Calculate row means on subset of columns

Calculate row means on a subset of columns:

Create a new data.frame which specifies the first column from DF as an column called ID and calculates the mean of all the other fields on that row, and puts that into column entitled 'Means':

data.frame(ID=DF[,1], Means=rowMeans(DF[,-1]))
  ID    Means
1  A 3.666667
2  B 4.333333
3  C 3.333333
4  D 4.666667
5  E 4.333333

Starting with your data frame DF, you could use the data.table package:

library(data.table)

## EDIT: As suggested by @MichaelChirico, setDT converts a
## data.frame to a data.table by reference and is preferred
## if you don't mind losing the data.frame
setDT(DF)

# EDIT: To get the column name 'Mean':

DF[, .(Mean = rowMeans(.SD)), by = ID]

#      ID     Mean
# [1,]  A 3.666667
# [2,]  B 4.333333
# [3,]  C 3.333333
# [4,]  D 4.666667
# [5,]  E 4.333333

You can create a new row with $ in your data frame corresponding to the Means

DF$Mean <- rowMeans(DF[,2:4])

Using dplyr:

library(dplyr)

# exclude ID column then get mean
DF %>%
  transmute(ID,
            Mean = rowMeans(select(., -ID)))

Or

# select the columns to include in mean
DF %>%
  transmute(ID,
            Mean = rowMeans(select(., C1:C3)))

#   ID     Mean
# 1  A 3.666667
# 2  B 4.333333
# 3  C 3.333333
# 4  D 4.666667
# 5  E 4.333333