# 2 Setting Up

## 2.1 R and R Studio

To begin using R, you must first download it here: https://www.r-project.org/

I also recommend using R Studio, which keeps all of your R-related materials easily accessible and visible in one screen. You can download it here: https://rstudio.com/

Once both are downloaded, open R Studio. You will have 4 quadrants in your R Studio workspace.

## 2.2 R Scripts

If you’ve worked with other statistical software or done any coding, you know the importance of having a reproducible document that contains all of your code, along with annotations. In R, this is called an R script.

When you open R Studio for the first time, you will need to start a new R script. Go to File > New File > R Script. R scripts will appear, by default, in the top left quadrant of R Studio. You can have multiple R scripts open at one time, all running on the same R session.

Note: You can also use R Markdown. I personally prefer using R scripts, especially if you are new to R. I also highly recommend taking advantage of multiple R scripts (e.g. separate scripts for data cleaning and analysis, etc).

Using an R script ensures that you remember what you did during analysis and that your analysis is replicable.

R scripts are color-coded. You can write notes using a “#”. Like an academic paper, R scripts should have a heading that includes the title of your project, your name, the date you last updated the R script, and a short description of what the R script does.

# Title
# Date last updated
# Description

## 2.3 R Console

R scripts represent the “input” for R. The “output” is returned in the console, which is the bottom left quadrant of R studio.

To run commands from an R script in the console, highlight the code and press Cmd+Return (on a Mac) or Ctrl+Enter (on a PC).

Note: You can type code directly into the console, but it’s not reproducible. It’s best practice to type all of your code in a script.

## 2.4 Working Directories

For any project class, assignment, etc. for which you’re using R, you should have a designated working directory. A working directory is a folder on your computer that contains everything you need for analysis, including R script(s) and data. Your R Script should live in your working directory (make sure you save it there! File > Save).

You will need to set your working directory within your R Script using the working directory’s file path. This is so that R can locate the files you’re using in your analysis.

Note: To get the file path for a folder on a Mac, right-click on the folder, press Option, and click on “Copy ‘Folder’ as Pathname.” To get the file path on a PC, click on the “Home” tab and then on the icon that says “copy path.”

setwd("/Users/amyjohnson/Box Sync/Stanford/SSDS/R Training PUBLIC")

If you are unsure of whether you have the correct working directory, you can always check your working directory using:

getwd()

## 2.5 A brief note on functions in R

The basic format for functions on R is this:

function(input)

You will see the output in your R console. You can save the output as an R object like this:

output <- function(input)

## 2.6 Installing packages

One of the best things about R is the variety of packages that are available to use. Packages are extensions of R’s basic software.

The first time you use an R package, you have to install it. You only need to install each package once. (Note the quotation marks!)

install.packages("tidyverse")

After the package is installed, you will have to tell R you want to use the package during this session by adding it to your library. (Note the lack of quotation marks!).

library(tidyverse)

Usually, at the top of every R script, the first code I write is a list of the packages I use in that script.

Note: This tutorial will use a mix of base R and tidyverse approaches. For more information about tidyverse see https://www.tidyverse.org/.

## 2.7 Documentation

You can access documentation using a “?” followed by the function name. Help pages will open in the bottom right quadrant of R Studio.

?mean

## 2.8 Activity

1. Open a new R script. Save it to a folder that will be your working directory.

2. Create a heading. Link to your working directory within the R script.

3. Install and load the tidyverse package.