jsonify

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "# "
)

library(jsonify)

To JSON

There are two types of R objects we need to handle when converting to JSON, simple and complex.

  1. Simple - scalars, vectors, matrices
  2. Complex - data.frames and lists

I've categorised them this way because 'simple' objects don't include any form of recursion. That is, a vector can't contain a data.frame or a list. But a list or data.frame can contain other data.frames, vectors, matrices, scalars, lists, and any combination thereof.

Simple

Simple objects ( scalars, vectors and matrices ) get converted to JSON ARRAYS

## scalar -> single array
to_json( 1 )
to_json( "a" )

## scalar (unboxed) -> single value
to_json( 1, unbox = TRUE )
to_json( "a", unbox = TRUE )

## vector -> array
to_json( 1:4 )
to_json( letters[1:4] )

## named vector - array (of the elements)
to_json( c("a" = 1, "b" = 2) )

## matrix -> array of arrays (by row)
to_json( matrix(1:4, ncol = 2) )
to_json( matrix(letters[1:4], ncol = 2))

## matrix -> array of arrays (by column)
to_json( matrix(1:4, ncol = 2), by = "column" )
to_json( matrix(letters[1:4], ncol = 2 ), by = "column" )

Complex - Lists

List of unnamed vectors gives an ARRAY of ARRAYS (since a vector gets converted to an array)

to_json( list( 1:2, c("a","b") )  )

A list with named elements gives an OBJECT with named ARRAYS

## List of vectors -> object with named arrays
to_json( list( x = 1:2 ) )

A combination of named and unnamed list elements gives both

to_json( list( x = 1:2, y = list( letters[1:2] ) ) )

Complex - Data Frames

A data.frame will, by default, treat each row as an object (to maintain the relationship inherent in a row of data )

## data.frame -> array of objects (by row) 
to_json( data.frame( x = 1:2, y = 3:4) )
to_json( data.frame( x = c("a","b"), y = c("c","d")))

You can set by = "column" to parse the data.frame by columns. And as each column (in this example) is a vector, each vector gets converted to an array. And since the vectors have names (the column names), we get an object of named arrays

## data.frame -> object of arrays (by column)
to_json( data.frame( x = 1:2, y = 3:4), by = "column" )
to_json( data.frame( x = c("a","b"), y = c("c","d") ), by = "column" )

Complex - Mixed objects

A data.frame where one columns is 'AsIs' a list

## data.frame where one colun is a list
df <- data.frame( id = 1, val = I(list( x = 1:2 ) ) )
to_json( df )

The data.frame is being parsed 'by row', so we get an array of objects. The second column is a list of a named vector, so the val column contains an object of a named array.

Here are the individual components to show how it's put together

## which we see is made up of
to_json( data.frame( id = 1 ) )
## and
to_json( list( x = 1:2 ) )

If we take the same example and parse it 'by column' we get the id column treated as a vector, but the list column remains the same

to_json( df, by = "column" )

We can build up a more complex example with nested lists inside columns of data.frames

df <- data.frame( id = 1, val = I(list(c(0,0))))
df
to_json( df )

df <- data.frame( id = 1:2, val = I(list( x = 1:2, y = 3:4 ) ) )
df
to_json( df )

df <- data.frame( id = 1:2, val = I(list( x = 1:2, y = 3:6 ) ), val2 = I(list( a = "a", b = c("b","c") ) ) )
df
pretty_json( df )

df <- data.frame( id = 1:2, val = I(list( x = 1:2, y = 3:6 ) ), val2 = I(list( a = "a", b = c("b","c") ) ), val3 = I(list( l = list( 1:3, l2 = c("a","b")), 1)) )
df
pretty_json( df )

From JSON

Use from_json() to convert from JSON to an R object.

## scalar / vector
js <- '[1,2,3]'
from_json( js )

## matrix
js <- '[[1,2],[3,4],[5,6]]'
from_json( js )

## data.frame
js <- '[{"x":1,"y":"a"},{"x":2,"y":"b"}]'
from_json( js )

Simplifying and NAs

By default from_json() will try and simplify

If an array contains objects with different keys, for example '[{"x":1},{"y":2}]', from_json() will not simplify this to a data.frame, because it would have to assume and insert NAs in rows where data is missing.

js <- '[{"x":1},{"y":2}]'
from_json( js )

You can override this default and use fill_na = TRUE to force it to a data.frame with NAs in place of missing values

js <- '[{"x":1},{"y":2}]'
from_json( js, fill_na = TRUE )


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jsonify documentation built on July 2, 2020, 2:55 a.m.