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In R, the apply() function can be used to apply a function to each row or column of a matrix or data frame. The syntax for the apply() function is apply(x, margin, FUN), where x is the matrix or data frame, margin indicates whether the function should be applied to each row (MARGIN = 1) or each column (MARGIN = 2), and FUN is the function to be applied. The apply() function will return the result of applying the function to each row or column of the matrix or data frame.
You can use the apply() function to apply a function to each row in a matrix or data frame in R.
This function uses the following basic syntax:
apply(X, MARGIN, FUN)
where:
- X: Name of the matrix or data frame.
- MARGIN: Dimension to perform operation across. Use 1 for row, 2 for column.
- FUN: The function to apply.
The following examples show how to use this syntax in practice.
Example 1: Apply Function to Each Row in Matrix
Suppose we have the following matrix in R:
#create matrix mat <- matrix(1:15, nrow=3) #view matrix mat [,1] [,2] [,3] [,4] [,5] [1,] 1 4 7 10 13 [2,] 2 5 8 11 14 [3,] 3 6 9 12 15
We can use the apply() function to apply different functions to the rows of the matrix:
#find mean of each row apply(mat, 1, mean) [1] 7 8 9 #find sum of each row apply(mat, 1, sum) [1] 35 40 45 #find standard deviation of each row apply(mat, 1, sd) [1] 4.743416 4.743416 4.743416 #multiply the value in each row by 2 (using t() to transpose the results) t(apply(mat, 1, function(x) x * 2)) [,1] [,2] [,3] [,4] [,5] [1,] 2 8 14 20 26 [2,] 4 10 16 22 28 [3,] 6 12 18 24 30 #normalize every row to 1 (using t() to transpose the results) t(apply(mat, 1, function(x) x / sum(x) )) [,1] [,2] [,3] [,4] [,5] [1,] 0.02857143 0.1142857 0.2 0.2857143 0.3714286 [2,] 0.05000000 0.1250000 0.2 0.2750000 0.3500000 [3,] 0.06666667 0.1333333 0.2 0.2666667 0.3333333
Note that if you’d like to find the mean or sum of each row, it’s faster to use the built-in rowMeans() or rowSums() functions:
#find mean of each row rowMeans(mat) [1] 7 8 9 #find sum of each row rowSums(mat) [1] 35 40 45
Example 2: Apply Function to Each Row in Data Frame
Suppose we have the following matrix in R:
#create data frame df <- data.frame(var1=1:3, var2=4:6, var3=7:9, var4=10:12, var5=13:15) #view data frame df var1 var2 var3 var4 var5 1 1 4 7 10 13 2 2 5 8 11 14 3 3 6 9 12 15
We can use the apply() function to apply different functions to the rows of the data frame:
#find mean of each row apply(df, 1, mean) [1] 7 8 9 #find sum of each row apply(df, 1, sum) [1] 35 40 45 #find standard deviation of each row apply(df, 1, sd) [1] 4.743416 4.743416 4.743416 #multiply the value in each row by 2 (using t() to transpose the results) t(apply(df, 1, function(x) x * 2)) var1 var2 var3 var4 var5 [1,] 2 8 14 20 26 [2,] 4 10 16 22 28 [3,] 6 12 18 24 30 #normalize every row to 1 (using t() to transpose the results) t(apply(df, 1, function(x) x / sum(x) )) var1 var2 var3 var4 var5 [1,] 0.02857143 0.1142857 0.2 0.2857143 0.3714286 [2,] 0.05000000 0.1250000 0.2 0.2750000 0.3500000 [3,] 0.06666667 0.1333333 0.2 0.2666667 0.3333333
Similar to matrices, if you’d like to find the mean or sum of each row, it’s faster to use the built-in rowMeans() or rowSums() functions:
#find mean of each row rowMeans(df) [1] 7 8 9 #find sum of each row rowSums(df) [1] 35 40 45