In this webscraping project in R, I webscrape the most recent La Liga Spanish football table.
Setup
The packages that are needed for webscraping and dealing with text are dplyr, tidyr, rvest, stringr. writexl package is for saving dataframes into Excel files and .csv files.
From rvest, the read_html() function is used for extracting the HTML page from the La Liga website.
## La Liga Spanish Soccer Table Web Scraping
# Reference: https://stackoverflow.com/questions/45450981/rvest-scrape-2-classes-in-1-tag
# Load libraries:
library(dplyr)
library(tidyr)
library(rvest)
library(stringr)
library(writexl)
## Extract La Liga Football League tables page.
page <- read_html("https://www.laliga.com/en-GB/laliga-santander/standing")
When it comes to extracting the table components from the La Liga standings table, I use xpaths along with html_nodes() for the job. In this case extracting ranks, club names and the rest outputs 60 items. I only need the first 20. This is why you see something like rank <- rank[1:20].
Ranks
# Extract the first 20 elements when I extract each part. There are 20 La Liga teams
# Rank:
rank <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[1]') %>%
html_text2() %>%
readr::parse_integer()
rank <- rank[1:20]
Club Names
# Club Names
club_names <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[2]/div[2]/p') %>%
html_text2()
club_names <- club_names[1:20]
club_names## [1] "Real Madrid" "FC Barcelona"
## [3] "Sevilla FC" "Atlético de Madrid"
## [5] "Real Betis" "Real Sociedad"
## [7] "Villarreal CF" "Athletic Club"
## [9] "Valencia CF" "CA Osasuna"
## [11] "RCD Espanyol de Barcelona" "RC Celta"
## [13] "Rayo Vallecano" "Getafe CF"
## [15] "Elche CF" "Granada CF"
## [17] "RCD Mallorca" "Cádiz CF"
## [19] "Levante UD" "Deportivo Alavés"
Club Abbreviation
# Club Abbreviations:
# Club Names
club_tickers <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[2]/div[1]/p') %>%
html_text2()
club_tickers <- club_tickers[1:20]
club_tickers## [1] "RMA" "BAR" "SEV" "ATM" "BET" "RSO" "VIL" "ATH" "VAL" "OSA" "ESP" "CEL"
## [13] "RAY" "GET" "ELC" "GRA" "MLL" "CAD" "LEV" "ALA"
Points
# Points:
points <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[3]') %>%
html_text2() %>%
readr::parse_integer()
points <- points[1:20]
points## [1] 72 60 60 57 56 54 46 45 42 41 39 36 34 32 32 29 29 28 22 22
Played Matches
# Played Matches Amount:
played <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[4]') %>%
html_text2() %>%
readr::parse_integer()
played <- played[1:20]
Wins
# Wins
wins <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[5]') %>%
html_text2() %>%
readr::parse_integer()
wins <- wins[1:20]
Draws
# Draws
draws <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[6]') %>%
html_text2() %>%
readr::parse_integer()
draws <- draws[1:20]
Losses
# Losses
losses <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[7]') %>%
html_text2() %>%
readr::parse_integer()
losses <- losses[1:20]
Goals For
# Goals For
goals_for <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[8]') %>%
html_text2() %>%
readr::parse_integer()
goals_for <- goals_for[1:20]
Goals Against
# Goals Against
goals_against <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[9]') %>%
html_text2() %>%
readr::parse_integer()
goals_against <- goals_against[1:20]
Goal Difference
# Goals Difference
goal_diff <- page %>%
html_nodes(xpath = '//*[@class="styled__StandingTableBody-e89col-5 cDiDQb"]/div/div[1]/div/div[1]/div[10]') %>%
html_text2() %>%
readr::parse_integer()
goal_diff <- goal_diff[1:20]
### Create Dataframe based on raw data:
laliga_table <- data.frame(
Rank = rank,
Club = club_names,
Club_Abbrev = club_tickers,
Points = points,
Played = played,
Wins = wins,
Draws = draws,
Losses = losses,
Goals_For = goals_for,
Goals_Against = goals_against,
Goal_Diff = goal_diff
)
## Preview table:
head(laliga_table, 10)## Rank Club Club_Abbrev Points Played Wins Draws Losses
## 1 1 Real Madrid RMA 72 31 22 6 3
## 2 2 FC Barcelona BAR 60 30 17 9 4
## 3 3 Sevilla FC SEV 60 31 16 12 3
## 4 4 Atlético de Madrid ATM 57 31 17 6 8
## 5 5 Real Betis BET 56 31 17 5 9
## 6 6 Real Sociedad RSO 54 31 15 9 7
## 7 7 Villarreal CF VIL 46 31 12 10 9
## 8 8 Athletic Club ATH 45 31 11 12 8
## 9 9 Valencia CF VAL 42 31 10 12 9
## 10 10 CA Osasuna OSA 41 31 11 8 12
## Goals_For Goals_Against Goal_Diff
## 1 63 26 37
## 2 60 31 29
## 3 44 22 22
## 4 57 38 19
## 5 56 37 19
## 6 32 30 2
## 7 49 30 19
## 8 36 30 6
## 9 43 44 -1
## 10 31 40 -9
Sys.Date()## [1] "2022-04-13"
### Save Raw data Dataframe as Excel File:
library(writexl)
write_xlsx(laliga_table, paste("LaLiga_Table", Sys.Date(), ".xlsx", sep = ""))
## Save dataframe as .csv File Option:
write.csv(laliga_table, paste("LaLiga_Table", Sys.Date(), ".csv", sep = ""), row.names = FALSE)