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 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 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
Additional Resources
How to Retrieve Row Numbers in R
How to Perform a COUNTIF Function in R
How to Perform a SUMIF Function in R