Hey guys, welcome back to my R-tips newsletter. As the R ecosystem continues to grow, staying updated with the most powerful and innovative packages can significantly enhance your data analysis workflow. Today I’m sharing a curated list of 25 R packages that you should master in 2024. Let’s go!

Table of Contents

Here’s what you’re learning today:

• Top 25 R Packages You Need To Learn: A curated list of 25 R packages that you should master in 2024.
• Next Steps: Join the R-Tips Newsletter where Matt will teach every one of the Top 25 R packages. I have 25 new R code tutorials coming in 2024 and 2025. Get ready!

Join the R-Tips Newsletter for a Deep Dive into the Top 25 R Packages in 2024 and 2025

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R-Tips Weekly

This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks. Pretty cool, right?

Here are the links to get set up. 👇

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Top R Packages to Learn in 2024: A Curated Guide

1. Janitor: Simplifying Data Cleaning

The janitor package is a must-have for anyone who works with messy data. Its intuitive functions like clean_names() help you quickly clean column names, making them consistent and easy to work with. The community praises this package for its “quality of life” enhancements that streamline data cleaning processes.

Highlight:

• clean_names(): Automatically cleans and standardizes data frame column names, turning them into snake_case, making them easier to manage and less error-prone.

Learn More: Janitor

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2. Skimr: Quick Data Summarization

If you’ve ever wished for a tool that could give you a quick summary of your data without diving deep into code, skimr is your answer. This package is an upgrade from the traditional glimpse(), offering a more comprehensive overview of your data.

Highlight:

• skim(): Provides a well-organized summary of your data, including the number of missing values, data types, and summary statistics for each column.

Learn More: Skimr

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3. bslib: Next-Gen UI for Shiny Apps

bslib is revolutionizing the way we create Shiny dashboards. It’s considered the next generation of shinydashboard, offering a more modern and customizable user interface.

Highlight:

• Themes: bslib allows you to create clean and modern UIs with built-in theming options, making your Shiny apps not only functional but also visually appealing.

Learn More: BSLib

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4. box: Modularize Your R Scripts

For those looking to write more maintainable and modular R code, box is a game-changer. This package lets you import specific functions from libraries, avoiding the pitfalls of loading entire libraries and potentially causing function conflicts.

Highlight:

• Modularization: Use box to import only the functions you need, leading to cleaner, more efficient code.

Learn More: Box

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5. data.table & tidytable: High-Performance Data Manipulation

When it comes to data manipulation, data.table is a powerhouse, known for its speed and efficiency. For those who prefer dplyr syntax but want the speed of data.table, tidytable offers the best of both worlds.

Highlight:

• data.table: Extremely fast data manipulation, especially for large datasets.
• tidytable: Combines the simplicity of dplyr with the power of data.table.

Learn More:

• Data.Table
• Tidytable

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6. renv: Reproducibility Made Easy

Package management and reproducibility are critical aspects of any data project. renv helps by keeping track of the packages and versions used in a project, ensuring that your work is reproducible across different environments.

Highlight:

• Project-specific libraries: renv creates isolated environments for your projects, storing package versions locally, making your scripts more portable and reproducible.

Learn More: Renv

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7. targets: Pipeline Management for Reproducible Workflows

For those dealing with complex workflows and large-scale simulations, the targets package is a lifesaver. It helps manage and automate data analysis pipelines, ensuring reproducibility and efficiency.

Highlight:

• Pipeline management: targets automates the running of analysis pipelines, making it easy to manage dependencies and ensure reproducibility.

Learn More: Targets

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8. naniar: Visualizing Missing Data

Handling missing data is a common challenge in data analysis, and naniar provides an elegant solution. The package offers powerful visualization tools to help you understand and manage missing values in your datasets.

Highlight:

• vis_miss(): Quickly visualize the pattern of missingness across your dataset, making it easier to decide how to handle missing data.

Learn More: naniar

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9. mlr3: Advanced Machine Learning

For machine learning practitioners, mlr3 offers a modern, object-oriented framework that simplifies the creation of complex machine learning pipelines.

Highlight:

• Flexibility: mlr3 is designed with a flexible architecture that supports a wide range of machine learning tasks, from basic classification to advanced hyperparameter tuning.

Learn More: MLR3

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10. gt: Making Professional Tables

Creating high-quality, publication-ready tables is made easy with the gt package. Whether you’re working on reports, presentations, or scientific papers, gt provides a flexible and powerful way to design and format tables.

Highlight:

• Table formatting: gt allows you to create visually appealing tables with detailed control over styling, making it an essential tool for presenting your data professionally.

Learn More: GT

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11. GWalkR: Tableau-Like Visualizations in R

For those who love the interactivity and visual power of Tableau, GWalkR brings similar capabilities into the R environment. It enables exploratory data analysis with interactive graphics directly from R, making it easier to understand complex data through visual exploration.

Highlight:

• Interactive visualizations: GWalkR allows users to create interactive, drag-and-drop visualizations, similar to Tableau, all within R, enhancing the exploratory data analysis process.

Learn More: GWalkR

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12. torch: Deep Learning in R

The torch package brings the power of deep learning to R. As an R implementation of the PyTorch framework, torch allows you to build and train deep neural networks, opening up a whole new world of machine learning possibilities within the R ecosystem.

Highlight:

• Deep learning: torch provides a flexible and efficient way to implement and train neural networks directly in R, integrating seamlessly with other R tools.

