Top 15 R Libraries for Data Science in 2025

R is popular for Data Science, offering a range of libraries designed for specific tasks. These libraries support data manipulation, visualization, machine learning and specialized data processing, such as text and image handling. With its wide array of functions and tools, R enables efficient and effective analysis, making it a valuable resource for data scientists.

This article explores the Top 15 R libraries that are essential for data science in 2025, highlighting their key features and use cases.

Overview of Top R Libraries for Data Science

Whether you are a seasoned data scientist or just starting your journey, these libraries will provide you with the tools you need to handle tough data problems. Here’s a streamlined list of the Top R libraries for Data Science, along with brief descriptions of their functionalities and uses:

1. dplyr

dplyr is one of the most commonly used libraries for data manipulation, simplifying operations on data frames. It offers a set of core functions that make data wrangling faster and more intuitive. These functions can be combined with group_by() to perform operations on grouped data.

Key Features of dplyr:

• mutate(): Adds new columns based on existing data, allowing for easy feature engineering.
• select(): Picks specific columns by name, making it easy to focus on the most relevant data.
• filter(): Filters rows based on logical conditions, enabling you to subset your data quickly.
• summarise(): Reduces a dataset to summary statistics, great for aggregation and descriptive analysis.
• arrange(): Orders rows based on column values, simplifying sorting.

Best for : Data wrangling, filtering and summarization

2. ggplot2

ggplot2 is an R data visualization library that is based on The Grammar of Graphics. ggplot2 can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API. It also allows you to add different types of data visualization components or layers in a single visualization. Once ggplot2 has been told which variables to map to which aesthetics in the plot, it does the rest of the work so that the user can focus on interpreting the visualizations and take less time to create them.

Key Features:

• Easily combine different elements (geoms, stats, scales) in a single plot.
• ggplot2 provides a flexible framework for styling and customizing plots.
• Automatically maps data to visual properties like size, color and shape.
• Easily create multiple plots based on a factor variable, making it simple to visualize subgroup differences.

Best for : Creating complex, customizable plots

3. Esquisse

Esquisse is a data visualization tool in R that helps create detailed visualizations using the ggplot2 package. It supports a variety of charts, including scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, dendrograms, 3-D charts and more. Esquisse also allows users to export graphs or access the code used to generate them. Its drag-and-drop interface makes it popular and easy to use, even for beginners.

Key Features:

• Drag-and-drop functionality for easy chart creation
• Supports multiple chart types (scatter, bar, line, etc.)
• Export visualizations and view underlying code

Best for : Easy and quick visualizations for beginner

4. Shiny

Shiny is an R package used to build interactive web applications. It seamlessly combines R with modern web technologies, enabling users to create web applications without the need for specialized web development skills. With Shiny, you can embed applications in R documents, create standalone web apps or design web-based dashboards. Shiny apps can be deployed to the cloud or hosted on your own servers, available under both open-source and commercial licenses.

Key Features:

• Build interactive web apps easily
• Embed apps in R documents or host on the web
• Extend functionality with HTML, CSS and JavaScript

Best for : Building interactive dashboards and web apps

5. mlr3

mlr3 is an R package designed for machine Learning, enabling us to implement a variety of supervised and unsupervised models. We can apply techniques like classification, regression, support vector machines, random forests, nearest neighbors, naive Bayes, decision trees and clustering with mlr3. It also integrates with the OpenML R package which supports online machine learning.

Key Features:

• Supports a wide range of machine learning models
• Integration with OpenML for online resources
• Improved functionality over its predecessor, mlr

Best for : Implementing machine learning algorithms with hyperparameter tuning

6. Lubridate

Lubridate is an R library designed to simplify working with date-time data. Handling date-time can be challenging in R due to unintuitive commands and variations based on the type of date-time object. Lubridate introduces new time span classes that make it easier for us to perform mathematical operations on date-time data.

Key Features:

• Simplifies date-time manipulation with intuitive functions
• Handles components like seconds, minutes and years easily
• Offers time span classes for mathematical operations

Best for : Parsing, manipulating and converting date-time formats

7. RCrawler

RCrawler is an R package for domain-based web crawling and web scraping, allowing us to extract structured data from websites for various applications. It supports web structure mining, text mining and web content mining, among others. With RCrawler, we can automatically navigate through all pages on a website and extract the necessary data with a single command.

