yyjsonr

yyjsonr

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 (v0.8.0). yysjon is MIT licensed - see LICENSE-yyjson.txt in this package for more details.

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

You can install from GitHub with:

# install.package('remotes')
remotes::install_github('coolbutuseless/yyjsonr')

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