Learn R: an interactive beginner's guide

How this page works. Every grey box below is a live R console. Edit the code however you like, then press Run. The first time you run anything, your browser quietly downloads R (about 30 MB), so give it 10–20 seconds on that first go. After that it's instant. Everything runs on your own machine; nothing is sent anywhere.

// lesson_01

R as a calculator

The simplest thing R can do is arithmetic. Type a sum, press Run, and R prints the answer. The [1] at the start of the answer is just R labelling the first item in its result. Ignore it for now.

Try changing the numbers below, then press Run. Everything here is editable.

R

2 + 2

10 * 5

# Anything after a # is a comment. R ignores it.
144 / 12

// lesson_02

Storing values in variables

Doing sums is handy, but the real power comes from giving values a name so you can reuse them. In R you assign a value to a name with the arrow <- (a less-than sign followed by a minus sign). Read it as "gets".

So price <- 20 means "the name price gets the value 20". After that, you can use price anywhere you'd use the number 20.

R

price <- 20
quantity <- 3

# Multiply the two names together
total <- price * quantity

# Print the result
total

Notice that assigning a value doesn't print anything. Writing the name total on its own line is what asks R to show it. Try adding a line that applies a 10% discount: total * 0.9.

// lesson_03

Vectors: many values at once

This is the idea that makes R click. Most languages make you loop over a list of numbers one at a time. R lets you hold a whole set of values in one place, called a vector, and do maths on all of them together.

You build a vector with c(), which stands for "combine". Then any operation you do applies to every value at once.

R

# A vector of five test scores
scores <- c(58, 72, 90, 64, 81)
scores

# Add 5 bonus marks to every score at once
scores + 5

# The colon makes a sequence: 1, 2, 3 ... 10
1:10

# Square every number in that sequence
(1:10) ^ 2

You can pull out individual values using square brackets. scores[1] gives the first score, scores[c(1, 3)] gives the first and third. R counts from 1, not 0, which trips up people coming from other languages.

// lesson_04

Types of data

R works with a few basic types of value. Numbers you've seen. There's also text (called character data, always in quotes) and true/false values (called logical). Understanding which is which saves a lot of confusion later.

R

name <- "Ada"          # character (text) — note the quotes
age <- 36              # numeric
is_learning_r <- TRUE  # logical — TRUE or FALSE, no quotes

# class() tells you what type something is
class(name)
class(age)
class(is_learning_r)

# Comparisons produce logical values
age > 30

// lesson_05

Functions and getting help

A function is a named command that does a job. You call it by writing its name followed by round brackets, with any inputs inside. You've already met c() and class(). R comes with hundreds ready to go.

R

values <- c(4, 8, 15, 16, 23, 42)

sum(values)      # add them all up
length(values)   # how many are there
max(values)      # the biggest
min(values)      # the smallest
sort(values)     # put them in order
round(3.14159, 2) # round to 2 decimal places

Stuck on what a function does? In real R you'd type ?mean to open its help page. You can also write your own functions once you're comfortable, but the built-in ones cover a huge amount.

// lesson_06

Data frames: data in tables

Real data usually comes as a table: rows and columns, like a spreadsheet. In R that's a data frame. R ships with a few built-in ones so you can practise without loading a file. Here's mtcars, data on 32 cars from a 1974 magazine.

R

# Show the first 6 rows
head(mtcars)

# How many rows and columns?
dim(mtcars)

# Pull out one column with the $ sign
mtcars$mpg

# The average miles-per-gallon across all cars
mean(mtcars$mpg)

You can filter a data frame too. mtcars[mtcars$mpg > 25, ] gives just the rows where miles-per-gallon is above 25. The comma matters: it means "these rows, all columns".

// lesson_07

Summary statistics

This is what R was built for. Once your data's in a vector or a column, describing it is a one-liner. summary() is a great first look at any set of numbers: it gives you the smallest, the largest, the average, and the middle.

R

mpg <- mtcars$mpg

mean(mpg)     # the average
median(mpg)   # the middle value
sd(mpg)       # standard deviation (how spread out)
range(mpg)    # lowest and highest

# One command for the lot
summary(mpg)

// lesson_08

Your first plot

R draws charts as easily as it does sums, and they'll appear right below the code. hist() draws a histogram (how often each range of values comes up). plot() draws points. Press Run and watch the picture appear.

R

# A histogram of car fuel economy
hist(mtcars$mpg,
     col = "#E8621C",
     border = "white",
     main = "Fuel economy of 32 cars",
     xlab = "Miles per gallon")

Now a scatter plot. Does a heavier car use more fuel? Each dot is one car.

R

plot(mtcars$wt, mtcars$mpg,
     pch = 19,
     col = "#1E1E24",
     main = "Heavier cars use more fuel",
     xlab = "Weight (1000s of lbs)",
     ylab = "Miles per gallon")

// lesson_09

Try it yourself

No new teaching here, just a blank console. Everything from the lessons above is loaded and ready. A few things to try:

Make a vector of your own numbers and find its mean().
Draw a histogram of mtcars$hp (horsepower).
Generate 1,000 random numbers with rnorm(1000) and plot them.

R

# Your turn. Write some R, then press Run.
random_numbers <- rnorm(1000)
hist(random_numbers, col = "#E8621C", border = "white",
     main = "1000 random values")

// where_next

Where to go next

When you're ready to work on your own machine instead of in a browser, here's the well-trodden path:

Install R from CRAN, the official home of the language. It's free.
Install RStudio (now made by Posit). It's the friendly workbench nearly everyone writes R in.
R for Data Science is the best free book for beginners, and it reads well cover to cover.
swirl teaches you R inside R itself, one nudge at a time.

The browser R on this page is powered by WebR, the real R language compiled to run in a web browser.

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