R for reproducible scientific analysis

Introduction to R and RStudio

Introduction to R and RStudio

R is a useful tool for scientific computing because

• it’s free, well-documented, and runs almost everywhere;
• it has a large (and growing) user base among scientists; and
• it has a large library of external packages available for performing diverse tasks.

We’ll be using RStudio: a free, open source R integrated development environment (IDE). It provides a built in editor, works on all platforms (including on servers) and provides many advantages such as integration with version control and project management.

Something to keep in mind as we use R is just one tool to conduct analyses efficiently and to do so in a way that is reproducible (by you and others). Thus, the goal here is not to teach you how to use R, but to use this software to teach you how to think about and analyze data.

Basic layout

When you first open RStudio, you will be greeted by three panels:

• The interactive R console (entire left)
• Workspace/History (tabbed in upper right)
• Files/Plots/Packages/Help (tabbed in lower right)

Once you open files, such as R scripts, an editor panel will also open in the top left.

Work flow within RStudio

You can type on the “command line” in the console window. However, just as with bash, you will want to save your code in a script. Saving code is easy in R Studio.

• Select File - New Project
• Select New Directory and Empty Project
  
• Give the project a name
  
• Browse to the subdirectory you would like to do your work

To keep track of the commands you are running in a script:

• Select File - New File - R Script

Now you have a fourth window to keep track of commands.

R Packages

It is possible to add functions to R by obtaining a package written by someone else. As of this writing, there are over 7,000 packages available on CRAN (the comprehensive R archive network). R and RStudio have functionality for managing packages:

• You can see what packages are installed by typing installed.packages()
• You can install packages by typing install.packages("packagename"), where packagename is the package name, in quotes.
• You can update installed packages by typing update.packages()
• You can remove a package with remove.packages("packagename")
• You can make a package you have installed available for use in your session with library(packagename)

Install the following packages: ggplot2, plyr, gapminder, curl if you have not done so already.