r - "unpacking" a factor list from a data.frame

Question

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!

Answer 1

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)))