R Academy · Lesson

Handling Nested JSON Structures

Flatten deeply nested JSON into tidy data frames for analysis.

Why Nested JSON Is Tricky

REST APIs often return deeply nested JSON where a single field may contain an array of objects, which in turn contain more objects. Flattening this structure into a tidy data frame requires understanding how jsonlite, purrr, and tidyr work together.

# Example nested JSON from a REST API:
json_str <- '{
  "user": {
    "id": 1,
    "name": "Alice",
    "orders": [
      {"order_id": 101, "total": 59.99, "status": "shipped"},
      {"order_id": 102, "total": 24.50, "status": "pending"}
    ]
  }
}'

# The challenge: 'orders' is an array of objects inside 'user'
cat('Nested JSON loaded as string, length:', nchar(json_str))

fromJSON() — Basic Parsing

jsonlite::fromJSON() converts a JSON string or file path into R objects. Simple flat JSON becomes a list or data frame. Nested JSON becomes a nested list — arrays of objects become data frames stored inside list columns.

library(jsonlite)

# Parse flat JSON
flat_json <- '{"name": "Alice", "age": 30, "score": 95.5}'
result <- fromJSON(flat_json)
cat('Name:', result$name, '\n')
cat('Age: ', result$age,  '\n')

# Parse an array of objects — becomes a data frame
array_json <- '[{"id":1,"val":10},{"id":2,"val":20},{"id":3,"val":30}]'
df <- fromJSON(array_json)
cat('Class:', class(df), '\n')
print(df)

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