# How to find the statistical summary of an R data frame with all the descriptive statistics?

When we find statistical summary of an R data frame, we only get the minimum value, first quartile, median, mean, third quartile, and maximum value but in descriptive there are many other useful measures such as variance, standard deviation, skewness, kurtosis, etc. Therefore, we can use basicStats function of fBasics package for this purpose.

library(fBasics)

Consider mtcars data in base R −

## Example

Live Demo

data(mtcars)
head(mtcars,20)

## Output

          mpg    cyl     disp    hp    drat    wt qsec vs am gear carb
Mazda RX4         21.0    6 160.0 110    3.90   2.620   16.46  0   1  4   4
Mazda RX4 Wag     21.0    6 160.0 110    3.90  2.875   17.02   0  1  4   4
Datsun 710        22.8    4 108.0 93     3.85  2.320   18.61   1   1   4   1
Hornet 4 Drive    21.4    6 258.0 110     3.08  3.215   19.44   1  0   3   1
Hornet Sportabout 18.7    8 360.0 175    3.15  3.440   17.02   0   0 3 2
Valiant           18.1    6 225.0 105    2.76  3.460   20.22   1 0 3 1
Duster 360        14.3    8 360.0 245    3.21  3.570   15.84   0 0 3 4
Merc 240D         24.4    4 146.7 62     3.69  3.190   20.00   1 0 4 2
Merc 230          22.8    4 140.8 95     3.92  3.150   22.90   1 0 4 2
Merc 280          19.2    6 167.6 123    3.92  3.440   18.30   1 0 4 4
Merc 280C         17.8    6 167.6 123    3.92  3.440   18.90   1 0 4 4
Merc 450SE        16.4    8 275.8 180    3.07  4.070   17.40   0 0 3 3
Merc 450SL        17.3    8 275.8 180    3.07  3.730   17.60   0 0 3 3
Merc 450SLC       15.2    8 275.8 180    3.07  3.780   18.00   0 0 3 3
Cadillac Fleetwood 10.4   8 472.0 205    2.93  5.250   17.98   0 0 3 4
Lincoln Continental 10.4  8 460.0 215    3.00  5.424   17.82    0 0 3 4
Chrysler Imperial 14.7 8  440.0 230     3.23   5.345   17.42   0 0 3 4
Fiat 128         32.4 4   78.7 66       4.08  2.200    19.47   1 1 4 1
Honda Civic      30.4 4   75.7 52       4.93  1.615    18.52   1 1 4 2
Toyota Corolla   33.9 4   71.1 65        4.22  1.835    19.90   1 1 4 1

Finding the statistical summary of mtcars data set −

>basicStats(mtcars)
               mpg        cyl        disp      hp         drat
nobs       32.000000  32.000000  32.000000 32.000000 32.000000
NAs        0.000000    0.000000  0.000000 0.000000 0.000000
Minimum   10.400000   4.000000   71.100000 52.000000 2.760000
Maximum   33.900000   8.000000   472.000000 335.000000 4.930000 1.
Quartile  15.425000   4.000000   120.825000 96.500000 3.080000 3.
Quartile  22.800000   8.000000   326.000000 180.000000 3.920000
Mean     20.090625   6.187500   230.721875 146.687500 3.596563
Median   19.200000   6.000000   196.300000 123.000000 3.695000
Sum     642.900000   198.000000   7383.100000 4694.000000 115.090000
SE Mean  1.065424   0.315709   21.909473 12.120317 0.094519 LCL
Mean    17.917679   5.543607   186.037211 121.967950 3.403790 UCL
Mean     22.263571   6.831393   275.406539 171.407050 3.789335
Variance  36.324103  3.189516   15360.799829 4700.866935 0.285881
Stdev     6.026948   1.785922   123.938694 68.562868 0.534679
Skewness  0.610655   -0.174612  0.381657 0.726024 0.265904
Kurtosis  -0.372766  -1.762120 -1.207212 -0.135551 -0.714701

