How to Wrangle JSON Data in R with jsonlite, purr and dplyr – Part I

Working with modern APIs you will often have to wrangle with data in JSON format.

This article presents some tools and recipes for working with JSON data with R in the tidyverse.

We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr and dplyr.

library(tidyverse)
library(here)
library(kableExtra)
pretty_print <- function(df, num_rows) {
  df %>%
  head(num_rows) %>%
    kable() %>%
    kable_styling(full_width = TRUE, position = 'center') %>%
    scroll_box(height = '300px')
}

Load JSON as nested named lists

This data has been converted from raw JSON to nested named lists using jsonlite::fromJSON with the simplify argument set to FALSE (that is, all elements are converted to named lists).

The data consists of market data for SPY options with various strikes and expiries. We got it from the options data vendor Orats, whose data API I enjoy almost as much as their orange website.

If you want to follow along, you can sign-up for a free trial of the API, and load the data directly from the Orats API with the following code (just define your API key in the ORATS_token variable):

library(httr)
ORATS_token <- 'YOUR_KEY_HERE'
res <- GET('https://api.orats.io/data/strikes?tickers=SPY', add_headers(Authorization = ORATS_token))
if (http_type(res) == 'application/json') {
  strikes <- jsonlite::fromJSON(content(res, 'text'), simplifyVector = FALSE)
} else {
  stop('No json returned')
}
if (http_error(res)) {
  stop(paste('API request error:',status_code(res), odata$message, odata$documentation_url))
} 

Now, if you want to read this data directly into a nicely formatted dataframe, replace the line:

strikes <- jsonlite::fromJSON(content(res, 'text'), simplifyVector = FALSE)

with

strikes <- jsonlite::fromJSON(content(res, 'text'), simplifyVector = TRUE, flatten = TRUE)

However, you should know that it isn’t always possible to coerce JSON into nicely shaped dataframes this easily – often the raw JSON won’t contain primitive types, or will have nested key-value pairs on the same level as your desired dataframe columns, to name a couple of obstacles.

In that case, it’s useful to have some tools – like the ones in this post – for wrangling your source data.

Stay tuned for the next installment in which Kris Longmore will look inside JSON lists.

Visit Robot Wealth website for additional insight on this topic and to download the complete set of scripts: https://robotwealth.com/how-to-wrangle-json-data-in-r-with-jsonlite-purr-and-dplyr/.