Goals for today

  1. Continue to explore and practice graphing with ggplot

  2. Continue to explore and practice website setup and styling with GitHub Pages

  3. Integrate 1) and 2) to publish your data visualization on a website



Exercise 1: Explore the penguins dataset with ggplot (50 mins)


General instructions

Today, we’ll use ggplot to visually explore patterns in morphological traits in three different species of penguin. This data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, and were compiled by Dr. Allison Horst and colleagues.




Step 1: Get to know our data

Take a few minutes to explore the palmerpenguins homepage. (Note that this website is built using GitHub Pages!)



Step 2: Set up a GitHub-linked RStudio project to work in

Please create a new GitHub repo in your personal account named penguins, clone the repo to your computer, and work on your data exploration in this new repo.

Open an .Rmd template file (File -> New File -> R Markdown…). Delete the boilerplate text under the setup chunk (you can keep that chunk) and make four level 2 headers:

  • Data
  • Single trait distribution
  • Relationship between multiple traits
  • Sexual dimorphism


Under the Data header, add a short description of the dataset we’re using today and a picture of penguins.



Step 3: Load the data

Today’s dataset is provided in the R package palmerpenguins.

Let’s start by installing the palmerpenguins package so we can access the data. After installing the package we need to load it with the library() function. We also need to load the tidyverse package because it contains ggplot.

library(palmerpenguins) #install.packages("palmerpenguins")
library(tidyverse)


Then, let’s explore the penguins dataset. You might e.g. want to use functions like View(), dim(), colnames() , and ?. You will see that the dataset includes the following variables:

Column names
species
island
bill_length_mm
bill_depth_mm
flipper_length_mm
body_mass_g
sex
year



Step 4: Explore patterns in the data

In groups or on your own, go and explore patterns in the data with ggplot. You can look back to our lecture 6 notes or the RStudio ggplot cheatsheet for inspiration.

We provide one possible solution for each question, but we highly recommend that you don’t look at them unless you are really stuck.



The distribution of a single trait

First, under the “Single trait distribution” header, add some text and code chunks to explore the distribution in any one of the morphological traits in the penguin dataset.

For example, what is the lowest and highest bill lengths do penguins in this dataset have? Do different species have different bill lengths? How much do they overlap?

One possible solution
click to expand 
penguins %>%
  ggplot() +
  geom_density(mapping = aes(x = bill_length_mm, fill=species), alpha=0.5)

penguins %>%
  ggplot() +
  geom_histogram(mapping = aes(x = bill_length_mm, fill=species)) +
  facet_wrap(~species, nrow=3)



The relationship between multiple traits

Then, under the “Relationship between multiple traits” header, add some text and code chunks to explore the relationship between different morphological traits in the penguins dataset.

For example, what is the relationship between body mass and flipper length in penguins in this dataset? Does this relationship differ between species? Given the same body mass, which species of penguins tend to have the longest flippers?

One possible solution
click to expand 
penguins %>%
  ggplot(mapping = aes(x = body_mass_g, y=flipper_length_mm, color=species)) +
  geom_point() +
  geom_smooth(method="lm")



The level of sexual dimorphism

Lastly, under the “Sexual dimorphism” header, add some text and code chunks to explore the level of sexual dimorphism in different morphological traits in the penguins dataset.

For example, what traits are sexually dimorphic in the penguins dataset? Is the level of sexual dimorphism the same in all three penguin species?

One possible solution
click to expand 
penguins %>%
  ggplot(mapping = aes(x = bill_length_mm, y=bill_depth_mm, color=sex)) +
  geom_point(size=1) +
  facet_wrap(~species)

penguins %>%
  ggplot(mapping = aes(x=flipper_length_mm, y=sex, color=sex)) +
  geom_boxplot(outlier.alpha = 0, alpha=0) +
  geom_jitter(width = 0, size=1) +
  coord_flip() +
  facet_wrap(~species)



Free exploration

If you have finished these suggested activities ahead of time, please feel free to explore other aspects of the data. For example, you can look at any temporal trend in the dataset, or patterns within the same species across different islands.

E.g., Sample size in different years and islands
click to expand 
penguins %>%
  ggplot() +
  geom_bar(mapping = aes(x=year, fill=island)) +
  facet_wrap(~species)



Recap (10 minutes)

Share your findings, challenges, and questions with the class.



Short break (10 minutes)



Exercise 2: Publish your data visualizations on a website and explore different website styling options (35 minutes)


General instructions

For this exercise, you will build a GitHub Pages website as described in Lecture 5 and display our penguins data visualization result on this website. For this website, you will each build your own, so there is no need to invite a collaborator. Just make sure your repo is public to be able to build the site.


Suggested activities

  • You can split your RMarkdown file into separate files, so each section (i.e. data, life expectancy, fertility, infant mortality) becomes a separate page and can get it own tab. You can e.g. split your content into files named index.Rmd, single_trait.Rmd, multiple_traits.Rmd, and sexual_dimorphism.Rmd, and the add those as tabs in a _site.yml file, as described in the lecture notes

  • You can then consider adding a table of contents and changing the styling (theme) of your website, as described here


Remember that it may take a little while for your website to update after you have pushed your changes to GitHub, but you can always check the current build (after running rmarkdown::render_site()) in your Viewer pane in RStudio.



Recap (10 minutes)

Share your findings, challenges, and questions with the class.



END LAB 3