README.md

tidyjson

CRAN_Status_Badge Build
Status Coverage
Status

CRAN
Activity CRAN
History

tidyjson
graphs

tidyjson provides tools for turning complex json into tidy data.

Installation

Get the released version from CRAN:

install.packages("tidyjson")

or the development version from github:

devtools::install_github("colearendt/tidyjson")

Examples

The following example takes a character vector of 500 documents in the worldbank dataset and spreads out all objects. Every JSON object key gets its own column with types inferred, so long as the key does not represent an array. When recursive=TRUE (the default behavior), spread_all does this recursively for nested objects and creates column names using the sep parameter (i.e. {"a":{"b":1}} with sep='.' would generate a single column: a.b).

library(dplyr)
library(tidyjson)

worldbank %>% spread_all
#> # A tbl_json: 500 x 9 tibble with a "JSON" attribute
#>    ..JSON document.id boardapprovalda… closingdate countryshortname project_name
#>    <chr>        <int> <chr>            <chr>       <chr>            <chr>       
#>  1 "{\"_…           1 2013-11-12T00:0… 2018-07-07… Ethiopia         Ethiopia Ge…
#>  2 "{\"_…           2 2013-11-04T00:0… <NA>        Tunisia          TN: DTF Soc…
#>  3 "{\"_…           3 2013-11-01T00:0… <NA>        Tuvalu           Tuvalu Avia…
#>  4 "{\"_…           4 2013-10-31T00:0… <NA>        Yemen, Republic… Gov't and C…
#>  5 "{\"_…           5 2013-10-31T00:0… 2019-04-30… Lesotho          Second Priv…
#>  6 "{\"_…           6 2013-10-31T00:0… <NA>        Kenya            Additional …
#>  7 "{\"_…           7 2013-10-29T00:0… 2019-06-30… India            National Hi…
#>  8 "{\"_…           8 2013-10-29T00:0… <NA>        China            China Renew…
#>  9 "{\"_…           9 2013-10-29T00:0… 2018-12-31… India            Rajasthan R…
#> 10 "{\"_…          10 2013-10-29T00:0… 2014-12-31… Morocco          MA Accounta…
#> # … with 490 more rows, and 3 more variables: regionname <chr>, totalamt <dbl>,
#> #   `_id.$oid` <chr>

Some objects in worldbank are arrays, which are not handled by spread_all. This example shows how to quickly summarize the top level structure of a JSON collection

worldbank %>% gather_object %>% json_types %>% count(name, type)
#> # A tibble: 8 x 3
#>   name                type       n
#>   <chr>               <fct>  <int>
#> 1 _id                 object   500
#> 2 boardapprovaldate   string   500
#> 3 closingdate         string   370
#> 4 countryshortname    string   500
#> 5 majorsector_percent array    500
#> 6 project_name        string   500
#> 7 regionname          string   500
#> 8 totalamt            number   500

In order to capture the data in the majorsector_percent array, we can use enter_object to enter into that object, gather_array to stack the array and spread_all to capture the object items under the array.

worldbank %>%
  enter_object(majorsector_percent) %>%
  gather_array %>%
  spread_all %>%
  select(-document.id, -array.index)
#> # A tbl_json: 1,405 x 3 tibble with a "JSON" attribute
#>    ..JSON                  Name                                    Percent
#>    <chr>                   <chr>                                     <dbl>
#>  1 "{\"Name\":\"Educat..." Education                                    46
#>  2 "{\"Name\":\"Educat..." Education                                    26
#>  3 "{\"Name\":\"Public..." Public Administration, Law, and Justice      16
#>  4 "{\"Name\":\"Educat..." Education                                    12
#>  5 "{\"Name\":\"Public..." Public Administration, Law, and Justice      70
#>  6 "{\"Name\":\"Public..." Public Administration, Law, and Justice      30
#>  7 "{\"Name\":\"Transp..." Transportation                              100
#>  8 "{\"Name\":\"Health..." Health and other social services            100
#>  9 "{\"Name\":\"Indust..." Industry and trade                           50
#> 10 "{\"Name\":\"Indust..." Industry and trade                           40
#> # … with 1,395 more rows

API

Spreading objects into columns

Object navigation

Array navigation

JSON inspection

JSON summarization

Creating tbl_json objects

Converting tbl_json objects

Included JSON data

Philosophy

The goal is to turn complex JSON data, which is often represented as nested lists, into tidy data frames that can be more easily manipulated.

Related Work

Tidyjson depends upon

Further, there are other R packages that can be used to better understand JSON data



Try the tidyjson package in your browser

Any scripts or data that you put into this service are public.

tidyjson documentation built on July 2, 2020, 12:15 a.m.