Introduction to R and RStudio (2024)

Overview

Teaching: 105 min
Exercises: 30 min

Questions

• How to find your way around RStudio?

• How to interact with R?

• How to manage your environment?

• How to install packages?

Objectives

• Describe the purpose and use of each pane in the RStudio IDE

• Locate buttons and options in the RStudio IDE

• Define a variable

• Assign data to a variable

• Manage a workspace in an interactive R session

• Use mathematical and comparison operators

• Call functions

• Manage packages

Motivation

Science is a multi-step process: once you’ve designed an experiment and collecteddata, the real fun begins! This lesson will teach you how to start this process usingR and RStudio. We will begin with raw data, perform exploratory analyses, and learnhow to plot results graphically. This example starts with a dataset fromgapminder.org containing population information for manycountries through time. Can you read the data into R? Can you plot the population forSenegal? Can you calculate the average income for countries on the continent of Asia?By the end of these lessons you will be able to do things like plot the populationsfor 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 ofthe R language as well as some best practices for organizing code forscientific projects that will make your life easier.

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

Basic layout

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

• The interactive R console (entire left)
• Environment/History (tabbed in upper right)
• Files/Plots/Packages/Help/Viewer (tabbed in lower right)

Once you open files, such as R scripts, an editor panel will also openin 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 intoa .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 short cut keys for the Run commandto push the current line, selected lines or modified lines to theinteractive 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 RStudioor using R’s source() function.

Tip: Running segments of your code

RStudio offers you great flexibility in running code from within the editorwindow. There are buttons, menu choices, and keyboard shortcuts. To run thecurrent 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 Windowsor Linux or Command-Enter on OS X. (This shortcut can also be seen by hoveringthe 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 re-select the section and Run, you can use the next buttonalong, Re-run the previous region. This will run the previous code blockincluding the modifications you have made.

Introduction to R

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

The first thing you will see in the R interactive session is a bunchof information, followed by a “>” and a blinking cursor. It operateson the 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 thisfor 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 tocomplete it:

> 1 +

+

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

Tip: Cancelling commands

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

Canceling 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 it’staking much longer than you expect), or to get rid of the code you’recurrently writing.

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

From highest to lowest precedence:

• Parentheses: (, )
• Exponents: ^ or **
• Divide: /
• Multiply: *
• Add: +
• Subtract: -

3 + 5 * 2

[1] 13

Use parentheses to group operations in order to force the order ofevaluation if it differs from the default, or to make clear what youintend.

(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 read3 + 5 * 2 ^ 2 # clear, if you remember the rules3 + 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:

2/10000

[1] 2e-04

Which is shorthand for “multiplied by 10^XX”. So 2e-4is 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’sarguments:

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. Youcan simply look them up on Google, or if you can remember thestart of the function’s name, use the tab completion in RStudio.

This is one advantage that RStudio has over R on its own, ithas auto-completion abilities that allow you to more easilylook up functions, their arguments, and the values that theytake.

Typing a ? before the name of a command will open the help pagefor that command. As well as providing a detailed description ofthe command and how it works, scrolling to the bottom of thehelp page will usually show a collection of code examples whichillustrate command usage. We’ll go through an example later.

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 shouldnever use == to compare two numbers unless they areintegers (a data type which can specifically representonly whole numbers).

Computers may only represent decimal numbers with acertain degree of precision, so two numbers which lookthe same when printed out by R, may actually havedifferent underlying representations and therefore bedifferent by a small margin of error (called Machinenumeric tolerance).

See Also

Tutorial: Getting Started with R and RStudio

Instead you should use the all.equal function.

Further reading: http://floating-point-gui.de/

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 laterin something called a variable. x now contains the value 0.025:

x

[1] 0.025

More precisely, the stored value is a decimal approximation ofthis 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 valuehave appeared. Our variable x can be used in place of a number in any calculation that expects a number:

log(x)

[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 taby <- x * 2

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

Variable names can contain letters, numbers, underscores and periods. Theycannot start with a number nor contain spaces at all. Different people usedifferent conventions for long variable names, these include

• periods.between.words
• underscores_between_words
• camelCaseToSeparateWords

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

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

x = 1/40

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

Vectorization

One final thing to be aware of is that R is vectorized, meaning thatvariables and functions can have vectors as values. For example

1:5

[1] 1 2 3 4 5

2^(1:5)

[1] 2 4 8 16 32

x <- 1:52^x

[1] 2 4 8 16 32

This is incredibly powerful; we will discuss this further in anupcoming lesson.

