Jfjelstul Worldcup R Package -

library(dplyr) library(ggplot2) # Clean and transform tournament attendance profiles attendance_trend <- tournaments %>% filter(type == "men") %>% mutate(avg_attendance = attendance / matches) # Generate timeline line plot ggplot(attendance_trend, aes(x = year, y = avg_attendance)) + geom_line(color = "#1b4b62", linewidth = 1.2) + geom_point(color = "#d95f02", size = 3) + scale_y_continuous(labels = scales::comma) + labs( title = "Evolution of Men's FIFA World Cup Attendance", subtitle = "Average gate attendance per match (1930-2018)", x = "Tournament Year", y = "Average Spectators" ) + theme_minimal() Use code with caution. Use Cases and Applications

# Get match data matches <- get_matches() jfjelstul worldcup r package

: Train statistical models (such as Poisson regressions or machine learning classifiers) on historical match results to forecast tournament outcomes. aes(x = year

cards %>% filter(card_type == "red") %>% count(team, sort = TRUE) %>% head(10) x = "Tournament Year"

: Historical ledgers documenting official tournament recognitions, including the Golden Ball and Golden Boot. Getting Started: Installation and Setup

: Match-by-match granular tracking of starts, minutes played, and team selection variants. 4. Micro-In-Game Events