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tidyjson is a complementary set of tools to tidyr for working with JSON data. It's primary objective is to turn JSON data into tidy tables for downstream use by dplyr or other relational, analytical or machine learning frameworks in R. Behind the scenes, tidyjson uses jsonlite to parse the JSON data. tidyjson is also designed to be used with the %>% operator imported into dplyr from the magrittr package.

tidyjson operates on the following principles:

  • Allow for structuring in tidy form arbitrarily nested (arrays or objects) JSON
  • Naturally handle 'ragged' arrays and / or objects (varying lengths by document)
  • Allow for extraction of data in values or key names
  • Integrate with pipelines built on dplyr and the %>% operator
  • Ensure edge cases are handled correctly (especially empty data)

You can install tidyjson from github directly by running:


tidyjson comes with several JSON examples:

  • commits: 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

Note that the tidyjson package closely follows the definition and semantics of the JSON standard.

An example of how tidyjson works is as follows:

library(tidyjson)   # this package
library(dplyr)      # for %>% and other dplyr functions

json <- '[{"name": "bob", "age": 32}, {"name": "susan", "age": 54}]'

json %>%            # Use the %>% pipe operator to pass json through a pipeline 
  as.tbl_json %>%   # Parse the JSON and setup a 'tbl_json' object
  gather_array %>%  # Gather (stack) the array by index
  spread_values(    # Spread (widen) values to widen the data.frame = jstring("name"),  # Extract the "name" object as a character column ""
    user.age = jnumber("age")     # Extract the "age" object as a numeric column "user.age"
# array.index user.age
#1           1           1       bob       32
#2           1           2     susan       54

For more complex uses, see the examples in help(commits), help(issues), help(worldbank) and help(companies).


The first step in using tidyjson is to convert your JSON into a tbl_json object. Almost every function in tidyjson accepts a tbl_json object as it's first parameter, and returns a tbl_json object for downstream use. tbl_json inherits from dplyr::tbl.

A tbl_json object is comprised of a data.frame with an additional attribute, JSON, that contains a list of JSON data of the same length as the number of rows in the data.frame. Each row of data in the data.frame corresponds to the JSON found in the same index of the JSON attribute.

The easiest way to construct a tbl_json object is directly from a character string or vector.

# Will return a 1 row data.frame with a length 1 JSON attribute
'{"key": "value"}' %>% as.tbl_json

# Will still return a 1 row data.frame with a length 1 JSON attribute as
# the character string is of length 1 (even though it contains a JSON array of
# length 2)
'[{"key1": "value1"}, {"key2": "value2"}]' %>% as.tbl_json

# Will return a 2 row data.frame with a length 2 JSON attribute
c('{"key1": "value1"}', '{"key2": "value2"}') %>% as.tbl_json

Behind the scenes, as.tbl_json is parsing the JSON strings and creating a data.frame with 1 column,, which keeps track of the character vector position (index) where the JSON data came from.


The rest of tidyjson is comprised of various verbs with operate on tbl_json objects and return tbl_json objects. They are meant to be used in a pipeline with the %>% operator.

Note that these verbs all operate on both the underlying data.frame and the JSON, iteratively moving data from the JSON into the data.frame. Any modifications of the underlying data.frame outside of these operations may produce unintended consequences where the data.frame and JSON become out of synch.


json_types inspects the JSON associated with each row of the data.frame, and adds a new column (type by default) that identifies the type according to the JSON standard.

types <- c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% as.tbl_json %>%
#[1] object  array   string  number  logical null
#Levels: object array string number logical null

This is particularly useful for inspecting your JSON data types, and can added after gather_array (or gather_keys) to inspect the types of the elements (or values) in arrays (or objects).


gather_array takes JSON arrays and duplicates the rows in the data.frame to correspond to the indices of the array, and puts the elements of the array into the JSON attribute. This is equivalent to "stacking" the array in the data.frame, and lets you continue to manipulate the remaining JSON in the elements of the array.

'[1, "a", {"k": "v"}]' %>% as.tbl_json %>% gather_array %>% json_types
# array.index   type
#1           1           1 number
#2           1           2 string
#3           1           3 object

This allows you to enter into an array and begin processing it's elements with other tidyjson functions. It retains the array.index in case the relative position of elements in the array is useful information.


Similar to gather_array, gather_keys takes JSON objects and duplicates the rows in the data.frame to correspond to the keys of the object, and puts the values of the object into the JSON attribute.

