🌐 R – Web Data Access (with Code Explanation)
🧲 Introduction – Fetching Data from the Web in R
In the world of data science, information isn’t always stored locally. Often, data is available through web APIs, URLs, or online datasets. R provides powerful tools for accessing, reading, and processing web-based data—whether it’s plain text, CSV, JSON, XML, or content from REST APIs.
🎯 In this guide, you’ll learn:
- How to fetch data from web URLs
- Read files (CSV, JSON, XML) hosted online
- Access and parse REST APIs using
httrandjsonlite - Line-by-line explanations of each example
🌍 1. Reading Web Files with Base R
✅ Reading CSV from a URL
url <- "https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv"
data <- read.csv(url)
head(data)
🔍 Explanation:
- The
urlvariable stores the CSV file’s web address. read.csv()works directly with URLs just like local files.head(data)shows the first few rows to confirm import.
🔗 2. Reading Web-Based JSON Data
✅ Using jsonlite::fromJSON()
library(jsonlite)
json_url <- "https://api.github.com/repos/hadley/ggplot2"
data <- fromJSON(json_url)
data$name
data$owner$login
🔍 Explanation:
fromJSON()downloads and converts the API JSON response into a list or data frame.- Access elements with
$like regular R objects.
🔄 3. Fetching REST API Data with httr
✅ Install and load httr:
install.packages("httr")
library(httr)
✅ Example: GET Request to OpenWeather API (mock URL)
res <- GET("https://api.openweathermap.org/data/2.5/weather?q=London&appid=your_api_key")
content <- content(res, "parsed")
content$weather[[1]]$main
🔍 Explanation:
GET()sends an HTTP GET request to the API.content()extracts and parses the JSON response into R-readable format.- Nested data is accessed with
$and[[ ]].
✅ Replace
"your_api_key"with a real API key.
📦 4. Reading XML Data from a Web URL
library(xml2)
xml_url <- "https://www.w3schools.com/xml/note.xml"
doc <- read_xml(xml_url)
xml_text(xml_find_all(doc, ".//body"))
🔍 Explanation:
read_xml()fetches and parses XML from the given URL.xml_find_all()selects the<body>tag.xml_text()extracts the content.
📥 5. Downloading Web Files to Local System
download.file("https://people.sc.fsu.edu/~jburkardt/data/csv/hw_200.csv",
destfile = "hw_data.csv",
method = "auto")
🔍 Explanation:
- Downloads the remote CSV to a local file named
"hw_data.csv". method = "auto"lets R pick the best available download method.
🔐 6. APIs with Headers or Tokens
For APIs requiring authentication:
res <- GET("https://api.example.com/data",
add_headers(Authorization = "Bearer YOUR_TOKEN"))
🔍 Explanation:
add_headers()lets you attach authentication or custom headers to the request.- Useful for OAuth tokens, API keys, etc.
🧠 Tips for Working with Web Data
| Task | Tip/Function |
|---|---|
| Check URL validity | Use httr::status_code() |
| Convert JSON → Data Frame | jsonlite::fromJSON(flatten = TRUE) |
| Parse nested lists | Use str() to explore structure |
| Handle rate-limited APIs | Use Sys.sleep() between requests |
📌 Summary – Recap & Next Steps
R provides seamless access to web-based resources—from static CSV files to dynamic REST APIs. These tools allow real-time integration of external data directly into your R workflow.
🔍 Key Takeaways:
- Use
read.csv()orread_xml()for static web files - Use
jsonlite::fromJSON()for reading JSON APIs - Use
httr::GET()andcontent()for full API control - Use
download.file()to save remote data locally - Add headers/tokens for authenticated API access
⚙️ Real-World Relevance:
Used in web scraping, public data portals (e.g., World Bank, GitHub, NASA APIs), financial and weather services, dashboards, and automated reporting.
❓ FAQs – Web Data in R
❓ Can I read a CSV file from a URL directly in R?
✅ Yes:
data <- read.csv("https://example.com/data.csv")
❓ How do I access an API that returns JSON?
✅ Use:
fromJSON("https://api.example.com/endpoint")
❓ What if the API requires authentication?
✅ Add headers using httr::add_headers() or pass a token:
GET("url", add_headers(Authorization = "Bearer TOKEN"))
❓ How do I avoid downloading the file multiple times?
✅ Use file.exists() to check before downloading:
if (!file.exists("data.csv")) download.file(url, "data.csv")
❓ How can I check the structure of nested JSON?
✅ Use:
str(fromJSON("file.json"))
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