A Complete Guide - R Language Reading Data from CSV, Excel, and Text Files

Last Updated: 03 Jul, 2025   
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R Language: Reading Data from CSV, Excel, and Text Files

Introduction

1. CSV Files

a. read.csv() Function

The read.csv() function is used to read CSV files. It is a specialized version of the more general read.table() function.

  • Syntax: read.csv(file, header=TRUE, sep=",", dec=".")

    • file: path to the CSV file.
    • header: logical indicating if the first row of the file contains names for the variables.
    • sep: field separator character, default is a comma.
    • dec: character used in the file for decimal points.

  • Example:

    # Reading CSV file
data <- read.csv("data.csv")
head(data)  # Display first few rows
b. read_csv() Function from readr Package

The read_csv() function from the readr package is an optimized and faster version of read.csv().

  • Installation: install.packages("readr")

  • Syntax: read_csv(file, col_names = TRUE)

    • file: path to the CSV file.
    • col_names: logical indicating if the first row has column names.

  • Example:

    # Using read_csv from readr package
library(readr)
data <- read_csv("data.csv")
head(data)

2. Excel Files

a. readxl Package

readxl is a package designed to read Excel files (both .xls and .xlsx).

  • Installation: install.packages("readxl")

  • Functions:

    • read_excel(path, sheet = 1, range = NULL, col_names = TRUE)
      • path: path to the Excel file.
      • sheet: sheet to read (can be numeric index or name).
      • range: cell range to read (e.g., A1:B5).
      • col_names: logical indicating if the first row has column names.

  • Example:

    # Reading Excel file
library(readxl)
data <- read_excel("data.xlsx", sheet = "Sheet1")
head(data)
b. openxlsx Package

openxlsx is another package for reading and writing Excel files.

  • Installation: install.packages("openxlsx")

  • Functions:

    • read.xlsx(file, sheet = 1, colNames = TRUE)
      • file: path to the Excel file.
      • sheet: sheet to read.
      • colNames: logical indicating if the first row has column names.

  • Example:

    # Using openxlsx package
library(openxlsx)
data <- read.xlsx("data.xlsx", sheet = 1)
head(data)

3. Text Files

a. read.table() and read.delim() Functions

read.table() is a general function for importing tabular data, which can be customized for various delimiters. read.delim() is specifically for files with tab-separated values (TSV).

  • Syntax:

    • read.table(file, header=FALSE, sep="", dec=".")
    • read.delim(file, header=FALSE)

  • Examples:

    # Reading text file
data <- read.table("data.txt", header=TRUE, sep="\t")
head(data)

# Reading tab-separated file
data_tsv <- read.delim("data.tsv", header=TRUE)
head(data_tsv)
b. read_fwf() Function from readr Package

read_fwf() is specifically for reading fixed-width formatted files.

  • Syntax: read_fwf(file, fwf_positions(positions, col_names=NULL))

    • file: path to the file.
    • fwf_positions(positions, col_names=NULL): width and column names.

  • Example:

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Step-by-Step Guide: How to Implement R Language Reading Data from CSV, Excel, and Text Files

1. Reading Data from a CSV File

A CSV (Comma-Separated Values) file is a common format for storing data. Let's assume we have a file named data.csv that contains the following sample data:

id,name,age
1,Alice,30
2,Bob,25
3,Charlie,40

Steps to Read CSV in R:

  1. Save Your CSV File:

    • Ensure that your CSV file (data.csv) is saved in a known location on your computer.

  2. Set Working Directory:

    • Use the setwd() function to set the working directory to the location where your CSV file is stored. 
      # Example: Set working directory to a folder named 'data' in your home directory
setwd("~/data")

  3. Use read.csv() Function:

    • Use the read.csv() function to read the data from the CSV file into an R data frame. 
      # Read data from the CSV file
my_data <- read.csv("data.csv")

# Print the first few rows of the data frame to check if it was loaded correctly
head(my_data)

Complete Example:

# Step 1: Set working directory (change this path to the location of your CSV file)
setwd("~/data")

# Step 2: Read the CSV file into a data frame
my_data <- read.csv("data.csv")

# Step 3: View the data frame
head(my_data)

# Optional: Install and load dplyr package for additional data manipulation functions
install.packages("dplyr")
library(dplyr)

# You can now use dplyr functions to manipulate the data, e.g., summarizing age
summary(my_data$age)

2. Reading Data from an Excel File

For reading data from Excel files, you can either use the readxl package or the gdata package. The readxl package is generally more user-friendly and modern. Let's use the readxl package as an example.

