R-Code/Waffle.R at main · harshkrishna17/R-Code · GitHub

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# Libraries

library(tidyverse)
library(worldfootballR)
library(ggtext)
library(extrafont)
library(waffle)
library(MetBrewer)

# Scraping

data <- fb_big5_advanced_season_stats(season_end_year = 2022, stat_type = "gca", team_or_player = "player")

# Data Wrangling

data1 <- data %>%
filter(Mins_Per_90 >= 9) %>%
select(Player, Mins_Per_90, SCA90_SCA, SCA_SCA, PassLive_SCA, PassDead_SCA, Drib_SCA, Sh_SCA, Fld_SCA, Def_SCA)

data1 <- data1[order(as.numeric(data1$SCA90_SCA),decreasing = TRUE),]
data1 <- data1[c(1:20),]

df <- data1[order(as.numeric(data1$SCA90_SCA),decreasing = TRUE),]
df <- df[c(1:20),]

Player <- data1$Player
Mins <- data1$Mins_Per_90
data1 <- subset(data1, select = -c(Player, Mins_Per_90, SCA90_SCA))

for(i in 1:ncol(data1)) {
    data1[, i] <- data1[, i] / Mins
}

SCA <- data1$SCA_SCA

for(i in 1:ncol(data1)) {
    data1[, i] <- round((data1[, i] / SCA) * 100, 0)
}

data1 <- data1 %>%
mutate(Total = PassLive_SCA + PassDead_SCA + Drib_SCA + Sh_SCA + Fld_SCA + Def_SCA)

## run this ifelse satatement as many times as necessarry until the Total comes out to be a 100 for all rows.

data1 <- data1 %>% mutate(Sh_SCA = ifelse(Total == 100, Sh_SCA,
                                          ifelse(Total < 100, Sh_SCA + 1,
                                                 ifelse(Total > 100, Sh_SCA - 1, NA)))) %>%
mutate(Total = PassLive_SCA + PassDead_SCA + Drib_SCA + Sh_SCA + Fld_SCA + Def_SCA)

data1$Player <- Player

data1 <- data1 %>%
pivot_longer(!Player, values_to = "SCAp90", names_to = "SCATypes") %>%
filter(!SCATypes == "SCA_SCA") %>%
filter(!SCATypes == "Total") %>%
count(Player, SCATypes, wt = SCAp90)

data1$Player <- factor(data1$Player, levels = print(df$Player))

# Custom theme function

theme_athletic <- function() {
  theme_minimal() +
    theme(plot.background = element_rect(colour = "#151515", fill = "#151515"),
          panel.background = element_rect(colour = "#151515", fill = "#151515")) +
    theme(plot.title = element_text(colour = "white", size = 24, family = "Fried Chicken Bold", hjust = 0.5),
          plot.subtitle = element_markdown(colour = "#525252", size = 18, hjust = 0.5),
          plot.caption = element_text(colour = "white", size = 15, hjust = 1),
          axis.title.x = element_text(colour = "white", face = "bold", size = 14),
          axis.title.y = element_text(colour = "white", face = "bold", size = 14),
          axis.text.x = element_blank(),
          axis.text.y = element_blank()) +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.background = element_blank()) +
    theme(legend.title = element_text(colour = "white"),
          legend.text = element_text(colour = "white"))
}

# Plotting

data1 %>%
ggplot(aes(fill = SCATypes, values = n)) +
geom_waffle(nrows = 10, size = 1.5, colour = "#151515", flip = TRUE) +
scale_fill_manual(values = met.brewer(name = "Gauguin", n = 6, type = "discrete")) +
facet_wrap(~Player) +
labs(title = "Big 5 Leagues Shot-Creating Actions Share [2021/22]",
     subtitle = "Top 20 Players with the most SCA per 90 so far",
     caption = "Minimum 9 90's Played\nData from FBref\nCreated by @placeholder2004") +
theme_athletic() +
theme(aspect.ratio = 1,
      strip.background = element_blank(),
      strip.text = element_text(colour = "white", size = 14),
      legend.position = "bottom",
      legend.text = element_text(size = 14))

# Save

setwd("C:/Users/harsh_1mwi2o4/Downloads")
ggsave("wafflebig5.png", width = 3100, height = 3500, units = "px")