library(tidyverse)
library(ggbump)
soccer <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2023/2023-04-04/soccer21-22.csv')
away_vic <- soccer %>%
group_by(AwayTeam) %>%
summarise(vic_away = sum(FTR == "A")) %>%
arrange(desc(vic_away)) %>%
rownames_to_column(var = "vic_away_rank") %>%
rename(team = AwayTeam) %>%
select(-vic_away)
home_vic <- soccer %>%
group_by(HomeTeam) %>%
summarise(vic_home = sum(FTR == "H")) %>%
arrange(desc(vic_home)) %>%
rownames_to_column(var = "vic_home_rank") %>%
rename(team = HomeTeam) %>%
select(-vic_home)
victories <- merge(home_vic, away_vic, by = "team") %>%
mutate(
vic_home_rank = as.numeric(vic_home_rank),
vic_away_rank = as.numeric(vic_away_rank)
) %>%
arrange(vic_home_rank) %>%
pivot_longer(
cols = c("vic_home_rank", "vic_away_rank"),
names_to = "home_away",
values_to = "rank"
) %>%
mutate(
home_away_enc = as.integer(fct_rev(factor(home_away)))
)
victories %>%
ggplot(aes(home_away_enc, rank, col = team)) +
geom_point(shape = "|", stroke = 5) +
ggbump::geom_bump(linewidth = 1.5) +
geom_text(
data = victories %>% filter(home_away_enc == 1),
aes(label = team),
hjust = 1,
nudge_x = -0.1,
size = 4,
fontface = "bold"
) +
geom_text(
data = victories %>% filter(home_away_enc == 2),
aes(label = team),
hjust = 0,
nudge_x = 0.1,
size = 4,
fontface = "bold"
) +
scale_y_reverse(breaks = 1:20) +
scale_x_continuous(breaks = 1:2, labels = c("Victories Home Rank", "Victories Away Rank")) +
coord_cartesian(xlim = c(0.5, 2.5)) +
theme_minimal() +
labs(
title = "Best defenders vs best visitors in Premier League 21/22",
caption = "2023-04-04 Tidytuesday data"
) +
theme(
axis.title = element_blank(),
plot.background = element_rect(fill = "#f5ede4", colour = NA),
plot.margin = margin(t = 2, b =4, l = 5, r = 5, unit = "mm"),
panel.grid = element_blank(),
legend.position = "none"
)
