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Type:

Package

Title:

Convert Between 'R' Objects and Javascript Object Notation(JSON)

Version:

1.2.3

Date:

2025-11-24

Description:

Conversions between 'R' objects and Javascript Object Notation (JSON) using the 'rapidjsonr' libraryhttps://CRAN.R-project.org/package=rapidjsonr.

License:

MIT + file LICENSE

Depends:

R (≥ 4.0.0)

Imports:

Rcpp (≥ 1.1.0)

LinkingTo:

rapidjsonr (≥ 1.2.1), Rcpp

RoxygenNote:

7.3.2

Suggests:

covr, testthat, knitr, rmarkdown

Encoding:

UTF-8

URL:

https://symbolixau.github.io/jsonify/,https://github.com/SymbolixAU/jsonify

BugReports:

https://github.com/SymbolixAU/jsonify/issues

NeedsCompilation:

yes

Packaged:

2025-11-24 13:03:46 UTC; david

Author:

David Cooley [aut, cre], Chris Muir [ctb], Brendan Knapp

[ctb]

Maintainer:

David Cooley <dcooley@symbolix.com.au>

Repository:

CRAN

Date/Publication:

2025-11-25 19:22:12 UTC

Coerce string to JSON

Description

Coerce string to JSON

Usage

as.json(x)

Arguments

x

string to coerce to JSON

Examples

js <- '{"x":1,"y":2}'as.json(js)

From JSON

Description

Converts JSON to an R object.

Usage

from_json(json, simplify = TRUE, fill_na = FALSE, buffer_size = 1024)

Arguments

json

JSON to convert to R object. Can be a string, url or link to a file.

simplify

logical, ifTRUE, coerces JSON to the simplest R object possible. See Details

fill_na

logical, ifTRUE andsimplify isTRUE, data.frames will be na-filled if there are missing JSON keys.Ignored ifsimplify isFALSE. See details and examples.

buffer_size

size of buffer used when reading a file from disk. Defaults to 1024

Details

Whensimplify = TRUE

single arrays are coerced to vectors
array of arrays (all the same length) are coerced to matrices
objects with the same keys are coerced to data.frames

Whensimplify = TRUE andfill_na = TRUE

objects are coerced to data.frames, and any missing values are filled with NAs

Examples

from_json('{"a":[1, 2, 3]}')from_json('{"a":8, "b":99.5, "c":true, "d":"cats", "e":[1, "cats", 3]}')from_json('{"a":8, "b":{"c":123, "d":{"e":456}}}')lst <- list("a" = 5L, "b" = 1.43, "c" = "cats", "d" = FALSE)js <- jsonify::to_json(lst, unbox = TRUE)from_json( js )## Return a data framefrom_json('[{"id":1,"val":"a"},{"id":2,"val":"b"}]')## Return a data frame with a list columnfrom_json('[{"id":1,"val":"a"},{"id":2,"val":["b","c"]}]')## Without simplifying to a data.framefrom_json('[{"id":1,"val":"a"},{"id":2,"val":["b","c"]}]', simplify = FALSE )## Missing JSON keys from_json('[{"x":1},{"x":2,"y":"hello"}]')## Missing JSON keys - filling with NAsfrom_json('[{"x":1},{"x":2,"y":"hello"}]', fill_na = TRUE )## Duplicate object keysfrom_json('[{"x":1,"x":"a"},{"x":2,"x":"b"}]')from_json('[{"id":1,"val":"a","val":1},{"id":2,"val":"b"}]', fill_na = TRUE )

from ndjson

Description

Converts ndjson into R objects

Usage

from_ndjson(ndjson, simplify = TRUE, fill_na = FALSE)

Arguments

ndjson

new-line delimited JSON to convert to R object. Can be a string, url or link to a file.

simplify

logical, ifTRUE, coerces JSON to the simplest R object possible. See Details

fill_na

logical, ifTRUE andsimplify isTRUE, data.frames will be na-filled if there are missing JSON keys.Ignored ifsimplify isFALSE. See details and examples.

Examples

js <- to_ndjson( data.frame( x = 1:5, y = 6:10 ) )from_ndjson( js )

Minify Json

Description

Removes indentiation from a JSON string

Usage

minify_json(json, ...)

Arguments

json

string of JSON

...

other argments passed toto_json

Examples

df <- data.frame(id = 1:10, val = rnorm(10))js <- to_json( df )jsp <- pretty_json(js)minify_json( jsp )

Pretty Json

Description

Adds indentiation to a JSON string

Usage

pretty_json(json, ...)

