jsonify
Converts between R objects and JSON.
js <- '[{"x":1,"y":"a"},{"x":2,"y":"b"}]'
( df <- from_json( js ) )
# x y
# 1 1 a
# 2 2 b
( to_json( df ) )
# [{"x":1,"y":"a"},{"x":2,"y":"b"}]There are already JSON converters, why did you build this one?
Because I wanted it available at the source ( C++ ) level for integrating into other packages.
What do you mean by “available at the source” ?
I want to be able to call the C++ code from another package, without going to & from R. Therefore, the C++ code is implemented in headers, so you can “link to” it in your own package.
For example, the LinkingTo section in DESCRIPTION will look
something like
LinkingTo:
Rcpp,
jsonifyAnd in a c++ source file you can #include the header and use the
available functions
// [[Rcpp::depends(jsonify)]]
#include "jsonify/jsonify.hpp"
Rcpp::StringVector my_json( Rcpp::DataFrame df ) {
return jsonify::api::to_json( df );
}Can I call it from R if I want to?
Yes. Just like the examples in this readme use to_json()
df <- data.frame(
id = 1:3
, val = letters[1:3]
)
jsonify::to_json( df )
# [{"id":1,"val":"a"},{"id":2,"val":"b"},{"id":3,"val":"c"}]Is it fast?
yeah it’s pretty good.
library(microbenchmark)
library(jsonlite)
n <- 1e6
df <- data.frame(
id = 1:n
, value = sample(letters, size = n, replace = T)
, val2 = rnorm(n = n)
, log = sample(c(T,F), size = n, replace = T)
, stringsAsFactors = FALSE
)
microbenchmark(
jsonlite = {
jlt <- jsonlite::toJSON( df )
},
jsonify = {
jfy <- jsonify::to_json( df )
},
times = 3
)
# Unit: seconds
# expr min lq mean median uq max neval
# jsonlite 2.017081 2.063732 2.540350 2.110383 2.801984 3.493585 3
# jsonify 1.186239 1.202719 1.514067 1.219198 1.677981 2.136763 3
microbenchmark(
jsonlite = {
df_jlt <- jsonlite::fromJSON( jlt )
},
jsonify = {
df_jfy <- jsonify::from_json( jfy )
},
times = 3
)
# Unit: seconds
# expr min lq mean median uq max neval
# jsonlite 5.034888 5.149688 5.229363 5.264489 5.326601 5.388713 3
# jsonify 4.551434 4.629683 4.678198 4.707932 4.741579 4.775227 3
n <- 1e4
x <- list(
x = rnorm(n = n)
, y = list(x = rnorm(n = n))
, z = list( list( x = rnorm(n = n)))
, xx = rnorm(n = n)
, yy = data.frame(
id = 1:n
, value = sample(letters, size = n, replace = T)
, val2 = rnorm(n = n)
, log = sample(c(T,F), size = n, replace = T)
)
)
microbenchmark(
jsonlite = {
jlt <- jsonlite::toJSON( x )
},
jsonify = {
jfy <- jsonify::to_json( x )
},
times = 5
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# jsonlite 18.52028 18.82241 19.32112 18.99683 19.18103 21.08508 5
# jsonify 17.72060 18.19092 19.58308 19.52457 21.14687 21.33241 5
microbenchmark(
jsonlite = {
df_jlt <- jsonlite::fromJSON( jlt )
},
jsonify = {
df_jfy <- jsonify::from_json( jfy )
},
times = 3
)
# Unit: milliseconds
# expr min lq mean median uq max neval
# jsonlite 62.53554 62.96435 63.12574 63.39316 63.42084 63.44853 3
# jsonify 42.47449 42.53826 43.38475 42.60204 43.83988 45.07773 3There’s no Date type in JSON, how have you handled this?
At its core Dates in R are numeric, so they are treated as numbers
when converted to JSON. However, the user can coerce to character
through the numeric_dates argument.
df <- data.frame(dte = as.Date("2018-01-01"))
jsonify::to_json( df )
# [{"dte":17532.0}]
df <- data.frame(dte = as.Date("2018-01-01"))
jsonify::to_json( df, numeric_dates = FALSE )
# [{"dte":"2018-01-01"}]And POSIXct and POSIXlt?
The same
jsonify::to_json( as.POSIXct("2018-01-01 10:00:00") )
# [1514761200.0]
jsonify::to_json( as.POSIXct("2018-01-01 10:00:00"), numeric_dates = FALSE)
# ["2017-12-31T23:00:00"]However, here the POSIXct values are returned in UTC timezone. This is by design.
POSIXlt will return each component of the date-time
x <- as.POSIXlt("2018-01-01 01:00:00", tz = "GMT")
jsonify::to_json( x )
# {"sec":[0.0],"min":[0],"hour":[1],"mday":[1],"mon":[0],"year":[118],"wday":[1],"yday":[0],"isdst":[0]}
jsonify::to_json( x, numeric_dates = FALSE)
# {"sec":[0.0],"min":[0],"hour":[1],"mday":[1],"mon":[0],"year":[118],"wday":[1],"yday":[0],"isdst":[0]}I see factors are converted to strings
Yep. Even though I constructed a data.frame() without setting
stringsAsFactros = FALSE, jsonify automatically treats factors as
strings.
Has this changed from v0.1?
Yes. And it’s to keep the data more inline with modern concepts and design patterns.
If you want factors, use factors_as_string = FALSE in the to_json()
call
jsonify::to_json( df, factors_as_string = FALSE )
# [{"dte":17532.0}]How do I install it?
Get the latest release version from CRAN
install.packages("jsonify")Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("SymbolixAU/jsonify")