Skim a dataframe and include labels and levels
Source:R/skim_with_labels_and_levels.R
skim_with_labels_and_levels.RdThis function takes a data.frame and returns a skim summary with variable names,
labels, and levels for categorical variables. It is a wrapper around the skimr::skim()
function.
See also
Other text_helpers:
df_to_string(),
vector_list_to_string()
Examples
# First add some labels to 'mtcars':
mtcars$car <- rownames(mtcars)
mtcars$car <- factor(mtcars$car, levels = rownames(mtcars))
attr(mtcars$car, "label") <- "Name of the car"
# Then skim the data:
mtcars |>
skim_with_labels_and_levels()
#> variable description levels skim_type n_missing complete_rate
#> 1 am <NA> NA numeric 0 1
#> 2 car Name of the car Mazda RX.... factor 0 1
#> 3 carb <NA> NA numeric 0 1
#> 4 cyl <NA> NA numeric 0 1
#> 5 disp <NA> NA numeric 0 1
#> 6 drat <NA> NA numeric 0 1
#> 7 gear <NA> NA numeric 0 1
#> 8 hp <NA> NA numeric 0 1
#> 9 mpg <NA> NA numeric 0 1
#> 10 qsec <NA> NA numeric 0 1
#> 11 vs <NA> NA numeric 0 1
#> 12 wt <NA> NA numeric 0 1
#> factor.ordered factor.n_unique factor.top_counts numeric.mean
#> 1 NA NA <NA> 0.406250
#> 2 FALSE 32 Maz: 1, Maz: 1, Dat: 1, Hor: 1 NA
#> 3 NA NA <NA> 2.812500
#> 4 NA NA <NA> 6.187500
#> 5 NA NA <NA> 230.721875
#> 6 NA NA <NA> 3.596563
#> 7 NA NA <NA> 3.687500
#> 8 NA NA <NA> 146.687500
#> 9 NA NA <NA> 20.090625
#> 10 NA NA <NA> 17.848750
#> 11 NA NA <NA> 0.437500
#> 12 NA NA <NA> 3.217250
#> numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100
#> 1 0.4989909 0.000 0.00000 0.000 1.00 1.000
#> 2 NA NA NA NA NA NA
#> 3 1.6152000 1.000 2.00000 2.000 4.00 8.000
#> 4 1.7859216 4.000 4.00000 6.000 8.00 8.000
#> 5 123.9386938 71.100 120.82500 196.300 326.00 472.000
#> 6 0.5346787 2.760 3.08000 3.695 3.92 4.930
#> 7 0.7378041 3.000 3.00000 4.000 4.00 5.000
#> 8 68.5628685 52.000 96.50000 123.000 180.00 335.000
#> 9 6.0269481 10.400 15.42500 19.200 22.80 33.900
#> 10 1.7869432 14.500 16.89250 17.710 18.90 22.900
#> 11 0.5040161 0.000 0.00000 0.000 1.00 1.000
#> 12 0.9784574 1.513 2.58125 3.325 3.61 5.424
#> numeric.hist
#> 1 ▇▁▁▁▆
#> 2 <NA>
#> 3 ▇▂▅▁▁
#> 4 ▆▁▃▁▇
#> 5 ▇▃▃▃▂
#> 6 ▇▃▇▅▁
#> 7 ▇▁▆▁▂
#> 8 ▇▇▆▃▁
#> 9 ▃▇▅▁▂
#> 10 ▃▇▇▂▁
#> 11 ▇▁▁▁▆
#> 12 ▃▃▇▁▂