Find the column index of least value for each row of an R matrix

To find the column index of least value for each row in an R matrix, we can use apply function.

For Example, if we have a matrix called M then we can find column that has the least value for each row by using the command as follows −

apply(M,1,which.min)

Example 1

Consider the matrix given below −

M1<-matrix(round(rexp(80),1),ncol=4) M1

The following dataframe is created

     [,1] [,2]  [,3] [,4] [1,]  1.3  0.1  0.6  1.0 [2,]  0.7  2.5  0.0 0.5 [3,]  0.9  0.7  0.9 0.8 [4,]  0.5  1.0  0.6 0.5 [5,]  0.3  1.2  1.6 0.4 [6,]  0.7  0.5  0.1 1.9 [7,]  1.4  0.1  0.1 0.2 [8,]  2.6  1.4  0.1 1.1 [9,]  0.1  2.9  0.6 0.5 [10,] 1.9  1.6  1.0 1.6 [11,] 2.6  0.7  0.3 3.5 [12,] 0.4  1.4 1.0 1.9 [13,] 0.8  0.5 0.8 4.3 [14,] 0.2  1.7 0.8 0.4 [15,] 0.1  0.9 0.3 1.0 [16,] 2.2  1.9 0.3 2.2 [17,] 0.2  0.4 1.9 0.6 [18,] 0.8  1.2 3.4 0.4 [19,] 0.7  1.4 2.0 0.2 [20,] 0.7  0.2 1.0 2.5

To find the column index for each row in M1 that has least value on the above created data frame, add the following code to the above snippet −

M1<-matrix(round(rexp(80),1),ncol=4) apply(M1,1,which.min)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 2 3 2 1 1 3 2 3 1 3 3 1 2 1 1 3 1 4 4 2 

Example 2

Consider the matrix given below −

M2<-matrix(round(rnorm(80),2),ncol=4) M2

The following dataframe is created

 [,1] [,2] [,3] [,4] [1,] 0.35 -0.41 -0.97 0.29 [2,] 0.88 -0.36 -0.13 0.23 [3,] 0.44 -0.26 -0.83 0.57 [4,] 1.46 -1.78 0.89 -0.07 [5,] -0.02 -0.98 0.75 1.32 [6,] 0.69 -1.08 0.75 0.84 [7,] -1.67 -1.16 -0.49 0.60 [8,] -0.98 -0.61 -1.12 0.97 [9,] -0.53 0.00 0.40 -1.01 [10,] -0.15 0.01 1.64 0.94 [11,] -0.01 0.50 0.18 -1.96 [12,] 0.01 0.95 -0.40 -1.06 [13,] -1.20 0.90 -0.83 0.88 [14,] -0.09 -1.44 -0.72 0.39 [15,] -0.41 0.87 0.27 0.57 [16,] -1.15 -1.31 0.76 -0.76 [17,] -0.42 0.88 -1.61 0.58 [18,] -0.99 1.21 0.05 0.25 [19,] -0.68 1.15 0.79 0.23 [20,] -0.44 0.64 0.16 0.54

To find the column index for each row in M2 that has least value on the above created data frame, add the following code to the above snippet −

M2<-matrix(round(rnorm(80),2),ncol=4) apply(M2,1,which.min)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 3 2 3 2 2 2 1 3 4 1 4 4 1 2 1 2 3 1 1 1 

Example 3

Consider the matrix given below −

M3<-matrix(rpois(100,10),ncol=5) M3

The following dataframe is created

    [,1] [,2] [,3] [,4] [,5] [1,] 16   15   5 3 10 [2,]   8   12 7 8 9 [3,]   8   18 13 11 9 [4,] 11   13 13 12 12 [5,] 12   10 11 8 9 [6,] 12 11 11 9 9 [7,]   9 6 5 10 5 [8,]   9 11 11 8 11 [9,] 15 9 9 9 11 [10,] 9 14 11 9 7 [11,] 12 14 8 16 9 [12,] 8 13 12 15 15 [13,] 6 11 12 8 10 [14,] 10 13 8 15 13 [15,] 7 13 7 10 6 [16,] 16 6 10 9 13 [17,] 7 12 12 11 10 [18,] 10 11 10 20 12 [19,] 14 13 7 8 12 [20,] 11 10 6 11 20

To find the column index for each row in M3 that has least value on the above created data frame, add the following code to the above snippet −

Consider the matrix given below −

M3<-matrix(rpois(100,10),ncol=5) apply(M3,1,which.min)

Output

If you execute all the above given snippets as a single program, it generates the following Output −

[1] 4 3 1 1 4 4 3 4 2 5 3 1 1 3 5 2 1 1 3 3