After completing this module, the learner should be able to:
- Describe the purpose of the RStudio script, console, environment, and plot windows.
- Organize files and directories for a set of analysis in an R Project.
- 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.
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.
The learning curve might be steeper than with other software, but with R, you can save all the steps you used to go from the data to the results. 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 deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
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 manuscripts 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.
An increasing number of journals and funding agencies expect analyses to be reproducible, so knowing R will give you an edge with these requirements.
With 10,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine analyses across many scientific disciplines to best suit the analyses you want to use on your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.
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.
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.
Thousands of people use R daily. Many of them are willing to help you through mailing lists and Stack Overflow.
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.
Let’s start by learning about RStudio, the Integrated Development Environment (IDE).
The RStudio IDE open source product is free under the Affero General Public License (AGPL) v3. 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 found 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, writting Shiny apps) that we will not cover during the workshop.
RStudio is divided into 4 “Panes”: the editor for your scripts and documents (top-left, in the default layout), the R console (bottom-left), your environment/history (top-right), and your files/plots/packages/help/viewer (bottom-right). The placement of these panes and their content can be customized (see menu, RStudio -> Preferences -> 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.
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 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.
RStudio provides a helpful set of tools to do this 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. Below, we will go through the steps for creating an “R Project” for this tutorial.
Filemenu, click on
New project, choose
New directory, then
Filestab on the right of the screen, click on
New Folderand create a folder named
datawithin your newly created working directory. (e.g.,
Your working directory should now look like this:
Using a consistent folder structure across your projects will help keep things organized, and will also make it easy find/file 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 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
...plot2.txtkept separate from a
data/processed/tree.survey.csvfile generated by the
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. For this workshop, we will need a
data/ folder to store our raw data, and we will create later a
data_output/ folder when we learn how to export data as CSV files.
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). We want our code and workflow to be reproducible. In other words, we want to write code in a way that anyone can easily replicate our results on their computer.
The console pane (in RStudio, the bottom left panel) is the place where R is waiting for you to tell it what to do, and where it will show the results of a command that has 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. It is better to enter the commands in the script editor, and save the script. This way, you have a complete record of what you did, you can easily show others how you did it and you can do it again later on if needed. RStudio allows you to execute commands directly from the script editor by using the
Enter shortcut. The command on the current line in the script or all of the commands in the currently selected text will be sent to the console and executed when you press
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
2 shortcuts allow you to jump between the script and the console windows.
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
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. If you’re in RStudio and this happens, click inside the console window and press
Esc; this will cancel the incomplete command and return you to the
If you need help with a specific function, let’s say
barplot(), you can type:
If you just need to remind yourself of the names of the arguments, you can use:
If you are looking for a function to do a particular task, you can use
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
If you can’t find what you are looking for, you can use the rdocumention.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 question that someone else already asked.
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.
The key to get help from someone is for them to grasp your problem rapidly. 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 row) 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
## structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4), ## Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9), Petal.Length = c(1.4, ## ...
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 questions is not related to a
data.frame, you can save any R object to a file:
The content of this file is however not human readable and cannot be posted directly on Stack Overflow. It can however be sent to someone by email who can read it with this command:
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.
## R version 3.3.3 (2017-03-06) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 16.10 ## ## locale: ##  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ##  LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 ##  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 ##  LC_PAPER=en_US.UTF-8 LC_NAME=C ##  LC_ADDRESS=C LC_TELEPHONE=C ##  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ##  stats graphics grDevices utils datasets base ## ## other attached packages: ##  BiocInstaller_1.24.0 ## ## loaded via a namespace (and not attached): ##  backports_1.0.5 magrittr_1.5 rprojroot_1.2 tools_3.3.3 ##  htmltools_0.3.5 yaml_2.1.14 Rcpp_0.12.9 stringi_1.1.2 ##  rmarkdown_1.3 knitr_1.15.1 methods_3.3.3 stringr_1.2.0 ##  digest_0.6.12 evaluate_0.10
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).