jsonlite (version 0.9.1)

toJSON: Stringify R objects to JSON and vice versa

Description

These functions are used to convert R objects into JSON and back. The toJSON and fromJSON functions use a class based encoding schema which follows conventions outlines in the vignette of this package. They implement a consitent and practical mapping between JSON structures and the standard data structures in R.

Usage

fromJSON(txt, simplifyVector = TRUE,
    simplifyDataFrame = simplifyVector,
    simplifyMatrix = simplifyVector)
  toJSON(x, dataframe = c("rows", "columns"),
    Date = c("ISO8601", "epoch"),
    POSIXt = c("string", "ISO8601", "epoch", "mongo"),
    factor = c("string", "integer"),
    complex = c("string", "list"),
    raw = c("base64", "hex", "mongo"),
    na = c("null", "string"), digits = 2, pretty = FALSE,
    ...)

Arguments

x

the object to be encoded

dataframe

how to encode data.frame objects: must be one of 'row' or 'column'

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'

digits

max number of digits (after the dot) to print for numeric values

na

how to print NA values. One of 'null' or 'string'. Defaults are class specific

pretty

adds indentation whitespace to JSON output. See prettify

txt

a string in json format

simplifyVector

automatically coerse JSON arrays containing only scalars into a vector

simplifyDataFrame

automatically coerse JSON arrays containing records (JSON objects with scalars) into a data frame.

simplifyMatrix

automatically coerse JSON arrays with vectors of equal length and mode into a matrix

...

arguments passed on to class specific print methods

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 use an alternative system to convert between R objects and JSON, which supports more classes but is much more verbose.

References

http://www.jsonlint.org

Examples

#stringify some data
jsoncars <- toJSON(mtcars, pretty=TRUE)
cat(jsoncars)

#parse it back
fromJSON(jsoncars)

#control scientific notation
toJSON(10 ^ (0:10), digits=8)
options(scipen=3)
toJSON(10 ^ (0:10), digits=8)

# Parse 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