describer
quickly and easily describes data using common descriptive statistics.
You can install the latest development version from CRAN:
install.packages("describer")
Or from GitHub with:
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
.count_elements
.mean_value
.sd_value
.q0_value
.q25_value
.q50_value
.q75_value
.q100_value
mpg
32
20.090625
6.0269481
10.400
15.42500
19.200
22.80
33.900
cyl
32
6.187500
1.7859216
4.000
4.00000
6.000
8.00
8.000
disp
32
230.721875
123.9386938
71.100
120.82500
196.300
326.00
472.000
hp
32
146.687500
68.5628685
52.000
96.50000
123.000
180.00
335.000
drat
32
3.596563
0.5346787
2.760
3.08000
3.695
3.92
4.930
wt
32
3.217250
0.9784574
1.513
2.58125
3.325
3.61
5.424
qsec
32
17.848750
1.7869432
14.500
16.89250
17.710
18.90
22.900
vs
32
0.437500
0.5040161
0.000
0.00000
0.000
1.00
1.000
am
32
0.406250
0.4989909
0.000
0.00000
0.000
1.00
1.000
gear
32
3.687500
0.7378041
3.000
3.00000
4.000
4.00
5.000
carb
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
.count_elements
.mean_value
.sd_value
.q0_value
.q25_value
.q50_value
.q75_value
.q100_value
4
mpg
11
26.6636364
4.5098277
21.400
22.8000
26.000
30.40000
33.900
4
cyl
11
4.0000000
0.0000000
4.000
4.0000
4.000
4.00000
4.000
4
disp
11
105.1363636
26.8715937
71.100
78.8500
108.000
120.65000
146.700
4
hp
11
82.6363636
20.9345300
52.000
65.5000
91.000
96.00000
113.000
4
drat
11
4.0709091
0.3654711
3.690
3.8100
4.080
4.16500
4.930
4
wt
11
2.2857273
0.5695637
1.513
1.8850
2.200
2.62250
3.190
4
qsec
11
19.1372727
1.6824452
16.700
18.5600
18.900
19.95000
22.900
4
vs
11
0.9090909
0.3015113
0.000
1.0000
1.000
1.00000
1.000
4
am
11
0.7272727
0.4670994
0.000
0.5000
1.000
1.00000
1.000
4
gear
11
4.0909091
0.5393599
3.000
4.0000
4.000
4.00000
5.000
4
carb
11
1.5454545
0.5222330
1.000
1.0000
2.000
2.00000
2.000
6
mpg
7
19.7428571
1.4535670
17.800
18.6500
19.700
21.00000
21.400
6
cyl
7
6.0000000
0.0000000
6.000
6.0000
6.000
6.00000
6.000
6
disp
7
183.3142857
41.5624602
145.000
160.0000
167.600
196.30000
258.000
6
hp
7
122.2857143
24.2604911
105.000
110.0000
110.000
123.00000
175.000
6
drat
7
3.5857143
0.4760552
2.760
3.3500
3.900
3.91000
3.920
6
wt
7
3.1171429
0.3563455
2.620
2.8225
3.215
3.44000
3.460
6
qsec
7
17.9771429
1.7068657
15.500
16.7400
18.300
19.17000
20.220
6
vs
7
0.5714286
0.5345225
0.000
0.0000
1.000
1.00000
1.000
6
am
7
0.4285714
0.5345225
0.000
0.0000
0.000
1.00000
1.000
6
gear
7
3.8571429
0.6900656
3.000
3.5000
4.000
4.00000
5.000
6
carb
7
3.4285714
1.8126539
1.000
2.5000
4.000
4.00000
6.000
8
mpg
14
15.1000000
2.5600481
10.400
14.4000
15.200
16.25000
19.200
8
cyl
14
8.0000000
0.0000000
8.000
8.0000
8.000
8.00000
8.000
8
disp
14
353.1000000
67.7713236
275.800
301.7500
350.500
390.00000
472.000
8
hp
14
209.2142857
50.9768855
150.000
176.2500
192.500
241.25000
335.000
8
drat
14
3.2292857
0.3723618
2.760
3.0700
3.115
3.22500
4.220
8
wt
14
3.9992143
0.7594047
3.170
3.5325
3.755
4.01375
5.424
8
qsec
14
16.7721429
1.1960138
14.500
16.0975
17.175
17.55500
18.000
8
vs
14
0.0000000
0.0000000
0.000
0.0000
0.000
0.00000
0.000
8
am
14
0.1428571
0.3631365
0.000
0.0000
0.000
0.00000
1.000
8
gear
14
3.2857143
0.7262730
3.000
3.0000
3.000
3.00000
5.000
8
carb
14
3.5000000
1.5566236
2.000
2.2500
3.500
4.00000
8.000
To mimic the exact pandas.describe()
behavior, use pd_describe
.
