R Packages for AI and Machine Learning

Written by Coursera Staff • Updated on

Learn about the various packages available in R to help with your artificial intelligence and machine learning projects.

[Featured Image] A programmer explores R packages while he sits at his computer in his office.

Programming languages offer tools known as libraries and packages to help simplify the process of performing programming tasks. A library provides you with a collection of already written code, so you can spend less time building certain areas of your program. The libraries contain packages that allow you to efficiently complete standard or commonly occurring parts of applications. As a result, you can direct your energy and effort away from the basic building blocks and toward the specifics of a project. R offers packages that are great for data analysis, understanding insights from machine learning applications, and more.

What is R?

An open-source programming language, R is particularly prevalent in statistical computing and data analysis. Compared to other programming languages, R truly stands out for its capabilities in statistics, with several tools available in this space, as well as for data visualizations. You can access R on various operating systems, including Windows, Linux, and macOS. You may also download R as RStudio, an integrated development environment that allows you to input files from different sources.

Professionals in various fields use R, namely data analysts, quantitative analysts, data scientists, and statisticians working in industries such as health care, finance, manufacturing, and academia. R is gaining traction in the world of artificial intelligence as well.

Why choose R for AI and machine learning?

R offers a wide range of programming benefits, even though it may not be the most commonly used programming language for artificial intelligence. R has several packages and libraries to assist with the development of artificial intelligence. Since it is known for its strong data analysis capabilities, this feature becomes useful when developing machine learning models. Some of the specific areas of AI where you can utilize R for building applications include computer vision, natural language processing, and predictive modeling. Another added benefit of programming with R is that you can find a large community of users online to provide support if you have questions or encounter any problems.

10 R packages for AI and machine learning

You can access various R packages and libraries to help you develop your artificial intelligence projects, including the following:

1. Tidyverse

The Tidyverse is a library that includes several useful packages such as ggplot2, which gives you the ability to graph your data, and tidyr for cleaning data. 

2. Gmodels

Gmodels is an R package designed for model fitting. Model fitting allows you to measure your machine learning model’s ability to adjust to the training data it receives accurately.

3. OneR

With the OneR package, you can utilize the One Rule machine learning classification algorithm to find the class that most frequently features a specific value.

4. Ranger

Ranger is a package that allows you to analyze data efficiently and create prediction trees based on regression, classification, and probability from high-dimensional data.

5. iml

Short for interpretable machine learning, iml provides you with the necessary tools to analyze machine learning models better to understand the model's features and their influence.

6. tm

The tm package allows you to perform text mining through its frameworks. With tm, you can read data in both .doc files and .txt files.

7. XGBoost

XGBoost, or extreme gradient boosting package, is a package that makes it possible to build predictive models at scale. It’s also useful in areas such as classification and regression.

8. pdp

With the partial dependence plots (pdp) package in R, you can build plots from different types of machine learning models to demonstrate partial dependence—or marginal effect.

9. H2O

H2O allows you to create several different machine learning algorithms and supports supervised and unsupervised learning. It’s important to note that if you’re using R version 3.1.0, you will need to upgrade to a later version to use H2O.

10. vip

The vip package in R helps create plots that visualize variable importance. Vip works with various machine learning models and has features to assist with supervised learning algorithms.

Alternative programming languages for artificial intelligence

The right programming language for AI depends on the specific area of artificial intelligence you’re working on. For example, data processing and machine learning are two important areas of AI development, each with its own unique demands. In some applications of artificial intelligence, other languages are superior to R. Therefore, based on the goal of your project and the task you are working on, several different programming languages can be useful for artificial intelligence. Here’s a quick look at some other programming languages outside of R that you can use for AI development:

  • Lisp: One area where the Lisp programming language differentiates itself within artificial intelligence is its ability to help you create prototypes quickly. However, Lisp offers fewer libraries compared to most popular AI programming languages.

  • C++: C++ offers libraries for deep learning and machine learning, making it a great option for AI projects. Other strengths of C++ include computer vision and neural network applications.

  • Python: Python is especially popular for artificial intelligence for several reasons. Not only is Python filled with libraries for machine learning, but it’s also one of the most learnable languages, which makes it a good option for those new to programming. Additionally, it’s in high demand by employers.

  • Java: Another option for AI programming is Java, which also offers several libraries to assist with your AI projects. One area where Java specializes is mobile application development, enabling you to implement artificial intelligence into your applications. 

Getting started with Coursera

On Coursera, you can find highly-rated courses to learn more about programming with R, artificial intelligence, and machine learning. Data Analysis with R Specialization from Duke University can help you learn how to use R and RStudio, analyze data, create visualizations, and utilize regression models.

If you’re interested in discovering more about building machine learning models, Advanced Learning Algorithms from DeepLearning.AI covers building and training neural networks and will give you an opportunity to gain skills with the XGBoost package.

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