This lab will not be graded - there is nothing to turn in. Please do sit with your lab group, which may be found on Sakai. Next week will be the first team lab; we will introduce the team workflow and some basic linear regression commands.

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 recently ran a data visualization communication contest on plastic waste as seen here.

Learning goals for this lab are

Getting started

The link to this assignment is at https://classroom.github.com/a/MZhDf2xM.

Ask your TA (or better yet, your lab group!) if you are unsure of how to clone your own private repository for this lab.

Update the name and date

You will write your answers in the document 230123-lab.Rmd. Before starting the exercises, be sure to update the author name in the YAML at the top of the .Rmd file. Knit the document and make sure the resulting PDF file has your name.

Packages

We’ll use the tidyverse package for this analysis. You can run run the following code to load this package.

library(tidyverse)

The 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 line of code. We will name it plastic_waste.

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)

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. If you’re unfamiliar with the function or the filter() function, check out a brief reference here.

Note that the pipe operator %>% is specific to the dplyr package; base R also has a pipe operator given by |>. Either of them will work just fine.

plastic_waste |>
  filter(plastic_waste_per_cap > 3.5)
## # A tibble: 1 x 10
##   code  entity              continent  year gdp_per_cap plastic_waste_p~ mismanaged_plas~
##   <chr> <chr>               <chr>     <dbl>       <dbl>            <dbl>            <dbl>
## 1 TTO   Trinidad and Tobago North Am~  2010      31261.              3.6             0.19
## # ... with 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.

  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. Follow along using the code below:

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 it’s best 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.

Now is a good time to commit and push your changes to GitHub with a short, informative commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

There is 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. Learn something new: violin plots! Read about them at http://ggplot2.tidyverse.org/reference/geom_violin.html, and 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. Next, visualize the relationship between plastic waste per capita and mismanaged plastic waste per capita using a scatterplot. Describe the relationship between the two variables.
  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. Do either of these pairs of variables appear to be more strongly linearly associated?

Now is another good time to commit and push your changes to GitHub with a short, informative commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

  1. Challenge: Recreate the following plot, and interpret what you see in context of the data. Use the code below to filter out a country with plastic waste per capita over 3 kg/per day. Use the filtered data set for the plot.
plastic_waste <- plastic_waste %>%
  filter(plastic_waste_per_cap < 3)

Hint: The colors are from the viridis color palette. Take a look at the functions starting with scale_color_viridis_* in the ggplot2 reference page.

Commit and push your changes to GitHub with an appropriate commit message again. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

This lab was adapted from Data Science in a Box.