1 7 0.0203 0.0205 0.0203
#> 2 45.2 6 0.0174 0.0175 0.0378
#> 3 37.8 5 0.0145 0.0146 0.0523
#> 4 39.6 5 0.0145 0.0146 0.0669
#> 5 45.5 5 0.0145 0.0146 0.0814
#> 6 46.2 5 0.0145 0.0146 0.0959
#> 7 46.5 5 0.0145 0.0146 0.110
#> 8 50 5 0.0145 0.0146 0.125
#> 9 50.5 5 0.0145 0.0146 0.140
#> 10 36 4 0.0116 0.0117 0.151
#> # ... with 155 more rows
#>
#> $bill_depth_mm
#> # A tibble: 81 x 5
#> bill_depth_mm n percent valid_percent cum_percent
#> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 17 12 0.0349 0.0351 0.0349
#> 2 15 10 0.0291 0.0292 0.0640
#> 3 17.9 10 0.0291 0.0292 0.0930
#> 4 18.5 10 0.0291 0.0292 0.122
#> 5 18.6 10 0.0291 0.0292 0.151
#> 6 17.3 9 0.0262 0.0263 0.177
#> 7 17.8 9 0.0262 0.0263 0.203
#> 8 18.1 9 0.0262 0.0263 0.230
#> 9 18.9 9 0.0262 0.0263 0.256
#> 10 19 9 0.0262 0.0263 0.282
#> # ... with 71 more rows
tbl(penguins, ends_with("_mm"))
#> $bill_length_mm
#> # A tibble: 165 x 5
#> bill_length_mm n percent valid_percent cum_percent
#> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 41.1 7 0.0203 0.0205 0.0203
#> 2 45.2 6 0.0174 0.0175 0.0378
#> 3 37.8 5 0.0145 0.0146 0.0523
#> 4 39.6 5 0.0145 0.0146 0.0669
#> 5 45.5 5 0.0145 0.0146 0.0814
#> 6 46.2 5 0.0145 0.0146 0.0959
#> 7 46.5 5 0.0145 0.0146 0.110
#> 8 50 5 0.0145 0.0146 0.125
#> 9 50.5 5 0.0145 0.0146 0.140
#> 10 36 4 0.0116 0.0117 0.151
#> # ... with 155 more rows
#>
#> $bill_depth_mm
#> # A tibble: 81 x 5
#> bill_depth_mm n percent valid_percent cum_percent
#> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 17 12 0.0349 0.0351 0.0349
#> 2 15 10 0.0291 0.0292 0.0640
#> 3 17.9 10 0.0291 0.0292 0.0930
#> 4 18.5 10 0.0291 0.0292 0.122
#> 5 18.6 10 0.0291 0.0292 0.151
#> 6 17.3 9 0.0262 0.0263 0.177
#> 7 17.8 9 0.0262 0.0263 0.203
#> 8 18.1 9 0.0262 0.0263 0.230
#> 9 18.9 9 0.0262 0.0263 0.256
#> 10 19 9 0.0262 0.0263 0.282
#> # ... with 71 more rows
#>
#> $flipper_length_mm
#> # A tibble: 56 x 5
#> flipper_length_mm n percent valid_percent cum_percent
#> <int> <int> <dbl> <dbl> <dbl>
#> 1 190 22 0.0640 0.0643 0.0640
#> 2 195 17 0.0494 0.0497 0.113
#> 3 187 16 0.0465 0.0468 0.160
#> 4 193 15 0.0436 0.0439 0.203
#> 5 210 14 0.0407 0.0409 0.244
#> 6 191 13 0.0378 0.0380 0.282
#> 7 215 12 0.0349 0.0351 0.317
#> 8 196 10 0.0291 0.0292 0.346
#> 9 197 10 0.0291 0.0292 0.375
#> 10 185 9 0.0262 0.0263 0.401
#> # ... with 46 more rows
tbl(penguins, c(island, sex, species))
#> $island
#> # A tibble: 3 x 4
#> island n percent cum_percent
#> <fct> <int> <dbl> <dbl>
#> 1 Biscoe 168 0.488 0.488
#> 2 Dream 124 0.360 0.849
#> 3 Torgersen 52 0.151 1
#>
#> $sex
#> # A tibble: 3 x 5
#> sex n percent valid_percent cum_percent
#> <fct> <int> <dbl> <dbl> <dbl>
#> 1 male 168 0.488 0.505 0.488
#> 2 female 165 0.480 0.495 0.968
#> 3 <NA> 11 0.0320 NA 1
#>
#> $species
#> # A tibble: 3 x 4
#> species n percent cum_percent
#> <fct> <int> <dbl> <dbl>
#> 1 Adelie 152 0.442 0.442
#> 2 Gentoo 124 0.360 0.802
#> 3 Chinstrap 68 0.198 1