How to Read a CSV File in R

Importing CSV files is a fundamental skill in R for data analysis. Here are the main approaches.

Method 1: Using read.csv() (Base R)

The simplest way to read a CSV file:

# Read a CSV file
df <- read.csv("data.csv")

# View the first few rows
head(df)

Common Options

# Specify that strings should not be converted to factors
df <- read.csv("data.csv", stringsAsFactors = FALSE)

# Skip rows
df <- read.csv("data.csv", skip = 2)

# No header row
df <- read.csv("data.csv", header = FALSE)

# Handle different delimiters
df <- read.csv2("data.csv")  # For semicolon-separated files

Method 2: Using readr (Tidyverse)

The readr package offers faster and more consistent CSV reading:

library(readr)

# Read a CSV file
df <- read_csv("data.csv")

# View the data
print(df)

Advantages of readr

# Automatically parses column types
# Shows a column specification
# Faster for large files
# Returns a tibble instead of data.frame

df <- read_csv("data.csv", 
               col_types = cols(
                 name = col_character(),
                 age = col_integer()
               ))

Method 3: Using data.table

For very large files, data.table is extremely fast:

library(data.table)

df <- fread("data.csv")

Summary

• Use read.csv() for quick, simple imports
• Use read_csv() from readr for tidyverse workflows
• Use fread() from data.table for large files