📁 File Handling in R – Read, Write, and Connect to External Data Sources

💼 Power up your data science workflow with R’s tools for importing, exporting, and integrating data from files, APIs, and databases.

🧲 Introduction – Import, Export & Manage Files and Data Connections in R

To analyze real-world data in R, you must master file handling and external data connections. R makes it easy to read from and write to files such as CSV, Excel, JSON, XML, and even binary formats. You can also fetch data from web APIs or connect directly to SQL databases using specialized libraries.

This section teaches you how to efficiently manage your data inputs and outputs, allowing you to automate workflows and seamlessly work with external data sources in both local and cloud-based environments.

🎯 In this guide, you’ll learn:

• How to read/write CSV, Excel, and binary files in R
• How to parse structured files like XML and JSON
• How to pull data from online sources via URLs and APIs
• How to connect and interact with relational databases

📘 Topics Covered

📄 R – CSV Files

CSV files are the most common format for storing tabular data. You can easily read or write them using base R:

data <- read.csv("input.csv")
write.csv(data, "output.csv")

Use optional arguments like header, sep, and na.strings for customized parsing.

📊 R – Excel Files

To read and write Excel files (.xls, .xlsx), use:

library(readxl)
data <- read_excel("report.xlsx")

library(openxlsx)
write.xlsx(data, "export.xlsx")

🧠 readxl is optimized for reading, while openxlsx supports both read and write operations.

💾 R – Binary Files

Binary files are efficient for raw data storage and can be read/written like this:

con <- file("data.bin", "wb")
writeBin(1:5, con)
close(con)

con <- file("data.bin", "rb")
readBin(con, "integer", n = 5)
close(con)

Useful for high-performance applications or inter-system data exchange.

🧾 R – XML Files

Parse structured data from XML:

library(xml2)
doc <- read_xml("file.xml")
titles <- xml_text(xml_find_all(doc, "//title"))

Perfect for working with hierarchical documents or scraping from XML feeds.

🧰 R – JSON Files

JSON is commonly used in APIs and configuration files.

library(jsonlite)
data <- fromJSON("data.json")
toJSON(data, pretty = TRUE)

Flatten nested structures with fromJSON(..., flatten = TRUE) for easier data wrangling.

🌍 R – Web Data

Read online datasets directly:

data <- readLines("https://example.com/data.csv")

library(httr)
res <- GET("https://api.example.com/data")
content(res, "text")

Includes support for headers, API keys, and pagination.

🛢️ R – Database

Use DBI with connectors for various database engines:

library(DBI)
con <- dbConnect(RSQLite::SQLite(), dbname = "local.db")
result <- dbGetQuery(con, "SELECT * FROM users")
dbDisconnect(con)

Handles large datasets efficiently using SQL queries without loading everything into memory.

📌 Summary – Recap & Next Steps

📦 File handling in R is one of the most vital skills for any data-driven project. Whether you’re dealing with spreadsheets, structured files, or database backends, R provides native functions and robust packages to make data access seamless and efficient.

📌 This knowledge empowers you to:

• Build repeatable data pipelines
• Integrate live data from APIs
• Connect to enterprise-grade database systems

🔍 Key Takeaways:

• Use read.csv() and read_excel() to import spreadsheets
• Use jsonlite, xml2, and httr for structured and remote data
• Connect to SQL databases using the DBI interface

⚙️ Real-World Relevance:
From data engineering pipelines to research dashboards, R’s file handling capabilities are crucial across all domains of data science and analytics.

❓ Frequently Asked Questions (FAQs)

Q1: How do I handle huge CSVs without crashing R?
✅ Use data.table::fread() or readr::read_csv() for efficient memory usage.

Q2: Can I read Excel sheets with formulas?
✅ Yes, formulas are not executed but their last computed values are read using readxl or openxlsx.

Q3: What’s the best way to authenticate to secure APIs in R?
✅ Use httr::authenticate() or API tokens in the headers with add_headers().

Q4: Can I automate file exports in R daily or weekly?
✅ Yes. Use R scripts with cron jobs or Windows Task Scheduler for automation.

Q5: Is it safe to write files directly to cloud drives like Dropbox or Google Drive?
✅ Yes, if those drives are locally synced, but use googledrive package for direct access via API.