Managing your environment

There are a few useful commands you can use to interact with the R session.

ls will list all of the variables and functions stored in the global environment(your working R session):

ls()

[1] "x" "y"

Like in the shell, ls will hide any variables or functions startingwith a “.” by default. To list all objects, type ls(all.names=TRUE)instead

Note here that we didn’t give any arguments to ls, but we stillneeded to give the parentheses to tell R to call the function.

If we type ls by itself, R will print out the source code for that function!

ls

function (name, pos = -1L, envir = as.environment(pos), all.names = FALSE, pattern, sorted = TRUE) { if (!missing(name)) { pos <- tryCatch(name, error = function(e) e) if (inherits(pos, "error")) { name <- substitute(name) if (!is.character(name)) name <- deparse(name) warning(gettextf("%s converted to character string", sQuote(name)), domain = NA) pos <- name } } all.names <- .Internal(ls(envir, all.names, sorted)) if (!missing(pattern)) { if ((ll <- length(grep("[", pattern, fixed = TRUE))) && ll != length(grep("]", pattern, fixed = TRUE))) { if (pattern == "[") { pattern <- "\\[" warning("replaced regular expression pattern '[' by '\\\\['") } else if (length(grep("[^\\\\]\\[<-", pattern))) { pattern <- sub("\\[<-", "\\\\\\[<-", pattern) warning("replaced '[<-' by '\\\\[<-' in regular expression pattern") } } grep(pattern, all.names, value = TRUE) } else all.names}<bytecode: 0x56172137ab30><environment: namespace:base>

You can use rm to delete objects you no longer need:

rm(x)

If you have lots of things in your environment and want to delete all of them,you can pass the results of ls to the rm function:

rm(list = ls())

In this case we’ve combined the two. Like the order of operations, anythinginside the innermost parentheses is evaluated first, and so on.

In this case we’ve specified that the results of ls should be used for thelist argument in rm. When assigning values to arguments by name, you mustuse the = operator!!

If instead we use <-, there will be unintended side effects, or you may get an error message:

rm(list <- ls())

Error in rm(list <- ls()): ... must contain names or character strings

Tip: Warnings vs. Errors

Pay attention when R does something unexpected! Errors, like above,are thrown when R cannot proceed with a calculation. Warnings on theother hand usually mean that the function has run, but it probablyhasn’t worked as expected.

In both cases, the message that R prints out usually give you clueshow to fix a problem.

R Packages

It is possible to add functions to R by writing a package, or byobtaining a package written by someone else. As of this writing, thereare over 10,000 packages available on CRAN (the comprehensive R archivenetwork). R and RStudio have functionality for managing packages:

• You can see what packages are installed by typinginstalled.packages()
• You can install packages by typing install.packages("packagename"),where packagename is the package name, in quotes.
• You can update installed packages by typing update.packages()
• You can remove a package with remove.packages("packagename")
• You can make a package available for use with library(packagename)

Challenge 1

Which of the following are valid R variable names?

min_heightmax.height_age.massMaxLengthmin-length2widthscelsius2kelvin

Solution to challenge 1

The following can be used as R variables:

min_heightmax.heightMaxLengthcelsius2kelvin

The following creates a hidden variable:

.mass

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

_agemin-length2widths

Challenge 2

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

mass <- 47.5age <- 122mass <- mass * 2.3age <- 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 of109.25 to the variable mass.

age <- age - 20

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

Challenge 3

Run the code from the previous challenge, and write a command tocompare 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

Clean up your working environment by deleting the mass and agevariables.

Solution to challenge 4

We can use the rm command to accomplish this task

rm(age, mass)

Challenge 5

Install the following packages: ggplot2, plyr, gapminder

Solution to challenge 5

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

install.packages("ggplot2")install.packages("plyr")install.packages("gapminder")

Key Points

• Use RStudio to write and run R programs.

• R has the usual arithmetic operators and mathematical functions.

• Use <- to assign values to variables.

• Use ls() to list the variables in a program.

• Use rm() to delete objects in a program.

• Use install.packages() to install packages (libraries).