R Programming with AI and Machine Learning: What You Need to Know

R is a programming language that specializes in data analysis, statistics, and data visualization. R and Python are the two preferred languages for data analysts and data scientists. In addition to being great for data, R also has a rich set of tools for artificial intelligence.
Let’s explore the tools that are available, and look at how you can add AI to your R programming skills.
Start by Learning AI Concepts
Before you start using the AI packages and tools available for R, you’ll need to spend some time learning AI concepts. With R, the most important concepts are:
Also, we want to draw attention to one type of ML you’ll definitely need to learn: gradient boosting. This is an advanced concept that involves training models in sequence, with each training becoming subsequently better than the previous. It’s used a lot in AI with R.
Pro tip: ChatGPT is also an excellent place to learn about such topics. It knows a great deal about its own technologies. For example, you can ask it, “Please provide me with a basic introduction to machine learning concepts,” then you can continue asking it for more advanced concepts are you go. Of course, we all know that ChatGPT has been known to make mistakes; it’s always helpful to double-check its output against your own web findings.
Interactive AI with R
R is an excellent tool for working interactively with data, running analyses, and visualizing data. If you also want to work interactively with AI, you’ll need to learn some basic packages first. (Note that while other languages use terms like libraries, R uses the term packages.)
- Data Cleaning Packages: When you’re working with data (whether with AI or not), your data needs to be cleansed. Data from random sources and multiple pipelines can have problems that will skew your analysis and anything you build from it (one common problem is empty values, which would likely be interpreted as zeros). A good package to learn here would be dplyr. (Note that dplyr is more than just a data cleanser, as it’s also for filtering, selecting, arranging, mutating, and summarizing data.)
- Data Visualization Packages: Because R is so popular for data visualization, there are many options here. Some of the most popular are
After learning these, you’re ready to move on to machine learning. Here are some libraries to learn:
- Caret: This is probably the most popular ML library for R. It’s for building predictive models. It’s easy to use and has a complete set of features. (Note that the page we’re linking to includes some great books and other resources for learning about Caret and similar libraries.)
- Mlr3: This is a newer machine language library. It’s object oriented and is quite easy to use. The creators even wrote a nice online book about it.
- Gradient Boosting: As mentioned, this is an important part of AI in R. There’s a library built specifically for it called XGBoost.
Deep learning is a specialized type of machine learning. The three most important libraries for deep learning are:
- Keras: This is probably the first of the three to learn, as it actually provides a simplified way to work with the next on the list, TensorFlow. Keras has grown in popularity, and many people prefer it over both TensorFlor and Torch.
- TensorFlow: This is a machine language and AI framework created by Google. The R documentation we’ve linked to is excellent with a full installation guide and plenty of examples.
- Torch: Torch is an older library for machine learning. It was originally created in 2002, and the developers have continued to expand it. Today it conforms with the latest AI tools. It’s mostly used in research and educational settings.
Each of these are accessible from many different languages, including R (the links we’ve provided for each are for the R packages). We recommend that you start with Keras and then learn TensorFlow; if you have colleagues who use Torch, you might then learn that one as well.
Natural language processing (NLP) refers to the process of reading and producing language that sounds like it came from a real person. If you’ve used ChatGPT’s various features and noticed how it sounds (mostly) human, you’ve witnessed NLP. There are several packages you can learn for NLP. Here are some important ones:
- tm (lowercase, stands for text mining): This is a package for processing text documents in various formats, including plain text and PDF. Although not technically an AI package, it’s useful for preprocessing documents such as removing extra whitespace before sending it into a NLP package. It can also do something called term-document matrix creation–which is a fancy way of saying it can build a table of different words used across multiple documents.
- Quanteda: This is a popular and somewhat complex NLP package. It includes core NLP features, as well as preprocessing features like those found in tm. It even includes visualization capabilities to plot information about the text.
- NLP (yes, it stands for Natural Language Processing) is a rather complete NLP processing package. It provides tokenizing, language processing, annotation, and more. The documentation, which includes code examples, is available as a PDF download.
There are also more libraries related to NLP, including some for sentiment analysis and part of speech tagging. Geeks for Geeks has a pretty good page about it.
Computer vision is another area that’s important to AI and accessible from within R. In addition to torch and keras mentioned earlier, an important library for computer vision is called OpenCV. It’s written in C++, but you can access it from R through the opencv package.
Finally, we want to draw attention to a special package that lets you use Python libraries within an R app. It’s called reticulate. This opens up an entire world of AI tools that are normally exclusive to Python.
Production AI Apps Written in R
R is a bit unique compared to other languages in that it’s typically used interactively, most often inside the app called R Studio. However, you can also use it to build production apps. If you’re interested in releasing AI apps, you’ll want to learn about Shiny.
Shiny is a package that simplifies the process of building a web app within R. This means if you want to build a website, you can use R for the backend, rather than the more popular languages such as JavaScript, Java, and C#. This could be a huge benefit, because it means you can access all these R packages (not just the AI ones) from your backend that you’re accustomed to using if you’re an R programmer.
One cool aspect of Shiny is that it also includes a front end that you can use for building interactive data-oriented dashboards. That means you don’t have to learn a separate front-end framework.
Conclusion
R provides a rich environment for building data and statistic applications, in addition to AI applications. You’ll likely want to use R Studio, which is the de facto standard IDE.
Learning AI takes time, so don’t rush it. Spend time working through what we’ve covered here; there’s enough to cover several months of studying. Practice as you go, and soon you’ll be an AI expert with R, and will be in good shape to land a great job.
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