Filling a Matrix with "For Loop" Taking Too Long - json

Question

I'm trying to create a data frame that is about 1,000,000 x 5 by using a for-loop, but it's been 5+ hours and I don't think it will finish very soon. I'm using the rjson library to read in the data from a large json file. Can someone help me with filling up this data frame in a faster way?
library(rjson)
# read in data from json file
file <- "/filename"
c <- file(file, "r")
l <- readLines(c, -1L)
data <- lapply(X=l, fromJSON)
# specify variables that i want from this data set
myvars <- c("url", "time", "userid", "hostid", "title")
newdata <- matrix(data[[1]][myvars], 1, 5, byrow=TRUE)
# here's where it goes wrong
for (i in 2:length(l)) {
newdata <- rbind(newdata, data[[i]][myvars])
}
newestdata <- data.frame(newdata)

Answer 1

This is taking forever because each iteration of your loop is creating a new, bigger object. Try this:
slice <- function(field, data) unlist(lapply(data, `[[`, field))
data.frame(Map(slice, myvars, list(data)))
This will create a data.frame and preserve your original data types: character, numeric, etc., if it matters. While forcing everything into a matrix will coerce everything into character class.

Answer 2

Without the data, it's hard to be sure, but there are a couple of things you are doing that are relatively slow. This should be faster, but again, without the data, I can't test:
newdata <- vapply(data, `[`, character(5L), myvars)
I'm also assuming that your data is character, which I think it has to be based on title.
Also, as others have noted, the reason yours is slow is because you are growing an object, which requires R to keep re-allocating memory. vapply will allocate the memory ahead of time because it knows the size of each iterations result, and how many items there are.