I am trying to turn a nested list structure into a dataframe. The list looks similar to the following (it is serialized data from parsed JSON read in using the httr package).
myList <- list(object1 = list(w=1, x=list(y=0.1, z="cat")), object2 = list(w=2, x=list(y=0.2, z="dog")))
unlist(myList) does a great job of recursively flattening the list, and I can then use lapply to flatten all the objects nicely.
flatList <- lapply(myList, FUN= function(object) {return(as.data.frame(rbind(unlist(object))))})
And finally, I can button it up using plyr::rbind.fill
myDF <- do.call(plyr::rbind.fill, flatList)
str(myDF)
#'data.frame': 2 obs. of 3 variables:
#$ w : Factor w/ 2 levels "1","2": 1 2
#$ x.y: Factor w/ 2 levels "0.1","0.2": 1 2
#$ x.z: Factor w/ 2 levels "cat","dog": 1 2
The problem is that w and x.y are now being interpreted as character vectors, which by default get parsed as factors in the dataframe. I believe that unlist() is the culprit, but I can't figure out another way to recursively flatten the list structure. A workaround would be to post-process the dataframe, and assign data types then. What is the best way to determine if a vector is a valid numeric or integer vector?
