# How to get the summary statistics including all basic statistical values for R data frame columns?

When we apply summary function in R, the output gives minimum, first quartile, median, mean, third quartile, and maximum but there are many other basic statistical values that help us to understand the variable such as range, sum, standard error of mean, variance, standard deviation, and coefficient of variation. Therefore, if we want to find all the values then we can use stat.desc function of pastecs package as shown in the below examples.

## Example1

Consider the below data frame −

Live Demo

> x1<-rnorm(20)
> x2<-rnorm(20)
> x3<-rnorm(20)
> df1<-data.frame(x1,x2,x3)
> df1

## Output

            x1          x2         x3
1   1.37057327  0.96585723 -1.6824440
2   0.43258556 -2.54077794 -1.5962218
3   0.68188832  1.08144561 -0.9956110
4   0.24553258  0.07541754 -0.3527252
5  -0.19946765  0.49262220 -0.7946248
6  -1.93924451  0.13544724 -0.4184053
7   0.27443524  0.08363552  0.8696729
8  -2.02613035 -0.67827697 -0.8940207
9   0.33772301 -1.51171368  0.4032073
10 -0.44463177  1.69245587  1.7037202
11  1.69256604 -0.60384845  0.7247898
12  0.11356829  1.05543184  0.9780191
13 -0.01516246  0.92529906  0.4805570
14 -0.78159893 -0.55414738 -0.4680645
15 -0.08974609  0.76847977 -0.2780631
16 -0.45456509  1.08361106 -1.6672789
17  1.13920983  0.24680491  1.3922984
18  0.55562889 -0.06529163 -0.7083794
19 -0.11607439  1.09421670  2.1602874
20 -0.78351132  0.48005020  0.3453250

Finding summary of df1 using summary function −

> summary(df1)

## Output

                x1                x2                x3
Min.   :-2.0261304   Min.   :-2.5408   Min.   :-1.6824
1st Qu.:-0.4471151   1st Qu.:-0.1875   1st Qu.:-0.8195
Median : 0.0492029   Median : 0.3634   Median :-0.3154
Mean   :-0.0003211   Mean   : 0.2113   Mean   :-0.0399
3rd Qu.: 0.4633464   3rd Qu.: 0.9883   3rd Qu.: 0.7610
Max.   : 1.6925660   Max.   : 1.6925   Max.   : 2.1603

Loading pastecs package and finding the statistical summary of df1 using stat.desc function −

> library(pastecs)
> stat.desc(df1)

## Output

                        x1         x2           x3
nbr.val       2.000000e+01 20.0000000  20.00000000
nbr.null      0.000000e+00  0.0000000   0.00000000
nbr.na        0.000000e+00  0.0000000   0.00000000
min          -2.026130e+00 -2.5407779  -1.68244397
max           1.692566e+00  1.6924559   2.16028742
range         3.718696e+00  4.2332338   3.84273139
sum          -6.421540e-03  4.2267187  -0.79796158
median        4.920292e-02  0.3634276  -0.31539416
mean         -3.210770e-04  0.2113359  -0.03989808
SE.mean       2.103941e-01  0.2262258   0.25081489
CI.mean.0.95  4.403600e-01  0.4734961   0.52496160
var           8.853137e-01  1.0235624   1.25816219
std.dev       9.409111e-01  1.0117126   1.12167829
coef.var     -2.930484e+03  4.7872246 -28.11359138

## Example2

Live Demo

> y1<-rpois(20,5)
> y2<-rpois(20,2)
> y3<-rpois(20,10)
> y4<-rpois(20,8)
> df2<-data.frame(y1,y2,y3,y4)
> df2

## Output

   y1 y2 y3 y4
1   4  4 10  6
2   4  1  9  8
3   2  3 12  9
4   4  0 11  4
5   7  3  7  7
6   6  0  9 18
7   5  1  7  3
8   6  2  5 10
9   5  1 10  5
10  6  1 12  7
11 11  2  8  7
12  4  2 10 11
13  4  3  7  6
14  4  0 11 15
15 10  1  8  8
16  5  0  6  8
17  3  1 13 14
18  4  1  8  5
19  5  1  5  4
20  8  2 13  5

Finding the statistical summary of df2 using stat.desc function −

> stat.desc(df2)

## Output

                      y1         y2          y3          y4
nbr.val       20.0000000 20.0000000  20.0000000  20.0000000
nbr.null       0.0000000  4.0000000   0.0000000   0.0000000
nbr.na         0.0000000  0.0000000   0.0000000   0.0000000
min            2.0000000  0.0000000   5.0000000   3.0000000
max           11.0000000  4.0000000  13.0000000  18.0000000
range          9.0000000  4.0000000   8.0000000  15.0000000
sum          107.0000000 29.0000000 181.0000000 160.0000000
median         5.0000000  1.0000000   9.0000000   7.0000000
mean           5.3500000  1.4500000   9.0500000   8.0000000
SE.mean        0.4988144  0.2562380   0.5547641   0.8795932
CI.mean.0.95   1.0440305  0.5363122   1.1611345   1.8410097
var            4.9763158  1.3131579   6.1552632  15.4736842
std.dev        2.2307657  1.1459310   2.4809803   3.9336604
coef.var       0.4169656  0.7902973   0.2741415   0.4917076