Lesson 1 of 7 · Course overview

Introduction to R

R is a free programming language built specifically for working with data. Pretty much every statistical method you’ve heard of has an R implementation, and a lot of them were first implemented in R. The graphics are excellent, the package ecosystem is huge, and you can use it for everything from a one-off plot to a peer-reviewed publication.

This lesson covers four things:

  1. What R actually is (and what it isn’t).
  2. Installing R and RStudio.
  3. A quick tour of the RStudio interface.
  4. Writing and running your first script.

What is R?

R is two things at once: a programming language and an interactive environment. You can type 2 + 2 at the prompt and get 4 back, just like a calculator. Or you can write a 500-line script that loads a CSV, fits a model, and produces a PDF report.

R was created in the early 1990s by statisticians at the University of Auckland, and it shows: the language has built-in support for vectors, missing values, factors, and other things that statisticians care about. It’s very good at “load a table, do something to every row, summarise the result.” It’s less great at writing a video game.

R is open source. Anyone can use it, anyone can contribute packages, and the Comprehensive R Archive Network (CRAN) hosts more than 20,000 add-on packages — for everything from genomics to finance to natural language processing.

📝 R vs. Python

You’ll hear “R or Python?” a lot. Honest answer: both are fine, you can do almost anything in either, and most working data scientists use both. R has the edge for statistics, reporting, and visualization out of the box. Python has the edge for general programming, web apps, and most of modern machine learning. If you already know Python, R will feel quirky for a week and then second-nature.

Installing R and RStudio

You need two things: R itself (the engine) and RStudio (a much nicer place to write R code than the bare R console).

Step 1: Install R

  1. Go to https://cran.r-project.org/ and click the link for your operating system (Windows, macOS, or Linux).
  2. Download the latest base R installer.
  3. Run the installer with the default options — for most users you don’t need to change anything.

Step 2: Install RStudio Desktop

  1. Go to https://posit.co/download/rstudio-desktop/.
  2. Download the free RStudio Desktop installer for your OS.
  3. Run the installer. RStudio will find your R installation automatically.

When you open RStudio for the first time, you should see a window with several panes. If you see a > prompt in one of them, you’re good to go.

⚠️ One install, not two languages

RStudio is just an editor — it runs R for you. If you uninstall R, RStudio breaks. If you install a new version of R, RStudio will pick it up automatically.

A tour of RStudio

By default RStudio shows four panes:

  • Source / Script editor (top-left): where you write R scripts and R Markdown documents. This is where you’ll spend most of your time.
  • Console (bottom-left): the live R session. Anything you run from the script gets sent here. You can also type directly into the console for one-off commands.
  • Environment / History (top-right): every variable, dataset, or function you’ve created in the current session shows up here. Great for sanity-checking what’s loaded.
  • Files / Plots / Packages / Help / Viewer (bottom-right): file browser, plot output, installed packages, the help system, and the viewer for HTML output (like rendered R Markdown).

A few keyboard shortcuts that pay for themselves immediately:

Shortcut What it does
Cmd/Ctrl + Enter Run the current line (or selection) from the script
Cmd/Ctrl + Shift + N New R script
Cmd/Ctrl + Shift + M Insert the pipe operator (|>)
Alt + - Insert the assignment operator (<-)
Cmd/Ctrl + L Clear the console

Your first R script

Let’s actually run some R. In RStudio, hit Cmd/Ctrl + Shift + N to open a new script. Type the following (don’t paste — really type it):

greeting <- "Hello, R!"
greeting
## [1] "Hello, R!"
2 + 2
## [1] 4
sqrt(144)
## [1] 12

Place your cursor on the first line and hit Cmd/Ctrl + Enter. The line gets sent to the console. Do it for each line. You should see:

  • greeting was assigned the value "Hello, R!" and now appears in the Environment pane.
  • 2 + 2 prints 4.
  • sqrt(144) prints 12.

A few things to notice:

  • <- is R’s assignment operator. You can use = too, but <- is the convention. Use the Alt + - shortcut.
  • Anything after # on a line is a comment — R ignores it.
  • Functions are called with parentheses: sqrt(144), mean(c(1, 2, 3)). Without the parentheses, R prints the function’s source code instead of running it.

Save the script with Cmd/Ctrl + S. Call it hello.R. Now you have your first R script.

Installing and using packages

R’s superpower is its packages. A package is a bundle of functions and data someone else wrote. To use one, you install it once and then load it whenever you need it.

install.packages("dplyr")
library(dplyr)
  • install.packages("dplyr") downloads and installs the dplyr package from CRAN. You only need to do this once per machine.
  • library(dplyr) loads it into your current session, so its functions are available. You do this every session.

You can also call a function from a package without loading the whole package by using :::

dplyr::filter(mtcars, mpg > 25)

This is helpful when you only need one function or want to be explicit about where it came from. We’ll use it occasionally throughout the course.

The packages we’ll use the most:

  • dplyr and tidyr — data manipulation
  • ggplot2 — plotting
  • readr — reading CSVs
  • rmarkdown and knitr — reproducible reports

You can install them all at once with the tidyverse umbrella package:

install.packages("tidyverse")

Getting help

When (not if) you forget how a function works, R has built-in help. Three ways to get it:

?mean
help("mean")
example("mean")

The help page opens in the bottom-right pane. Every help page has the same sections: Description, Usage, Arguments, Value, Examples. The Examples section is gold — copy them, run them, modify them.

If you don’t even know the name of the function you want, ?? does a fuzzy search across all installed packages:

??"linear regression"

And of course, Stack Overflow, the Posit Community forum, and now LLMs are all good for “how do I…” questions.

Putting it together

Here’s a tiny script that uses everything you just learned: a built-in dataset (mtcars — fuel efficiency for 32 cars from 1974), a function call, and a quick plot.

head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
mean(mtcars$mpg)
## [1] 20.09
plot(mtcars$wt, mtcars$mpg,
  main = "Heavier cars get worse gas mileage",
  xlab = "Weight (1000 lbs)",
  ylab = "Miles per gallon")

If you typed that in and got a similar plot back, you’re in business.

✏️ Exercise 1.1 — Your first script

Open a new R script in RStudio and write code that:

  1. Stores your name in a variable called me.
  2. Stores your age (or guess, doesn’t matter) in a variable called age.
  3. Prints both, plus the value of age * 365 (your age in days, roughly).
Show solution 
me <- "Cavan"
age <- 30
me
## [1] "Cavan"
age
## [1] 30
age * 365
## [1] 10950
✏️ Exercise 1.2 — Help yourself

R has a function called seq() that you’ve never seen before. Without searching the web, figure out what it does and use it to generate the numbers from 0 to 1 in steps of 0.1.

Show solution

Run ?seq to open the help page. The relevant arguments are from, to, and by:

seq(from = 0, to = 1, by = 0.1)
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

You can also write it more compactly — R uses positional arguments:

seq(0, 1, 0.1)
##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

What’s next

You can now run R, you have RStudio set up, and you know how to install packages and ask R for help. That’s the whole runway. Next up: actually doing things with R.

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