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Introduction to the R programming languange

Info

  • R is a powerful, popular open-source scripting language
  • RStudio allows you to run R in an easy-to-use interface and makes it easy to find help
  • A popular language for it's use in statistical analysis and visualisation tools
  • Create an RStudio project, and know the benefits of working within a project
  • Be able to locate and change the current working directory with getwd() and setwd()
  • Compose an R script file containing comments and commands
  • Understand what an R function is
  • Locate help for an R function using ?, ??, and args()
  • Use R to visualise population-specific frequencies of medically relevant genetic variants

A brief history of R

R has been around since 1995, and was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is based off the S programming language developed at Bell Labs and was developed to teach intro statistics.

Advantages of using R

At more than 20 years old, R is fairly mature and growing in popularity. However, programming isn't a popularity contest. Here are key advantages of analyzing data in R:

  • R is open source. This means R is free - an advantage if you are at an institution where you have to pay for your own MATLAB or SAS license. Open source is important to your colleagues in parts of the world where expensive software in inaccessible. It also means that R is actively developed by a community (see r-project.org), and there are regular updates.
  • R is widely used. Ok, maybe programming is a popularity contest. Because R is used in many areas (not just bioinformatics), you are more likely to find help online when you need it. Chances are, almost any error message you run into, someone else has already experienced.
  • R is powerful. R runs on multiple platforms (Windows/MacOS/Linux). It can work with much larger datasets than popular spreadsheet programs like Microsoft Excel, and because of its scripting capabilities is far more reproducible. Also, there are thousands of available software packages for science, including genomics and other areas of life science.

Introducing RStudio Server

In these lessons, we will be making use of a software called RStudio, an Integrated Development Environment (IDE). RStudio, like most IDEs, provides a graphical interface to R, making it more user-friendly, and providing dozens of useful features. We will introduce additional benefits of using RStudio as you cover the lessons.

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  • Source: This pane is where you will write/view R scripts. Some outputs (such as if you view a dataset using View()) will appear as a tab here.
  • Console/Terminal/Jobs: This is actually where you see the execution of commands. This is the same display you would see if you were using R at the command line without RStudio. You can work interactively (i.e. enter R commands here), but for the most part we will run a script (or lines in a script) in the source pane and watch their execution and output here. The "Terminal" tab give you access to the BASH terminal (the Linux operating system, unrelated to R). RStudio also allows you to run jobs (analyses) in the background. This is useful if some analysis will take a while to run. You can see the status of those jobs in the background.
  • Environment/History: Here, RStudio will show you what datasets and objects (variables) you have created and which are defined in memory. You can also see some properties of objects/datasets such as their type and dimensions. The "History" tab contains a history of the R commands you've executed R.
  • Files/Plots/Packages/Help/Viewer: This multipurpose pane will show you the contents of directories on your computer. You can also use the "Files" tab to navigate and set the working directory. The "Plots" tab will show the output of any plots generated. In "Packages" you will see what packages are actively loaded, or you can attach installed packages. "Help" will display help files for R functions and packages. "Viewer" will allow you to view local web content (e.g. HTML outputs).

Create an RStudio project

One of the first benefits we will take advantage of in RStudio is something called an RStudio Project. An RStudio project allows you to more easily:

  • Save data, files, variables, packages, etc. related to a specific analysis project
  • Restart work where you left off
  • Collaborate, especially if you are using version control such as git.

  1. To create a project, go to the File menu, and click New Project

    image

  2. In the window that opens select Existing Directory
    Then select Browse....
    Choose and then click "~/Documents/IndigiDataIntroToR".

  3. Finally click Create Project. In the "Files" tab of your output pane (more about the RStudio layout in a moment), you should see an RStudio project file, IndigiDataIntroToR.Rproj. All RStudio projects end with the ".Rproj" file extension.

Creating your first R script

Now that we are ready to start exploring R, we will want to keep a record of the commands we are using. To do this we can create an R script:

Click the File menu and select New File and then R Script. Before we go any further, save your script by clicking the save/disk icon that is in the bar above the first line in the script editor, or click the File menu and select save. In the "Save File" window that opens, name your file "indigidataIntroToR". The new script indigidataIntroToR.R should appear under "files" in the output pane. By convention, R scripts end with the file extension .R.

