Importing data is a crucial step in any data analysis project, and R offers a wide range of tools and packages to facilitate this process. Whether you have data in CSV, Excel, JSON, HTML, databases, or other file formats, R provides efficient methods to load and manipulate data. In this tutorial, we will explore various techniques for importing data into R, focusing on the most commonly used data types and formats.

Getting Started with R

Before we dive into the data importing process, let’s ensure you have R and the required packages installed. If you haven’t already, download and install R from the official website and RStudio, a popular integrated development environment for R.

To perform data analysis and import data in R, we will be using several packages, including readr, tidyverse, RSQLite, jsonlite, XML, and quantmod. Ensure you have these packages installed by running the following code:

install.packages(c('readr', 'tidyverse', 'RSQLite', 'jsonlite', 'XML', 'quantmod'))

Importing Data from CSV and TXT Files

Using read_csv from readr package

One of the most common file formats for data storage is CSV (Comma-Separated Values). To import data from a CSV file into R, we can use the read_csv function from the readr package:

data <- read_csv('data/hotel_bookings_clean.csv')

Alternative: Using read.table

Alternatively, you can use the read.table function to import CSV files:

data <- read.table('data/hotel_bookings_clean.csv', sep=",", header = TRUE)

Importing a TXT File

For importing text files, such as .txt files, we can use the readLines function to load the file and then convert it into a dataframe:

data <- read.table('data/drake_lyrics.txt', header = FALSE)

Importing Data from Excel and JSON Files

Importing Data from Excel

To import data from Excel files, we’ll use the read_excel function from the readxl package:

data <- read_excel("data/Tesla Deaths.xlsx", sheet = 1)

Importing Data from JSON

JSON (JavaScript Object Notation) is a widely used data format for web APIs and web applications. To import JSON data into R, we can use the fromJSON function from the jsonlite package:

json_data <- fromJSON(file = 'data/drake_data.json')
data <-[1])

Importing Data from Databases and XML/HTML Files

Importing Data from a Database using SQL

To import data from a database, such as SQLite, MySQL, or PostgreSQL, we’ll use the DBI package along with the respective database-specific packages. Let’s see an example of importing data from an SQLite database:

conn <- dbConnect(RSQLite::SQLite(), "data/mental_health.sqlite")
data <- dbGetQuery(conn, "SELECT * FROM Survey")

Importing XML Data

R provides various packages to work with XML data. For example, we can use the xml2 package to import and parse XML data:

plant_xml <- read_xml('')
plant_nodes <- xml_find_all(plant_xml, "//PLANT")
data <- xmlToDataFrame(nodes = plant_nodes)

Importing HTML Tables

To import HTML tables from web pages, we can use the rvest package, which allows us to scrape web data easily:

url <- ""
html_data <- read_html(url)
tables <- html_nodes(html_data, "table")
data <- html_table(tables[25])

Importing Less Commonly Used Data Types

Importing Data from SAS and SPSS Files

For importing data from SAS and SPSS files, we can use the haven package:


# Importing SAS File
data_sas <- read_sas('data/lond_small.sas7bdat')

# Importing SPSS File
data_spss <- read_sav('data/airline_passengers.sav')

Importing Data from Matlab Files

To import data from Matlab files (.mat) into R, we’ll use the R.matlab package:

data_matlab <- readMat("data/cross_dsads.mat")

Importing Large Datasets

Importing large datasets can be challenging, but R provides efficient solutions. Let’s explore some methods for handling large data:

Using fread from data.table package

The fread function from the data.table package is specifically designed for fast and memory-efficient data import. It can handle large datasets with ease:

data_large <- fread("data/US_Accidents_Dec21_updated.csv")

Using read.table.ffdf from ff package

The ff package allows us to import large datasets as well. We can use the read.table.ffdf function to read data in chunks:

data_large <- read.table.ffdf(file = "data/US_Accidents_Dec21_updated.csv", nrows = 10000, header = TRUE, sep = ',')


In this tutorial, we have explored various methods for importing data into R from different file formats and data types. R’s flexibility and powerful packages make it a valuable tool for data analysis, especially when dealing with diverse data sources. By mastering these data importing techniques, you can streamline your data analysis workflow and gain deeper insights from your datasets.

Remember to install the required packages and experiment with different datasets to become proficient in importing data into R. Happy data importing and analyzing!


Can I import data from Excel files into R?

Yes, you can import data from Excel files into R. R provides the read_excel function from the readxl package, which allows you to read data from Excel files (.xlsx) and load it into R as a dataframe. You can specify the sheet name or number, and the function will extract the data from the chosen sheet. This makes it easy to work with Excel data in R for further analysis.

Is it possible to import data from JSON files in R?

Yes, it is possible to import data from JSON files in R. R has the fromJSON function from the jsonlite package, which enables you to read JSON data and convert it into R data structures, such as lists or data frames. This makes it convenient to work with JSON data obtained from web APIs or other sources in R for analysis and visualization.

How can I load data from a database using SQL in R?

You can load data from a database using SQL in R by using the DBI package along with the database-specific packages. For example, to load data from an SQLite database, you can use the RSQLite package. First, create a connection to the database using the dbConnect function, then use dbGetQuery to execute your SQL query and retrieve the data into R as a dataframe. This allows you to perform data analysis on large datasets stored in databases directly within R.

What are the steps to import data from XML and HTML files in R?

To import data from XML and HTML files in R, you can use the xml2 package for parsing XML and the rvest package for web scraping. For XML data, use the read_xml function to read the file, then use xml_find_all to extract specific elements from the XML file. To import HTML tables from web pages, use the read_html function from rvest to read the HTML, then use html_nodes and html_table to extract the desired tables. These steps allow you to efficiently import and work with XML and HTML data in R.

Are there any less commonly used data formats like SAS, SPSS, and Matlab that can be imported into R?

Yes, R supports importing less commonly used data formats such as SAS, SPSS, and Matlab files. The haven package provides functions like read_sas and read_sav to read SAS and SPSS files, respectively. For Matlab files, the R.matlab package offers the readMat function. These packages allow you to import data from these specialized formats into R for further analysis and integration with R’s powerful statistical tools.


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