Week 4: Apply 3

Import data

# excel file
data <- read_excel("data/Data.xlsx") %>%
    
    # Convert weekly_attendance to numeric
    mutate(weekly_attendance = as.numeric(weekly_attendance))

## New names:
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`

## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `weekly_attendance = as.numeric(weekly_attendance)`.
## Caused by warning:
## ! NAs introduced by coercion

data

## # A tibble: 10,846 × 14
##    team    `Team City` Population team_name  year  total   home   away  week
##    <chr>   <chr>            <dbl> <chr>     <dbl>  <dbl>  <dbl>  <dbl> <dbl>
##  1 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     1
##  2 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     2
##  3 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     3
##  4 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     4
##  5 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     5
##  6 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     6
##  7 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     7
##  8 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     8
##  9 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451     9
## 10 Arizona Phoenix        1608139 Cardinals  2000 893926 387475 506451    10
## # ℹ 10,836 more rows
## # ℹ 5 more variables: weekly_attendance <dbl>, ...11 <lgl>, ...12 <chr>,
## #   ...13 <lgl>, ...14 <dbl>

Plot data

data %>%
    
    ggplot(aes(team_name)) +
    geom_bar() +
    coord_flip()