          wt     qsec           vs     am        gea      r carb
nobs     32.000000 32.000000 32.000000 32.000000 32.000000 32.000000
NAs     0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Minimum  1.513000 14.500000 0.000000 0.000000 3.000000 1.000000
Maximum  5.424000 22.900000 1.000000 1.000000 5.000000 8.000000 1.
Quartile  2.581250 16.892500 0.000000 0.000000 3.000000 2.000000 3.
Quartile  3.610000 18.900000 1.000000 1.000000 4.000000 4.000000
Mean    3.217250 17.848750 0.437500 0.406250 3.687500 2.812500
Median 3.325000 17.710000 0.000000 0.000000 4.000000 2.000000
Sum 102.952000 571.160000 14.000000 13.000000 118.000000 90.000000
SE Mean 0.172968 0.315890 0.089098 0.088210 0.130427 0.285530
LCL Mean 2.864478 17.204488 0.255783 0.226345 3.421493 2.230158
UCL Mean 3.570022 18.493012 0.619217 0.586155 3.953507 3.394842
Variance 0.957379 3.193166 0.254032 0.248992 0.544355 2.608871
Stdev 0.978457 1.786943 0.504016 0.498991 0.737804 1.615200
Skewness 0.423146 0.369045 0.240258 0.364016 0.528854 1.050874
Kurtosis -0.022711 0.335114 -2.001938 -1.924741 -1.069751 1.257043

Let’s have a look at two more examples using trees data and pressure data in base R.

The trees data example −

## Example

Live Demo

data(trees)
head(trees,20)

## Output

  Girth Height Volume
1  8.3   70     10.3
2  8.6   65     10.3
3  8.8   63     10.2
4  10.5  72     16.4
5  10.7  81     18.8
6  10.8  83     19.7
7  11.0  66     15.6
8  11.0  75     18.2
9  11.1  80     22.6
10 11.2  75     19.9
11 11.3  79     24.2
12 11.4  76     21.0
13 11.4  76     21.4
14 11.7  69     21.3
15 12.0  75     19.1
16 12.9  74     22.2
17 12.9  85     33.8
18 13.3  86     27.4
19 13.7  71     25.7
20 13.8  64     24.9

>basicStats(trees)
Girth Height Volume
nobs 31.000000 31.000000 31.000000 NAs 0.000000 0.000000 0.000000
Minimum 8.300000 63.000000 10.200000 Maximum 20.600000 87.000000 77.000000 1. Quartile 11.050000 72.000000 19.400000 3.
Quartile 15.250000 80.000000 37.300000 Mean 13.248387 76.000000 30.170968 Median 12.900000 76.000000 24.200000 Sum 410.700000 2356.000000 935.300000 SE Mean 0.563626 1.144411 2.952324
LCL Mean 12.097309 73.662800 24.141517 UCL Mean 14.399466 78.337200 36.200418 Variance 9.847914 40.600000 270.202796 Stdev 3.138139 6.371813 16.437846 Skewness 0.501056 -0.356877 1.013274 Kurtosis -0.710941 -0.723368 0.246039

The pressure data example −

## Example

Live Demo

data(pressure)
head(pressure,20)

## Output

   temperature   pressure
1     0            0.0002
2     20           0.0012
3     40           0.0060
4     60           0.0300
5     80           0.0900
6    100           0.2700
7    120           0.7500
8    140           1.8500
9    160           4.2000
10   180           8.8000
11   200          17.3000
12   220          32.1000
13   240          57.0000
14  260           96.0000
15  280          157.0000
16  300          247.0000
17  320          376.0000
18  340          558.0000
19  360          806.0000

basicStats(pressure)
temperature pressure
nobs 19.000000 19.000000
NAs 0.000000 0.000000
Minimum 0.000000 0.000200
Maximum 360.000000 806.000000
1. Quartile 90.000000 0.180000
3. Quartile 270.000000 126.500000
Mean 180.000000 124.336705
Median 180.000000 8.800000
Sum 3420.000000 2362.397400
SE Mean 25.819889 51.531945
LCL Mean 125.754426 16.072107
UCL Mean 234.245574 232.601304 Variance 12666.666667 50455.285428 Stdev 112.546287 224.622540
Skewness 0.000000 1.835588
Kurtosis -1.390471 2.334429

Updated on: 08-Oct-2020

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