Learn More: Torch for R Users

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13. Plumber: Build APIs in R

Plumber is a powerful package that allows you to turn your R scripts into APIs effortlessly. Whether you’re deploying a model or sharing a data analysis pipeline, Plumber makes it easy to create web APIs from R, facilitating integration with other systems.

Highlight:

• API creation: With Plumber, you can build RESTful APIs directly from R scripts, enabling easy deployment and integration of R functionalities in web applications.

Learn More: Plumber

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14. Vetiver: Model Deployment in R and Python

For data scientists working in both R and Python, Vetiver offers a unified framework for model deployment. It simplifies the process of making models available for production, ensuring that your work can be easily integrated into real-world applications.

Highlight:

• Cross-language deployment: Vetiver supports both R and Python, providing a seamless way to deploy machine learning models across different environments.

Learn More: Vetiver

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15. fs: Efficient File System Operations

The fs package simplifies working with the file system in R. It provides a consistent and user-friendly interface for tasks like file management, directory navigation, and handling file paths, making it easier to automate workflows involving files.

Highlight:

• File system management: fs streamlines operations such as reading, writing, and organizing files, improving efficiency in handling file-based tasks.

Learn More: FS

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16. correlationfunnel: Turn Correlations into Insights

correlationfunnel is designed to help you understand the relationships between variables in your data. It provides tools to quickly visualize and analyze correlations, turning them into actionable insights that can drive decision-making.

Highlight:

• Correlation analysis: correlationfunnel helps you identify and visualize important correlations, enabling you to uncover hidden patterns and trends in your data.

Learn More: Correlation Funnel

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17. clock: Super-Powered Date and Time Handling

clock takes date and time manipulation to the next level, offering a robust alternative to the popular lubridate package. It provides more powerful and precise tools for working with dates and times, making it a valuable addition to any R user’s toolkit.

Highlight:

• Date and time manipulation: clock offers advanced features for handling dates and times, providing greater flexibility and accuracy than traditional methods.

Learn More: Clock

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18. furrr: Parallelized Iterative Processing

The furrr package combines the power of purrr with the parallel processing capabilities of the future library. It enables you to perform iterative tasks in parallel, significantly speeding up computations.

Highlight:

• Parallel processing: furrr allows you to map functions over lists or vectors in parallel, leveraging multiple CPU cores for faster execution.

Learn More: Furrr

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19. patchwork: Combine Multiple Plots

When you’re working with multiple ggplot2 plots, patchwork makes it easy to combine them into a single cohesive graphic. This package is perfect for creating complex layouts and multi-panel plots.

Highlight:

• Plot composition: patchwork provides an intuitive syntax for arranging and combining ggplot2 plots into one unified visualization.

Learn More: Patchwork

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20. echarts4r: Interactive Visualizations

echarts4r brings the power of Apache ECharts to R, enabling the creation of highly interactive and customizable visualizations. This package is perfect for users who need dynamic and responsive charts for web applications or dashboards.

Highlight:

• Interactive charts: echarts4r offers a wide range of interactive chart types, all customizable and designed for seamless integration into web-based platforms.

Learn More: echarts4R

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21. officer: Generate Microsoft Office Documents

officer is your go-to package for creating and editing Microsoft Word and PowerPoint documents directly from R. This package is essential for automating the generation of reports and presentations.

Highlight:

• Document automation: officer allows you to programmatically create and customize Word documents and PowerPoint presentations, streamlining the process of report generation.

Learn More: Officer

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22. golem: Production-Grade Shiny Applications

golem is an opinionated framework for building robust, production-ready Shiny applications. It enforces best practices and provides a structured environment that ensures your applications are maintainable and ready for deployment.

Highlight:

• Application development: golem provides tools and a standardized framework to help you develop Shiny apps that are reliable, maintainable, and scalable.

Learn More: Golem

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23. rhino: Fullstack Shiny Development

rhino is designed for developers who want to create Shiny applications with a fullstack software engineering approach. It supports modern development practices like version control, testing, and CI/CD pipelines, making it a powerful tool for building complex applications.

Highlight:

• Fullstack development: rhino helps you apply fullstack development principles to Shiny apps, ensuring they are robust, scalable, and production-ready.

Learn More: Rhino

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24. ROI: R Optimization Infrastructure

ROI provides a unified framework for optimization in R, supporting various optimization problems and solvers. Whether you’re working on linear programming, integer programming, or other optimization tasks, ROI offers a flexible and comprehensive solution.

Highlight:

• Optimization: ROI simplifies the implementation of optimization tasks in R, providing a consistent interface for various solvers and problem types.

Learn More: ROI

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25. mapgl: Next-Level Mapping with Mapbox GL and MapLibre GL

mapgl makes the latest versions of Mapbox GL JS and MapLibre GL JS available to R users. This package is designed to bring powerful mapping capabilities into your R projects while maintaining a familiar interface for users accustomed to other R mapping packages.

Highlight:

• Advanced mapping: mapgl provides access to cutting-edge mapping libraries, enabling the creation of highly interactive and customizable maps within R, perfect for spatial data analysis and visualization.

Learn More: MapGL

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Conclusions:

These 25 R packages are not just tools but critical components of a modern data scientist’s toolkit. Whether you’re cleaning data, building Shiny apps, creating professional tables, or managing complex workflows, these packages will help you stay ahead of the curve in 2024. Dive into these packages and elevate your R programming skills to new heights!

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