I have a stacked data frame
我有一个堆叠的数据框架。
a <- c(1,1,1,1,2,2,3,3,3,3,3,4,4,4,4)
b <- c(200,201,201,200,220,220,200,220,203,204,204,203,220,200,200)
d <- c(500,500,500,500,500,501,501,501,501,501,502,502,502,502,502)
f <- c("G","G","M","M", "G","G","M","M","M","G","M","G","M","G","G")
df <- data.frame(a,d,b,f)
I use dcast from reshape2 to unstack the data as follows
我使用reshape2中的dcast来将数据解压如下。
dcast(df,a+d+b ~ f)
a d b G M
1 1 500 200 1 1
2 1 500 201 1 1
3 2 500 220 1 0
4 2 501 220 1 0
5 3 501 200 0 1
6 3 501 203 0 1
7 3 501 204 1 0
8 3 501 220 0 1
9 3 502 204 0 1
10 4 502 200 2 0
11 4 502 203 1 0
12 4 502 220 0 1
It defaults to length since I have not put any aggregating function. What I would like however is to get
它默认为长度，因为我没有添加任何聚合函数。然而我想要的是得到!
a d b col_1 col_2
1 500 200 G M
1 500 201 G M
2 500 220 G NA
...and so on
I want to "widen" or unstack the data frame by transposing column f for a particular a+d+b combination and appending it to the frame. Is there an elegant way without having to loop through the combinations?
我想要“加宽”或将数据帧通过转置列f (a+d+b)组合，并将其添加到框架中。是否有一种优雅的方式，而不需要对组合进行循环?
EDIT: Not necessarily just 2 levels G & M in col f. I just want to put up col_1 col_2 col_3 which will transpose out the column f per unique a+d+b combination. I've done it with a for loop; but with a large data set it is unwieldy. I was looking to make the code quicker!
编辑:不一定只有两个级别的G & M在col f中，我只是想要设置col_1 col_2 col_3，它将转置出f / a+d+b组合。我做了一个for循环;但是，由于有大量的数据集，它是难以处理的。我想让代码更快!
1 个解决方案
#1
3
dcast(df, a+d+b ~ f, fun.aggregate = function(x) as.character(x)[1])
#Using f as value column: use value.var to override.
# a d b G M
#1 1 500 200 G M
#2 1 500 201 G M
#3 2 500 220 G <NA>
#4 2 501 220 G <NA>
#5 3 501 200 <NA> M
#6 3 501 203 <NA> M
#7 3 501 204 G <NA>
#8 3 501 220 <NA> M
#9 3 502 204 <NA> M
#10 4 502 200 G <NA>
#11 4 502 203 G <NA>
#12 4 502 220 <NA> M
Re comment: perhaps you want this then:
评论:也许你想要这个:
library(data.table)
dt = data.table(df)
dt[, lapply(1:3, function(i) as.character(f)[i]), by = list(a, d, b)]
# a d b V1 V2 V3
# 1: 1 500 200 G M NA
# 2: 1 500 201 G M NA
# 3: 2 500 220 G NA NA
# 4: 2 501 220 G NA NA
# 5: 3 501 200 M NA NA
# 6: 3 501 220 M NA NA
# 7: 3 501 203 M NA NA
# 8: 3 501 204 G NA NA
# 9: 3 502 204 M NA NA
#10: 4 502 203 G NA NA
#11: 4 502 220 M NA NA
#12: 4 502 200 G G NA
#1
3
dcast(df, a+d+b ~ f, fun.aggregate = function(x) as.character(x)[1])
#Using f as value column: use value.var to override.
# a d b G M
#1 1 500 200 G M
#2 1 500 201 G M
#3 2 500 220 G <NA>
#4 2 501 220 G <NA>
#5 3 501 200 <NA> M
#6 3 501 203 <NA> M
#7 3 501 204 G <NA>
#8 3 501 220 <NA> M
#9 3 502 204 <NA> M
#10 4 502 200 G <NA>
#11 4 502 203 G <NA>
#12 4 502 220 <NA> M
Re comment: perhaps you want this then:
评论:也许你想要这个:
library(data.table)
dt = data.table(df)
dt[, lapply(1:3, function(i) as.character(f)[i]), by = list(a, d, b)]
# a d b V1 V2 V3
# 1: 1 500 200 G M NA
# 2: 1 500 201 G M NA
# 3: 2 500 220 G NA NA
# 4: 2 501 220 G NA NA
# 5: 3 501 200 M NA NA
# 6: 3 501 220 M NA NA
# 7: 3 501 203 M NA NA
# 8: 3 501 204 G NA NA
# 9: 3 502 204 M NA NA
#10: 4 502 203 G NA NA
#11: 4 502 220 M NA NA
#12: 4 502 200 G G NA