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describer quickly and easily describes data using common
descriptive statistics.
You can install:
the latest released version from CRAN:
install.packages("describer")the latest development version from Github:
if (packageVersion("devtools") < 1.6) {
install.packages("devtools")
}
devtools::install_github("paulhendricks/describer")If you encounter a clear bug, please file a minimal reproducible example on github.
library(dplyr, warn.conflicts = FALSE)
library(describer)
mtcars %>%
describe %>%
knitr::kable(format = "markdown")| .column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
|---|---|---|---|---|---|---|---|---|---|---|
| mpg | numeric | double | 32 | 20.090625 | 6.0269481 | 10.400 | 15.42500 | 19.200 | 22.80 | 33.900 |
| cyl | numeric | double | 32 | 6.187500 | 1.7859216 | 4.000 | 4.00000 | 6.000 | 8.00 | 8.000 |
| disp | numeric | double | 32 | 230.721875 | 123.9386938 | 71.100 | 120.82500 | 196.300 | 326.00 | 472.000 |
| hp | numeric | double | 32 | 146.687500 | 68.5628685 | 52.000 | 96.50000 | 123.000 | 180.00 | 335.000 |
| drat | numeric | double | 32 | 3.596563 | 0.5346787 | 2.760 | 3.08000 | 3.695 | 3.92 | 4.930 |
| wt | numeric | double | 32 | 3.217250 | 0.9784574 | 1.513 | 2.58125 | 3.325 | 3.61 | 5.424 |
| qsec | numeric | double | 32 | 17.848750 | 1.7869432 | 14.500 | 16.89250 | 17.710 | 18.90 | 22.900 |
| vs | numeric | double | 32 | 0.437500 | 0.5040161 | 0.000 | 0.00000 | 0.000 | 1.00 | 1.000 |
| am | numeric | double | 32 | 0.406250 | 0.4989909 | 0.000 | 0.00000 | 0.000 | 1.00 | 1.000 |
| gear | numeric | double | 32 | 3.687500 | 0.7378041 | 3.000 | 3.00000 | 4.000 | 4.00 | 5.000 |
| carb | numeric | double | 32 | 2.812500 | 1.6152000 | 1.000 | 2.00000 | 2.000 | 4.00 | 8.000 |
mtcars %>%
group_by(cyl) %>%
do(describe(.)) %>%
knitr::kable(format = "markdown")| cyl | .column_name | .column_class | .column_type | .count_elements | .mean_value | .sd_value | .q0_value | .q25_value | .q50_value | .q75_value | .q100_value |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | mpg | numeric | double | 11 | 26.6636364 | 4.5098277 | 21.400 | 22.8000 | 26.000 | 30.40000 | 33.900 |
| 4 | cyl | numeric | double | 11 | 4.0000000 | 0.0000000 | 4.000 | 4.0000 | 4.000 | 4.00000 | 4.000 |
| 4 | disp | numeric | double | 11 | 105.1363636 | 26.8715937 | 71.100 | 78.8500 | 108.000 | 120.65000 | 146.700 |
| 4 | hp | numeric | double | 11 | 82.6363636 | 20.9345300 | 52.000 | 65.5000 | 91.000 | 96.00000 | 113.000 |
| 4 | drat | numeric | double | 11 | 4.0709091 | 0.3654711 | 3.690 | 3.8100 | 4.080 | 4.16500 | 4.930 |
| 4 | wt | numeric | double | 11 | 2.2857273 | 0.5695637 | 1.513 | 1.8850 | 2.200 | 2.62250 | 3.190 |
| 4 | qsec | numeric | double | 11 | 19.1372727 | 1.6824452 | 16.700 | 18.5600 | 18.900 | 19.95000 | 22.900 |
| 4 | vs | numeric | double | 11 | 0.9090909 | 0.3015113 | 0.000 | 1.0000 | 1.000 | 1.00000 | 1.000 |
| 4 | am | numeric | double | 11 | 0.7272727 | 0.4670994 | 0.000 | 0.5000 | 1.000 | 1.00000 | 1.000 |
| 4 | gear | numeric | double | 11 | 4.0909091 | 0.5393599 | 3.000 | 4.0000 | 4.000 | 4.00000 | 5.000 |
| 4 | carb | numeric | double | 11 | 1.5454545 | 0.5222330 | 1.000 | 1.0000 | 2.000 | 2.00000 | 2.000 |
| 6 | mpg | numeric | double | 7 | 19.7428571 | 1.4535670 | 17.800 | 18.6500 | 19.700 | 21.00000 | 21.400 |
| 6 | cyl | numeric | double | 7 | 6.0000000 | 0.0000000 | 6.000 | 6.0000 | 6.000 | 6.00000 | 6.000 |
| 6 | disp | numeric | double | 7 | 183.3142857 | 41.5624602 | 145.000 | 160.0000 | 167.600 | 196.30000 | 258.000 |
| 6 | hp | numeric | double | 7 | 122.2857143 | 24.2604911 | 105.000 | 110.0000 | 110.000 | 123.00000 | 175.000 |
| 6 | drat | numeric | double | 7 | 3.5857143 | 0.4760552 | 2.760 | 3.3500 | 3.900 | 3.91000 | 3.920 |
| 6 | wt | numeric | double | 7 | 3.1171429 | 0.3563455 | 2.620 | 2.8225 | 3.215 | 3.44000 | 3.460 |
| 6 | qsec | numeric | double | 7 | 17.9771429 | 1.7068657 | 15.500 | 16.7400 | 18.300 | 19.17000 | 20.220 |
