问题
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.
回答1:
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.
回答2:
reshape(my.df,
idvar = "ID",
timevar = "TIME",
direction = "wide")
gives
ID X.1 Y.1 X.2 Y.2 X.3 Y.3 X.4 Y.4 X.5 Y.5
1 A 1 16 4 19 7 22 10 25 13 28
2 B 2 17 5 20 8 23 11 26 14 29
3 C 3 18 6 21 9 24 12 27 15 30
回答3:
Using the data.table_1.9.5, this can be done without the melt as it can handle multiple value.var columns. You can install it from here
library(data.table)
dcast(setDT(my.df), ID~TIME, value.var=c('X', 'Y'))
# ID 1_X 2_X 3_X 4_X 5_X 1_Y 2_Y 3_Y 4_Y 5_Y
#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
回答4:
Note -Sept 2019: within tidyr, the gather()+spread() approach (described in this answer) has more or less been replaced by the pivot_wider() approach (described in `this newer tidyr answer). For current info about the transition, see the pivoting vignette.
Here's a solution with the tidyr package, which has essentially replaced reshape and reshape2. As with those two packages, the strategy it to make the dataset longer first, and then wider.
library(magrittr); requireNamespace("tidyr"); requireNamespace("dplyr")
my.df %>%
tidyr::gather(key=variable, value=value, c(X, Y)) %>% # Make it even longer.
dplyr::mutate( # Create the spread key.
time_by_variable = paste0(variable, "_", TIME)
) %>%
dplyr::select(ID, time_by_variable, value) %>% # Retain these three.
tidyr::spread(key=time_by_variable, value=value) # Spread/widen.
After the tidyr::gather() call, the intermediate dataset is:
ID TIME variable value
1 A 1 X 1
2 B 1 X 2
3 C 1 X 3
...
28 A 5 Y 28
29 B 5 Y 29
30 C 5 Y 30
The eventual result is:
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
tidyr::unite() is an alternative, suggested by @JWilliman. This is functionally equivalent to the dplyr::mutate() and dplyr::select() combination above, when the remove parameter is true (which is the default).
If you're not accustomed to this type of manipulation, the tidyr::unite() may be a small obstacle because it's one more function you have to learn & remember. However, it's benefits include (a) more concise code (ie, four lines are replaced by one) and (b) fewer places to repeat variable names (ie, you don't have to repeat/modify variables in the dplyr::select() clause).
my.df %>%
tidyr::gather(key=variable, value=value, c(X, Y)) %>% # Make it even longer.
tidyr::unite("time_by_variable", variable, TIME, remove=T) %>% # Create the spread key `time_by_variable` while simultaneously dropping `variable` and `TIME`.
tidyr::spread(key=time_by_variable, value=value) # Spread/widen.
回答5:
The pivot_wider() function is tidyr's 2nd generation approach (released in tidyr 1.0.0).
library(magrittr); requireNamespace("tidyr");
my.df %>%
tidyr::pivot_wider(
names_from = c(TIME), # Can accommodate more variables, if needed.
values_from = c(X, Y)
)
Result:
# A tibble: 3 x 11
ID X_1 X_2 X_3 X_4 X_5 Y_1 Y_2 Y_3 Y_4 Y_5
<fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
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
This is probably preferable to the previous tidyr approach (that uses a combination of gather() and spread()).
More capabilities are described in the pivoting vignette.
This example is particularly concise because your desired specifications match the defaults of the id_cols and names_sep.
来源：https://stackoverflow.com/questions/10589693/convert-data-from-long-format-to-wide-format-with-multiple-measure-columns