Getting Started: R and RStudio

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Learning Objectives

• Describe the purpose of the RStudio Script, Console, Environment, and Plots panes.
• Organize files and directories for a set of analyses as an R Project, and understand the purpose of the working directory.
• Use the built-in RStudio help interface to search for more information on R functions.
• Demonstrate how to provide sufficient information for troubleshooting with the R user community.

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What is R? What is RStudio?

The term “R” is used to refer to both the programming language and the software that interprets the scripts written using it.

RStudio is currently a very popular way to not only write your R scripts but also to interact with the R software. To function correctly, RStudio needs R and therefore both need to be installed on your computer.

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Knowing your way around RStudio

Let’s start by learning about RStudio, which is an Integrated Development Environment (IDE) for working with R.

The RStudio IDE open-source product is free under the Affero General Public License (AGPL) v3. The RStudio IDE is also available with a commercial license and priority email support from RStudio, Inc.

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 the workshop.

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 shortcuts, autocompletion, and highlighting for the major file types you use while developing in R, RStudio will make typing easier and less error-prone.

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Getting set up: Global Options

In R, the workspace is the current working environment, which includes all defined objects loaded into R - this includes objects, data, functions, etc. By default, when you exit R, this environment is saved to a file called .RData. Sometimes, saving a workspace can be cumbersome, especially if you are working with larger datasets or a lot of objects. So, let’s turn that option to save off. In RStudio, go to ‘Tools’ –> ‘Global Options’ and select the ‘Never’ option for ‘Save workspace to .RData on exit’:

Set ‘Save workspace to .RData on exit’ to ‘Never’.

Getting set up: Creating the Project

It is good practice to keep a set of related data, analyses, and text self-contained in a single project folder in RStudio. 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.

RStudio provides a helpful set of tools to set up a project through its “Projects” interface, which not only creates a working directory for you, but also remembers its location (allowing you to quickly navigate to it) and optionally preserves custom settings and open files to make it easier to resume work after a break. Go through the steps for creating an “R Project” for this tutorial below.

1. Start RStudio.
2. Under the File menu, click on New Project. Choose Existing Directory, then browse through your local files to select the folder created for your workshop download.
3. Click on Create Project. An .Rproj file is then automatically added to the project folder, with the name of the folder. This file marks the location of your project’s working directory.

The working directory

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. As mentioned before, the .Rproj file created when you first created the project is located in the working directory. RStudio will now set your working directory as the folder where your xxx.Rproj file lives. To verify the location of your working directly, type the following in your console:

getwd()

The returned string will be the absolute path to your project, such as C:/Users/RFeynman/Projects/R-Carpentry

In the fictional example above, the name of the project file would be R-Carpentry.Rproj

Getting set up: File organization for your Future Self (and Others, too!)

During this workshop you will be creating subfolders and you will add files to those subfolders. Note that they will be stored in levels below the working directory and their file paths will be relative to the location of the working directory. In that way, the entire project is structured to be portable – if you move the project to another location on your computer, or to another computer, RStudio will treat the project with integrity: it will recognize the .Rproj file; set the working directory to that same location; and accurately follow the structure of the subfolders and their files.

Using a consistent folder structure across your projects will help keep things organized, and will also make it easy to find/file things in the future. This can be especially helpful when you have multiple projects. In general, one recommended structure would include 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 cleanup and preprocessing programmatically (i.e., with scripts, rather than manually) as possible. Separating raw data from processed data is also a good idea. For example, you could have files data/raw/tree_survey.plot1.txt and ...plot2.txt kept separate from a data/processed/tree.survey.csv file generated by the scripts/01.preprocess.tree_survey.R script.
• documents/ This would be a place to keep outlines, drafts, and other text.
• scripts/ This would be the location to keep your R scripts for different analyses or plotting, and potentially a separate folder for your functions (more on that later).

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

Example of a working directory structure.

For this workshop, we will need a data/ folder to store our raw data, and we will use data_output/ for when we learn how to export data as CSV files, and fig_output/ folder for the figures that we will save. If you downloaded and expanded the .zip file, these directories, along with a img/ folder, should be in a directory called carpentry-workshop.

If you want to add a directory, you can do so in two ways. Let’s add a folder called documents:

• Method 1: Under the Files tab on the right of the screen, click on New Folder and create a folder named documents within your newly created working directory (e.g., ~/carpentry-workshop/documents).
• Method 2: Type dir.create("documents") at your R console.