'{"name": "bob", "age": 32}' %>% as.tbl_json %>% gather_keys %>% json_types
#  key   type
#1           1 name string
#2           1  age number

This allows you to enter into the keys of the objects just like gather_array let you enter elements of the array.


spread_values lets you dive into (potentially nested) JSON objects and extract specific values. spread_values takes jstring, jnumber or jlogical function calls as arguments in order to specify the type of the data that should be captured at each desired key location.

These values can be of varying types at varying depths, e.g.,

'{"name": {"first": "bob", "last": "jones"}, "age": 32}' %>% as.tbl_json %>% 
  spread_values( = jstring("name", "first"), age = jnumber("age"))
# age
#1           1        bob  32


The append_values_X functions let you take the remaining JSON and add it as a column X (for X in "string", "number", "logical") insofar as it is of the JSON type specified. For example:

'{"first": "bob", "last": "jones"}' %>% as.tbl_json %>% 
  gather_keys() %>% append_values_string()
#   key string
#1           1 first    bob
#2           1  last  jones

Any values that do not conform to the type specified will be NA in the resulting column. This includes other scalar types (e.g., numbers or logicals if you are using append_values_string) and also any rows where the JSON is still an object or an array.


enter_object lets you dive into a specific object key in the JSON attribute, so that all further tidyjson calls happen inside that object (all other JSON data outside the object is discarded). If the object doesn't exist for a given row / index, then that data.frame row will be discarded.

c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% 
  as.tbl_json %>% spread_values( = jstring("name")) %>%
  enter_object("children") %>% 
  gather_array %>% append_values_string("children")
# array.index children
#1           1         bob           1    sally
#2           1         bob           2   george

This is useful when you want to limit your data to just information found in a specific key.


When beginning to work with JSON data, you often don't have easy access to a schema describing what is in the JSON. One of the benefits of document oriented data structures is that they let developers create data without having to worry about defining the schema explicitly.

Thus, the first step is to usually understand the structure of the JSON. A first step can be to look at individual records with jsonlite::prettify:


Examining various random records can begin to give you a sense of what the JSON contains and how it it structured. However, keep in mind that in many cases documents that are missing data (either unknown or unrelevant) may omit the entire JSON structure.

Next, you can begin working with the data in R.

# Inspect the types of objects
read_json("myfile.json") %>% json_types %>% table

Then, if you want to work with a single row of data for each JSON object, use spread_values to get at (potentially nested) key-value pairs.

If all you care about is data from a certain sub-object, then use enter_object to dive into that object directly. Make sure you first use spread_values to capture any top level identifiers you might need for analytics, summarization or relational uses downstream.

If you want to access arrays, use gather_array to stack their elements, and then proceed as though you had separate documents. (Again, first spread any top-level keys you need.)

Finally, if you have data where information is encoded in both keys and values, then consider using gather_keys and append_values_X where X is the type of JSON scalar data you expect in the values.

It's important to remember that any of the above can be combined together iteratively to do some fairly complex data extraction. For example:

json <- '{
  "name": "bob",
  "shopping cart": 
        "date": "2014-04-02",
        "basket": {"books": 2, "shirts": 0}
        "date": "2014-08-23",
        "basket": {"books": 1}
json %>% as.tbl_json %>% 
  spread_values(customer = jstring("name")) %>% # Keep the customer name
  enter_object("shopping cart") %>%             # Look at their cart
  gather_array %>%                              # Expand the data.frame and dive into each array element
  spread_values(date = jstring("date")) %>%     # Keep the date of the cart
  enter_object("basket") %>%                    # Look at their basket
  gather_keys("product") %>%                    # Expand the data.frame for each product and capture it's name
  append_values_number("quantity")              # Capture the values as the quantity
# customer array.index       date product quantity
#1           1      bob           1 2014-04-02   books        2
#2           1      bob           1 2014-04-02  shirts        0
#3           1      bob           2 2014-08-23   books        1

Note that there are often situations where there are multiple arrays or objects of differing types that exist at the same level of the JSON hierarchy. In this case, you need to use enter_object to enter each of them in separate pipelines to create separate data.frames that can then be joined relationally.

Finally, don't forget that once you are done with your JSON tidying, you can use dplyr to continue manipulating the resulting data at your leisure!


Tools for using dplyr with JSON data







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