Week 4: Apply 3

Import data

# excel file
data <- read_excel("data/myData_Shoals.xlsx")
data

## # A tibble: 96,429 × 12
##    reported_date_time  reported_date_time_utc posted_date         city     state
##    <dttm>              <dttm>                 <dttm>              <chr>    <chr>
##  1 2022-08-29 02:03:00 2022-08-29 02:03:00    2022-09-09 00:00:00 Pinehur… NC   
##  2 2022-08-19 21:51:00 2022-08-19 21:51:00    2022-10-08 00:00:00 Rapid C… MI   
##  3 2022-08-13 01:30:00 2022-08-13 01:30:00    2022-09-09 00:00:00 Clevela… OH   
##  4 2022-08-06 17:00:00 2022-08-06 17:00:00    2022-09-09 00:00:00 Bloomin… IN   
##  5 2022-08-04 03:40:00 2022-08-04 03:40:00    2022-09-09 00:00:00 Irvine   CA   
##  6 2022-07-22 12:00:00 2022-07-22 12:00:00    2022-09-09 00:00:00 Moore    OK   
##  7 2022-07-19 12:27:00 2022-07-19 12:27:00    2022-09-09 00:00:00 Short P… VA   
##  8 2022-07-14 14:56:00 2022-07-14 14:56:00    2022-09-09 00:00:00 Norwalk  CT   
##  9 2022-07-13 15:40:00 2022-07-13 15:40:00    2022-09-09 00:00:00 Blayney  New …
## 10 2022-07-13 00:10:00 2022-07-13 00:10:00    2022-09-09 00:00:00 Greybull WY   
## # ℹ 96,419 more rows
## # ℹ 7 more variables: country_code <chr>, shape <chr>, reported_duration <chr>,
## #   duration_seconds <dbl>, summary <chr>, has_images <lgl>, day_part <chr>

Plot prices

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