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"}]Because I wanted it available at the source ( C++ ) level for integrating into other packages.
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
And 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 );
}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"}]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 3Date 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"}]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]}Yep. Even though I constructed a data.frame() without setting stringsAsFactros = FALSE, jsonify automatically treats factors as strings.
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
Get the latest release version from CRAN
Or the development version from GitHub with: