tidyjson provides tools for turning complex json into tidy data.
Get the released version from CRAN:
or the development version from github:
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 500In 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 rowsspread_all() for spreading all object values into new columns, with nested objects having concatenated names
spread_values() for specifying a subset of object values to spread into new columns using the json_chr(), json_dbl() and json_lgl() functions. It is possible to specify multiple parameters to extract data from nested objects (i.e. json_chr('a','b')).
enter_object() for entering into an object by name, discarding all other JSON (and rows without the corresponding object name) and allowing further operations on the object value
gather_object() for stacking all object name-value pairs by name, expanding the rows of the tbl_json object accordingly
gather_array() for stacking all array values by index, expanding the rows of the tbl_json object accordinglyjson_types() for identifying JSON data types
json_length() for computing the length of JSON data (can be larger than 1 for objects and arrays)
json_complexity() for computing the length of the unnested JSON, i.e., how many terminal leaves there are in a complex JSON structure
is_json family of functions for testing the type of JSON data
json_structure() for creating a single fixed column data.frame that recursively structures arbitrary JSON data
json_schema() for representing the schema of complex JSON, unioned across disparate JSON documents, and collapsing arrays to their most complex type representation
as.tbl_json() for converting a string or character vector into a tbl_json object, or for converting a data.frame with a JSON column using the json.column argument
tbl_json() for combining a data.frame and associated list derived from JSON data into a tbl_json object
read_json() for reading JSON data from a file
as.character.tbl_json for converting the JSON attribute of a tbl_json object back into a JSON character stringcommits: commit data for the dplyr repo from github API
issues: issue data for the dplyr repo from github API
worldbank: world bank funded projects from jsonstudio
companies: startup company data from jsonstudio
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.
Work on a single JSON document, or on a collection of related documents
Create pipelines with %>%, producing code that can be read from left to right
Guarantee the structure of the data produced, even if the input JSON structure changes (with the exception of spread_all)
Work with arbitrarily nested arrays or objects
Handle ‘ragged’ arrays and / or objects (varying lengths by document)
Allow for extraction of data in values or object names
Ensure edge cases are handled correctly (especially empty data)
Integrate seamlessly with dplyr, allowing tbl_json objects to pipe in and out of dplyr verbs where reasonable
Tidyjson depends upon
%>% pipe operatorFurther, there are other R packages that can be used to better understand JSON data