I recently wrote a 80-page guide to how to get a programming job without a degree, curated from my experience helping students do just that at Springboard, a leading data science bootcamp. This excerpt is a part where I focus on an overview of the R programming language.

Description: R is an open-source programming language most often used by statisticians, data scientists, and academics who will use it to explore large data sets and distill insights from it. It offers multiple libraries that are useful for data processing tasks. Developed by John Chambers and other colleagues at Bell Laboratories, it is a refined version of its precursor language S.

It has strong libraries for data visualization, time series analysis and a variety of statistical analysis tasks.

It’s free software that runs on a variety of operating systems, from UNIX to Windows to OSX. It runs on the open source license of the GNU general public license and is thus free to use and adapt.

Salary: Median salaries for R users tend to vary, with the main split being the difference between data analysts who use R to query existing data pipelines and data scientists who build those data pipelines and train different models programmatically on top of larger data sets. The difference can be stark, with around a $90,000 median salary for data scientists who use R, vs about a $60,000 median salary for data analysts who use R.

Uses: R is often used to analyze datasets, especially in an academic context. Most frameworks that have evolved around R focus on different methods of data processing. The ggplot family of libraries has been widely recognized as some of the top programming modules for data visualization.

Differentiation: R is often compared to Python when it comes to data analysis and data science tasks. Its strong data visualization and manipulation libraries along with its data analysis-focused community help make it a strong contender for any data transformation, analysis, and visualization tasks.

Most Popular Github Projects:

1- Mal

Mal is a Clojure inspired lisp interpreter which can be implemented in the R programming language. With 4,500 stars, Mal requires one of the lowest amount of stars to qualify for the top repository of a programming language. It speaks to the fact that most of the open-source work done on the R programming language resides outside of Github.

2- Prophet

Prophet is a library that is able to do rich time series analysis by adjusting forecasts to account for seasonal and non-linear trends. It was created by Facebook and forms a part of the strong corpus of data analysis frameworks and libraries that exist for the R programming language.

3- ggplot2

Ggplot2 is a data visualization library for R that is based on the Grammar of Graphics. It is a library often used by data analysts and data scientists to display their results in charts, heatmaps, and more.

4- H2o-3

H2o-3 is the open source machine learning library for the R programming language, similar to scikit-learn for Python. It allows people using the R programming language to run deep learning and other machine learning techniques on their data, an essential utility in an era where data is not nearly as useful without machine learning techniques.

5- Shiny

Shiny is an easy web application framework for R that allows you to build interactive websites in a few lines of code without any JavaScript. It uses an intuitive UI (user interface) component based on Bootstrap. Shiny can take all of the guesswork out of building something for the web with the R programming language.

Example Sites: There are not many websites built with R, which is used more for data analysis tasks and projects that are internal to one computer. However, you can build things with R Markdown and build different webpages. You might also use a web development framework such as Shiny if you wanted to create simple interactive web applications around your data.

Frameworks: The awesome repository comes up again with a great list of different R packages and frameworks you can use. A few that are worth mentioning are packages such as dplyr that help you assemble data in an intuitive tabular fashion, ggplot2 to help with data visualization and plotly to help with interactive web displays of R analysis. R libraries and frameworks are some of the most robust for doing ad hoc data analysis and displaying the results in a variety of formats.

Learning Path: This article helps frame the resources you need to learn R, and how you should learn it, starting from syntax and going to specific packages. It makes for a great introduction to the field, even if you’re an absolute beginner. If you want to apply R to data science projects and different data analysis tasks, Datacamp will help you learn the skills and mentality you need to do just that — you’ll learn everything from machine learning practices with R to how to do proper data visualization of the results.

Resources: R-bloggers is a large community of R practitioners and writers who aim to share knowledge about R with each other. This list of 60+ resources on R can be used in case you ever get lost trying to learn R.

I hope this is helpful for you! Want more material like this? Check out my guide to how to get a programming job without a degree.