• What is R?
  • Why learn R?
  • What is RStudio?
  • Create an R Project
    • Working directory
    • Exercise - creating folders
  • Interacting with R
  • How to learn more after these sessions?
  • Seeking help
    • Additional information


What is R?

R is a statistical programming language. It is very popular in the Data Science field, including Bioinformatics. The term “R” is used to refer to both the programming language and the software that interprets the scripts written using it.


Why learn R?

R does not involve lots of pointing and clicking

The learning curve might be steeper than with other software, but with R, the results of your analysis do not rely on remembering a succession of pointing and clicking, but instead on a series of written commands, and that’s a good thing! So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results; you just have to run your script again.

Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.

Working with scripts forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.


R code is great for reproducibility

Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.

R integrates with other tools to generate reports from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically after running the code again.

An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.


R is interdisciplinary and extendible

With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine statistical approaches from many scientific disciplines to best suit the analytical framework you need to analyse your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.


R works on data of different sizes

The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.

R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.

R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.


R produces high-quality graphics

The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.


R has a large and welcoming community

Thousands of people use R daily. Many of them are willing to help you through mailing lists and websites such as Stack Overflow, or on the RStudio community.


Not only is R free, but it is also open-source and cross-platform

Anyone can inspect the source code to see how R works. Because of this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.


What is RStudio?

RStudio is currently a very popular Integrated Development Environment (IDE) for working with R. An IDE is an application used by software developers that facilitates programming by offering source code editing, building and debugging tools all integarated into one application. To function correctly, RStudio needs R and therefore both need to be installed on your computer.

The RStudio Desktop open-source product is free under the Affero General Public License (AGPL) v3. Other versions of RStudio are also available.

We will use RStudio IDE to write code, navigate the files on our computer, inspect the variables we are going to create, and visualize the plots we will generate. RStudio can also be used for other things (e.g., version control, developing packages, writing Shiny apps) that we will not cover during these sessions.

RStudio interface screenshot. Clockwise from top left: Source, Environment/History, Files/Plots/Packages/Help/Viewer, Console.
RStudio interface screenshot. Clockwise from top left: Source, Environment/History, Files/Plots/Packages/Help/Viewer, Console.

RStudio is divided into 4 “Panes”: the Source for your scripts and documents (top-left, in the default layout), your Environment/History (top-right), your Files/Plots/Packages/Help/Viewer (bottom-right), and the R Console (bottom-left). The placement of these panes and their content can be customized (see menu, Tools -> Global Options -> Pane Layout).

One of the advantages of using RStudio is that all the information you need to write code is available in a single window. Additionally, with many short cuts, auto-completion and highlighting for the major file types you use while developing in R, RStudio will make typing easier and less error-prone.


Create an R Project

Before starting to write code in RStudio, we need to create an R Project. The idea behind an R project is to have a workspace where you can keep all the files and settings associated with the project together. In that way, next time you open the R Project it would be easier to resume work.

To create an “R Project”:

  1. Start RStudio.
  2. Under the File menu, click on New Project. Choose New Directory, then New Project.
  3. Enter a name for this new folder (or “directory”), and choose a convenient location for it. This will be your working directory for the rest of the day (e.g., ~/my-first-project).
  4. Click on Create Project.


Working directory

Whenever we are working on a project, it is good practice to keep a set of related data, analyses, and text self-contained in a single folder, called the working directory. All of the scripts within this folder can then use relative paths to files in the working directory that indicate where inside the project a file is located (as opposed to absolute paths, which point to where a file is on a specific computer). Working this way makes it a lot easier to move your project around on your computer and share it with others without worrying about whether or not the underlying scripts will still work.

Absolute vs Relative paths examples

Relative path: data/dataset1.txt

Absolute path: C:/Users/User1/Documents/R/my-first-project/data/dataset1.txt

Using RStudio projects makes it easy to organise your files in the project and ensures that your working directory is set properly. RStudio shows your current working directory at the top of your window:

Another way to check your working directory is by typing getwd() in the console pane. If for some reason your working directory is not what it should be, you can change it in the RStudio interface by navigating in the file browser where your working directory should be, and clicking on the blue gear icon “More”, and select “Set As Working Directory”. Alternatively you can use setwd("/path/to/working/directory") to reset your working directory (not recommended). However, your scripts should not include this line because it will fail on someone else’s computer.

Using a consistent folder structure across your projects will help keep things organized, and will also make it easy to find things in the future. This can be especially helpful when you have multiple projects. In general, you may create directories (folders) for scripts, data, and documents.

  • data/ Use this folder to store your raw data and intermediate datasets you may create for the need of a particular analysis. For the sake of transparency and provenance, you should always keep a copy of your raw data accessible and do as much of your data clean up and preprocessing programmatically (i.e., with scripts, rather than manually). Separating raw data from processed data is also a good idea. For example, you could have files data/raw/survey.plot1.txt and data/raw/survey.plot2.txt kept separate from a data_output/survey.csv file generated by the scripts/01.preprocess.survey.R script.
  • documents/ This would be a place to keep documentation and other text documents
  • scripts/ This would be the location to keep your R scripts for different analyses or plotting.

You may want additional directories or sub directories depending on your project needs, but these should form the backbone of your working directory.

Example of a working directory structure.
Example of a working directory structure.

For this course, we will keep things a bit simple and we’ll use a data_raw folder to store our raw data and data_processed for when we learn how to export data as CSV files. We will also need a scripts folder to store our scripts and a fig_output folder for the figures that we will save.

These directories do not exist yet, so we will have to create them after we have started our R project:

  • Under the Files tab on the right of the screen, click on New Folder to create a folder within your newly created working directory (e.g., ~/my-first-project/data).

