I am having trouble figuring out the most elegant and flexible way to switch data from long format to wide format when I have more than one measure variable I want to bring along.
For example, here's a simple data frame in long format. ID is the subject, TIME is a time variable, and X and Y are measurements made of ID at TIME:
> my.df <- data.frame(ID=rep(c("A","B","C"), 5), TIME=rep(1:5, each=3), X=1:15, Y=16:30)
> my.df
ID TIME X Y
1 A 1 1 16
2 B 1 2 17
3 C 1 3 18
4 A 2 4 19
5 B 2 5 20
6 C 2 6 21
7 A 3 7 22
8 B 3 8 23
9 C 3 9 24
10 A 4 10 25
11 B 4 11 26
12 C 4 12 27
13 A 5 13 28
14 B 5 14 29
15 C 5 15 30
If I just wanted to turn the values of TIME into column headers containing the include X, I know I can use cast() from the reshape package (or dcast() from reshape2):
> cast(my.df, ID ~ TIME, value="X")
ID 1 2 3 4 5
1 A 1 4 7 10 13
2 B 2 5 8 11 14
3 C 3 6 9 12 15
But what I really want to do is also bring along Y as another measure variable, and have the column names reflect both the measure variable name and the time value:
ID X_1 X_2 X_3 X_4 X_5 Y_1 Y_2 Y_3 Y_4 Y_5
1 A 1 4 7 10 13 16 19 22 25 28
2 B 2 5 8 11 14 17 20 23 26 29
3 C 3 6 9 12 15 18 21 24 27 30
(FWIW, I don't really care if all the X's are first followed by the Y's, or if they are interleaved as X_1, Y_1, X_2, Y_2, etc.)
I can get close to this by cast-ing the long data twice and merging the results, though the column names need some work, and I would need to tweak it if I needed to add a 3rd or 4th variable in addition to X and Y:
merge(
cast(my.df, ID ~ TIME, value="X"),
cast(my.df, ID ~ TIME, value="Y"),
by="ID", suffixes=c("_X","_Y")
)
Seems like some combination of functions in reshape2 and/or plyr should be able to do this more elegantly that my attempt, as well as handling multiple measure variables more cleanly. Something like cast(my.df, ID ~ TIME, value=c("X","Y")), which isn't valid. But I haven't been able to figure it out.
In order to handle multiple variables like you want, you need to melt the data you have before casting it.
library("reshape2")
dcast(melt(my.df, id.vars=c("ID", "TIME")), ID~variable+TIME)
which gives
ID X_1 X_2 X_3 X_4 X_5 Y_1 Y_2 Y_3 Y_4 Y_5
1 A 1 4 7 10 13 16 19 22 25 28
2 B 2 5 8 11 14 17 20 23 26 29
3 C 3 6 9 12 15 18 21 24 27 30
EDIT based on comment:
The data frame
num.id = 10
num.time=10
my.df <- data.frame(ID=rep(LETTERS[1:num.id], num.time),
TIME=rep(1:num.time, each=num.id),
X=1:(num.id*num.time),
Y=(num.id*num.time)+1:(2*length(1:(num.id*num.time))))
gives a different result (all entries are 2) because the ID/TIME combination does not indicate a unique row. In fact, there are two rows with each ID/TIME combinations. reshape2 assumes a single value for each possible combination of the variables and will apply a summary function to create a single variable is there are multiple entries. That is why there is the warning
Aggregation function missing: defaulting to length
You can get something that works if you add another variable which breaks that redundancy.
my.df$cycle <- rep(1:2, each=num.id*num.time)
dcast(melt(my.df, id.vars=c("cycle", "ID", "TIME")), cycle+ID~variable+TIME)
This works because cycle/ID/time now uniquely defines a row in my.df.