Parsing JSON records to a dataframe

What is the most efficient method of parsing a dataframe-like structure
that has been json encoded in record-based format rather than vector
based. For example a structure like this:

[ {"name":"joe", "gender":"male", "age":41}, {"name":"anna",
"gender":"female", "age":23} ]

RJSONIO parses this as a list of lists, which I would then have to apply
as.data.frame to and append them to an existing dataframe, which is
terribly slow. 

unlist is pretty fast. The solution below assumes that you know how your
structure is, so it is not very flexible, but it should show you that the
conversion to data.frame is not the bottleneck.

# json
library(RJSONIO)
# [ {"name":"joe", "gender":"male", "age":41},
#  {"name":"anna", "gender":"female", "age":23} ]
n = 300000
d = data.frame(name=rep(c("joe","anna"),n),
           gender=rep(c("male","female"),n),
           age = rep(c("23","41"),n))
dj = toJSON(d)

system.time(d1 <- fromJSON(dj))
#  user  system elapsed
#   4.06    0.26    4.32

system.time(
  dd <- data.frame(
    name = unlist(d1$name),
    gender = unlist(d1$gender),
    age=as.numeric(unlist(d1$age)))
)
#   user  system elapsed
#   1.13    0.05    1.18

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