Webscraping epl 19/20 goals for and goals against data from wikipedia

webscraping_epl1920_github

#Importing libraries
library(rvest) #for webscrapping
library(ggplot2) #for data visualisation
library(plyr) #for renaming values
library("ggrepel") #for avoiding overlapping of labels

#Specify url
url <- "https://en.wikipedia.org/wiki/2019%E2%80%9320_Premier_League"

#Scraped page
scraped_page <- read_html(url)

#Scraping the teams
Teams <- scraped_page %>%
  html_nodes("h2+ .wikitable th+ td") %>%
  html_text() %>%
  as.character()

#Preview 
head(Teams)

#Removing "\n" in the teams 
Teams <- gsub("\n","",Teams)

#Scraping "Goals For"
Goals_for <- scraped_page %>%
  html_nodes("h2+ .wikitable td:nth-child(7) , th:nth-child(7) abbr") %>%
  html_text() %>%
  as.numeric

#Preview
head(Goals_for)

#Remove NA case
Goals_for <- na.omit(Goals_for)

#Scraping "Goals Against"
Goals_against<- scraped_page %>%
  html_nodes("h2+ .wikitable td:nth-child(8)") %>%
  html_text() %>%
  as.numeric

#Preview
head(Goals_against)

#Combining all scraped data into a dataframe
df <- data.frame(Teams, Goals_for, Goals_against)

#Cleaning the data 
df$Teams <- mapvalues(df$Teams, 
                             from=c("Liverpool (Q)","Manchester City[a]"), 
                             to=c("Liverpool", "Manchester City"))

#Creating a cluster column
df$Cluster = 0

#Using for loop and ifelse statements to create different clusters 
for(i in 1: nrow(df)) {
  if (df$Goals_for[i] > mean(df$Goals_for) & df$Goals_against[i] < mean(df$Goals_against)) {
    df$Cluster[i] = 1
  } else if (df$Goals_for[i] < mean(df$Goals_for) & df$Goals_against[i] < mean(df$Goals_against)) {
    df$Cluster[i] = 2 
  } else if (df$Goals_for[i] > mean(df$Goals_for) & df$Goals_against[i] > mean(df$Goals_against)) {
    df$Cluster[i] = 3
  } else {
    df$Cluster[i] = 4
  }
}

#Making the cluster columns a factor 
df$Cluster = factor(df$Cluster, levels = c(1, 2, 3, 4), 
                    labels = c("Strong attack\nStrong defence",
                               "Poor attack\nStrong defence",
                               "Strong attack\nPoor defence",
                               "Poor attack\nPoor defence"))

#Data visualisation
ggplot(df, aes(x = Goals_for, y = Goals_against, label = Teams, colour = Cluster))+
  geom_point()+
  geom_vline(xintercept=mean(Goals_for), linetype="dashed", alpha = 0.4, colour = "red") +
  geom_hline(yintercept=mean(Goals_against), linetype="dashed", alpha = 0.4, colour = "red") +
  geom_text_repel(aes(Goals_for, Goals_against, label = Teams), 
                  size = 2, colour = "black", fontface = "bold")+
  labs(title = "EPL 2019/2020: Goals For vs Goals Against",
       x = "Goals For", y = "Goals Against",
       colour = " ")+
  theme(legend.position = "top")
ggsave("output.jpeg", dpi = 300)