I'm new to R / having the option to easily re-organize data, and have hunted around for a solution but can't find exactly what I'd like to do. Reshape2's melt/cast doesn't quite seem to work and I haven't mastered plyr well enough to factor it in here.
Basically I have a data.frame with a structure outlined below, with a category column in which each element is a variable-length list of categories (more compact because the # columns is much larger, and I actually have multiple category_lists that I'd like to keep separate):
>mydf
ID category_list xval yval
1 ID1 cat1, cat2, cat3 xnum1 ynum1
2 ID2 cat2, cat3 xnum2 ynum2
3 ID3 cat1 xnum3 ynum3
I want to do manipulations with the categories as factors (and the values associated, i.e. columns 3/4), so I think I need something like this in the end, where IDs and x/y/other column values are duplicated according to the length of the category list:
ID category xval yval
1 ID1 cat1 xnum1 ynum1
2 ID1 cat2 xnum1 ynum1
3 ID1 cat3 xnum1 ynum1
4 ID2 cat2 xnum2 ynum2
5 ID2 cat3 xnum2 ynum2
6 ID3 cat3 xnum2 ynum2
If there's another solution to factor/facet on the category_list, that would be a simpler solution but I haven't come across methods that support this, e.g. the following throws an error
>ggplot(mydf, aes(x=x, y=y)) + geom_point() + facet_grid(~cat_list)
Error in layout_base(data, cols, drop = drop) : At least one layer must contain all variables used for facetting
Thanks!
The answer will depend on the format of category_list. If in fact it is a list for each row
Something like
mydf <- data.frame(ID = paste0('ID',1:3),
category_list = I(list(c('cat1','cat2','cat3'), c('cat2','cat3'), c('cat1'))),
xval = 1:3, yval = 1:3)
or
library(data.table)
mydf <- as.data.frame(data.table(ID = paste0('ID',1:3),
category_list = list(c('cat1','cat2','cat3'), c('cat2','cat3'), c('cat1')),
xval = 1:3, yval = 1:3) )
Then you can use plyr and merge to create your long form data
newdf <- merge(mydf, ddply(mydf, .(ID), summarize, cat_list = unlist(category_list)), by = 'ID')
ID category_list xval yval cat_list
1 ID1 cat1, cat2, cat3 1 1 cat1
2 ID1 cat1, cat2, cat3 1 1 cat2
3 ID1 cat1, cat2, cat3 1 1 cat3
4 ID2 cat2, cat3 2 2 cat2
5 ID2 cat2, cat3 2 2 cat3
6 ID3 cat1 3 3 cat1
or a non-plyr approach that doesn't require merge
do.call(rbind,lapply(split(mydf, mydf$ID), transform, cat_list = unlist(category_list)))
A plodding but seemingly robust solution:
## Some example data
df <- as.data.frame(cbind(ID = paste0("ID", 1:2),
category_list = list(4:1, 2:3),
xvar = 8:9,
yvar = 10:9))
## Calculate number of times each row of df will be repeated
nn <- sapply(df$category_list, length)
ii <- rep(seq_along(nn), times=nn)
## Reshape data.frame
transform(df[ii,],
category = unlist(df$category_list),
category_list = NULL,
row.names = NULL)
# ID xvar yvar category
# 1 ID1 8 10 4
# 2 ID1 8 10 3
# 3 ID1 8 10 2
# 4 ID1 8 10 1
# 5 ID2 9 9 2
# 6 ID2 9 9 3
A possibility:
x <- read.table(textConnection('
ID category_list xval yval
ID1 "cat1, cat2, cat3" xnum1 ynum1
ID2 "cat2, cat3" xnum2 ynum2
ID3 "cat1" xnum3 ynum3'),
header=TRUE,stringsAsFactors=FALSE)
library(plyr)
ddply(x,"ID",transform,category=strsplit(category_list,",")[[1]])
## ID category_list xval yval category
## 1 ID1 cat1, cat2, cat3 xnum1 ynum1 cat1
## 2 ID1 cat1, cat2, cat3 xnum1 ynum1 cat2
## 3 ID1 cat1, cat2, cat3 xnum1 ynum1 cat3
## 4 ID2 cat2, cat3 xnum2 ynum2 cat2
## 5 ID2 cat2, cat3 xnum2 ynum2 cat3
This will be a non-plyr approach:
cbind( x[ rep(1:nrow(x),
times=sapply(x$category_list,
function(xx) sapply( strsplit(xx, ","), length) ) ),
-2], # to get rid of the old category column
new_cats = unlist( strsplit(x$category_list, ",") ) )
# this used Bolker's example. If these are factor will need to add `as.character`
ID xval yval new_cats
1 ID1 xnum1 ynum1 cat1
1.1 ID1 xnum1 ynum1 cat2
1.2 ID1 xnum1 ynum1 cat3
2 ID2 xnum2 ynum2 cat2
2.1 ID2 xnum2 ynum2 cat3
3 ID3 xnum3 ynum3 cat1
Another base R possibility using by:
do.call(rbind,
by(mydf,
mydf$ID,
function(x) {
data.frame(
ID=x$ID,
category_list = unlist(strsplit(x$category_list,",")),
xval=x$xval,
yval=x$yval
)
}
)
)
Result:
ID category_list xval yval
ID1.1 ID1 cat1 xnum1 ynum1
ID1.2 ID1 cat2 xnum1 ynum1
ID1.3 ID1 cat3 xnum1 ynum1
ID2.1 ID2 cat2 xnum2 ynum2
ID2.2 ID2 cat3 xnum2 ynum2
ID3 ID3 cat1 xnum3 ynum3
Note: Original answer deleted as my answer was based on a different data structure than what the OP seems to actually have.
Scenario 1: Column is a list
Using @mnel's sample data:
mydf <- data.frame(ID = paste0('ID',1:3),
category_list = I(list(c('cat1','cat2','cat3'), c('cat2','cat3'), c('cat1'))),
xval = 1:3, yval = 1:3)
Using listCol_l from my "splitstackshape" package
library(splitstackshape)
listCol_l(mydf, "category_list")
# ID xval yval category_list_ul
# 1: ID1 1 1 cat1
# 2: ID1 1 1 cat2
# 3: ID1 1 1 cat3
# 4: ID2 2 2 cat2
# 5: ID2 2 2 cat3
# 6: ID3 3 3 cat1
Using unnest from the "tidyr" package
library(tidyr)
unnest(mydf, "category_list")
# ID category_list xval yval
# 1 ID1 cat1 1 1
# 2 ID1 cat2 1 1
# 3 ID1 cat3 1 1
# 4 ID2 cat2 2 2
# 5 ID2 cat3 2 2
# 6 ID3 cat1 3 3
Scenario 2: Column is a concatenated string
Using @BenBolker's sample data:
x <- read.table(textConnection('
ID category_list xval yval
ID1 "cat1, cat2, cat3" xnum1 ynum1
ID2 "cat2, cat3" xnum2 ynum2
ID3 "cat1" xnum3 ynum3'),
header=TRUE,stringsAsFactors=FALSE)
Using cSplit from my "splitstackshape" package
library(splitstackshape)
cSplit(x, "category_list", ",", "long")
# ID category_list xval yval
# 1: ID1 cat1 xnum1 ynum1
# 2: ID1 cat2 xnum1 ynum1
# 3: ID1 cat3 xnum1 ynum1
# 4: ID2 cat2 xnum2 ynum2
# 5: ID2 cat3 xnum2 ynum2
# 6: ID3 cat1 xnum3 ynum3
来源：https://stackoverflow.com/questions/14226575/unpacking-a-factor-list-from-a-data-frame