Plastic Pollution

Plots of total plastic waste categorized by brands and countries.

Roupen Khanjian
02-07-2021 
library(tidyverse) # Easily Install and Load the 'Tidyverse', CRAN v1.3.0
library(lubridate) # Make Dealing with Dates a Little Easier, CRAN v1.7.10
library(skimr) # Compact and Flexible Summaries of Data, CRAN v2.1.2
library(ggfortify) # Data Visualization Tools for Statistical Analysis Results, CRAN v0.4.11
library(plotly) # Create Interactive Web Graphics via 'plotly.js', CRAN v4.9.3
library(ggpol) # Visualizing Social Science Data with 'ggplot2', CRAN v0.0.7
library(ggthemes) # Extra Themes, Scales and Geoms for 'ggplot2', CRAN v4.2.4
library(patchwork) # The Composer of Plots, CRAN v1.1.1
library(RColorBrewer) # ColorBrewer Palettes, CRAN v1.1-2

plastics <- readr::read_csv(
  'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-26/plastics.csv')
  
plastics_longer <- plastics %>% 
  pivot_longer(empty:grand_total, 
               names_to = "plastic_type", 
               values_to = "count")


# change nestle to nestle
plastics_longer <- plastics_longer %>% 
  mutate(company =
           case_when(
             str_detect(parent_company, c("Nestlé")) ~ "Nestle",
             str_detect(parent_company, c("Nestlè")) ~ "Nestle",
             str_detect(parent_company, c("nestle")) ~ "Nestle",
             str_detect(parent_company, c("NESTLE")) ~ "Nestle",
             str_detect(parent_company, c("nestlé")) ~ "Nestle",
             str_detect(parent_company, c("Nestlé")) ~ "Nestle",
             str_detect(parent_company, c("nestlé")) ~ "Nestle",
             str_detect(parent_company, c("Pepsico")) ~ "PepsiCo",
             str_detect(parent_company, c("PepsiCo")) ~ "PepsiCo",
             !str_detect(parent_company, c("Nestlé")) ~ parent_company,
             !str_detect(parent_company, c("Nestle")) ~ parent_company,
             !str_detect(parent_company, c("nestle")) ~ parent_company,
             !str_detect(parent_company, c("NESTLE")) ~ parent_company,
             !str_detect(parent_company, c("nestlé")) ~ parent_company,
             !str_detect(parent_company, c("Nestlé")) ~ parent_company,
             !str_detect(parent_company, c("nestlé")) ~ parent_company,
             !str_detect(parent_company, c("Pepsico")) ~ parent_company,
             !str_detect(parent_company, c("PepsiCo")) ~ parent_company
           )) %>% 
  mutate(country = 
           case_when(
             str_detect(country, c("NIGERIA")) ~ "Nigeria",
             !str_detect(country, c("NIGERIA")) ~ country
           ))


top_countries <- plastics_longer %>%
  filter(plastic_type != "grand_total") %>%
  filter(country != "EMPTY") %>%
  count(country,
        wt = count,
        sort = TRUE) %>%
  slice_max(n, n = 10)

top_countries_names <- plastics_longer %>% 
  right_join(top_countries, by = "country")

plastics_longer_countries <- plastics_longer %>%
  mutate(country =
           case_when(
             country %in% top_countries[1,1] ~ country,
             country %in% top_countries[2,1] ~ country,
             country %in% top_countries[3,1] ~ country,
             country %in% top_countries[4,1] ~ country,
             country %in% top_countries[5,1] ~ country,
             country %in% top_countries[6,1] ~ country,
             country %in% top_countries[7,1] ~ country,
             country %in% top_countries[8,1] ~ country,
             country %in% top_countries[9,1] ~ country,
             country %in% top_countries[10,1] ~ country,
             !country %in% top_countries["country"] ~ "Other"
           ))

countries_data <- plastics_longer_countries %>%
  filter(plastic_type != "grand_total") %>%
  filter(country != "EMPTY") %>%
  count(country,
        wt = count,
        sort = TRUE) %>%
  slice_max(n, n = 12)

