🌐 R – Reading XML and JSON Data (with Code Explanation)

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🧲 Introduction – Parsing Web & Nested Data in R

Modern data often comes in structured hierarchical formats like XML (used in config and APIs) or JSON (standard for web APIs, NoSQL). R provides packages to import and parse both XML and JSON easily into usable data frames or lists.

🎯 In this guide, you’ll learn:

• How to read and parse XML and JSON files in R
• Convert them into structured R objects
• Understand how to extract nested data with practical code and explanations

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📄 1. Reading XML Files in R

✅ Step 1: Install and Load the xml2 Package

install.packages("xml2")
library(xml2)

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✅ Step 2: Read an XML File

xml_data <- read_xml("books.xml")

🔍 Explanation:

• read_xml() reads the XML structure into an R xml_document object.
• books.xml is your local or web-based XML file.

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✅ Step 3: Explore XML Content

xml_name(xml_data)        # Root node name
xml_children(xml_data)    # Get child nodes

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✅ Step 4: Extract Specific Tags

Assume this structure:

<book>
  <title>R Programming</title>
  <author>John Doe</author>
</book>

titles <- xml_find_all(xml_data, ".//title")
xml_text(titles)

🔍 Explanation:

• xml_find_all() selects all <title> tags.
• xml_text() extracts the text values inside those nodes.

🧾 Output:

[1] "R Programming"

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🧾 2. Reading JSON Files in R

✅ Step 1: Install and Load the jsonlite Package

install.packages("jsonlite")
library(jsonlite)

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✅ Step 2: Read a JSON File

json_data <- fromJSON("students.json")

🔍 Explanation:

• fromJSON() reads the JSON file and automatically converts it into a list or data frame, depending on structure.
• Perfect for nested objects like:

{
  "name": "Alice",
  "scores": [90, 85, 92]
}

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✅ Step 3: Access JSON Data

json_data$name      # "Alice"
json_data$scores    # 90 85 92

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✅ Step 4: Nested JSON Structure Example

{
  "students": [
    { "name": "Alice", "age": 23 },
    { "name": "Bob", "age": 25 }
  ]
}

json_data <- fromJSON("students.json")
df <- json_data$students

🧾 Output:

   name age
1 Alice  23
2   Bob  25

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🌐 Load JSON or XML from Web URLs

🔗 Read JSON from URL

fromJSON("https://api.example.com/data.json")

🔗 Read XML from URL

read_xml("https://example.com/data.xml")

✅ Works directly with API endpoints and web-accessible files.

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⚠️ Handling Common Issues

Problem	Fix or Tip
File not found	Use getwd() to check working directory
Invalid XML/JSON structure	Validate using online validators before parsing
Nested structure too deep	Use str() to explore and extract manually
Encoding problems	Use encoding = "UTF-8" in readLines() if needed

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📌 Summary – Recap & Next Steps

R makes it simple to work with web and hierarchical data like XML and JSON. These formats are widely used in web APIs, data exchange, and configuration files.

🔍 Key Takeaways:

• Use xml2::read_xml() and jsonlite::fromJSON() for parsing
• Access nested content using indexing or helper functions
• Use xml_text(), xml_find_all(), str(), and $ for exploration
• Load from local or web sources with ease
• Convert structured data into data frames for analysis

⚙️ Real-World Relevance:
Used in API integrations (e.g., weather, finance, healthcare), configuration systems, cloud dashboards, and data science automation.

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❓ FAQs – Reading XML and JSON in R

❓ How do I read an XML file from a URL in R?
✅ Use read_xml():

read_xml("https://example.com/file.xml")

❓ What package is best for parsing JSON in R?
✅ jsonlite is widely used and converts JSON into data frames or lists easily.

❓ How do I convert nested JSON into a data frame?
✅ Use fromJSON() and access nested keys:

df <- fromJSON("file.json")$students

❓ Can I write back to JSON or XML in R?
✅ Yes:

• Use toJSON() from jsonlite for JSON.
• Use xml2::write_xml() or writeLines() for XML.

❓ How do I validate XML or JSON before reading?
✅ Use online tools like jsonlint.com or xmlvalidation.com to verify structure.

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