Learning goals

Visualising numerical and categorical data and interpreting visualisations
Recreating visualizations
Getting more practice using R, RStudio, and Quarto

Getting started

Open the project for this assignment in the course Posit Cloud workspace.

Open the Quarto document lab-02.qmd and render it before making any changes. Make sure the document renders without errors.

This first render is a useful check that the project, packages, data, and files are all working correctly.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

Update the YAML, changing the author name to your name.
Render the document.
Open the rendered document and confirm that your name appears correctly.
Recall that RStudio is divided into four panes. Without looking, can you name them all and briefly describe their purpose?
Verify that the dataset has loaded into the Environment. How many observations are in the dataset? Clicking on the dataset in the Environment will allow you to inspect it more carefully. Alternatively, you can type View(plastic_waste) into the Console to do this.

⊕Hint: If you’re not sure, run the command ?NA, which will lead you to the documentation.

Have a quick look at the data and notice that there are cells taking the value NA – what does this mean?

🧶 ✅ Throughout this lab, remember to render your document regularly. After rendering, check the output to make sure your code, figures, and written responses appear as you expect.

Packages

We’ll use the tidyverse package for this analysis. Run the following code to load this package.

library(tidyverse)

If the package is not installed, run the following in your Console:

install.packages("tidyverse")

You only need to install a package once. Do not put install.packages() commands in the code you render for your assignment.

Data

The dataset for this assignment can be found as a CSV file in the data folder of your project. You can read it in using the following:

plastic_waste <- read_csv("data/plastic-waste.csv")

The variable descriptions are as follows:

code: 3 Letter country code
entity: Country name
continent: Continent name
year: Year
gdp_per_cap: GDP per capita constant 2011 international $, rate
plastic_waste_per_cap: Amount of plastic waste per capita in kg/day
mismanaged_plastic_waste_per_cap: Amount of mismanaged plastic waste per capita in kg/day
mismanaged_plastic_waste: Tonnes of mismanaged plastic waste
coastal_pop: Number of individuals living on/near coast
total_pop: Total population according to Gapminder

Exercises

Let’s start by taking a look at the distribution of plastic waste per capita in 2010.

ggplot(data = plastic_waste, aes(x = plastic_waste_per_cap)) +
  geom_histogram(binwidth = 0.2)

## Warning: Removed 51 rows containing non-finite outside the scale
## range (`stat_bin()`).

One country stands out as an unusual observation at the top of the distribution. One way of identifying this country is to filter the data for countries where plastic waste per capita is greater than 3.5 kg/person.

plastic_waste |>
  filter(plastic_waste_per_cap > 3.5)

## # A tibble: 1 × 10
##   code  entity              continent      year gdp_per_cap
##   <chr> <chr>               <chr>         <dbl>       <dbl>
## 1 TTO   Trinidad and Tobago North America  2010      31261.
##   plastic_waste_per_cap mismanaged_plastic_waste_per_cap
##                   <dbl>                            <dbl>
## 1                   3.6                             0.19
## # ℹ 3 more variables: mismanaged_plastic_waste <dbl>,
## #   coastal_pop <dbl>, total_pop <dbl>

Did you expect this result? You might consider doing some research on Trinidad and Tobago to see why plastic waste per capita is so high there, or whether this is a data error. If it is a data error, you will want to filter out this observation for the following exercises.

✏️ Plot, using histograms, the distribution of plastic waste per capita faceted by continent. How do the continents compare to each other in terms of their plastic waste per capita?

⊕NOTE: From this point onwards the plots and the output of the code are not displayed in the lab instructions, but you can and should run the code and view the results yourself.

Another way of visualizing numerical data is using density plots.

ggplot(data = plastic_waste, aes(x = plastic_waste_per_cap)) +
  geom_density()

And compare distributions across continents by colouring density curves by continent.

ggplot(
  data = plastic_waste,
  mapping = aes(
    x = plastic_waste_per_cap,
    color = continent
  )
) +
  geom_density()

The resulting plot may be a little difficult to read, so let’s also fill the curves in with colours as well.

ggplot(
  data = plastic_waste,
  mapping = aes(
    x = plastic_waste_per_cap,
    color = continent,
    fill = continent
  )
) +
  geom_density()

The overlapping colours make it difficult to tell what’s happening with the distributions in continents plotted first, and hence covered by continents plotted over them. We can change the transparency level of the fill color to help with this. The alpha argument takes values between 0 and 1: 0 is completely transparent and 1 is completely opaque. There is no way to tell what value will work best, so you just need to try a few.

ggplot(
  data = plastic_waste,
  mapping = aes(
    x = plastic_waste_per_cap,
    color = continent,
    fill = continent
  )
) +
  geom_density(alpha = 0.7)

This still doesn’t look great…

Recreate the density plots above using a different (lower) alpha level that works better for displaying the density curves for all continents.
✏️ Describe why we defined the color and fill of the curves by mapping aesthetics of the plot but we defined the alpha level as a characteristic of the plotting geom.

🧶 ✅ Complete Exercises 1–3 and render the document. Check that your plots appear correctly and that your written responses for Exercises 1 and 3 are included and refer to the output produced by your code.

And yet another way to visualize this relationship is using side-by-side box plots.

ggplot(
  data = plastic_waste,
  mapping = aes(
    x = continent,
    y = plastic_waste_per_cap
  )
) +
  geom_boxplot()

✏️ Convert your side-by-side box plots from the previous task to violin plots. What do the violin plots reveal that box plots do not? What features are apparent in the box plots but not in the violin plots?

⊕Remember: We use geom_point() to make scatterplots.

✏️ Visualize the relationship between plastic waste per capita and mismanaged plastic waste per capita using a scatterplot. Describe the relationship.
✏️ Colour the points in the scatterplot by continent. Does there seem to be any clear distinction between continents with respect to how plastic waste per capita and mismanaged plastic waste per capita are associated? Discuss.
✏️ Visualize the relationship between plastic waste per capita and total population as well as plastic waste per capita and coastal population. You will need to make two separate plots. Do either of these pairs of variables appear to be more strongly linearly associated? Discuss.

🧶 ✅ Complete Exercises 4–7 and render the document. Check that all of your plots appear correctly and that each exercise marked with ✏️ includes a written response that interprets the visualization.

Wrapping up

We don’t expect you to complete all of the exercises within the lab session. Ideally, you should have got to this point. If you still have some time left, move on to the remaining exercise below. If not, finish it after the lab session.

If you haven’t had time to finish the exercises above, please ask for help before you leave!

⊕Hint: The x-axis is a calculated variable. One country with plastic waste per capita over 3 kg/day has been filtered out. And the data are not only represented with points on the plot but also a smooth curve. The term “smooth” should help you pick which geom to use.

✏️ Recreate the following plot, and interpret what you see in the context of the data.

⊕Hint: Not sure how to get the same color scheme? Revisit the slides from this week where we introduce color-blind friendly palettes.

🧶 ✅ Complete Exercise 8 and render your document one final time. Read through the rendered document from beginning to end. Make sure all code runs, all figures appear correctly, and every exercise marked with ✏️ includes a written response before you submit your work.