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yyjsonr

yyjsonr

CRAN R-CMD-check

{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 (v0.9.0 April 8, 2024). yyjson is MIT licensed - see LICENSE-yyjson.txt in this package for more details - readLines(system.file('LICENSE-yyjson.txt', package = 'yyjsonr', mustWork = TRUE))

What’s in the box

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).

Vanilla JSON

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() opts_write_json()
validate validate_json_str() validate_json_file()

NDJSON

string file raw conn options
read read_ndjson_str() read_ndjson_file() opts_read_json()
write write_ndjson_str() write_ndjson_file() opts_write_json()

GeoJSON

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()

Speed

In the following plots, bigger is better, with yyjsonr results in blue.

JSON

NDJSON

GeoJSON

Note: Benchmarks were run on Apple M2 Mac. See files man/benchmark/benchmark*.Rmd for details.

Installation

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'))

Simple JSON example

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

Simple GeoJSON example

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...

Limitations

  • Some datatypes not currently supported. Please file an issue on GitHub if these types are critical for you. Providing test cases also appreciated!:
    • Complex numbers
    • POSIXlt
    • Matrices of POSIXct / Date

Acknowledgements

  • R Core for developing and maintaining the language.
  • CRAN maintainers, for patiently shepherding packages onto CRAN and maintaining the repository