library(dplyr) library(ggplot2) # Calculate goals per match by tournament type wc_scoring <- tournaments %>% mutate(goals_per_match = goals / matches) %>% group_by(type) %>% summarize( avg_goals_match = mean(goals_per_match, na.rm = TRUE), total_goals = sum(goals), total_matches = sum(matches) ) print(wc_scoring) Use code with caution. Example 2: Compiling a Player Discipline Profile
“It’s July 13, 2014. Mario Götze controls the ball on his chest in the 113th minute of the World Cup final and volleys it past Sergio Romero. Germany wins its fourth title. That moment — every pass, every foul, every substitution — is captured in a single R package.” jfjelstul worldcup r package
Or: “Every Red Card, Every Header, Every Heartbreak — Analyzing 90+ Years of World Cup Data” Germany wins its fourth title
: Roster allocations mapping club affiliations, player ages, positions, and shirt numbers. and types (open play
: Detailed documentation tracking individual goal scorers, timestamps, and types (open play, headers, penalties).
library(worldcup) library(dplyr) library(ggplot2)
Highlight the data structure: