Tidy Tuesday NFL challenge

tidy_nfl.R

library(tidyverse)
library(hrbrthemes)

# download https://github.com/rfordatascience/tidytuesday/blob/master/data/tidy_tuesday_week2.xlsx
football <- read_xlsx("data/tidy_tuesday_week2.xlsx")

# get the top 16 paid players in each position for each year
to_plot <- football %>%
  mutate(Team = 1:nrow(.)) %>%
  gather(position, salary, -c(year, Team)) %>%
  group_by(position, year) %>%
  arrange(desc(salary)) %>%
  mutate(rank = 1:n()) %>%
  filter(rank <= 16) %>%
  ungroup %>%
  mutate(salary = salary/1e6) %>% # convert to millions
  mutate(position_other = 
           fct_other(position, # we only care about running backs and the highest paid, QBs
                     keep=c("Quarterback",
                            "Running Back")))

to_plot %>%
  filter(position_other == "Other") %>% # plot every other position at rear of image
  ggplot(data=., aes(x= year + (9-rank)/16,  # like a dodge
                     y=salary,
                     color = position_other,
                     group = interaction(year, position))) +
  geom_path(size=1, alpha=0.075, color="black") +
  theme_ipsum_rc() +
  ylim(c(0,40)) + # set limits to be pretty-ish
  scale_x_continuous(breaks = unique(to_plot$year)) +
  ylab("Salary ($m)") +
  xlab("Year") +
  geom_path(data = filter(to_plot,
                          position_other != "Other"),
            size=1, alpha=0.75) +
  scale_color_manual(values=c("purple", "red"),
                     name="Position") +
  theme(legend.position = "bottom", 
        panel.grid.minor.x = element_blank()) +
  ggtitle("Top 16 annual salaries per position (2011-2018)", 
          subtitle = "Data: http://www.spotrac.com/rankings/\nGraphic: @samclifford")