| 6 | vs | numeric | double | 7 | 0.5714286 | 0.5345225 | 0.000 | 0.0000 | 1.000 | 1.00000 | 1.000 |
| 6 | am | numeric | double | 7 | 0.4285714 | 0.5345225 | 0.000 | 0.0000 | 0.000 | 1.00000 | 1.000 |
| 6 | gear | numeric | double | 7 | 3.8571429 | 0.6900656 | 3.000 | 3.5000 | 4.000 | 4.00000 | 5.000 |
| 6 | carb | numeric | double | 7 | 3.4285714 | 1.8126539 | 1.000 | 2.5000 | 4.000 | 4.00000 | 6.000 |
| 8 | mpg | numeric | double | 14 | 15.1000000 | 2.5600481 | 10.400 | 14.4000 | 15.200 | 16.25000 | 19.200 |
| 8 | cyl | numeric | double | 14 | 8.0000000 | 0.0000000 | 8.000 | 8.0000 | 8.000 | 8.00000 | 8.000 |
| 8 | disp | numeric | double | 14 | 353.1000000 | 67.7713236 | 275.800 | 301.7500 | 350.500 | 390.00000 | 472.000 |
| 8 | hp | numeric | double | 14 | 209.2142857 | 50.9768855 | 150.000 | 176.2500 | 192.500 | 241.25000 | 335.000 |
| 8 | drat | numeric | double | 14 | 3.2292857 | 0.3723618 | 2.760 | 3.0700 | 3.115 | 3.22500 | 4.220 |
| 8 | wt | numeric | double | 14 | 3.9992143 | 0.7594047 | 3.170 | 3.5325 | 3.755 | 4.01375 | 5.424 |
| 8 | qsec | numeric | double | 14 | 16.7721429 | 1.1960138 | 14.500 | 16.0975 | 17.175 | 17.55500 | 18.000 |
| 8 | vs | numeric | double | 14 | 0.0000000 | 0.0000000 | 0.000 | 0.0000 | 0.000 | 0.00000 | 0.000 |
| 8 | am | numeric | double | 14 | 0.1428571 | 0.3631365 | 0.000 | 0.0000 | 0.000 | 0.00000 | 1.000 |
| 8 | gear | numeric | double | 14 | 3.2857143 | 0.7262730 | 3.000 | 3.0000 | 3.000 | 3.00000 | 5.000 |
| 8 | carb | numeric | double | 14 | 3.5000000 | 1.5566236 | 2.000 | 2.2500 | 3.500 | 4.00000 | 8.000 |
To mimic the exact pandas.describe() behavior, use
reshape2 to melt() and cast() the
data into an appropriate form. This is not recommendated as all columns
will be coerced into character vectors.
library(reshape2)
pandas_describe_mtcars <-
mtcars %>%
describe %>%
melt(id.vars = ".column_name", variable.name = ".variable") %>%
dcast(.variable ~ .column_name, value.var = "value")
pandas_describe_mtcars %>%
knitr::kable(format = "markdown")| .variable | am | carb | cyl | disp | drat | gear | hp | mpg | qsec | vs | wt |
|---|---|---|---|---|---|---|---|---|---|---|---|
| .column_class | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric | numeric |
| .column_type | double | double | double | double | double | double | double | double | double | double | double |
| .count_elements | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 |
| .mean_value | 0.40625 | 2.8125 | 6.1875 | 230.721875 | 3.5965625 | 3.6875 | 146.6875 | 20.090625 | 17.84875 | 0.4375 | 3.21725 |
| .sd_value | 0.498990917235846 | 1.61519997763185 | 1.78592164694654 | 123.938693831382 | 0.534678736070971 | 0.737804065256947 | 68.5628684893206 | 6.0269480520891 | 1.78694323609684 | 0.504016128774185 | 0.978457442989697 |
| .q0_value | 0 | 1 | 4 | 71.1 | 2.76 | 3 | 52 | 10.4 | 14.5 | 0 | 1.513 |
| .q25_value | 0 | 2 | 4 | 120.825 | 3.08 | 3 | 96.5 | 15.425 | 16.8925 | 0 | 2.58125 |
| .q50_value | 0 | 2 | 6 | 196.3 | 3.695 | 4 | 123 | 19.2 | 17.71 | 0 | 3.325 |
| .q75_value | 1 | 4 | 8 | 326 | 3.92 | 4 | 180 | 22.8 | 18.9 | 1 | 3.61 |
| .q100_value | 1 | 8 | 8 | 472 | 4.93 | 5 | 335 | 33.9 | 22.9 | 1 | 5.424 |
str(pandas_describe_mtcars)
#> 'data.frame': 10 obs. of 12 variables:
#> $ .variable: Factor w/ 10 levels ".column_class",..: 1 2 3 4 5 6 7 8 9 10
#> $ am : chr "numeric" "double" "32" "0.40625" ...
#> $ carb : chr "numeric" "double" "32" "2.8125" ...
#> $ cyl : chr "numeric" "double" "32" "6.1875" ...
#> $ disp : chr "numeric" "double" "32" "230.721875" ...
#> $ drat : chr "numeric" "double" "32" "3.5965625" ...
#> $ gear : chr "numeric" "double" "32" "3.6875" ...
#> $ hp : chr "numeric" "double" "32" "146.6875" ...
#> $ mpg : chr "numeric" "double" "32" "20.090625" ...
#> $ qsec : chr "numeric" "double" "32" "17.84875" ...
#> $ vs : chr "numeric" "double" "32" "0.4375" ...
#> $ wt : chr "numeric" "double" "32" "3.21725" ...The original author of describer is Paul Hendricks. 
The lead maintainer of describer is Paul Hendricks.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.