multiple groups of row means in R

multiple_groups_row_means.R

# We have a dataframe with many columns. 
# 15 of these columns correspond to responses to questions about hunger.
# They group into 5 subscales (each with 3 questions).
# I want to calculate these subscales (means) as well as an overall score (mean of all).
# This then needs to applied to the control condition (C) and a fasted condition (F) scores
# Once the means are calculated I no longer need the raw scores, so bonus is to automatically remove these columns. Need to retain all other columns in orginal dataframe.

# The code below works just fine, but seems pretty verbose. Is there a more R-like, concise way to do this? 
# It seems like a split-apply-combine, but applied to columns rather than rows?

rawdata$C.hunger1 <- apply(rawdata[,c('CQ1.1', 'CQ1.2', 'CQ1.3')], 1, function(x) mean(x) )
rawdata$C.hunger2 <- apply(rawdata[,c('CQ2.1', 'CQ2.2', 'CQ2.3')], 1, function(x) mean(x) )
rawdata$C.hunger3 <- apply(rawdata[,c('CQ3.1', 'CQ3.2', 'CQ3.3')], 1, function(x) mean(x) )
rawdata$C.hunger4 <- apply(rawdata[,c('CQ4.1', 'CQ4.2', 'CQ4.3')], 1, function(x) mean(x) )
rawdata$C.hunger5 <- apply(rawdata[,c('CQ5.1', 'CQ5.2', 'CQ5.3')], 1, function(x) mean(x) )
rawdata$C.hunger <- apply(rawdata[,c('C.hunger1', 'C.hunger2', 'C.hunger3', 'C.hunger4', 'C.hunger5')], 1, function(x) mean(x) )

# repeat this process for the fasting (F) condition
rawdata$F.hunger1 <- apply(rawdata[,c('FQ1.1', 'FQ1.2', 'FQ1.3')], 1, function(x) mean(x) )
rawdata$F.hunger2 <- apply(rawdata[,c('FQ2.1', 'FQ2.2', 'FQ2.3')], 1, function(x) mean(x) )
rawdata$F.hunger3 <- apply(rawdata[,c('FQ3.1', 'FQ3.2', 'FQ3.3')], 1, function(x) mean(x) )
rawdata$F.hunger4 <- apply(rawdata[,c('FQ4.1', 'FQ4.2', 'FQ4.3')], 1, function(x) mean(x) )
rawdata$F.hunger5 <- apply(rawdata[,c('FQ5.1', 'FQ5.2', 'FQ5.3')], 1, function(x) mean(x) )
rawdata$F.hunger <- apply(rawdata[,c('F.hunger1', 'F.hunger2', 'F.hunger3', 'F.hunger4', 'F.hunger5')], 1, function(x) mean(x) )