NOTE
I'm currently working with the SymbolixAU team to add the code/functionality from this package into their CRAN package jsonify. If that all works out as planned, then all future development of this code will be happening in that repo.
jsonparse
R package for parsing JSON. There are already a few R packages that parse JSON data ( jsonlite, rjson ), the intent behind this one is to try to build a package that is faster than the existing options. This project is very young, currently the functions can only handle values of type int, double, logical, and character.
This package is built using the rapidjson C++ library (via the rapidjsonr R package), and Rcpp.
As an additional resource, check out the jsonify package, which uses the rapidjson library to convert R objects to json.
Please report issues, comments, or feature requests.
Installation
Install from this repo:
# install.packages("devtools")
devtools::install_github("ChrisMuir/jsonparse")Example Usage
library(jsonparse)
library(jsonify)# Create json string, using package jsonify
json_str <- jsonify::to_json(
list(
"string_key" = "cats",
"int_key" = 5L,
"double_key" = 99.4,
"bool_key" = TRUE,
"vector_key" = c(9L, 10L, 11L, 12L),
"list_key" = list("dogs", 55.3)
)
)
# print json_str
json_str#> {"string_key":["cats"],"int_key":[5],"double_key":[99.4],"bool_key":[true],"vector_key":[9,10,11,12],"list_key":[["dogs"],[55.3]]}
jsonparse::from_json(json_str)#> $string_key
#> [1] "cats"
#> $int_key
#> [1] 5
#> $double_key
#> [1] 99.4
#> $bool_key
#> [1] TRUE
#> $vector_key
#> [1] 9 10 11 12
#> $list_key
#> $list_key[[1]]
#> [1] "dogs"
#> $list_key[[2]]
#> [1] 55.3
Benchmarks
library(jsonlite)
jl_fromJSON <- jsonlite::fromJSON
library(rjson)
rj_fromJSON <- rjson::fromJSONTest 1
json_str <- jsonify::to_json(
list(
"ints" = 1L:100000L,
"doubles" = rnorm(100000),
"strings" = stringi::stri_rand_strings(100000, 8),
"bools" = sample(c(TRUE, FALSE), size = 100000, replace = TRUE)
)
)
microbenchmark::microbenchmark(
jsonparse = from_json(json_str),
rjson = rj_fromJSON(json_str),
jsonlite = jl_fromJSON(json_str, simplifyVector = FALSE)
)#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> jsonparse 24.01423 27.23423 29.97406 29.60571 32.0372 44.45918 100
#> rjson 100.33898 109.40579 119.47500 117.18489 126.3026 226.08668 100
#> jsonlite 207.57313 219.68605 230.28911 226.09717 239.4743 277.65422 100
Test 2
json_str <- lapply(1:10000, function(x) {
list(
"string_key" = "cats",
"int_key" = 5L,
"double_key" = 99.4,
"bool_key" = TRUE,
"vector_key" = c(9L, 10L, 11L, 12L),
"list_key" = list("dogs", 55.3)
)
})
json_str <- jsonify::to_json(json_str)
microbenchmark::microbenchmark(
jsonparse = from_json(json_str),
rjson = rj_fromJSON(json_str),
jsonlite = jl_fromJSON(json_str, simplifyVector = FALSE)
)#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> jsonparse 15.64111 17.31473 22.00976 19.56679 22.38043 105.1796 100
#> rjson 39.75470 47.17560 57.10193 52.63964 60.26783 168.0407 100
#> jsonlite 106.23252 111.21012 118.63235 115.01797 119.71804 238.5919 100