| Year | Home Team | Away Team | Total Goals | |------|-----------|-----------|-------------| | 1954 | Austria | Switzerland | 12 | | 1938 | Sweden | Cuba | 8 | | 1954 | Hungary | South Korea | 9 |
library(worldcup) library(dplyr) goals %>% group_by(match_id) %>% summarise(total_goals = n(), .groups = "drop") %>% arrange(desc(total_goals)) %>% left_join(matches, by = "match_id") %>% select(year, home_team, away_team, total_goals) %>% slice(1:3)
In the world of sports data science, few events are as rich with narrative and statistical depth as the FIFA World Cup. For R users looking to analyze penalty shootout pressure, goal distribution patterns, or player longevity, accessing clean, comprehensive historical data is the first hurdle.
| Year | Home Team | Away Team | Total Goals | |------|-----------|-----------|-------------| | 1954 | Austria | Switzerland | 12 | | 1938 | Sweden | Cuba | 8 | | 1954 | Hungary | South Korea | 9 |
library(worldcup) library(dplyr) goals %>% group_by(match_id) %>% summarise(total_goals = n(), .groups = "drop") %>% arrange(desc(total_goals)) %>% left_join(matches, by = "match_id") %>% select(year, home_team, away_team, total_goals) %>% slice(1:3) worldcup r package jfjelstul
In the world of sports data science, few events are as rich with narrative and statistical depth as the FIFA World Cup. For R users looking to analyze penalty shootout pressure, goal distribution patterns, or player longevity, accessing clean, comprehensive historical data is the first hurdle. | Year | Home Team | Away Team