An IDE for R

Start fresh

Running code

You can run…

• code that you type directly in the console
  • code you won’t need to run again
• code in an .R script
• 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: .R files
  • plain text files with R code
  • can be run line-by-line in RStudio (or all at once)
• Quarto/R Markdown: .qmd or .Rmd files
  • 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)

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”

# create values
vals <- c(1, 645, 329)

Now vals holds those values

• this is called a vector

Objects

We can retrieve those values by running just the name of the object

vals

[1]   1 645 329

We can also perform operations on them using functions like mean()

mean(vals)

[1] 325

If we want to keep the result of that operation, we need to use <- again

mean_val <- mean(vals)

Types of data (classes)

We could also create a character vector:

chars <- c("dog", "cat", "rhino")
chars

[1] "dog"   "cat"   "rhino"

Or a logical vector:

logs <- c(TRUE, FALSE, FALSE)
logs

[1]  TRUE FALSE FALSE

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:

# turn the vector of numbers into a 2-row matrix
mat <- matrix(c(234, 7456, 12, 654, 183, 753), nrow = 2)
mat

     [,1] [,2] [,3]
[1,]  234   12  183
[2,] 7456  654  753

Indices

The numbers in square brackets are indices, which we can use to pull out values:

# extract second animal
chars[2]

[1] "cat"

We can pull out rows or columns from matrices:

# extract second row
mat[2, ]

[1] 7456  654  753

# extract first column
mat[, 1]

[1]  234 7456

Packages

• You have to install a package once¹

install.packages("survival")

• You then have to load the package every time you want to use it

library(survival)

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.”

install.packages("survival")
library(survival)
survfit(...)

library(survival)
coxph(...)