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

**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