Introduction to R and RStudio

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 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

• 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 just 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 inculding 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 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 when you open up the basic R GUI.

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 the idea of a “Read, Evaluate, Print loop” (REPL): 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.

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 just 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 learnt back in school.

From highest to lowest precedence:

• Parentheses: (, )
• Exponents: ^
• 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:

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

Functions

Most of R’s functionality comes from its functions. A function takes zero, one or multiple arguments, depending on the function, and returns a value. To call a function enter it’s name followed by a pair of brackets - include any arguments in the brackets.

log(10)

## [1] 2.302585

To find out more about a function called function_name type ?function_name. To search for the functions associated with a topic type ??topic or ??"multiple topics". 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.

Exercise 1 Which function calculates sums? And what arguments does it take?

log(x = 10, base = 2)

## [1] 3.321928

log(10, 2)

## [1] 3.321928

log(2, 10)

## [1] 0.30103

Mathematical functions

R has many built in mathematical functions.

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.

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 by giving them a name, and using the assignment operator <- (To save finger strokes, type Alt-):

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:

x

## [1] 0.025

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] -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.

Exercise 3 Create an object called x with the value 7. What is the value of x^x. Save the value in a object called i. If you assign the value 20 to the object x does the value of i change? What does this indicate about how R assigns values to objects?

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 <-.

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"

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!

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. Just 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.

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.

Challenge 1

Which of the following are valid R variable names?

min_height
max.height
_age
.mass
MaxLength
min-length
2widths
celsius2kelvin

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.

Project management with RStudio