library(reshape2)
pandas_describe_mtcars <-
mtcars %>%
pd_describe
pandas_describe_mtcars %>%
knitr::kable(format = "markdown")
.variable
am
carb
cyl
disp
drat
gear
hp
mpg
qsec
vs
wt
.count_elements
32.0000000
32.0000
32.000000
32.0000
32.0000000
32.0000000
32.00000
32.000000
32.000000
32.0000000
32.0000000
.mean_value
0.4062500
2.8125
6.187500
230.7219
3.5965625
3.6875000
146.68750
20.090625
17.848750
0.4375000
3.2172500
.sd_value
0.4989909
1.6152
1.785922
123.9387
0.5346787
0.7378041
68.56287
6.026948
1.786943
0.5040161
0.9784574
.q0_value
0.0000000
1.0000
4.000000
71.1000
2.7600000
3.0000000
52.00000
10.400000
14.500000
0.0000000
1.5130000
.q25_value
0.0000000
2.0000
4.000000
120.8250
3.0800000
3.0000000
96.50000
15.425000
16.892500
0.0000000
2.5812500
.q50_value
0.0000000
2.0000
6.000000
196.3000
3.6950000
4.0000000
123.00000
19.200000
17.710000
0.0000000
3.3250000
.q75_value
1.0000000
4.0000
8.000000
326.0000
3.9200000
4.0000000
180.00000
22.800000
18.900000
1.0000000
3.6100000
.q100_value
1.0000000
8.0000
8.000000
472.0000
4.9300000
5.0000000
335.00000
33.900000
22.900000
1.0000000
5.4240000
str(pandas_describe_mtcars)
#> 'data.frame': 8 obs. of 12 variables:
#> $ .variable: chr ".count_elements" ".mean_value" ".sd_value" ".q0_value" ...
#> $ am : num 32 0.406 0.499 0 0 ...
#> $ carb : num 32 2.81 1.62 1 2 ...
#> $ cyl : num 32 6.19 1.79 4 4 ...
#> $ disp : num 32 230.7 123.9 71.1 120.8 ...
#> $ drat : num 32 3.597 0.535 2.76 3.08 ...
#> $ gear : num 32 3.688 0.738 3 3 ...
#> $ hp : num 32 146.7 68.6 52 96.5 ...
#> $ mpg : num 32 20.09 6.03 10.4 15.43 ...
#> $ qsec : num 32 17.85 1.79 14.5 16.89 ...
#> $ vs : num 32 0.438 0.504 0 0 ...
#> $ wt : num 32 3.217 0.978 1.513 2.581 ...
To cite package ‘describer’ in publications use:
Paul Hendricks (2015). describer: Describe Data in R Using Common Descriptive Statistics. R package version 0.2.0. https://CRAN.R-project.org/package=describer
A BibTeX entry for LaTeX users is
@Manual{,
title = {describer: Describe Data in R Using Common Descriptive Statistics},
author = {Paul Hendricks},
year = {2015},
note = {R package version 0.2.0},
url = {https://CRAN.R-project.org/package=describer},
}
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