Sample Excel File (data.xlsx):

| id | name | age | |----|---------|-----| | 1 | Alice | 30 | | 2 | Bob | 25 | | 3 | Charlie | 40 |

Steps to Read Excel File in R:

  1. Install and Load readxl Package:

    • If you haven't already installed readxl, do so by running install.packages("readxl"). Load the package using library(readxl). 
      # Install readxl package (only need to do once)
install.packages("readxl")

# Load the readxl package
library(readxl)

  2. Read Excel File:

    • Use the read_excel() function to read data from an Excel file. 
      # Read data from the Excel file into a data frame
my_excel_data <- read_excel("data.xlsx", sheet = 1)

# Print the first few rows of the data frame to check if it was loaded correctly
head(my_excel_data)

Complete Example:

# Step 1: Install and load the readxl package
install.packages("readxl")
library(readxl)

# Step 2: Set working directory (change this path to the location of your Excel file)
setwd("~/data")

# Step 3: Read the Excel file into a data frame
# Here, sheet = 1 indicates that we are reading from the first sheet in the workbook
my_excel_data <- read_excel("data.xlsx", sheet = 1)

# Step 4: View the data frame
head(my_excel_data)

# Optional: Install and load dplyr package for additional data manipulation functions
install.packages("dplyr")
library(dplyr)

# You can now use dplyr functions to manipulate the data, e.g., summarizing age
summary(my_excel_data$age)

3. Reading Data from a Text File

Text files can be structured in different ways, including fixed-width, delimited by spaces, or using commas (similar to CSV). For simplicity, let's consider a delimited text file with the same content as data.csv.

Sample Text File (data.txt):

id,name,age
1,Alice,30
2,Bob,25
3,Charlie,40

Note: Sometimes, text files might have different delimiters, such as tabs (\t). Adjust the sep parameter accordingly.

Steps to Read Text File in R:

  1. Save Your Text File:

    • Ensure that your text file (data.txt) is saved in a known location on your computer.

  2. Set Working Directory:

    • Use the setwd() function to set the working directory to the location where your text file is stored. 
      # Example: Set working directory to a folder named 'data' in your home directory
setwd("~/data")

  3. Use read.table() or read.delim() Function:

    • Use read.table() with the appropriate separator (sep) or read.delim() for tab-separated files to read the data from a text file into an R data frame.
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Top 10 Interview Questions & Answers on R Language Reading Data from CSV, Excel, and Text Files

Top 10 Questions and Answers: Reading Data from CSV, Excel, and Text Files in R

1. How do I read a CSV file into R?

Answer: Use the read.csv() function to read a CSV file into an R data frame.

data <- read.csv("path/to/your/file.csv")

Make sure to replace "path/to/your/file.csv" with the actual path of your CSV file.

2. Can I specify a delimiter when reading a CSV file?

Answer: Yes, use the sep parameter in read.csv().

data <- read.csv("path/to/your/file.csv", sep = ";")

If your CSV uses a semicolon (;) as the delimiter (common in some European countries), you can specify sep = ";".

3. How do I handle missing values while reading a CSV file in R?

Answer: Use the na.strings parameter in read.csv() to define which strings represent missing values.

data <- read.csv("path/to/your/file.csv", na.strings = c("", "NA", "."))

This tells R that empty fields (""), fields containing "NA", and fields containing "." should be treated as missing values.

4. How do I read a specific sheet from an Excel file in R?

Answer: Use the readxl package, specifically the read_excel() function. First, install the package if you don't already have it:

install.packages("readxl")
library(readxl)
data <- read_excel("path/to/your/file.xlsx", sheet = "Sheet1")

You can also specify the sheet by its index number.

data <- read_excel("path/to/your/file.xlsx", sheet = 1)

5. What if my Excel file is password protected?

Answer: The readxl package does not support reading password-protected Excel files directly. You would need to open the file, remove the password protection, and save it before reading it into R.

6. How do I read multiple sheets from an Excel file?

Answer: You can use lapply() to loop through a list of sheet names or indices:

library(readxl)
sheets <- excel_sheets("path/to/your/file.xlsx")
all_sheets <- lapply(sheets, function(x) read_excel("path/to/your/file.xlsx", sheet = x))
names(all_sheets) <- sheets

7. How do I read a text file in R?

Answer: Use the read.table() function for fixed-width formatted text files, or readLines() for line-by-line reading and strsplit() for splitting lines into columns manually. For delimited text files, use read.delim() or read_csv() from the readr package.

data <- read.table("path/to/your/textfile.txt", header = TRUE, sep = "\t")
data <- read.delim("path/to/your/textfile.txt", header = TRUE)  # For tab-delimited files
library(readr)
data <- read_csv("path/to/your/textfile.txt")  # For comma-separated files

8. How can I handle large text files efficiently in R?

Answer: Use the readr package, which is optimized for performance. The read_csv() and read_tsv() functions are memory-efficient and faster than the base read.csv() and read.table() functions.

library(readr)
data <- read_csv("path/to/large/file.csv")

9. How do I skip rows or columns while reading a CSV file?

Answer: Use the skip parameter to skip a certain number of rows, and colClasses or select (in readr) to specify which columns to include or exclude.

# Skip first two rows:
data <- read.csv("path/to/your/file.csv", skip = 2)

# Select only specified columns:
data <- read_csv("path/to/your/file.csv", select = c(col1, col3))  # Using readr
data <- read.csv("path/to/your/file.csv", colClasses = c("NULL",NA,"numeric","NULL"))  # Base R

10. What if my data has a different encoding than UTF-8?

Answer: Use the fileEncoding parameter in read.csv() or encoding parameter in read_csv() from the readr package.

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