stacking columns into 1 column in R

Question

I have a data frame that looks like:

ID Time U1 U2 U3 U4 ...
1  20    1  2 3  5 .. 
2  20    2  5 9  4 ..
3  20    2  5 6  4 ..
.
.

And I would need to keep it like: 

ID Time  U
1  20    1
1  20    2
1  20    3
1  20    5
2  20    2
2  20    5
2  20    9
2  20    4
3  20    2
3  20    5
3  20    6
3  20    4

I tried with:

X <- read.table("mydata.txt", header=TRUE, sep=",")
X_D <- as.data.frame(X)
X_new <- stack(X_D, select = -c(ID, Time))

But I haven't managed to get the data into that form. Honestly, I have little experience with stacking/transposing, so any help is greatly appreciated!

Answer 1

Here's the stack approach:

dat2a <- data.frame(dat[1:2], stack(dat[3:ncol(dat)]))
dat2a
#    ID Time values ind
# 1   1   20      1  U1
# 2   2   20      2  U1
# 3   3   20      2  U1
# 4   1   20      2  U2
# 5   2   20      5  U2
# 6   3   20      5  U2
# 7   1   20      3  U3
# 8   2   20      9  U3
# 9   3   20      6  U3
# 10  1   20      5  U4
# 11  2   20      4  U4
# 12  3   20      4  U4

This is very similar to melt from "reshape2":

library(reshape2)
dat2b <- melt(dat, id.vars=1:2)
dat2b
#    ID Time variable value
# 1   1   20       U1     1
# 2   2   20       U1     2
# 3   3   20       U1     2
# 4   1   20       U2     2
# 5   2   20       U2     5
# 6   3   20       U2     5
# 7   1   20       U3     3
# 8   2   20       U3     9
# 9   3   20       U3     6
# 10  1   20       U4     5
# 11  2   20       U4     4
# 12  3   20       U4     4

And, very similar to @TylerRinker's answer, but not dropping the "times", is to just use sep = "" to help R guess time and variable names.

dat3 <- reshape(dat, direction = "long", idvar=1:2, 
                varying=3:ncol(dat), sep = "", timevar="Measure")
dat3
#        ID Time Measure U
# 1.20.1  1   20       1 1
# 2.20.1  2   20       1 2
# 3.20.1  3   20       1 2
# 1.20.2  1   20       2 2
# 2.20.2  2   20       2 5
# 3.20.2  3   20       2 5
# 1.20.3  1   20       3 3
# 2.20.3  2   20       3 9
# 3.20.3  3   20       3 6
# 1.20.4  1   20       4 5
# 2.20.4  2   20       4 4
# 3.20.4  3   20       4 4

In all three of those, you end up with four columns, not three, like you describe in your desired output. However, as @ndoogan points out, by doing so, you're loosing information about your data. If you're fine with that, you can always drop that column from the resulting data.frame quite easily (for example, dat2a <- dat2a[-4].