Programming with R

Introduction to R and RStudio


Teaching: 20 min
Exercises: 10 min
  • How to find your way around RStudio?

  • How to interact with R?

  • How to manage your environment?

  • How to install packages?

  • To gain familiarity with the various panes in the RStudio IDE

  • To gain familiarity with the buttons, short cuts and options in the RStudio IDE

  • To understand variables and how to assign to them

  • To be able to manage your workspace in an interactive R session

  • To be able to use mathematical and comparison operations

  • To be able to call functions

  • Introduction to package management


Science is a multi-step process: once you’ve designed an experiment and collected data, the real fun begins! This lesson will teach you how to start this process using R and RStudio. We will begin with raw data, perform exploratory analyses, and learn how to plot results graphically. This example starts with a dataset from containing population information for many countries through time. Can you read the data into R? Can you plot the population for Senegal? Can you calculate the average income for countries on continent of Asia? By the end of these lessons you will be able to do things like plot the populations for all of these countries in under a minute!

Before Starting The Workshop

Please ensure you have the latest version of R and RStudio installed on your machine. This is important, as some packages used in the workshop may not install correctly (or at all) if R is not up to date.

Download and install the latest version of R here

Download and install RStudio here

Introduction to RStudio

Welcome to the R portion of the Software Carpentry workshop.

Throughout this lesson, we’re going to teach you some of the fundamentals of the R language as well as some useful packages for data analysis.

We’ll be using RStudio: a free, open source R integrated development environment. It provides a built in editor, works on all platforms (including on servers) and provides many advantages such as integration with version control and project management.

Basic layout

When you first open RStudio, you will be greeted by three panels:

Once you open files, such as R scripts, an editor panel will also open in the top left.

Work flow within RStudio

There are two main ways one can work within RStudio.

  1. Test and play within the interactive R console then copy code into a .R file to run later.
    • This works well when doing small tests and initially starting off.
    • It quickly becomes laborious
  2. Start writing in an .R file and use RStudio’s command / short cut to push current line, selected lines or modified lines to the interactive R console.
    • This is a great way to start; all your code is saved for later
    • You will be able to run the file you create from within RStudio or using R’s source function.

Tip: Running segments of your code

RStudio offers you great flexibility in running code from within the editor window. There are buttons, menu choices, and keyboard shortcuts. To run the current line, you can 1. click on the Run button above the editor panel, or 2. select “Run Lines” from the “Code” menu, or 3. hit Ctrl-Enter in Windows or Linux or Command-Enter on OS X. (This shortcut can also be seen by hovering the mouse over the button). To run a block of code, select it and then Run. If you have modified a line of code within a block of code you have just run, there is no need to reselct the section and Run, you can use the next button along, Re-run the previous region. This will run the previous code block including the modifications you have made.

Once you begin using R scripts regularly, you might want to work on more than one project within RStudio without losing your progress on another. RStudio contains some project management tools that can help. You can learn more about these tools here.

Introduction to R

Much of your time in R will be spent in the R interactive console. This is where you will run all of your code, and can be a useful environment to try out ideas before adding them to an R script file. This console in RStudio is the same as the one you would get if you typed in R in your command-line environment.

The first thing you will see in the R interactive session is a bunch of information, followed by a “>” and a blinking cursor. In many ways this is similar to the shell environment you learned about during the shell lessons: it operates on the same idea of a “Read, evaluate, print loop”: you type in commands, R tries to execute them, and then returns a result.

Using R as a calculator

The simplest thing you could do with R is do arithmetic:

1 + 100
[1] 101

And R will print out the answer, with a preceding “[1]”. Don’t worry about this for now, we’ll explain that later. For now think of it as indicating output.

Like bash, if you type in an incomplete command, R will wait for you to complete it:

> 1 +

Any time you hit return and the R session shows a “+” instead of a “>”, it means it’s waiting for you to complete the command. If you want to cancel a command you can simply hit “Esc” and RStudio will give you back the “>” prompt.

Tip: Cancelling commands

If you’re using R from the commandline instead of from within RStudio, you need to use Ctrl+C instead of Esc to cancel the command. This applies to Mac users as well!

Cancelling a command isn’t only useful for killing incomplete commands: you can also use it to tell R to stop running code (for example if its taking much longer than you expect), or to get rid of the code you’re currently writing.

When using R as a calculator, the order of operations is the same as you would have learned back in school.

From highest to lowest precedence:

3 + 5 * 2
[1] 13

Use parentheses to group operations in order to force the order of evaluation if it differs from the default, or to make clear what you intend.

(3 + 5) * 2
[1] 16

This can get unwieldy when not needed, but clarifies your intentions. Remember that others may later read your code.

(3 + (5 * (2 ^ 2))) # hard to read
3 + 5 * 2 ^ 2       # clear, if you remember the rules
3 + 5 * (2 ^ 2)     # if you forget some rules, this might help

The text after each line of code is called a “comment”. Anything that follows after the hash (or octothorpe) symbol # is ignored by R when it executes code.

Really small or large numbers get a scientific notation:

[1] 2e-04

Which is shorthand for “multiplied by 10^XX”. So 2e-4 is shorthand for 2 * 10^(-4).

