How to create a new data frame for the mean of rows of some columns from an R data frame?

Finding row means help us to identity the average performance of a case if all the variables are of same nature and it is also an easy job. But if some of the columns have different type of data then we have to extract columns for which we want to find the row means. Therefore, we can create a new data frame with row means of the required columns using rowMeans function.

Example

 Live Demo

Consider the below data frame −

set.seed(88) Group<-LETTERS[1:10] x1<-rpois(20,2) x2<-rpois(20,5) x3<-rpois(20,10) df<-data.frame(Group,x1,x2,x3) df

Output

 Group x1 x2 x3 1 A 2 3 10 2 B 0 6 7 3 C 3 7 9 4 D 2 8 9 5 E 6 8 9 6 F 8 6 4 7 G 0 4 5 8 H 3 7 10 9 I 3 5 11 10 J 5 4 10 11 A 2 3 9 12 B 3 7 8 13 C 2 6 6 14 D 1 4 7 15 E 0 7 12 16 F 1 8 9 17 G 0 5 11 18 H 2 6 9 19 I 3 7 5 20 J 3 9 6

Creating a new data frame with column Group as in original df and RowMeans for the mean of columns x1, x2, and x3 −

row_means_df<-data.frame(Group=df[,1],RowMeans=rowMeans(df[,-1])) row_means_df Group RowMeans 1 A 5.000000 2 B 4.333333 3 C 6.333333 4 D 6.333333 5 E 7.666667 6 F 6.000000 7 G 3.000000 8 H 6.666667 9 I 6.333333 10 J 6.333333 11 A 4.666667 12 B 6.000000 13 C 4.666667 14 D 4.000000 15 E 6.333333 16 F 6.000000 17 G 5.333333 18 H 5.666667 19 I 5.000000 20 J 6.000000

Creating a new data frame with column Group as in original df and RowMeans for the mean of columns x2 and x3 that is 3 and 4 −

row_means_3.4_cols_df<-data.frame(Group=df[,1],RowMeans=rowMeans(df[,-c(1,2)])) row_means_3.4_cols_df Group RowMeans 1 A 6.5 2 B 6.5 3 C 8.0 4 D 8.5 5 E 8.5 6 F 5.0 7 G 4.5 8 H 8.5 9 I 8.0 10 J 7.0 11 A 6.0 12 B 7.5 13 C 6.0 14 D 5.5 15 E 9.5 16 F 8.5 17 G 8.0 18 H 7.5 19 I 6.0 20 J 7.5