Introduction to R
Using R Studio
An IDE for R
An integrated development environment is software that makes coding easier
- see objects you’ve imported and created
- autocomplete
- syntax highlighting
- run part or all of your code
Start fresh
- If you have used R previously, an old workspace may still be active when you open RStudio
- You always want to start with a fresh session
- Go to Tools -> Global Options, and under General, change these settings:
Tip
Now, you can just quit and restart RStudio if something goes wrong! You can also go to Session -> Restart R to clear your session.
Using R
Running code
You can run…
- code that you type directly in the console
- code you won’t need to run again
- code in an
.Rscript - code in a
.qmd(Quarto) or.Rmd(R Markdown) file- code you want to render to an html, word, or pdf file
Running code
Make sure your cursor is on the line you want to run -> cmd+return (Mac) or ctrl+enter (Windows)
- This will automatically bring your cursor to the next line
You can also highlight a section of code and hit cmd+return/ctrl+enter or use the “Run” dropdown in RStudio
Types of documents
- R scripts:
.Rfiles- plain text files with R code
- can be run line-by-line in RStudio (or all at once)
- Quarto/R Markdown:
.qmdor.Rmdfiles- plain text files with R code and text
- can be run line-by-line or in “chunks” in RStudio
- can be rendered to produce a report in HTML, PDF, Word, etc.
Quarto
We’ll generally use .qmd files (Quarto documents) in this course
- Encourage good coding practices
- Every time a document is rendered, the code is run from top to bottom
- When coding line-by-line, sometimes you can get code that only works out of order or depends on something you’ve only run in the console
- When writing code, run line-by-line/in chunks, but render often
Running code in quarto
- Run “line-by-line” by putting your cursor in the chunk and hitting cmd+return/ctrl+enter
- Run the whole chunk by clicking the green arrow
- Run all chunks above this one by clicking the grey arrow with green bar
- This mimics what will happen when you render (start a new R session first)
Demo
R objects
Dataframes
The most common object we’ll be using R is a dataframe
- this is the equivalent to a dataset in Stata, but you can have many in your environment at the same time
- we’ll also use other objects: in R, a function is an object, a figure is an object, a single number can be an object, etc.
We’ll talk briefly about some of these
- Also see the optional reading (Chapter 2 of Hands-On Programming with R) for more practice
The biggest difference between R and Stata is that R can have many different objects in its environment
- datasets, numbers, figures, etc.
- you have to be explicit about storing and retrieving objects
- e.g., what dataset a variable belongs to
R uses <- to store objects in the environment
I call this the “assignment arrow”
Now vals holds those values
- this is called a vector
Warning
No assignment arrow means that the object will be printed to the console (and lost forever!)
Objects
We can retrieve those values by running just the name of the object
We can also perform operations on them using functions like mean()
If we want to keep the result of that operation, we need to use <- again
Types of data (classes)
We could also create a character vector:
Or a logical vector:
Tip
We’ll see more options as we go along!
Types of objects
We created vectors with the c() function (c stands for concatenate)
We could also create a matrix of values with the matrix() function:
Indices
The numbers in square brackets are indices, which we can use to pull out values:
We can pull out rows or columns from matrices:
Demo
Packages
- Some functions are built into R
mean(),lm(),table(), etc.
- They actually come from built-in packages
base,stats,graphics, etc.
- Anyone (yes, anyone) build their own package to add to the functionality of R
{ggplot2},{dplyr},{data.table},{survival}, etc.![]()
1
1Packages
- You have to install a package once1
- You then have to load the package every time you want to use it
Packages
“You only have to buy the book once, but you have to go get it out of the bookshelf every time you want to read it.”
Several days later…
Package details
- When you use
install.packages, packages are downloaded from CRAN (The Comprehensive R Archive Network)- This is also where you downloaded R
- Packages can be hosted lots of other places, such as Bioconductor (for bioinformatics), and Github (for personal projects or while still developing)
- The folks at CRAN check to make things “work” in some sense, but don’t check on the statistical methods…
- But because R is open-source, you can always read the code yourself
- Two functions from different packages can have the same name… if you load them both, you may have some trouble