You can write numbers in scientific notation too:

5e3  # Note the lack of minus here
[1] 5000

Mathematical functions

R has many built in mathematical functions. To call a function, we simply type its name, followed by open and closing parentheses. Anything we type inside the parentheses is called the function’s arguments:

sin(1)  # trigonometry functions
[1] 0.841471
log(1)  # natural logarithm
[1] 0
log10(10) # base-10 logarithm
[1] 1
exp(0.5) # e^(1/2)
[1] 1.648721

Don’t worry about trying to remember every function in R. You can simply look them up on Google, or if you can remember the start of the function’s name, use the tab completion in RStudio.

This is one advantage that RStudio has over R on its own, it has auto-completion abilities that allow you to more easily look up functions, their arguments, and the values that they take.

Comparing things

We can also do comparison in R:

1 == 1  # equality (note two equals signs, read as "is equal to")
[1] TRUE
1 != 2  # inequality (read as "is not equal to")
[1] TRUE
1 <  2  # less than
[1] TRUE
1 <= 1  # less than or equal to
[1] TRUE
1 > 0  # greater than
[1] TRUE
1 >= -9 # greater than or equal to
[1] TRUE

Tip: Comparing Numbers

A word of warning about comparing numbers: you should never use == to compare two numbers unless they are integers (a data type which can specifically represent only whole numbers).

Computers may only represent decimal numbers with a certain degree of precision, so two numbers which look the same when printed out by R, may actually have different underlying representations and therefore be different by a small margin of error (called Machine numeric tolerance).

Instead you should use the all.equal function.

Further reading:

Variables and assignment

We can store values in variables using the assignment operator <-, like this:

x <- 1/40

Notice that assignment does not print a value. Instead, we stored it for later in something called a variable. x now contains the value 0.025:

[1] 0.025

More precisely, the stored value is a decimal approximation of this fraction called a floating point number.

Look for the Environment tab in one of the panes of RStudio, and you will see that x and its value have appeared. Our variable x can be used in place of a number in any calculation that expects a number:

[1] -3.688879

Notice also that variables can be reassigned:

x <- 100

x used to contain the value 0.025 and and now it has the value 100.

Assignment values can contain the variable being assigned to:

x <- x + 1 #notice how RStudio updates its description of x on the top right tab

The right hand side of the assignment can be any valid R expression. The right hand side is fully evaluated before the assignment occurs.

In addition to numbers, we can also assign character values to variables.

y <- "green"

You can see the new variable show up in the Environment tab of your RStudio window.

Variable names can contain letters, numbers, underscores and periods. They cannot start with a number nor contain spaces at all.

Different people use different conventions for long variable names, these include

What you use is up to you, but be consistent.

It is also possible to use the = operator for assignment:

z = 1/40

But this is much less common among R users. The most important thing is to be consistent with the operator you use. There are occasionally places where it is less confusing to use <- than =, and it is the most common symbol used in the community. So the recommendation is to use <-.


When doing mathematical operations, we used commands such as sin and log. These commands are called functions and they are chunks of code that have been written by R developers. Built-in functions such as the ones we’ve used so far are included in R because they make R easier to use. It is helpful to write your own functions for bits of code you use frequently. For more information on writing your own functions, you can read through the supplemental lesson Functions Explained.

R Packages

It is possible to add functions to R by writing a package, or by obtaining a package written by someone else. It is often useful to use a package written by someone else to do something you need to do over writing your own code. As of this writing, there are over 7,000 packages available on CRAN (the comprehensive R archive network). R and RStudio have functionality for managing packages:

For example, to install the gapminder package which contains the dataset we are going to use for today’s lessons:


If you have not previously installed this package, you will see a lot of text scrolling by as R installs the components of this package and any dependancies.

Once the package is installed, you can make it available for use by using the library command:


The Packages tab in RStudio can also be used to manage packages. Simply click the Install button to install packages. To load packages, just click the checkbox in front of the package you wish to load. You should see the same messaging in your console for both approaches.

Other useful commands for working with packages:

Challenge 1

Which of the following are valid R variable names?


Solution to Challenge 1

The following can be used as R variables:


The following creates a hidden variable:


The following will not be able to be used to create a variable


Challenge 2

What will be the value of each variable after each statement in the following program?

mass <- 47.5
age <- 122
mass <- mass * 2.3
age <- age - 20

Solution to Challenge 2

mass <- 47.5

This will give a value of 47.5 for the variable mass.

age <- 122

This will give a value of 122 for the variable age.

mass <- mass * 2.3

This will multiply the existing value of 47.5 by 2.3 to give a new value of 109.25 to the variable mass.

age <- age - 20

This will subtract 20 from the existing value of 122 to give a new value of 102 to the variable age.

Challenge 3

Run the code from the previous challenge, and write a command to compare mass to age. Is mass larger than age?

Solution to Challenge 3

One way of answering this question in R is to use the > to set up the following:

mass > age
[1] TRUE

This should yield a boolean value of TRUE since 109.25 is greater than 102.

Challenge 4

Install the packages ggplot2 and dplyr

After installing, load both of these packages so they are active. Try using both ways that we discussed.

(Note: We will be using these packages in future lessons, so ask a helper for assistance if you have difficulties)

Solution to Challenge 4

We can use the install.packages command to install the required packages.


You can also install packages through the Packages tab in the lower right pane of RStudio by clicking Install and then typing in the names of the packages we want to install.

To load the packages use the following commands:


Or you can click the corresponding checkbox next to the name of the package from the list in the Packages tab.

Key Points