# toJSON, fromJSON

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##### Convert R objects to/from JSON

These functions are used to convert between JSON data and R objects. The toJSON and fromJSON functions use a class based mapping, which follows conventions outlined in this paper: https://arxiv.org/abs/1403.2805 (also available as vignette).

##### Usage
fromJSON(txt, simplifyVector = TRUE, simplifyDataFrame = simplifyVector, simplifyMatrix = simplifyVector, flatten = FALSE, ...)
toJSON(x, dataframe = c("rows", "columns", "values"), matrix = c("rowmajor", "columnmajor"), Date = c("ISO8601", "epoch"), POSIXt = c("string", "ISO8601", "epoch", "mongo"), factor = c("string", "integer"), complex = c("string", "list"), raw = c("base64", "hex", "mongo"), null = c("list", "null"), na = c("null", "string"), auto_unbox = FALSE, digits = 4, pretty = FALSE, force = FALSE, ...)
##### Arguments
txt
a JSON string, URL or file
simplifyVector
coerce JSON arrays containing only primitives into an atomic vector
simplifyDataFrame
coerce JSON arrays containing only records (JSON objects) into a data frame
simplifyMatrix
coerce JSON arrays containing vectors of equal mode and dimension into matrix or array
flatten
automatically flatten nested data frames into a single non-nested data frame
...
arguments passed on to class specific print methods
x
the object to be encoded
dataframe
how to encode data.frame objects: must be one of 'rows', 'columns' or 'values'
matrix
how to encode matrices and higher dimensional arrays: must be one of 'rowmajor' or 'columnmajor'.
Date
how to encode Date objects: must be one of 'ISO8601' or 'epoch'
POSIXt
how to encode POSIXt (datetime) objects: must be one of 'string', 'ISO8601', 'epoch' or 'mongo'
factor
how to encode factor objects: must be one of 'string' or 'integer'
complex
how to encode complex numbers: must be one of 'string' or 'list'
raw
how to encode raw objects: must be one of 'base64', 'hex' or 'mongo'
null
how to encode NULL values within a list: must be one of 'null' or 'list'
na
how to print NA values: must be one of 'null' or 'string'. Defaults are class specific
auto_unbox
automatically unbox all atomic vectors of length 1. It is usually safer to avoid this and instead use the unbox function to unbox individual elements. An exception is that objects of class AsIs (i.e. wrapped in I()) are not automatically unboxed. This is a way to mark single values as length-1 arrays.
digits
max number of decimal digits to print for numeric values. Use I() to specify significant digits. Use NA for max precision.
pretty
adds indentation whitespace to JSON output. Can be TRUE/FALSE or a number specifying the number of spaces to indent. See prettify
force
unclass/skip objects of classes with no defined JSON mapping
##### Details

The toJSON and fromJSON functions are drop-in replacements for the identically named functions in packages rjson and RJSONIO. Our implementation uses an alternative, somewhat more consistent mapping between R objects and JSON strings.
The serializeJSON and unserializeJSON functions in this package use an alternative system to convert between R objects and JSON, which supports more classes but is much more verbose.
A JSON string is always unicode, using UTF-8 by default, hence there is usually no need to escape any characters. However, the JSON format does support escaping of unicode characters, which are encoded using a backslash followed by a lower case "u" and 4 hex characters, for example: "Z\u00FCrich". The fromJSON function will parse such escape sequences but it is usually preferable to encode unicode characters in JSON using native UTF-8 rather than escape sequences.

##### References

Jeroen Ooms (2014). The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects. arXiv:1403.2805. https://arxiv.org/abs/1403.2805

##### Aliases
• toJSON, fromJSON
• fromJSON
• toJSON
• jsonlite
• toJSON
##### Examples
library(jsonlite) # Stringify some data jsoncars <- toJSON(mtcars, pretty=TRUE) cat(jsoncars) # Parse it back fromJSON(jsoncars) # Parse escaped unicode fromJSON('{"city" : "Z\\u00FCrich"}') # Decimal vs significant digits toJSON(pi, digits=3) toJSON(pi, digits=I(3)) ## Not run: retrieve data frame # data1 <- fromJSON("https://api.github.com/users/hadley/orgs") # names(data1) # data1$login # # # Nested data frames: # data2 <- fromJSON("https://api.github.com/users/hadley/repos") # names(data2) # names(data2$owner) # data2$owner$login # # # Flatten the data into a regular non-nested dataframe # names(flatten(data2)) # # # Flatten directly (more efficient): # data3 <- fromJSON("https://api.github.com/users/hadley/repos", flatten = TRUE) # identical(data3, flatten(data2)) # ## End(Not run) 
Documentation reproduced from package jsonlite, version 1.2, License: MIT + file LICENSE

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