# Countries plot
c1 <- ggplot(data = countries_data) +
  geom_arcbar(aes(shares = n, r0 = 4, r1 = 10, fill = country)) +
  scale_fill_manual(values=c("cadetblue4", "violetred3", "olivedrab",
                              "hotpink", "gold2", "aquamarine3", "lemonchiffon2",
                              "lightcyan3", "royalblue", "slateblue3", "yellow")) +
  coord_fixed() +
  theme_void() +
  labs(title = "Top 10 Countries in Total Plastic Waste",
       subtitle = "All other countries are combined\ninto one category 'Other'",
       fill = "Countries") +
  theme(legend.key.size = unit(0.25, 'cm'), #change legend key size
        plot.title = element_text(size = 12, face = "bold", color = "white"),
        plot.subtitle = element_text(size = 10, color = "white"),
        legend.title = element_text(size=10.5, face = "bold", color = "white"), 
        legend.text = element_text(size=9, color = "white")) 

top_brands <- plastics_longer %>%
  filter(plastic_type != "grand_total") %>%
  filter(!company %in% c("Grand Total",
                                "Unbranded",
                                "null",
                                "NULL",
                                "Assorted")) %>% 
  count(company,
        wt = count,
        sort = TRUE) %>%
  slice_max(n, n = 10)

top_brands_names <- plastics_longer %>% 
  right_join(top_brands, by = "company")

plastics_longer_brands <- plastics_longer %>%
  mutate(company =
           case_when(
             company %in% top_brands[1,1] ~ company,
             company %in% top_brands[2,1] ~ company,
             company %in% top_brands[3,1] ~ company,
             company %in% top_brands[4,1] ~ company,
             company %in% top_brands[5,1] ~ company,
             company %in% top_brands[6,1] ~ company,
             company %in% top_brands[7,1] ~ company,
             company %in% top_brands[8,1] ~ company,
             company %in% top_brands[9,1] ~ company,
             company %in% top_brands[10,1] ~ company,
             !company %in% top_brands["company"] ~ "Other"
           ))

brands_data <- plastics_longer_brands %>%
  filter(plastic_type != "grand_total") %>%
  count(company,
        wt = count,
        sort = TRUE) %>%
  slice_max(n, n = 11)

# Brands plot
b1 <- ggplot(data = brands_data) +
  geom_arcbar(aes(shares = n, r0 = 5, r1 = 10, fill = company)) +
  coord_fixed() +
  scale_fill_manual(values=c("cadetblue4", "violetred3", "olivedrab",
                              "hotpink", "gold2", "aquamarine3", "lemonchiffon2",
                              "lightcyan3", "royalblue", "slateblue3", "yellow")) +
  theme_void() +
  labs(title = "Top 10 Brands in Total Plastic Waste",
       subtitle = "All other brands are combined\ninto one category 'Other'",
       fill = "Company") +
  theme(legend.key.size = unit(0.25, 'cm'), #change legend key size
        plot.title = element_text(size = 12, face = "bold", color = "white"),
        plot.subtitle = element_text(size = 10, color = "white"),
        legend.title = element_text(size=10.5, face = "bold", color = "white"),
        legend.text = element_text(size=9, color = "white")) 

patchwork <- b1 / c1

patchwork +
  plot_annotation(
    caption = "By Roupen Khanjian"
  ) & theme(plot.background = element_rect(fill = "black",
                                                 color = "black"),
                  strip.background = element_rect(fill = "black",
                                                 color = "black"),
            plot.caption = element_text(color = "white"))

Citation

For attribution, please cite this work as

Khanjian (2021, Feb. 7). Roupen Khanjian: Plastic Pollution. Retrieved from https://khanjian.github.io/roupen-website/tidytuesday/plastics/

BibTeX citation

@misc{khanjian2021plastic,
  author = {Khanjian, Roupen},
  title = {Roupen Khanjian: Plastic Pollution},
  url = {https://khanjian.github.io/roupen-website/tidytuesday/plastics/},
  year = {2021}
}