Say I have a list called df such that colnames(df) yields:
"A" "B" "C" "D" "E" "F"
I would like aggregate data in the following way:
aggregate(cbind(`C`,`D`,`E`,`F`)~A+B, data = df, FUN = sum)
Of course I could do it "manually" but in my true data I have a very big amount of columns, so I am trying to change the colnames(df)[3:6] output to yield:
`C`,`D`,`E`,`F`
instead. So far I have tried to use toString(colnames(df)[3:6]) which yields:
"C, D, E, F"
But this is not read properly by cbind.
Any suggestions?
Instead of the cbind you could also use a matrix created from the subsetted data frame.
aggregate(as.matrix(df[names(df)[3:6]])~A+B, data=df, FUN=sum)
# A B C D E F
# 1 0.36 -0.11 2.02 2.29 -0.13 -2.66
# 2 -0.56 0.40 -0.09 1.30 -0.28 -0.28
# 3 1.37 0.63 1.51 -0.06 -1.39 0.64
Or, to answer your question literally try
(ev <- sprintf("cbind(%s)", toString(names(df)[3:6])))
# [1] "cbind(C, D, E, F)"
I don't think the backticks are needed. Are they?
And then, of course:
aggregate(eval(parse(text=ev))~A+B, data=df, FUN=sum)
# A B C D E F
# 1 -2.44 -1.78 1.90 -1.76 0.46 -0.61
# 2 1.32 -0.17 -0.43 0.46 0.70 0.50
# 3 -0.31 1.21 -0.26 -0.64 1.04 -1.72
Data:
df <- structure(list(A = c(-2.44, 1.32, -0.31), B = c(-1.78, -0.17,
1.21), C = c(1.9, -0.43, -0.26), D = c(-1.76, 0.46, -0.64), E = c(0.46,
0.7, 1.04), F = c(-0.61, 0.5, -1.72)), class = "data.frame", row.names = c(NA,
-3L))