Motivation

Science is a multi-step process: once you’ve designed an experiment and collected data, the real fun begins! This lesson will explore how to start this process using R and RStudio.

Before Starting The Tutorial

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

If you have administrative rights on your computer:

Download and install the latest version of R here

Download and install RStudio here

Introduction to RStudio

Throughout this lesson, we’re going to teach you some of the fundamentals of the R language as well as some best practices for organizing 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.

Tip: Key RStudio Setting

By default RStudio will automatically save your session variables in your project directory in a file called .RData. These are saved when you exit a project and restored when you open it up again. We highly recommend turning this feature off. As you will learn in this course, all outputs will be created from code. Typically you do not need to save intermediate steps. If you don’t plan to use this feature you can toggle it in the Project Options -> General tab.

Basic layout

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

RStudio layout
RStudio layout

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

RStudio layout with .R file open
RStudio layout with .R file open

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 short cut keys for the Run command to push the 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+Return in Windows or Linux or ⌘+Return 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 reselect 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.

Introduction to R

Much of your time in R will be spent in the R interactive console. This is where you can run your code line-by-line, 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. It operates on 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.

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

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 it’s 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:

2/10000
[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:

sum(1, 2, 3, 4, 5)
[1] 15
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.

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 to 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

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

Variables and assignment

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

x <- 5

Notice that assignment does not print a value. Instead, we stored it for later in something called a variable. x now contains the value 5:

x
[1] 5

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:

log(x)
[1] 1.609438

Notice also that variables can be reassigned:

x <- 100

x used to contain the value 5 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
y <- 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. 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:

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 <-.

Exercise 1 (5 minutes)

Which of the following are valid R variable names?

min_height
max.height
_age
.mass
MaxLength
min-length
2widths
celsius2kelvin
Solution to exercise 1

The following can be used as R variables:

min_height
max.height
MaxLength
celsius2kelvin

The following creates a hidden variable:

.mass

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

_age
min-length
2widths

Exercise 2 (5 minutes)

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 exercise 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.

Exercise 3 (5 minutes)

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

Solution to exercise 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.

Vectorization

One final thing to be aware of is that R is vectorized, meaning that variables and functions can have vectors as values. In contrast to physics and mathematics, a vector in R describes a set of values in a certain order of the same data type. For example

1:5
[1] 1 2 3 4 5
2 * (1:5)
[1]  2  4  6  8 10
x <- 1:5
2 * x
[1]  2  4  6  8 10
y <- c(1, 3, 5, 7, 9)
y
[1] 1 3 5 7 9

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):

ls()
[1] "age"            "fix_fig_path"   "knitr_fig_path" "mass"          
[5] "x"              "y"

Tip: hidden objects

Like in the shell, ls will hide any variables or functions starting with 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 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!

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: 0x7f7c9942b3b8>
<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())

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 the other hand usually mean that the function has run, but it probably hasn’t worked as expected.

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

Exercise 4 (2 minutes)

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

Solution to exercise 4


We can use the rm command to accomplish this task

rm(age, mass)

R Packages

Think of packages like apps on your smart phone. Your phone can do a lot of things right out of the box, but you can get apps to make some existing functionality better (like a better timer), or allow you to do new and amazing things with your phone (like play Cwazy Cupcakes).

Similarly, R comes with many functions built in, but it is possible to add functions to R by obtaining a package written by someone else. As of this writing, there are over 10,000 packages available on CRAN (the comprehensive R archive network).

R and RStudio have functionality for managing packages:

Exercise 5 (2 minutes)

Install the following packages: ggplot2, dplyr.

Solution to exercise 5


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

install.packages("ggplot2")
install.packages("dplyr")