• Organize your code, output, and plots
• Auto-complete code and highlight syntax
• Help view data and objects
• Enable easy integration of R code into documents with Quarto

It can also…

• Manage git repositories
• Run interactive tutorials
• Handle other languages like Python, SQL, \(\LaTeX\), and HTML

Then, let’s get oriented with the interface:

• Top Left: Code editor pane, data viewer (browse with tabs)

• Bottom Left: Console for running code (> prompt)

• Top Right: List of objects in environment, code history tab.

• Bottom Right: Tabs for browsing files, viewing plots, managing packages, and viewing help files.

You can change the layout in Preferences > Pane Layout

• Highlight lines in the editor window and click Run at the top or hit Ctrl+Enter or ⌘+Enter to run them all.

• With your caret (|) on a line you want to run, hit Ctrl+Enter or ⌘+Enter. Note your caret moves to the next line, so you can run code sequentially with repeated presses.

• Type individual lines in the console and press Enter.

The console will show the lines you ran followed by any printed output.

The [1] in the output indicates the numeric index of the first element on that line.

Now in your blank R document in the editor, try typing the line sqrt(400) and either clicking Run or hitting Ctrl+Enter or ⌘+Enter.

If we didn’t have a good guess as to what sqrt() will do, we can type ?sqrt in the console and look at the Help panel on the right.

?sqrt

Arguments are the inputs to a function. In this case, the only argument to sqrt() is x which can be a number or a vector of numbers.

Help files provide documentation on how to use functions and what functions produce.

You can give an object a name using the assignment operator: <-

Operators like <- are functions that look like symbols but typically sit between their arguments (e.g. numbers or objects) instead of having them inside () like in sqrt(x).

We do math with operators, e.g., x + y. + is the addition operator!

Object names can contain _ and . in them, but cannot begin with numbers. Try to be consistent in naming objects.

Auto-complete means long names are better than vague ones!

Good names save confusion later.

You can treat the object’s name as if it were the values assigned to it.

You can create a vector using the function c() which stands for “combine”.

Assigning to an existing name overwrites that object.

You can provide a vector as an argument for many functions.

Categorical data can also be stored as a factor, which has an underlying numeric representation.¹

factor.vector <- factor(string.vector)
factor.vector

[1] Atlantic Pacific  Arctic   Pacific 
Levels: Arctic Atlantic Pacific

You can open saved files in R as well:

load("new_object.RData")

But where are these files being saved and loaded from?

Similarly, we can set a working directory like so:

setwd("C:/Users/")
getwd()

[1] "C:/Users"

Most data sets you will work with will be read into R and stored as a dataframe.

Consequently, we will focus on manipulating and analyzing these dataframes.

data(USArrests)

data(USArrests) loads this dataframe into the Global Environment (as a promise¹).

View(USArrests) pops up a Viewer pane (“interactive” use only, don’t put in R Markdown document!) or…

summary() displays summary information:¹

summary(USArrests)

     Murder          Assault         UrbanPop          Rape      
 Min.   : 0.800   Min.   : 45.0   Min.   :32.00   Min.   : 7.30  
 1st Qu.: 4.075   1st Qu.:109.0   1st Qu.:54.50   1st Qu.:15.07  
 Median : 7.250   Median :159.0   Median :66.00   Median :20.10  
 Mean   : 7.788   Mean   :170.8   Mean   :65.54   Mean   :21.23  
 3rd Qu.:11.250   3rd Qu.:249.0   3rd Qu.:77.75   3rd Qu.:26.18  
 Max.   :17.400   Max.   :337.0   Max.   :91.00   Max.   :46.00  

We can load packages like {swirl} using the library() function.

library(swirl)

You could then use swirl() to start tutorials.