In addition to reviewing this Page, you might find it useful to read my "Getting Started in R" document, which offers useful background on R and RStudio, as well as step-by-step instructions for getting up and running.

Download R (link provides a list of sites that host the R software; try Duke or Washington University) and RStudio -- R is a free (open source) software environment for statistical computing and graphics. RStudio is an integrated development environment that is easier to operate in than R and is perhaps the most popular way for people to use R. You'll need to download both, but my recommendation is to use RStudio. This blog post provides a helpful talk through of both R and RStudio.

Although working in RStudio and learning/using R code directly may be the most gratifying, it can also be somewhat challenging. If you're struggling with learning the coding language, you can explore some of the Graphical User Interfaces (GUIs) that have been developed for R. They are reviewed here, with links to their respective reviews and sites. Some of them offer more traditional point-and-click menu systems, along with the ability to see the corresponding R code. You may find BlueSky (mirrors SPSS) and R Commander to be the most familiar. In the end, though, I recommend trying to make a go of it with RStudio. There's value in learning to work with the code directly.

Here's a good walk-through of how to get started with R and RStudio.

Generic R Commands

You can find my generic R scripts (Word documents) for various tests here.

Helpful Resources

These are some readings and websites that I found to be quite useful in getting to understand and learn R.

Very Useful Packages

R makes use of "packages" that users write to either accomplish new tasks or accomplish old tasks better. There are thousands of packages. Here are some of the packages that you are likely to make heavy use of.

tidyverse
car
haven
psych
ggplot2
dplyr

Data Visualization with R

Much of what people are looking to do with data is to understand it better through visualization. The following resources will help you develop proficiency with data visualization in R.

The R Graph Gallery -- This website contains an array of graphs and the coding behind them.
Plotly Graph Gallery -- This website contains an array of plotly produced graphs and the coding behind them.
UCLA IDRE slide presentation on ggplot2
ggplot2 cheat sheet
Hadley Wickham's (creator of ggplot2) book (online version)
Kieran Healy's Data Visualization: A Practical Introduction. An online version of the book can be found here (click on the menu at the top).
Winston Chang's R Graphics Cookbook (free online version).
Michael Freeman (CC sociology alum) and Joel Ross, Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. An online version that appears to be similar to the book can be found here.
Shiny -- An R package that makes it easy to build interactive web apps straight from R.
Lovelace et al., "Introduction to Visualizing Spatial Data in R"
I like this site and his list of resources.