17 Lab: Global plastic waste

Plastic pollution is a major and growing problem, negatively affecting oceans and wildlife health. Our World in Data has a lot of great data at various levels including globally, per country, and over time. For this lab, we focus on data from 2010.

Additionally, National Geographic ran a data visualization communication contest on plastic waste as seen here.

Learning goals

  • Visualizing numerical and categorical data and interpreting visualizations
  • Recreating visualizations
  • Getting more practice using with R, RStudio, Git, and GitHub

Getting started

Go to the GitHub course organization and locate the assignment repo template, which should be named lab-02-plastic-waste. If you’re in the right place, it should look like the following.

Fork or use the template to make your own repo, and then clone it in RStudio.

First, open the R Markdown document lab-02.Rmd and Knit it. Make sure it compiles without errors. The output will be in the file markdown .md file with the same name.

Packages

Like always, we’ll use the tidyverse package for this analysis. Run the following code in the Console to load this package.

library(tidyverse)

Data

The dataset for this assignment can be found as a csv file in the data folder of your repository. 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

Warm up

  • 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?

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)
##   code              entity     continent year gdp_per_cap plastic_waste_per_cap
## 1  TTO Trinidad and Tobago North America 2010    31260.91                   3.6
##   mismanaged_plastic_waste_per_cap mismanaged_plastic_waste coastal_pop
## 1                             0.19                    94066     1358433
##   total_pop
## 1   1341465

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.

1.1. Plot, using histograms, the distribution of plastic waste per capita faceted by continent. What can you say about how 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 coloring 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 colors as well.

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

The overlapping colors make it difficult to tell what’s happening with the distributions. The first plotted in continents get covered by continents plotted over them.

We can change the transparency level of the fill color to help with this problem.

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 plot still doesn’t look great…

2.1. Recreate the density plots above using a different (lower) alpha level that works better for displaying the density curves for all continents.

2.2. Explain 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.

🧶 ✅ ⬆️ Now is a good time to knit your document and commit and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

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()

3.1. 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.

4.1. Visualize the relationship between plastic waste per capita and mismanaged plastic waste per capita using a scatterplot. Describe the relationship.

4.2. Color the points in the scatterplot by continent. Does there seem to be any clear distinctions between continents with respect to how plastic waste per capita and mismanaged plastic waste per capita are associated?

4.3. 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?

🧶 ✅ ⬆️ Now is another good time to knit your document and commit and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Wrapping up

If you still have some time left, move on to the remaining exercises below.

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.

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

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards and review the md document on GitHub to make sure you’re happy with the final state of your work.

Once you’re done, check to make sure your latest changes are on GitHub and readable.2


  1. (If previous versions of this lab, there was an automated check to see if for your R Markdown document knitted properly. It required using github actions, which made this lab a tad too complicated.)↩︎