Here is the data I have:
am group v1 v2 v3 v4
1 2015-10-31 A 693 803 700 17%
2 2015-10-31 B 524 859 302 77%
3 2015-10-31 C 266 675 86 7%
4 2015-10-31 D 376 455 650 65%
5 2015-11-30 A 618 715 200 38%
6 2015-11-30 B 249 965 215 54%
7 2015-11-30 C 881 106 184 24%
8 2015-11-30 D 033 047 492 46%
9 2015-12-31 A 229 994 720 19%
10 2015-12-31 B 539 543 332 57%
11 2015-12-31 C 100 078 590 24%
12 2015-12-31 D 517 413 716 57%
Question: How can I transpose the data such that
v1-v4
and am
as column variables group
variable is replicated by the number of v1-v4
The result I'd like to produce:
group metric 2015-10-31 2015-11-30 2015-12-31
A v1 693 618 229
A v2 803 715 994
A v3 700 200 720
A v4 17% 38% 19%
B v1 524 249 539
B v2 859 965 543
B v3 302 215 332
B v4 77% 54% 57%
...
What I have tried so far:
name <- mydata$am
data <- as.data.frame(t(mydata[, -1]))
colnames(mydata) <- name
This doesn't handle group
variable the way I want.
Thanks for your help.
The basic idea would be to go to a "long" format first, and then go into a "wide" format.
Here are a few ways to do this....
melt
+ dcast
library(data.table) ## or library(reshape2)
dcast(melt(as.data.table(mydf), id.vars = c("am", "group")),
group + variable ~ am, value.var = "value")
recast
(This is basically the same as above, but in one step.)
library(reshape2)
recast(mydf, group + variable ~ am, id.var = c("am", "group"))
gather
+ spread
library(dplyr)
library(tidyr)
mydf %>%
gather(key, value, v1:v4) %>%
spread(am, value)
reshape
reshape(cbind(mydf[c(1, 2)], stack(mydf[-c(1, 2)])),
direction = "wide", idvar = c("group", "ind"), timevar = "am")