Mastering Data Import in R

Mastering Data Import in R — Your 2025 Guide

Importing data into R remains a vital first step in any analytics workflow—and with today’s diverse file formats and data sources, getting it right matters more than ever. Here's how to confidently navigate data importing in R—smartly, efficiently, and using the most relevant tools available.

Set Up for Success: Clean Start & Environment

Start each session fresh:

• Use at least one of these to avoid ghost errors:

rm(list = ls())

• Define and verify your working directory: setwd("path/to/your/directory") getwd()

This ensures clarity in your script, avoids confusion over file paths, and keeps your workspace clean.

Import Flat Files: Text, CSV, and Co.

Base R

read.table("data.txt", header = TRUE, sep = "\t")
read.csv("data.csv") # comma-separated
read.csv2("data_semicolon.csv") # semicolon-separated

Modern Tidyverse Style

library(readr)
df_csv <- read_csv("data.csv")
df_tsv <- read_delim("data.tsv", delim = "\t")

These functions are faster and work seamlessly with tidy workflows.

Work with JSON, XML, and HTML Data

JSON:

library(jsonlite)
json_data <- fromJSON("data.json") # from file or URL
json_df <- as.data.frame(json_data)

XML / HTML:

library(xml2)
xml_doc <- read_xml("data.xml")
html_tab <- xml2::read_html("webpage.html") %>%
rvest::html_table(fill = TRUE) %>%
.[[1]]

Excel and Spreadsheet Files

The readxl package is the gold standard:

library(readxl)
df_default <- read_excel("workbook.xlsx") # reads first sheet
df_sheet3 <- read_excel("workbook.xlsx", sheet = 3)

It's fast, avoids external dependencies, and handles headers gracefully.

Data from Statistical & Scientific Files

Haven streamlines data importing from other stats tools:

library(haven)
df_sas <- read_sas("data.sas7bdat")
df_spss <- read_sav("data.sav")
df_stata <- read_dta("data.dta")

For MATLAB files:

library(R.matlab)
df_mat <- readMat("data.mat") # imports MATLAB files directly

And for Octave files:

library(foreign)
df_oct <- read.octave("data.octave")

Connect to Databases and Visualize Large Data

ODBC connections let you pull data straight from relational systems:

library(RODBC)
conn <- odbcConnect("DSN_Name", uid = "user", pwd = "password")
df1 <- sqlFetch(conn, "Table1")
df2 <- sqlQuery(conn, "SELECT * FROM Table2")
odbcClose(conn)

Perfect for relational data sources—SQL Server, Access, and others.

Pro Tips: Best Practices & Workflow Polish

• Keep column headers clean: no spaces, no special characters; use snake_case or camelCase.
• Use consistent naming and avoid duplicates.
• Treat missing values appropriately—let missing values map to NA.
• Use consistent code style (like the tidyverse style guide) for readability and maintainability.

Final Thoughts

Importing data into R now spans powerful tools like readr, readxl, haven, and ODBC connectors. By starting clean, applying modern packages, and enforcing consistent naming and style, you streamline your analysis—and set yourself up for speed and clarity.

This article was originally published on Perceptive Analytics.

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