Arguments

json

string of JSON

...

other argments passed toto_json

Examples

df <- data.frame(id = 1:10, val = rnorm(10))js <- to_json( df )pretty_json(js)## can also use directly on an R objectpretty_json( df )

To JSON

Description

Converts R objects to JSON

Usage

to_json(  x,  unbox = FALSE,  digits = NULL,  numeric_dates = TRUE,  factors_as_string = TRUE,  by = "row")

Arguments

x

object to convert to JSON

unbox

logical indicating if single-value arrays should be 'unboxed', that is, not contained inside an array.

digits

integer specifying the number of decimal places to round numerics.Default isNULL - no rounding

numeric_dates

logical indicating if dates should be treated as numerics. Defaults to TRUE for speed. If FALSE, the dates will be coerced to character in UTC time zone

factors_as_string

logical indicating if factors should be treated as strings. Defaults to TRUE.

by

either "row" or "column" indicating if data.frames and matrices should be processedrow-wise or column-wise. Defaults to "row"

Examples

to_json(1:3)to_json(letters[1:3])## factors treated as stringsto_json(data.frame(x = 1:3, y = letters[1:3], stringsAsFactors = TRUE ))to_json(data.frame(x = 1:3, y = letters[1:3], stringsAsFactors = FALSE ))to_json(list(x = 1:3, y = list(z = letters[1:3])))to_json(seq(as.Date("2018-01-01"), as.Date("2018-01-05"), length.out = 5))to_json(seq(as.Date("2018-01-01"), as.Date("2018-01-05"), length.out = 5), numeric_dates = FALSE)psx <- seq(  as.POSIXct("2018-01-01", tz = "Australia/Melbourne"),   as.POSIXct("2018-02-01", tz = "Australia/Melbourne"),   length.out = 5  )to_json(psx)to_json(psx, numeric_dates = FALSE)## unbox single-value arraysto_json(list(x = 1), unbox = TRUE)to_json(list(x = 1, y = c("a"), z = list(x = 2, y = c("b"))), unbox = TRUE)## rounding numbers using the digits argumentto_json(1.23456789, digits = 2)df <- data.frame(x = 1L:3L, y = rnorm(3), z = letters[1:3], stringsAsFactors = TRUE )to_json(df, digits = 0 )## keeping factorsto_json(df, digits = 2, factors_as_string = FALSE )

To ndjson

Description

Converts R objects to ndjson

Usage

to_ndjson(  x,  unbox = FALSE,  digits = NULL,  numeric_dates = TRUE,  factors_as_string = TRUE,  by = "row")

Arguments

x

object to convert to JSON

unbox

logical indicating if single-value arrays should be 'unboxed', that is, not contained inside an array.

digits

integer specifying the number of decimal places to round numerics.Default isNULL - no rounding

numeric_dates

logical indicating if dates should be treated as numerics. Defaults to TRUE for speed. If FALSE, the dates will be coerced to character in UTC time zone

factors_as_string

logical indicating if factors should be treated as strings. Defaults to TRUE.

by

either "row" or "column" indicating if data.frames and matrices should be processedrow-wise or column-wise. Defaults to "row"

Details

Lists are converted to ndjson non-recursively. That is, each of the objectsin the list at the top level are converted to a new-line JSON object. Any nestedsub-elements are then contained within that JSON object. See examples

Examples

to_ndjson( 1:5 )to_ndjson( letters )mat <- matrix(1:6, ncol = 2)to_ndjson( x = mat )to_ndjson( x = mat, by = "col" )df <- data.frame(  x = 1:5  , y = letters[1:5]  , z = as.Date(seq(18262, 18262 + 4, by = 1 ), origin = "1970-01-01" )  )to_ndjson( x = df )to_ndjson( x = df, numeric_dates = FALSE )to_ndjson( x = df, factors_as_string = FALSE )to_ndjson( x = df, by = "column" )to_ndjson( x = df, by = "column", numeric_dates = FALSE )## Lists are non-recurisve; only elements `x` and `y` are converted to ndjsonlst <- list(  x = 1:5  , y = list(    a = letters[1:5]    , b = data.frame(i = 10:15, j = 20:25)  )) to_ndjson( x = lst )to_ndjson( x = lst, by = "column")

validate JSON

Description

Validates JSON

Usage

validate_json(json)

Arguments

json

character or json object

Value

logical vector

Examples

validate_json('[]')df <- data.frame(id = 1:5, val = letters[1:5])validate_json( to_json(df) )validate_json('{"x":1,"y":2,"z":"a"}')validate_json( c('{"x":1,"y":2,"z":"a"}', to_json(df) ) )validate_json( c('{"x":1,"y":2,"z":a}', to_json(df) ) )