R for reproducible scientific analysis

Introduction to R and RStudio

Learning Objectives

  • 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 the data/gapminder-FiveYearData.csv dataset 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!

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 best practices for organising code for scientific projects that will make your life easier.

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:

  • 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 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
  1. 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.

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 commandline 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 ouput.

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.

When using R as a calculator, the order of operations is the same as you would have learnt 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 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 autocompletion abilities that allow you to more easily look up functions, their arguments, and the values that they take.

Typing a ? before the name of a command will open the help page for that command. As well as providing a detailed description of the command and how it works, scrolling ot the bottom of the help page will usually show a collection of code examples which illustrate 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

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.

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

  • 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 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 <-.


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

[1] 1 2 3 4 5
[1]  2  4  8 16 32
x <- 1:5
[1]  2  4  8 16 32

This is incredibly powerful; we will discuss this further in an upcoming 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):

[1] "hook_error" "hook_in"    "hook_out"   "x"         

Note here that we didn’t given any arguments to ls, but we still needed 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!

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: 0x7fb223e01a78>
<environment: namespace:base>

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


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, anything inside the innermost parentheses is evaluated first, and so on.

In this case we’ve specified that the results of ls should be used for the list argument in rm. When assigning values to arguments by name, you must use 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

R Packages

It is possible to add functions to R by writing a package, or by obtaining a package written by someone else. 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:

  • You can see what packages are installed by typing installed.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?


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

Challenge 3

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

Challenge 4

Clean up your working environment by deleting the mass and age variables.

Challenge 5

Install the following packages: ggplot2, plyr, gapminder

Challenge Solutions

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


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.

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.

Solution to challenge 4

We can use the rm command to accomplish this task

rm(age, mass)

Solution to challenge 4

We can use the install.packages() command to install the required packages. We can also install them in one go like so:

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