How to Normalize the Values in Matrix R

1. Use the sweep Function to Sweep Out Arrays in R
2. Use the scale Function to Normalize the Values in R Matrix

This article will introduce how to normalize the values in an R matrix.

Use the sweep Function to Sweep Out Arrays in R

The sweep function is used to sweep out the summary statistic from the array. It takes the input array as the first argument and the summary statistic as the third argument. The second argument of the function represents the vector of indices that need to correspond to the elements of the third argument vector. The fourth argument denotes the function that’s used to sweep out the array. In this case, we pass the division operator, which can be supplied with quoted notation - "/". The function returns the array with the same shape as the input array. We utilize the colSums function to calculate the column sums of the given input array and pass the result as the summary statistic.

require(stats)

v1 <- c(1.1, 1.2, 4.3, 1.3, 3.9, 2.1, 5.3, 3.8, 7.7, 8.8, 6.7, 2.6)
m1 <- matrix(v1, ncol = 4)

sweep(m1, 2, colSums(m1), FUN = "/")

Output:

          [,1]      [,2]      [,3]      [,4]
[1,] 0.1666667 0.1780822 0.3154762 0.4861878
[2,] 0.1818182 0.5342466 0.2261905 0.3701657
[3,] 0.6515152 0.2876712 0.4583333 0.1436464

Note that, sweep function can also with the default value of the function parameter. If the user does not supply it explicitly, the function is assumed to be the subtraction operator. Mind that when the custom function object is passed, it should have two arguments. The following code snippet subtracts the median of each column from the elements in the corresponding column of the matrix.

require(stats)

v1 <- c(1.1, 1.2, 4.3, 1.3, 3.9, 2.1, 5.3, 3.8, 7.7, 8.8, 6.7, 2.6)
m1 <- matrix(v1, ncol = 4)

med.att <- apply(m1, 2, median)
sweep(m1, 2, med.att)

Output:

     [,1] [,2] [,3] [,4]
[1,] -0.1 -0.8  0.0  2.1
[2,]  0.0  1.8 -1.5  0.0
[3,]  3.1  0.0  2.4 -4.1

Use the scale Function to Normalize the Values in R Matrix

Another useful function for matrix data normalization is scale, which divides each column of the input matrix by the corresponding value from the third argument named - scale. Note that scale takes the center argument that is used for column centering (more details can be found on this page). In this case, we assign FALSE to the latter argument, indicating that column centering needs not to be done. The colSums function is utilized to calculate the sums for each column of the input matrix and pass it as the scale argument.

require(stats)

v1 <- c(1.1, 1.2, 4.3, 1.3, 3.9, 2.1, 5.3, 3.8, 7.7, 8.8, 6.7, 2.6)
m1 <- matrix(v1, ncol = 4)

c1 <- colSums(m1)
scale(m1, center = FALSE, scale = c1)

Output:

          [,1]      [,2]      [,3]      [,4]
[1,] 0.1666667 0.1780822 0.3154762 0.4861878
[2,] 0.1818182 0.5342466 0.2261905 0.3701657
[3,] 0.6515152 0.2876712 0.4583333 0.1436464
attr(,"scaled:scale")
[1]  6.6  7.3 16.8 18.1