How to divide data frame row values by row variance in R?

To divide data frame row values by row variance R, we can follow the below steps −

• First of all, create a data frame.
• Then, use apply function to divide the data frame row values by row variance.

Create the data frame

Let's create a data frame as shown below −

 Live Demo

x<-rpois(25,1) y<-rpois(25,1) z<-rpois(25,1) df<-data.frame(x,y,z) df

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

  x y z 1 1 1 1 2 4 0 0 3 0 3 0 4 2 1 0 5 1 0 0 6 1 1 1 7 1 3 2 8 1 1 2 9 0 1 1 10 1 1 1 11 3 0 3 12 3 0 1 13 4 2 0 14 1 0 0 15 2 1 1 16 2 0 0 17 3 2 1 18 0 0 0 19 1 0 2 20 0 1 2 21 1 0 1 22 0 1 2 23 1 0 0 24 0 2 2 25 2 1 1

Divide the data frame row values by row variance

Using apply function to divide the row values of df by row variance −

 Live Demo

x<-rpois(25,1) y<-rpois(25,1) z<-rpois(25,1) df<-data.frame(x,y,z) df_new<-t(apply(df,1, function(x) x/var(x))) df_new

Output

         x      y      z [1,]    Inf    Inf    Inf [2,] 0.750000 0.0 0.0000000 [3,] 0.000000 1.0 0.0000000 [4,] 2.000000 1.0 0.0000000 [5,] 3.000000 0.0 0.0000000 [6,]    Inf    Inf    Inf [7,] 1.000000 3.0 2.0000000 [8,] 3.000000 3.0 6.0000000 [9,] 0.000000 3.0 3.0000000 [10,]    Inf   Inf    Inf [11,] 1.000000 0.0 1.0000000 [12,] 1.285714 0.0 0.4285714 [13,] 1.000000 0.5 0.0000000 [14,] 3.000000 0.0 0.0000000 [15,] 6.000000 3.0 3.0000000 [16,] 1.500000 0.0 0.0000000 [17,] 3.000000 2.0 1.0000000 [18,]    NaN    NaN    NaN [19,] 1.000000 0.0 2.0000000 [20,] 0.000000 1.0 2.0000000 [21,] 3.000000 0.0 3.0000000 [22,] 0.000000 1.0 2.0000000 [23,] 3.000000 0.0 0.0000000 [24,] 0.000000 1.5 1.5000000 [25,] 6.000000 3.0 3.0000000