Lesson11_solution.R

##Solution
library(tidyverse)

#plastic pollution dataset
plastics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-01-26/plastics.csv')

#wrangle data
data <- plastics %>%
  group_by(parent_company) %>%
  summarize(total = sum(grand_total, na.rm = TRUE)) %>%
  arrange(desc(total)) %>%
  slice(4:14) %>%
  mutate(parent_company = ifelse(parent_company == "NULL", "Unknown", 
                                 parent_company))

#how I did it
ggplot(data) +
  geom_col(aes(x = total, y = reorder(parent_company, total)), fill = "orchid4") +
  labs(title = "Plastic items collected on beaches, by manufacturer") +
  scale_x_continuous(labels = scales::label_comma()) +
  theme(
    axis.title = element_blank(),
    panel.background = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank(),
    axis.ticks.y = element_blank(),
    panel.grid.minor.x = element_blank(),
    panel.grid.major = element_line(color = "#e7e7e7", size = 0.4),
    axis.ticks = element_line(color = "#e7e7e7", size = 0.4)
  )