Worldcup R Package Jfjelstul !!better!! < Firefox >

Because this is a data package, the most helpful files are the dataset documentation. In your RStudio console, type:

Joshua Fjelstul’s worldcup package removes the "data cleaning" hurdle, allowing you to go straight to insights. Whether you are building a predictive model for the next tournament or writing a blog post about football history, this is an essential tool in your R library. To help you get the most out of this data, let me know:

library(dplyr) library(ggplot2) worldcup::matches %>% group_by(tournament_id) %>% summarize(avg_goals = mean(home_team_score + away_team_score)) %>% ggplot(aes(x = tournament_id, y = avg_goals)) + geom_line() + theme_minimal() + labs(title = "Average Goals per World Cup Match") Use code with caution. 🔍 Use Cases for Researchers worldcup r package jfjelstul

Let's take a look at the matches dataset, which contains information on all World Cup matches from 1950 to 2018.

That hurdle is elegantly removed by the worldcup R package, created by . Because this is a data package, the most

The package relies on ID columns (e.g., match_id , team_id , player_id ). Don't try to join on names (e.g., "USA" vs "United States"). Always use the provided ID columns to avoid mismatches.

data(matches) head(matches)

While many datasets exist on platforms like Kaggle, they are often messy or incomplete. The worldcup package stands out because it is: