How to find the proportion of row values in an R data frame?

R ProgrammingServer Side ProgrammingProgramming

The proportion of row values can be calculated if we divide each row value with the sum of all values in a particular row. Therefore, the total sum of proportions will be equal to 1. This can be done by dividing the data frame with the row sums and for this purpose we can use the below syntax −

Syntax

data_frame_name/rowSums(data_frame_name)

Consider the below data frame −

Example

 Live Demo

set.seed(111)
x1<-rpois(20,2)
x2<-rpois(20,5)
x3<-round(runif(20,2,5),0)
x4<-round(runif(20,2,4),0)
df1<-data.frame(x1,x2,x3,x4)
df1

Output

   x1 x2 x3 x4
1   2  4  4  2
2   3  4  3  3
3   1  4  4  2
4   2  4  5  3
5   1  9  4  4
6   2  4  3  2
7   0  6  3  2
8   2  4  5  3
9   2  7  4  2
10  0  5  4  3
11  2  2  4  4
12  2  5  4  3
13  0  5  2  3
14  0  5  3  2
15  1  4  5  3
16  2  6  4  3
17  1  2  4  3
18  5  7  3  4
19  1  6  2  2
20  2  7  4  4

Finding the proportions for each row of data frame df1 −

Example

df1<-df1/rowSums(df1)
df1

Output

       x1        x2         x3        x4
1 0.16666667  0.3333333   0.3333333  0.1666667
2 0.23076923  0.3076923  0.2307692   0.2307692
3 0.09090909  0.3636364  0.3636364   0.1818182
4 0.14285714  0.2857143  0.3571429   0.2142857
5 0.05555556  0.5000000  0.2222222   0.2222222
6 0.18181818  0.3636364  0.2727273   0.1818182
7 0.00000000  0.5454545  0.2727273   0.1818182
8 0.14285714  0.2857143  0.3571429   0.2142857
9 0.13333333  0.4666667  0.2666667   0.1333333
10 0.00000000 0.4166667   0.3333333  0.2500000
11 0.16666667  0.1666667  0.3333333   0.3333333
12 0.14285714  0.3571429  0.2857143   0.2142857
13 0.00000000  0.5000000  0.2000000   0.3000000
14 0.00000000  0.5000000  0.3000000   0.2000000
15 0.07692308  0.3076923  0.3846154   0.2307692
16 0.13333333  0.4000000  0.2666667   0.2000000
17 0.10000000  0.2000000  0.4000000   0.3000000
18 0.26315789  0.3684211  0.1578947   0.2105263
19 0.09090909  0.5454545  0.1818182   0.1818182
20 0.11764706  0.4117647  0.2352941   0.2352941

Let’s have a look at another example −

Example

y1<-sample(0:5,20,replace=TRUE)
y2<-sample(0:9,20,replace=TRUE)
y3<-sample(1:10,20,replace=TRUE)
y4<-sample(1:50,20) y5<-sample(10:100,20)
df2<-data.frame(y1,y2,y3,y4,y5)
df2

Output

   y1  y2  y3   y4   y5
1  4   5   3   48   87
2  4   6  10   41   76
3  2   5   7   26   36
4  2   1   5   44   82
5  4   8   2   4   80
6  1   1   3  35   12
7  5   5   9  10   84
8  3   3   6   1   93
9  1   3   8   9   15
10 0   4   4   19   83
11 4   5   4   24   65
12 0   7   10   3   49
13 1   5   6   27   64
14 1   5   2   47   10
15 1   6   3   45   56
16 4   0   2   33   28
17 2   9   3   32   96
18 0   3   6   5   52
19 0   7   5   15   61
20 2   6   3   31   98

Finding the proportions for each row of data frame df2 −

Example

df2<-df2/rowSums(df2)
df2

Output

       y1        y2            y3          y4         y5
1 0.027210884  0.034013605  0.02040816  0.326530612  0.5918367
2 0.029197080  0.043795620  0.07299270  0.299270073  0.5547445
3 0.026315789  0.065789474  0.09210526  0.342105263  0.4736842
4 0.014925373  0.007462687  0.03731343  0.328358209  0.6119403
5 0.040816327  0.081632653  0.02040816  0.040816327  0.8163265
6 0.019230769  0.019230769  0.05769231  0.673076923  0.2307692
7 0.044247788  0.044247788  0.07964602  0.088495575  0.7433628
8 0.028301887  0.028301887  0.05660377  0.009433962  0.8773585
9 0.027777778  0.083333333  0.22222222  0.250000000  0.4166667
10 0.000000000  0.036363636  0.03636364  0.172727273  0.7545455
11 0.039215686  0.049019608  0.03921569  0.235294118  0.6372549
12 0.000000000  0.101449275  0.14492754  0.043478261  0.7101449
13 0.009708738  0.048543689  0.05825243  0.262135922  0.6213592
14 0.015384615  0.076923077  0.03076923  0.723076923  0.1538462
15 0.009009009  0.054054054  0.02702703  0.405405405 0.5045045
16 0.059701493  0.000000000  0.02985075  0.492537313 0.4179104
17 0.014084507  0.063380282  0.02112676  0.225352113 0.6760563
18 0.000000000  0.045454545  0.09090909  0.075757576 0.7878788
19 0.000000000  0.079545455  0.05681818  0.170454545 0.6931818
20 0.014285714  0.042857143  0.02142857  0.221428571 0.7000000
raja
Published on 09-Oct-2020 14:41:15
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