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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/serializeJSON.R
\name{serializeJSON}
\alias{serializeJSON}
\alias{unserializeJSON}
\title{serialize R objects to JSON}
\usage{
serializeJSON(x, digits = 8, pretty = FALSE)
unserializeJSON(txt)
}
\arguments{
\item{x}{an \R{} object to be serialized}
\item{digits}{max number of digits (after the dot) to print for numeric values}
\item{pretty}{add indentation/whitespace to JSON output. See \code{\link[=prettify]{prettify()}}}
\item{txt}{a JSON string which was created using \code{serializeJSON}}
}
\description{
The \code{\link[=serializeJSON]{serializeJSON()}} and \code{\link[=unserializeJSON]{unserializeJSON()}} functions convert between
\R{} objects to JSON data. Instead of using a class based mapping like
\code{\link[=toJSON]{toJSON()}} and \code{\link[=fromJSON]{fromJSON()}}, the serialize functions base the encoding
schema on the storage type, and capture all data and attributes from any object.
Thereby the object can be restored almost perfectly from its JSON representation, but
the resulting JSON output is very verbose. Apart from environments, all standard storage
types are supported.
}
\note{
JSON is a text based format which leads to loss of precision when printing numbers.
}
\examples{
jsoncars <- serializeJSON(mtcars)
mtcars2 <- unserializeJSON(jsoncars)
identical(mtcars, mtcars2)
set.seed('123')
myobject <- list(
mynull = NULL,
mycomplex = lapply(eigen(matrix(-rnorm(9),3)), round, 3),
mymatrix = round(matrix(rnorm(9), 3),3),
myint = as.integer(c(1,2,3)),
mydf = cars,
mylist = list(foo='bar', 123, NA, NULL, list('test')),
mylogical = c(TRUE,FALSE,NA),
mychar = c('foo', NA, 'bar'),
somemissings = c(1,2,NA,NaN,5, Inf, 7 -Inf, 9, NA),
myrawvec = charToRaw('This is a test')
);
identical(unserializeJSON(serializeJSON(myobject)), myobject);
}
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