Remember to try and avoid capitalisation and spaces (use the underscore instead).


Exercise - creating folders

  1. Create an R project, if you haven’t done so already.
  2. Create a data_raw, data_output, scripts and fig_output folder within your working directory.

Your working directory should now look along the lines of this:

How it should look like at the beginning of this lesson

How it should look like at the beginning of this lesson

The working directory is an important concept to understand. It is the place from where R will be looking for and saving the files. When you write code for your project, it should refer to files in relation to the root of your working directory and only need files within this structure.


Interacting with R

The basis of programming is that we write down instructions for the computer to follow, and then we tell the computer to follow those instructions. We write, or code, instructions in R because it is a common language that both the computer and we can understand. We call the instructions commands and we tell the computer to follow the instructions by executing (also called running) those commands.

There are two main ways of interacting with R: by using the console or by using script files (plain text files that contain your code). The console pane (in RStudio, the bottom left panel) is the place where commands written in the R language can be typed and executed immediately by the computer. It is also where the results will be shown for commands that have been executed. You can type commands directly into the console and press Enter to execute those commands, but they will be forgotten when you close the session.

Because we want our code and workflow to be reproducible, it is better to type the commands we want in the script editor, and save the script. This way, there is a complete record of what we did, and anyone (including our future selves!) can easily replicate the results on their computer.

RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter short cut (on Macs, Cmd + Return will work, too). The command on the current line in the script (indicated by the cursor) or all of the commands in the currently selected text will be sent to the console and executed when you press Ctrl + Enter. You can find other keyboard short cuts in this RStudio cheatsheet about the RStudio IDE.

At some point in your analysis you may want to check the content of a variable or the structure of an object, without necessarily keeping a record of it in your script. You can type these commands and execute them directly in the console. RStudio provides the Ctrl + 1 and Ctrl + 2 short cuts allow you to jump between the script and the console panes.

If R is ready to accept commands, the R console shows a > prompt. If it receives a command (by typing, copy-pasting or sent from the script editor using Ctrl + Enter), R will try to execute it, and when ready, will show the results and come back with a new > prompt to wait for new commands.

If R is still waiting for you to enter more data because it isn’t complete yet, the console will show a + prompt. It means that you haven’t finished entering a complete command. This is because you have not ‘closed’ a parenthesis or quotation, i.e. you don’t have the same number of left-parentheses as right-parentheses, or the same number of opening and closing quotation marks. When this happens, and you thought you finished typing your command, click inside the console window and press Esc; this will cancel the incomplete command and return you to the > prompt.


How to learn more after these sessions?

The material we cover during these sessions will give you an initial taste of how you can use R to analyse data for your own research. However, you will need to learn more to do advanced operations such as cleaning your data set, using statistical methods, or creating beautiful graphics. The best way to become proficient and efficient at R, as with any other tool, is to use it to address your actual research questions. As a beginner, it can feel daunting to have to write a script from scratch, and given that many people make their code available online, modifying existing code to suit your purpose might make it easier for you to get started.


Seeking help

What if you’re stuck? It happens. A lot. It’s normal and there is no need to panic. Thankfully, because it happens so often to so many people there are plenty of ways to get unstuck. Below are a few options to help you get going again.


Use the built-in RStudio help interface to search for more information on R functions

RStudio help interface.
RStudio help interface.

One of the fastest ways to get help, is to use the RStudio help interface. This panel by default can be found at the lower right hand panel of RStudio. As seen in the screenshot, by typing the word “Mean”, RStudio tries to also give a number of suggestions that you might be interested in. The description is then shown in the display window.


I know the name of the function I want to use, but I’m not sure how to use it

If you need help with a specific function, let’s say barplot(), you can type:

?barplot

If you just need to remind yourself of the names of the arguments, you can use:

args(lm)


I want to use a function that does X, there must be a function for it but I don’t know which one…

If you are looking for a function to do a particular task, you can use the help.search() function, which is called by the double question mark ??. However, this only looks through the installed packages for help pages with a match to your search request

??kruskal

If you can’t find what you are looking for, you can use the rdocumentation.org website that searches through the help files across all packages available.

Finally, a generic Google or internet search “R <task>” will often either send you to the appropriate package documentation or a helpful forum where someone else has already asked your question.


I am stuck… I get an error message that I don’t understand

Start by googling the error message. However, this doesn’t always work very well because often package developers rely on the error catching provided by R. You end up with general error messages that might not be very helpful to diagnose a problem (e.g. “subscript out of bounds”). If the message is very generic, you might also include the name of the function or package you’re using in your query.


Additional information

A good place to check is Stack Overflow. Search using the [r] tag. Most questions have already been answered, but the challenge is to use the right words in the search to find the answers: http://stackoverflow.com/questions/tagged/r


Asking for help

The key to receiving help from someone is for them to rapidly grasp your problem. You should make it as easy as possible to pinpoint where the issue might be.

Try to use the correct words to describe your problem. Be as precise as possible when describing your problem.

If possible, try to reduce what doesn’t work to a simple reproducible example. If you can reproduce the problem using a very small data set instead of your 50,000 rows and 10,000 columns one, provide the small one with the description of your problem. When appropriate, try to generalise what you are doing so even people who are not in your field can understand the question. For instance instead of using a subset of your real dataset, create a small (3 columns, 5 rows) generic one. For more information on how to write a reproducible example see this article by Hadley Wickham.


Where to ask for help?

  • The person sitting next to you during the course. Don’t hesitate to talk to your neighbour during the course, compare your answers, and ask for help. You might also be interested in organizing regular meetings following the course to keep learning from each other.
  • Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
  • Stack Overflow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 minutes. Remember to follow their guidelines on how to ask a good question.