[tidytuesday] FIFA World Cup

data
Author

Tony Duan

Published

November 29, 2022

1. download data

Code 
library(tidytuesdayR)
tuesdata <- tidytuesdayR::tt_load('2022-11-29')

    Downloading file 1 of 2: `wcmatches.csv`
    Downloading file 2 of 2: `worldcups.csv`

2. read data

Code 
wcmatches <- tuesdata$wcmatches
worldcups  <- tuesdata$worldcups

3. data Dictionary

Code 
library(tidyverse)
glimpse(wcmatches)
Rows: 900
Columns: 15
$ year           <dbl> 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1…
$ country        <chr> "Uruguay", "Uruguay", "Uruguay", "Uruguay", "Uruguay", …
$ city           <chr> "Montevideo", "Montevideo", "Montevideo", "Montevideo",…
$ stage          <chr> "Group 1", "Group 4", "Group 2", "Group 3", "Group 1", …
$ home_team      <chr> "France", "Belgium", "Brazil", "Peru", "Argentina", "Ch…
$ away_team      <chr> "Mexico", "United States", "Yugoslavia", "Romania", "Fr…
$ home_score     <dbl> 4, 0, 1, 1, 1, 3, 0, 0, 1, 6, 1, 0, 0, 4, 3, 6, 6, 4, 2…
$ away_score     <dbl> 1, 3, 2, 3, 0, 0, 4, 3, 0, 3, 0, 1, 4, 0, 1, 1, 1, 2, 3…
$ outcome        <chr> "H", "A", "A", "A", "H", "H", "A", "A", "H", "H", "H", …
$ win_conditions <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ winning_team   <chr> "France", "United States", "Yugoslavia", "Romania", "Ar…
$ losing_team    <chr> "Mexico", "Belgium", "Brazil", "Peru", "France", "Mexic…
$ date           <date> 1930-07-13, 1930-07-13, 1930-07-14, 1930-07-14, 1930-0…
$ month          <chr> "Jul", "Jul", "Jul", "Jul", "Jul", "Jul", "Jul", "Jul",…
$ dayofweek      <chr> "Sunday", "Sunday", "Monday", "Monday", "Tuesday", "Wed…
Code 
glimpse(worldcups)
Rows: 21
Columns: 10
$ year         <dbl> 1930, 1934, 1938, 1950, 1954, 1958, 1962, 1966, 1970, 197…
$ host         <chr> "Uruguay", "Italy", "France", "Brazil", "Switzerland", "S…
$ winner       <chr> "Uruguay", "Italy", "Italy", "Uruguay", "West Germany", "…
$ second       <chr> "Argentina", "Czechoslovakia", "Hungary", "Brazil", "Hung…
$ third        <chr> "USA", "Germany", "Brazil", "Sweden", "Austria", "France"…
$ fourth       <chr> "Yugoslavia", "Austria", "Sweden", "Spain", "Uruguay", "W…
$ goals_scored <dbl> 70, 70, 84, 88, 140, 126, 89, 89, 95, 97, 102, 146, 132, …
$ teams        <dbl> 13, 16, 15, 13, 16, 16, 16, 16, 16, 16, 16, 24, 24, 24, 2…
$ games        <dbl> 18, 17, 18, 22, 26, 35, 32, 32, 32, 38, 38, 52, 52, 52, 5…
$ attendance   <dbl> 434000, 395000, 483000, 1337000, 943000, 868000, 776000, …

4. data cleanning

replay West Germany to Germany across all column with str_replace function.

Code 
worldcups=worldcups %>% mutate_all(list(~str_replace(., "West Germany", "Germany")))

4.Visualization

Code 
worldcups_data001=worldcups %>% group_by(winner) %>% count() %>% arrange(desc(n)) %>% head(5) %>% rename(country=winner) %>% mutate(rank='1:winner')

worldcups_data002=worldcups %>% group_by(second) %>% count() %>% arrange(desc(n)) %>% head(5) %>% rename(country=second) %>% mutate(rank='2:second')

worldcups_data003=worldcups %>% group_by(third) %>% count() %>% arrange(desc(n)) %>% head(5) %>% rename(country=third) %>% mutate(rank='3:third')

worldcups_data004=rbind(worldcups_data001,worldcups_data002,worldcups_data003)
worldcups_data004
# A tibble: 15 × 3
# Groups:   country [9]
   country            n rank    
   <chr>          <int> <chr>   
 1 Brazil             5 1:winner
 2 Germany            4 1:winner
 3 Italy              4 1:winner
 4 Argentina          2 1:winner
 5 France             2 1:winner
 6 Germany            4 2:second
 7 Argentina          3 2:second
 8 Netherlands        3 2:second
 9 Brazil             2 2:second
10 Czechoslovakia     2 2:second
11 Germany            4 3:third 
12 Brazil             2 3:third 
13 France             2 3:third 
14 Poland             2 3:third 
15 Sweden             2 3:third
Code 
ggplot(worldcups_data004, aes(x=country, y=n)) + 
  geom_bar(stat = "identity")+coord_flip()+facet_wrap(~rank)

Reference

https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-11-29

https://sparkbyexamples.com/r-programming/replace-using-dplyr-package-in-r/

https://www.datacamp.com/tutorial/facets-ggplot-r