We are going to keep the scripts in the root of our working directory because we are only going to use one file and it will make things easier.

Your working directory should now look like this:

Starting directory structure in RStudio.

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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.

Note: In RStudio, the History tab will record all console entries until it is cleared. If you forget to use a script, or decide to use one later, you can export lines from the History to either the Console, or to a file.

RStudio allows you to execute commands directly from the script editor by using the Ctrl + Enter shortcut (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 shortcuts in this RStudio cheatsheet about the RStudio IDE.

Practice With Scripts!

Let’s practice with scripts, as well as get our R environment ready for work.

1. From the Files tab (bottom-right pane), double-click the install_packages.R file to open it in the File viewer (top left).
2. This file contains commands to download and install a number of packages we will need throughout our lessons. We could install each package one by one by parsing through the lines and using the shortcut above, or we can run all the commands by clicking the Source button in the top right of the file pane. Let’s do that.
3. A whole bunch of packages will now be downloaded and installed into R. Note: if you get a message asking you to restart R, select Yes.
4. Repeat steps 1 through 3 with the file load_packages.R.

Scripts are a great way to setup your working environment in terms of making sure any needed packages are installed and loaded prior to starting your analysis!

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 shortcuts 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 the workshop?

The material we cover during this workshop will give you an initial taste of how you can use R to analyze data for your own research. However, you will need to learn more to do advanced operations such as cleaning your dataset, 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.

How to actually learn programming.

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Seeking help

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

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

> ??krusal

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.

However, you should check 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

The Introduction to R can also be dense for people with little programming experience but it is a good place to understand the underpinnings of the R language.

The R FAQ is dense and technical but it is full of useful information.

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. For instance, a package is not the same thing as a library. Most people will understand what you meant, but others have really strong feelings about the difference in meaning. The key point is that it can make things confusing for people trying to help you. 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 frame 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 generalize 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.

To share an object with someone else, if it’s relatively small, you can use the function dput(). It will output R code that can be used to recreate the exact same object as the one in memory:

dput(head(iris)) # iris is an example data frame that comes with R and head() is a function that returns the first part of the data frame

If the object is larger, provide either the raw file (i.e., your CSV file) with your script up to the point of the error (and after removing everything that is not relevant to your issue). Alternatively, in particular if your question is not related to a data frame, you can save any R object to a file:

saveRDS(iris, file="/tmp/iris.rds")

The content of this file is however not human readable and cannot be posted directly on Stack Overflow. Instead, it can be sent to someone by email who can read it with the readRDS() command (here it is assumed that the downloaded file is in a Downloads folder in the user’s home directory):

some_data <- readRDS(file="~/Downloads/iris.rds")

Last, but certainly not least, always include the output of sessionInfo() as it provides critical information about your platform, the versions of R and the packages that you are using, and other information that can be very helpful to understand your problem.

sessionInfo()

Where to ask for help?

• The person sitting next to you during the workshop. Don’t hesitate to talk to your neighbor during the workshop, compare your answers, and ask for help. You might also be interested in organizing regular meetings following the workshop 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 min. Remember to follow their guidelines on how to ask a good question.
• The R-help mailing list: it is read by a lot of people (including most of the R core team), a lot of people post to it, but the tone can be pretty dry, and it is not always very welcoming to new users. If your question is valid, you are likely to get an answer very fast but don’t expect that it will come with smiley faces. Also, here more than anywhere else, be sure to use correct vocabulary (otherwise you might get an answer pointing to the misuse of your words rather than answering your question). You will also have more success if your question is about a base function rather than a specific package.
• If your question is about a specific package, see if there is a mailing list for it. Usually it’s included in the DESCRIPTION file of the package that can be accessed using packageDescription("name-of-package"). You may also want to try to email the author of the package directly, or open an issue on the code repository (e.g., GitHub).
• There are also some topic-specific mailing lists (GIS, phylogenetics, etc…), the complete list is here.

More resources

• The Posting Guide for the R mailing lists.
• How to ask for R help useful guidelines
• This blog post by Jon Skeet has quite comprehensive advice on how to ask programming questions.
• The reprex package is very helpful to create reproducible examples when asking for help. The [rOpenSci community call “How to ask questions so they get answered”], rOpenSci Blog and video recording includes a presentation of the reprex package and of its philosophy.