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Intro to R Programming

R is a powerful tool for data analysis and visualization; this guide will provide resources to get you started with this programming language!

Getting Started with R

Welcome to the R Programming LibGuide. If you are interested in learning R programming or need additional resources while using R, this LibGuide can help you get started.

The R workshops held at the Leatherby Libraries will introduce you to the fundamentals of R programming. The workshops are designed for absolute beginners or for those looking for basic refreshers. No prior programming knowledge is required to attend these workshops.

What is R?

R is a free & open-source programming language supported by the R Project for Statistical Computing.

R is a powerful tool for data manipulation, analysis, and graphics. 
R is versatile across operating systems (Windows, macOS, Linux, and Unix). R code is great for collaborative work, as it is reproducible & shareable!

Why learn R?

R is used by statisticians, data miners, data analysts, and researchers in a variety of fields: business analytics, scientific research, software/application development, statistical reporting, and many more.

Compared to other statistical programs, R gives you the freedom to program new statistical methods in a straightforward manner. You can calculate advanced statistics not available in other software statistical packages, along with advanced graphics capabilities. 

The caveat of R can be the steep learning curve, as there is no one comprehensive guide and no commercial support, but R programming will become easier with more practice. Additionally, R utilizes more memory and have slower processing speed compared to other programming languages, so if you are processing extremely large data sets, you may need to remote into high-performance computing (HPC) clusters.

How R Works

The analysis process in R is highly interactive: it is a cycle of running a command, and processing the results through another command until the data is fully analyzed or plotted. In a statistical software, you may run an analysis with set parameters; you then sift through the output to extract the required results. Meanwhile, R processes parameters step-by-step, and is more flexible and customizable for complex statistical analyses.