Week 4: Apply 3

Import data

# csv file
data <- read_csv("data/myData.csv")

## Rows: 900 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (11): country, city, stage, home_team, away_team, outcome, win_conditio...
## dbl   (3): year, home_score, away_score
## date  (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data

## # A tibble: 900 × 15
##     year country city      stage home_…¹ away_…² home_…³ away_…⁴ outcome win_c…⁵
##    <dbl> <chr>   <chr>     <chr> <chr>   <chr>     <dbl>   <dbl> <chr>   <chr>  
##  1  1930 Uruguay Montevid… Grou… France  Mexico        4       1 H       <NA>   
##  2  1930 Uruguay Montevid… Grou… Belgium United…       0       3 A       <NA>   
##  3  1930 Uruguay Montevid… Grou… Brazil  Yugosl…       1       2 A       <NA>   
##  4  1930 Uruguay Montevid… Grou… Peru    Romania       1       3 A       <NA>   
##  5  1930 Uruguay Montevid… Grou… Argent… France        1       0 H       <NA>   
##  6  1930 Uruguay Montevid… Grou… Chile   Mexico        3       0 H       <NA>   
##  7  1930 Uruguay Montevid… Grou… Bolivia Yugosl…       0       4 A       <NA>   
##  8  1930 Uruguay Montevid… Grou… Paragu… United…       0       3 A       <NA>   
##  9  1930 Uruguay Montevid… Grou… Uruguay Peru          1       0 H       <NA>   
## 10  1930 Uruguay Montevid… Grou… Argent… Mexico        6       3 H       <NA>   
## # … with 890 more rows, 5 more variables: winning_team <chr>,
## #   losing_team <chr>, date <date>, month <chr>, dayofweek <chr>, and
## #   abbreviated variable names ¹​home_team, ²​away_team, ³​home_score,
## #   ⁴​away_score, ⁵​win_conditions

Plot data

data %>%
    
    ggplot(aes(winning_team)) +
    geom_bar()