The data this week comes from Break Free from Plastic courtesy of Sarah Sauve.

Data

The unit of measurement is “count” which I’m not totally clear about. Each row represents the country of cleanup (per year, per company, per type), so a count of 3 isn’t clear. They also mention that the data come from particular audits, so maybe they are random samples of some kind. A fuller analysis would want to understand the units and also whether the observations are truly representative of the larger population of plasic which is being wasted.

plastics <- readr::read_csv("plastics.csv") %>%
  select(-num_events, - volunteers)

Visualizing Waste

The bar plot below uses a color palette based on Joe Biden’s inauguration (coats and mittens) by @ciannabp (https://github.com/ciannabp/inauguration).

#devtools::install_github("ciannabp/inauguration")
library(inauguration)
inauguration("inauguration_2021_bernie")

Let’s start by looks at the US data only. Note that the palette only has 7 colors, so if you need more colors for your graph, the palette will fail.

plastics %>%
  filter(country == "United States of America") %>%
  filter(!parent_company %in% c("Unbranded", "null", "Grand Total")) %>%
  group_by(year) %>%
  arrange(desc(grand_total)) %>%
  top_n(7) %>%
  mutate(comapny_ordered = fct_reorder(parent_company, -grand_total)) %>%
  pivot_longer(empty:grand_total, names_to = "plastic_type", values_to = "count") %>%
  filter(plastic_type != "grand_total") %>%
  filter(year == 2019) %>%
  ggplot(aes(x = comapny_ordered, y = count)) +
  geom_bar(aes(fill = plastic_type), stat = "identity") +
  theme(axis.text.x=element_text(angle=45,vjust=1,hjust=1)) +
  scale_fill_manual(values = inauguration("inauguration_2021_bernie")) +
  xlab("Company Name")

  #facet_grid(~year)

Important Closing

praise()
## [1] "You are priceless!"