tabulator provides efficient tabulation with big data in R, with Stata-like output. It contains three distinct functions:
tab() shows, for each unique value of a selected variable, the number of observations, proportion of observations, and cumulative proportion.quantiles() shows quantile values.tabcount() shows the number of unique categories for a selected variable.All functions work with data frames, data tables, and tibbles.
To get a bug fix or to use a feature from the development version, you can install the development version of tabulator from GitHub.
library(tabulator)
data(iris)
iris %>% tab(Sepal.Length)
#> Sepal.Length N prop cum_prop
#> 1: 5.0 10 0.07 0.07
#> 2: 5.1 9 0.06 0.13
#> 3: 6.3 9 0.06 0.19
#> 4: 5.7 8 0.05 0.24
#> 5: 6.7 8 0.05 0.29
#> 6: 5.8 7 0.05 0.34
#> 7: 5.5 7 0.05 0.39
#> 8: 6.4 7 0.05 0.43
#> 9: 4.9 6 0.04 0.47
#> 10: 5.4 6 0.04 0.51
#> 11: 6.0 6 0.04 0.55
#> 12: 6.1 6 0.04 0.59
#> 13: 5.6 6 0.04 0.63
#> 14: 4.8 5 0.03 0.67
#> 15: 6.5 5 0.03 0.70
#> 16: 4.6 4 0.03 0.73
#> 17: 5.2 4 0.03 0.75
#> 18: 6.9 4 0.03 0.78
#> 19: 6.2 4 0.03 0.81
#> 20: 7.7 4 0.03 0.83
#> 21: 4.4 3 0.02 0.85
#> 22: 5.9 3 0.02 0.87
#> 23: 6.8 3 0.02 0.89
#> 24: 7.2 3 0.02 0.91
#> 25: 4.7 2 0.01 0.93
#> 26: 6.6 2 0.01 0.94
#> 27: 4.3 1 0.01 0.95
#> 28: 4.5 1 0.01 0.95
#> 29: 5.3 1 0.01 0.96
#> 30: 7.0 1 0.01 0.97
#> 31: 7.1 1 0.01 0.97
#> 32: 7.6 1 0.01 0.98
#> 33: 7.3 1 0.01 0.99
#> 34: 7.4 1 0.01 0.99
#> 35: 7.9 1 0.01 1.00
#> Sepal.Length N prop cum_prop
iris %>% tabcount(Sepal.Length)
#> [1] 35
iris %>% quantiles(Petal.Width)
#> p Petal.Width
#> 1: 0.0 0.10
#> 2: 0.1 0.20
#> 3: 0.2 0.20
#> 4: 0.3 0.40
#> 5: 0.4 1.16
#> 6: 0.5 1.30
#> 7: 0.6 1.50
#> 8: 0.7 1.80
#> 9: 0.8 1.90
#> 10: 0.9 2.20
#> 11: 1.0 2.50