• R is a programming language

  • Like MATLAB, Python, Java, C, and others

• R is interpreted, meaning that commands are processed and run line by line, which is often more user-friendly

• Interpreted languages often run (much) more slowly 🐌 than compiled languages (e.g., C and C++).

• : great options for machine learning, web scraping, etc. (but R has options for these endeavors as well!)

• : good for stability of code, popular in certain industries (e.g., healthcare 🔬💊, agriculture 🌱, finance 💸)

Other software tools do not require programming:

, ,

Why use R?

• Reproducibility

• Open-source (free)

• Effective data visualizations

• Popular in academia

R = (R Core) + (R Packages)

• R packages 📦 are collections of code that extend or improve R’s functionality.

• Most R packages are available to download at CRAN (Comprehensive R Archive Network)

• Newer, rapidly-changing packages may be available on GitHub (which we will learn more about soon)

Resources from the Big Book of R, a collection of R-related (mostly free) online books, blog posts, and other resources:

• Sport analytics 🏀

• Text and Natural Language Processing 🗣️

• Data Journalism 📰

• Social Sciences 🌐

• Life Sciences ⚕️

• Linear Algebra for Data Science 🔢

• : programming language with a focus on data analytics and visualization

• : tool for dynamic and reproducible data analysis reports that integrates formatted text with R code and output

• R Markdown allows you to combine formatted text with R code and its resulting output.

• Possible output formats:

Documents: , ,

Presentations: , , \(\LaTeX\) /

Many more!

• There are many ways R Markdown documents can be customized / used

• Posit (the company that owns RStudio) has a gallery displaying some neat uses of R Markdown

• Projects I made with R Markdown on my website

Let’s get started learning more about R and R Markdown with an activity!