A look at Premier League Wages
1. Loading in the Necessary Packages
library(worldfootballR)
library(tidyverse)
library(reactable)
library(ggthemes)
library(htmltools)
library(scales)
library(ggchicklet)
library(sysfonts)
library(showtext)
2. Using the WorldFootballR package to read in squad wages for each team and save the data as a csv file
prem <- fb_league_urls(country = 'ENG', gender = 'M', season_end_year = 2023, tier = "1st")
prem_teams <- fb_teams_urls(prem, time_pause = 3)
wages <- data.frame()
for (i in prem_teams) {
wage <- fb_squad_wages(team_urls = i)
wages <- rbind(wages,wage)
}
wages %>%
arrange(desc(Nation))
#write.csv(wages, 'wages.csv')
wages_csv <- read.csv('wages.csv')
3. Cleaning the Data and calculating averages for each team
wages$AnnualWageUSD <- as.integer(wages$AnnualWageUSD)
wages_clean <- wages_csv %>%
select(Team, 'Name' = Player, 'Nationality' = Nation, 'Position' = Pos, Age, 'Salary' = AnnualWageUSD) %>%
na.omit() %>%
mutate(is_english = grepl('ENG', Nationality)) %>%
arrange(desc(Salary))
wages_clean
wagebyteam <- wages_clean %>%
group_by(Team) %>%
summarise(avg = mean(Salary))
wagebyteam <- wagebyteam %>%
mutate(avg_salary = (avg/1000000)) %>%
arrange(desc(Team))
wagebyteam$avg_salary <- round(wagebyteam$avg_salary, 3)
#EDITING TEAM NAMES
wagebyteam$Team <- gsub("Wolverhampton Wanderers", "Wolverhampton", wagebyteam$Team)
wagebyteam$Team <- gsub("Tottenham Hotspur", "Tottenham", wagebyteam$Team)
wagebyteam$Team <- gsub("Brighton and Hove Albion", "Brighton", wagebyteam$Team)
wagebyteam$avg_salary <- as.numeric(wagebyteam$avg_salary)
4. Creating list of team color hex codes and loading in custom fonts from Google Fonts
teammain <- c('Arsenal' = '#EF0107',
'Aston Villa' = '#670E36',
'Bournemouth' = '#DA291C',
'Brentford' = '#D20000',
'Brighton' = '#0057B8',
'Chelsea' = '#034694',
'Crystal Palace' = '#1B458F',
'Everton' = '#003399',
'Fulham' = '#000000',
'Leeds United' = '#FFCD00',
'Leicester City' = '#003090',
'Liverpool' = '#00B2A9',
'Manchester City' = '#6CABDD',
'Manchester United' = '#DA291C',
'Newcastle United' = '#241F20',
'Nottingham Forest' = '#E53233',
'Southampton' = '#D71920',
'Tottenham' = '#132257',
'West Ham United' = '#7A263A',
'Wolverhampton' = '#FDB913')
font.add.google(name = 'Source Code Pro', family = 'source-code-pro')
font.add.google(name = 'Staatliches', family = 'staatliches')
showtext.auto()
5. Plotting the data and customizing the chart using the ggplot2 package
wagebyteam.epl.plot <- ggplot(data = wagebyteam, aes( y = avg_salary, x = Team, fill = Team)) +
geom_bar(stat = 'identity', width = .6) +
geom_chicklet(radius = grid::unit(1.5, "mm")) +
scale_y_continuous(limits=c(0, 11), breaks=c(0, 2, 4, 6, 8, 10)) +
scale_fill_manual(values = teammain) +
coord_flip() +
theme_wsj() +
ylab("Average Player Salary (in Millions)") +
labs(title = "Average Annual Wages") +
labs(subtitle = "2022-23 Premier League Season") +
theme(
plot.title = element_text(size = 22,
family = 'staatliches',
color = '#292727'),
plot.subtitle = element_text(size=9),
legend.position = "none",
axis.title.x = element_text(
size = 9, lineheight = 1,
family = "source-code-pro",
face = 'plain',
colour = "#292727"),
axis.text.y = element_text(
size = 9, lineheight = 1,
family = 'source-code-pro',
face = 'plain',
color = '#292727')
)
6. Creating an interactive and searchable database of player wages using the reactable package
wage_search <- wages %>%
mutate('Percentile' = ntile(WeeklyWageGBP, 100)) %>%
select(Team, 'Name' = Player, 'Nationality' = Nation, 'Position' = Pos, WeeklyWageGBP, Percentile) %>%
na.omit()
wage_search$Team <- gsub("Wolverhampton Wanderers", "Wolverhampton", wage_search$Team)
wage_search$Team <- gsub("Tottenham Hotspur", "Tottenham", wage_search$Team)
wage_search$Team <- gsub("Brighton and Hove Albion", "Brighton", wage_search$Team)
wage_search <- wage_search %>%
mutate(Nationality = replace(Nationality, Name == 'Alejandro Garnacho', 'ARG')) %>%
mutate(Nationality = replace(Nationality, Name == 'Samuel Edozie', 'ENG')) %>%
mutate(Nationality = replace(Nationality, Name == 'Roméo Lavia', 'BEL' )) %>%
mutate(Nationality = replace(Nationality, Name == 'Juan Larios', 'ESP'))
wsj_cream = '#F7F2E6'
highlight_cream = '#FEFCF4'
text_black = '#292727'
glizzy_grey = '#C4C4C4'
green_gradient <- function(x) rgb(colorRamp(c('#F7F2E6','#4DFF6B'))(x), maxColorValue = 255)
wage_search_db <- reactable(wage_search,
columns = list(
Team = colDef(width = 200),
Name = colDef(width = 250),
Nationality = colDef(name = 'NAT',
align = 'center',
width = 75),
Position = colDef(name = 'POS',
align = 'center',
width = 75,),
WeeklyWageGBP = colDef(name = "Weekly Wage (£)",
align = 'center',
width = 200,
format = colFormat(separators = TRUE)),
Percentile = colDef(name = '%',
align = 'center',
width = 75,
style = function(value) {
normalized <- (value - min(wage_search$Percentile)) / (max(wage_search$Percentile) - min(wage_search$Percentile))
color <- green_gradient(normalized)
list(background = color)
})),
language = reactableLang(searchPlaceholder = 'Filter Data',
noData = 'No Results'),
theme = reactableTheme(
backgroundColor = wsj_cream,
highlightColor = highlight_cream,
borderColor = glizzy_grey,
cellPadding = '8px 12px',
style = list(fontFamily = 'Roboto Mono',
fontSize = '15px',
color = text_black),
searchInputStyle = list(width = '100%',
backgroundColor = wsj_cream,
color = text_black,
borderColor = glizzy_grey)),
defaultSorted = 'Name',
fullWidth = FALSE,
searchable = TRUE,
highlight = TRUE,
bordered = TRUE,
wrap = FALSE,
paginationType = "simple",
minRows = 10)