{yyjsonr} is a fast JSON parser/serializer, which converts R data to/from JSON.
In most cases it is around 2x to 10x faster than {jsonlite} at both reading and writing JSON.
It is a wrapper for the yyjson C library (v0.11.1 May 2025). yyjson is MIT licensed - see LICENSE-yyjson.txt in this package for more details - readLines(system.file('LICENSE-yyjson.txt', package = 'yyjsonr', mustWork = TRUE))
This package contains specialized functions for each type of operation (read/write/validate) and the storage location of the JSON (string/file/raw vector/connection).
string file raw conn options read read_json_str() read_json_file() read_json_raw() read_json_conn() opts_read_json() write write_json_str() write_json_file() write_json_raw() opts_write_json() validate validate_json_str() validate_json_file() string file raw conn options read read_ndjson_str() read_ndjson_file() read_ndjson_raw() opts_read_json() write write_ndjson_str() write_ndjson_file() write_ndjson_raw() opts_write_json() string file raw conn options read read_geojson_str() read_geojson_file() opts_read_geojson() write write_geojson_str() write_geojson_file() opts_write_geojson()In the following plots, bigger is better, with yyjsonr results in blue.
Note: Benchmarks were run on Apple M2 Mac. See files man/benchmark/benchmark*.Rmd for details.
This package can be installed from CRAN
install.packages('yyjsonr')
You can install the latest development version from GitHub with:
# install.package('remotes')
remotes::install_github('coolbutuseless/yyjsonr')
Pre-built source/binary versions can also be installed from R-universe
install.packages('yyjsonr', repos = c('https://coolbutuseless.r-universe.dev', 'https://cloud.r-project.org'))
library(yyjsonr)
str <- write_json_str(head(iris, 3), pretty = TRUE)
cat(str)
#> [
#> {
#> "Sepal.Length": 5.1,
#> "Sepal.Width": 3.5,
#> "Petal.Length": 1.4,
#> "Petal.Width": 0.2,
#> "Species": "setosa"
#> },
#> {
#> "Sepal.Length": 4.9,
#> "Sepal.Width": 3.0,
#> "Petal.Length": 1.4,
#> "Petal.Width": 0.2,
#> "Species": "setosa"
#> },
#> {
#> "Sepal.Length": 4.7,
#> "Sepal.Width": 3.2,
#> "Petal.Length": 1.3,
#> "Petal.Width": 0.2,
#> "Species": "setosa"
#> }
#> ]
read_json_str(str)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
When parsing GeoJSON, {yyjsonr} returns an sf object.
read_geojson_str(geojsonsf::geo_melbourne) |> head() |> sf:::print.sf() #> Simple feature collection with 6 features and 7 fields #> Geometry type: POLYGON #> Dimension: XY #> Bounding box: xmin: 144.8958 ymin: -37.86631 xmax: 145.0371 ymax: -37.75423 #> Geodetic CRS: WGS 84 #> SA2_NAME polygonId SA3_NAME AREASQKM fillColor #> 1 Abbotsford 70 Yarra 1.7405 #440154 #> 2 Albert Park 59 Port Phillip 4.6747 #450457 #> 3 Alphington - Fairfield 41 Darebin - South 2.8853 #46075A #> 4 Armadale 66 Stonnington - West 2.1835 #460A5D #> 5 Ascot Vale 44 Essendon 3.8361 #460C5F #> 6 Brunswick 36 Brunswick - Coburg 5.1425 #472D7B #> strokeColor strokeWeight geometry #> 1 #440154 1 POLYGON ((144.9925 -37.8024... #> 2 #450457 1 POLYGON ((144.9449 -37.8437... #> 3 #46075A 1 POLYGON ((145.0204 -37.7654... #> 4 #460A5D 1 POLYGON ((145.0117 -37.8535... #> 5 #460C5F 1 POLYGON ((144.8994 -37.7704... #> 6 #472D7B 1 POLYGON ((144.9497 -37.7627...
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