Getting to work with R: navigating directories

Now that we have covered the more aesthetic aspects of RStudio, we can get to work using some commands. We will write, execute, and save the commands we learn in our indigidataIntroToR.R script that is loaded in the Source pane. First, lets see what directory we are in. To do so, type the following command into the script:

R-project

getwd()

To execute this command, make sure your cursor is on the same line the command is written. Then click the Run button that is just above the first line of your script in the header of the Source pane.

In the console, we expect to see the following output :

[1] "/home/Documents/IndigiDataIntroToR"

  • Notice, at the Console, you will also see the instruction you executed above the output in blue.

Since we will be learning several commands, we may already want to keep some short notes in our script to explain the purpose of the command. Entering a # before any line in an R script turns that line into a comment, which R will not try to interpret as code. Edit your script to include a comment on the purpose of commands you are learning, e.g.:

R-project

`# this command shows the current working directory

getwd()`

Exercise : Work interactively in R

What happens when you try to enter the getwd() command in the Console pane?

Solution

You will get the same output you did as when you ran getwd() from the source. You can run any command in the Console, however, executing it from the source script will make it easier for us to record what we have done, and ultimately run an entire script, instead of entering commands one-by-one.

For the purposes of this exercise we want you to be in the directory "/home/<USERID>/IndigiDataIntroToR". What if you weren't? You can set your home directory using the setwd() command. Alternatively, RStudio has a helpful button for setting your working directory (note that this is a very simplified way to set your working directory, and this is actually a bit more complex in practice.)

Using functions in R, without needing to master them

A function in R (or any computing language) is a short program that takes some input and returns some output. Functions may seem like an advanced topic (and they are), but you have already used at least one function in R. getwd() is a function! The next sections will help you understand what is happening in any R script.

Exercise: What do these functions do?

Try the following functions by writing them in your script. See if you can guess what they do, and make sure to add comments to your script about your assumed purpose. - dir() - sessionInfo() - date() - Sys.time()

Solution
  • dir() # Lists files in the working directory
  • sessionInfo() # Gives the version of R and additional info including on attached packages
  • date() # Gives the current date
  • Sys.time() # Gives the current time
  • .libPaths() # Shows what libraries are available

Notice: Commands are case sensitive!

You have hopefully noticed a pattern - an R function has three key properties:

  • Functions have a name (e.g. dir, getwd); note that functions are case sensitive!

  • Following the name, functions have a pair of ()

  • Inside the parentheses, a function may take 0 or more arguments.

An argument may be a specific input for your function and/or may modify the function's behavior. For example the function round() will round a number with a decimal:

R-project

# This will round a number to the nearest integer
round(3.14)

Getting help with function arguments

What if you wanted to round to one significant digit? round() can do this, but you may first need to read the help to find out how. To see the help (In R sometimes also called a "vignette") enter a ? in front of the function name:

R-project

?round()

The "Help" tab will show you information (often, too much information). You will slowly learn how to read and make sense of help files. Checking the "Usage" or "Examples" headings is often a good place to look first. If you look under "Arguments," we also see what arguments we can pass to this function to modify its behavior. You can also see a function's argument using the args() function:

R-project

args(round)

round() takes two arguments, x, which is the number to be rounded, and a digits argument. The = sign indicates that a default (in this case 0) is already set. Since x is not set, round() requires we provide it, in contrast to digits where R will use the default value 0 unless you explicitly provide a different value. We can explicitly set the digits parameter when we call the function:

R-project

round(3.14159, digits = 2)

Or, R accepts what we call "positional arguments", if you pass a function arguments separated by commas, R assumes that they are in the order you saw when we used args(). In the case below that means that x is 3.14159 and digits is 2.

R-project

round(3.14159, 2)

Finally, what if you are using ? to get help for a function in a package not installed on your system, such as when you are running a script which has dependencies.

R-project

geom_point()

will return an error:

Error

Error in .helpForCall(topicExpr, parent.frame()) :
   no methods for ‘geom_point’ and no documentation for it as a function

Use two question marks (i.e. ??geom_point()) and R will return results from a search of the documentation for packages you have installed on your computer in the "Help" tab. Finally, if you think there should be a function, for example a statistical test, but you aren't sure what it is called in R, or what functions may be available, use the help.search() function.

Exercise: Searching for R functions

Use help.search() to find R functions for the following statistical functions. Remember to put your search query in quotes inside the function's parentheses.

  • Chi-Squared test
  • Student t-test
  • mixed linear model
Success

While your search results may return several tests, we list a few you might find:

  • Chi-Squared test: stats::Chisquare
  • Student t-test: stats::t.test
  • mixed linear model: stats::lm.glm