Getting started with R

This document is here to give students who are unfamiliar with R or who haven’t used R for a long time some basic familiarity with the language. We will cover the basics of

1. Installing and running R
2. Performing basic operations in R

Installing and running R

What is R and why are we using it?

R is a programming language (a syntax for humans to tell computers what to do) and a runtime environment (a program that translates a language into something a computer understands). This puts it in the same family as computer-sciency languages like Python, C++, Java, etc. But what makes R distinct is that it is a programming language designed specifically for statistical analysis, data manipulation, and the creation of figures and charts—i.e. designed specifically for doing science. R (along with its predecessors S and S-plus) has been used by statisticians, mathematicians, and computer scientists since the 1970s, and since about the mid 00s it has gained a considerable following in the social sciences. I am teaching this course using R for a few reasons:

• R is becoming the de-facto standard for social data science. There are a large number of software tools for statistical and data analysis (SPSS, Stata, SAS, …), but R is fast becoming the most common. Getting comfortable using R will be a boon for students planning to work with data in academia, industry, government, or anywhere else that values social data analysis.
• R is free. R is open-source software, which means its underlying source code is available for anyone to look at, tinker with, and potentially contribute to. This leads to more reliable and reproducible research. It also means you don’t need to pay anything to download and install R.
• R has a thriving ecosystem of add-on packages. Anyone can write an add-on package (called a ‘library’) for R, which means that scientists who are working on the cutting edge of social-science methodology will often implement their ideas in R first. R can do just about anything with the right libraries—such as the network-analysis library igraph that we will be relying on heavily in this class.
• R is versatile. R has a slightly steeper learning curve than certain software like Stata, but it is also much more versatile. Once you’ve put in the effort to learn to use R for one task (say network analysis) you will find it easy to translate that knowledge to other domains (regression analysis, data visualization, text analysis, etc.).

Installing R There are many ways to run, and interact with R, but one of the simplest is to use the RStudio interface. RStudio is a bare-bones graphical interface to R that makes a few of the more arcane and common tasks much easier. It also provides a single graphical window where you can write scripts, execute commands, explore your data, see visualsizations, and access documentation.

R and RStudio are installed separately. If you are using an Apple Mac or a Microsoft Windows computer:

1. Install R by going to https://cran.rstudio.com/ and downloading the installer for your computer.
2. Install RStudio by going to https://rstudio.com/products/rstudio/download/ and download the free version of RStudio Desktop.

Once both of these have run, you should be able to open RStudio.

Note: the remainder of this lab is adapted from Data Carpentry’s “Introduction to R and RStudio” (CC-BY 4.0 by The Carpentries.)

Introduction to RStudio

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.

Workflow within RStudio

There are two main ways one can work within RStudio. Both of them create an “.R” file, which is just a text file on your computer that contains a series of R commands (an R script).

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 shortcut 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.

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. This where 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

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.

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:

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

3 + 5 * 2

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

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

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

Don’t worry about trying to remember every function in R. You can 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 != 2  # inequality (read as "is not equal to")

1 < 2  # less than

1 <= 1  # less than or equal to

1 > 0  # greater than

1 >= -9 # greater than or equal to

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:

x

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)

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
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.

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

mass <- 47.5

age <- 122

mass <- mass * 2.3

age <- age - 20

mass > age

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 <-.

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

min_height
max.height
MaxLength
celsius2kelvin

.mass

_age
min-length
2widths