Key Features:

• Automated web crawling to gather structured data
• Single command to extract data from multiple pages
• Efficient parallel processing with concurrent nodes

Best for : Automated web crawling and scraping

8. knitr

Knitr is a tool for R users who wants to create dynamic reports. It allows us to integrate various types of code, such as Markdown, LyX, LaTeX, AsciiDoc and HTML, directly into our R code. This feature is especially helpful for researchers who need to transform their data analysis into a report. Knitr streamlines and automates the process, offering an upgrade over Sweave, another R function, by addressing some of its limitations.

Key Features:

• Combines code and text for dynamic reporting
• Supports multiple document formats (HTML, PDF, etc.)
• Streamlines the reporting process for researchers

Best for : Creating dynamic reports and documents (HTML, PDF, etc.)

9. DT

DT is an R package that provides an interface to the JavaScript library DataTables, allowing us to display R matrices and data frames as interactive tables. The key function in DT is datatable() which helps us create a data table for displaying R objects. We can also style our tables using CSS classes within DT.

Key Features:

• Creates interactive tables with sorting and searching
• Integrates seamlessly with R data frames and matrices
• Customizable with CSS for styling

Best for: Displaying data frames in a searchable, interactive table format

10. Plotly

Plotly is an open-source tool for creating graphs that works with R. It is an R package built on top of the Plotly JavaScript library, allowing us to create interactive visualizations. We can display these charts in Jupyter notebooks, web apps (via Dash) or save them as standalone HTML files. Plotly provides over 40 chart types, from basic scatter plots to more advanced visualizations like 3-D charts and contour plots.

Key Features:

• Supports over 40 types of charts and visualizations
• Open-source and integrates with R
• Easy to share in various formats (HTML, Jupyter notebooks)

Best for: Creating interactive charts and visualizations for reports

11. caret

caret is a tool designed for regression analysis and classification. It centers around the train function which examines the effect of resampling on tuning parameters for model performance. caret works with a variety of algorithms in both regression and classification tasks. It also generates tables and plots to provide insights and support during the model training process.

Key Features:

• Supports multiple algorithms for regression and classification
• Generates insightful tables and plots during training
• Facilitates model tuning and evaluation with resampling

Best for: Training and evaluating classification and regression models

12. ROCR

ROCR is an R package designed for evaluating and visualizing the performance of classification models. It helps create key metrics such as ROC (Receiver Operating Characteristic) curves and precision-recall curves, offering a clear assessment of model accuracy and effectiveness. We can use ROCR to improve the visual representation and understanding of classification model performance.

Key Features:

• Generates ROC and precision-recall curves for model evaluation
• User-friendly interface for easy analysis
• Provides clear insights into model performance

Best for: Visualizing performance of classification models (ROC curves, etc.)

13. Glmnet

glmnet is a widely-used R package for building regression models with regularization techniques like LASSO and elastic-net. It helps in selecting important variables, preventing overfitting and making linear and logistic regression models more understandable and effective. glmnet's flexibility extends to various types of regression tasks, making it a versatile tool for data analysts.

Key Features:

• Implements LASSO and elastic-net regularization
• Aids in variable selection and reduces overfitting
• Versatile for various regression tasks (linear and logistic)

Best for: Preventing overfitting in regression models

14. Markdown

Markdown simplifies the process of creating dynamic documents by seamlessly blending code, text and visual elements within a single document. With support for multiple output formats such as HTML, PDF and Word, it empowers users to generate reproducible research and reports effortlessly.

Key Features:

• Blends code, text and visuals in one document
• Supports various output formats (HTML, PDF, Word)
• Facilitates reproducible research and reporting

Best for : Reproducible research, mixing code, text and visuals

15. RSQLite

RSQLite is a tool for R users who need to work with SQLite databases. It allows us to manage, query and modify SQLite databases directly from R. RSQLite simplifies database handling in R, making it easier for data scientists and analysts.

Key Features:

• Simplifies database management within R
• Allows direct querying of SQLite databases
• Enhances data handling capabilities for R users

Best for: Managing SQLite databases directly within R

In this article, we discussed how R, with its libraries like dplyr, ggplot2 and Shiny, supported data analysis, visualization and machine learning, making it a reliable choice for data science tasks.