Learning Objectives

• Describe the purpose of RStudio’s script, console, environment, and file/plot/help windows.
• Create an R project.
• Organize files and directories for a set of analyses as an R Project.
• Use the built-in RStudio help interface to search for more information on R functions.

Presentation of Azure Jupyter Notebooks

Jupyter (formerly IPython), is a multi-lingual REPL.

Azure Notebooks is a free service that provides Jupyter notebooks along with supporting packages for R, Python and F# as a service. This means you can just login and get going since no installation/setup is necessary.

What can you do with a Jupyter Notebooks

• You can use Jupyter notebooks to write Python 2, Python 3 and R code or any of ~40 other languages interactively
• You can do your research, do analysis, produce a report, save it as PDF, HTML, a notebook, etc.
• You can hold a classroom or give a seminar with students following along in a copy of your Shared notebook
• You make a code and workflow are reproducible

Our end goal is not just to “do stuff” but to do it in a way that anyone can easily and exactly replicate our workflow and results. For more information or help on using a Jupyter notebook http://nbviewer.jupyter.org/github/ipython/ipython/blob/3.x/examples/Notebook/Index.ipynb.

Before we get started

You will need to have created a Microsoft account to use Azure notebooks if you did not already please go here https://notebooks.azure.com/ and signup for an account.

• Sign in to Azure Notebooks https://notebooks.azure.com/
• Click the Libraries Menu item (upper left)
• Click the “New Library” button and fill in the dialog with “bioworkshop” as the values for name and id and make the library private.
• Click the “New” button and create a notebok named “uhbiogeoworkshop.ipynb” and select R as the language and click the blue “New” button - this will create a new Jupyter notebook.
• In the table of notebooks your “uhbiogeoworkshop.ipynb” should be availabe if you click it it will open the notebook

Now you have Jupyter notebook to interact with for todays workshop.

Interacting with R

There are three main ways of interacting with R: using the R-shell, Jupyter notebooks or by using script files (plain text files that contain your code).

The R shell 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. You can type commands directly into the shell, but they will be forgotten when you close the session. It is better to enter the commands in a Jypter notebook or script file, 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.

If R shell 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, 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.

Basics of R

R is a versatile, open source programming/scripting language that’s useful both for statistics but also data science. Inspired by the programming language S.

• Open source software under GPL.
• Superior (if not just comparable) to commercial alternatives. R has over 7,000 user contributed packages at this time. It’s widely used both in academia and industry.
• Available on all platforms.
• Not just for statistics, but also general purpose programming.
• For people who have experience in programmming: R is both an object-oriented and a so-called functional language
• Large and growing community of peers.

Organizing your working directory

You should separate the original data (raw data) from intermediate datasets that you may create for the need of a particular analysis. For instance, you may want to create a data/ directory within your working directory that stores the raw data, and have a data_output/ directory for intermediate datasets and a figure_output/ directory for the plots you will generate.

For Azure Notebooks all data uploaded is with the notebook so there is no folder structure - other implementations of Jupyter notebooks might access a file system.

Seeking help

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)

If the function is part of a package that is installed on your computer but don’t remember which one, you can type:

??geom_point

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 help.search() (but only looks through the installed packages):

help.search("kruskal")

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

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”).

However, you should check stackoverflow. 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 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.

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

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:

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

The content of this file is however not human readable and cannot be posted directly on stackoverflow. 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.

sessionInfo()

Where to ask for help?

• Your friendly colleagues: if you know someone with more experience than you, they might be able and willing to help you.
• Stackoverlow: if your question hasn’t been answered before and is well crafted, chances are you will get an answer in less than 5 min.
• The R-help: 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 everywhere 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.
• 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