Create frequency tables for multiple factor columns in R
You were nearly there. Just one small change in your function would have got you there. The x in function(x) ... needs to be passed through to the table() call:
levs <- c("Not Impt at all", "Somewhat Impt", "Neutral", "Impt", "Very Impt")
sapply(x.sample, function(x) table(factor(x, levels=levs, ordered=TRUE)))
A little re-jig of the code might make it a bit easier to read too:
sapply(lapply(x.sample,factor,levels=levs,ordered=TRUE), table)
# Q9_A Q9_B Q9_C
#Not Impt at all 3 4 4
#Somewhat Impt 0 0 0
#Neutral 0 0 0
#Impt 1 0 0
#Very Impt 6 6 6
Coming a bit late, but here's a reshape2 possible solution. It could have been very straightforward with recast but we need to handle empty factor levels here so we need to specify both factorsAsStrings = FALSE within melt and drop = FALSE within dcast, while recast can't pass arguments to melt (only to dcast), so here goes
library(reshape2)
x.sample$indx <- 1
dcast(melt(x.sample, "indx", factorsAsStrings = FALSE), value ~ variable, drop = FALSE)
# value Q9_A Q9_B Q9_C
# 1 Impt 1 0 0
# 2 Neutral 0 0 0
# 3 Not Impt at all 3 4 4
# 4 Somewhat Impt 0 0 0
# 5 Very Impt 6 6 6
If we wouldn't care about empty levels a quick solution would be just
recast(x.sample, value ~ variable, id.var = "indx")
# value Q9_A Q9_B Q9_C
# 1 Impt 1 0 0
# 2 Not Impt at all 3 4 4
# 3 Very Impt 6 6 6
Alternatively, if speed is a concern, we can do the same using data.atble
library(data.table)
dcast(melt(setDT(x.sample), measure.vars = names(x.sample), value.factor = TRUE),
value ~ variable, drop = FALSE)
# value Q9_A Q9_B Q9_C
# 1: Impt 1 0 0
# 2: Neutral 0 0 0
# 3: Not Impt at all 3 4 4
# 4: Somewhat Impt 0 0 0
# 5: Very Impt 6 6 6