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

• Visualising numerical and categorical data and interpreting visualisations
• Recreating visualizations
• Getting more practice using with R and RStudio

Packages

We’ll use the tidyverse package for this analysis.

library(tidyverse)

Data

The dataset for this assignment can be found as a csv file. You can read it in using the following (make sure you save the data in your working directory).

plastic_waste <- read_csv("data03/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

• 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.
• 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)

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 plastic_waste_per_cap
  <chr> <chr>               <chr>        <dbl>       <dbl>                 <dbl>
1 TTO   Trinidad and Tobago North Ameri…  2010      31261.                   3.6
# ℹ 4 more variables: mismanaged_plastic_waste_per_cap <dbl>,
#   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.

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?

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 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…

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

2. 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.

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()

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?

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

3. 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. 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?

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