Introduction and welcome

Welcome to the Sensometrics Workshop “Publication-quality data visualizations using the R tidyverse”!

This workshop is going to be conducted not using slides, but through livecoding. That means we are going to run code lines in the console or highlight and run code in scripts and other files. It is also an opportunity and encouragement for you to follow along. Along with introducing ourselves for today’s workshop, we’re going to discuss a bit about how that will work here.

Introductions

Leah Hamilton, PhD

Leah Hamilton is an Assistant Professor of Sensory & Flavor Science at Virginia State University, in the US. Her primary research interest is flavor language, including the ways that people talk about flavors using their own words in different contexts. In her new position at Virginia State University, she’ll be working closely with plant breeders and agricultural scientists to develop sensory-driven specialty crops and support the work of small and underprivileged farmers in the mid-Atlantic US.

Jacob Lahne, PhD

Jacob Lahne is an Associate Professor of Food Science & Technology at Virginia Tech, in the United States. He runs the Virginia Tech Sensory Evaluation Laboratory, as well as teaching courses in data analytics and coding for food-science research. His main research focuses are sensory data-analysis methodologies and investigating the sensory properties of fermented and distilled foods and beverages.

Today’s agenda

Today’s workshop is going to take ~3 hours, and we’ll be covering the following material:

Importing Data
1. Reading tabular data
2. Checking data fidelity
Basics of Tidy Data Analysis
Intro to ggplot2
Other Packages & Extras
1. Adding layers and geoms (e.g., ggrepel)
2. Plot-builders (e.g., factoextra)
3. Combining plots
Fine-Tuning Publication-Quality ggplots
1. Exporting plots
2. Formatting text
3. Ordering categorical variables

How we’re going to run

This workshop is going to be run with livecoding, as noted above. This means we won’t be using slides or a prepared video, but running through code step by step to show how these tools are used in practice. We encourage you to also follow along with livecoding, because the best way to learn coding is to actually do it.

Recommended approach for livecoding

We recommend that you download the pre-made archive of code and data from the workshop github repo. This archive, when unzipped, will have a folder structure and a .Rproj file. We recommend that you close out RStudio, unzip the archive, and double click the .Rproj file in that folder, which will open a new session of RStudio with proper settings (like the home directory) for the files for this workshop.

In that folder, you will find a data/ folder with the necessary data for the workshop, and a script named sensometrics-all-code.R. This latter file contains all of the code demonstrated in this workshop for your future reference. You can also follow along with the code at the workshop’s page hosted on github.io (which you’re reading right now), and which will remain available after this workshop.

Once you’re in RStudio, go to the File > New File > R Script menu to open a new script. Scrips are basically workbooks for you to store sequential lines of code to be run in the Console. It is where you can livecode along! Even though we are giving you all of the code you need right now, you will learn a lot more if you actively write out the code to follow along, rather than just running the entire code file.

The Console is the place to run code that is short and easy to type, or that you’re experimenting with. It will allow you to write a single line of code, and after you hit return, R will execute the command. This is great for “interactive programming”, but it isn’t so great for building up a complex workflow, or for following along with this workshop!

You can write multiple lines of code in your R Script, then execute each one in any order (although keeping a logical sequence from top to bottom will help you keep track of what you’re doing). In an R script, everything is expected to be valid R code.

You can't write this in an R script because it is plain text.  This will
cause an error.

# If you want to write text or notes to yourself, use the "#" symbol at the start of 
# every line to "comment" out that line.  You can also put "#" in the middle of
# a line in order to add a comment - everything after will be ignored.

1 + 1 # this is valid R syntax

print("hello world") # this is also valid R syntax

To run code from your R script, put your cursor on the line you want to run and either hit the run button with the green arrow at the top left or (my preferred method) type cmd + return (on Mac) or ctrl + return (on PC).

Dealing with errors

Coding means making mistakes. This is fine–as you will surely see today, we will make a ton of trivial errors and have to fix things on the fly. If you run into trouble, try looking carefully at what you’ve done and see if you can see what went wrong. You can also make use of the help files in R.

You can always get help on a particular function by typing ?<search term>, which will make the help documentation for whatever you’ve searched for appear. For example, try typing the following to get help for the sessionInfo() command:

?sessionInfo

But what if you don’t know what to search for?

By typing ??<search term> you will search all help files for the search term. R will return a list of matching articles to you in the help pane. This is considerably slower, since it’s searching hundreds or thousands of text files. Try typing ??install into your console to see how this works.

You will notice that there are two types of results in the help list for install. The help pages should be familiar. But what are “vignettes”? Try clicking on one to find out.

Vignettes are formatted, conversational walkthroughs that are increasingly common (and helpful!) in R packages. Rather than explaining a single function they usually explain some aspect of a package, and how to use it. And, even better for our purposes, they are written in R Markdown. Click the “source” link next to the vignette name in order to see how the author wrote it in R Markdown. This is a great way to learn new tricks.

While you can find vignettes as we just did, a better way is to use the function browseVignettes(). This opens a web browser window that lists all vignettes installed on your computer. You can then use cmd/ctrl + F to search using terms in the web browser and quickly find package names, function names, or topics you are looking for.

PSA: not-knowing is normal!

Above, I mentioned “help files”. How do we get help when we (inevitably) run into problems in R? There are a couple steps you will find helpful in the future:

Look up the help file for whatever you’re doing. Do this by using the syntax ?<search item> (for example ?c gets help on the vector command) as a shortcut on the console.
Search the help files for a term you think is related. Can’t remember the command for making a sequence of integers? Go to the “Help” pane in RStudio and search in the search box for “sequence”. See if some of the top results get you what you need.
The internet. Seriously. I am not kidding even a little bit. R has one of the most active and (surprisingly) helpful user communities I’ve ever encountered. Try going to google and searching for “How do I make a sequence of numbers in R?” You will find quite a bit of useful help. I find the following sites particularly helpful
1. Stack Overflow
2. Cross Validated/Stack Exchange
3. Seriously, Google will get you most of the way to helpful answers for many basic R questions.

We may come back to this, but I want to emphasize that looking up help is normal. I do it all the time. Learning to ask questions in helpful ways, how to quickly parse the information you find, and how to slightly alter the answers to suit your particular situation are key skills.

Getting Help

If you get stuck and can’t figure out what went wrong, we are here to help! Because we have 2 instructors for this workshop, one of us is available to help at any time.

When you run into trouble, please raise your hand. We’ll be keeping an eye out, and whichever instructor isn’t livecoding will come to help you. If your issue is a common one or something we think is worth noting, don’t worry–we’ll make time to discuss it!

Virginia Tech, jlahne@vt.edu ↩︎
Virginia State University, lhamilton@vsu.edu ↩︎