25  Car Wash

A car wash business based in Rexburg, Idaho USA wants to see if the temperature hurts their bottom line. They have point of sale data for the months of April, May, June, and July. You will need to aggregate the data into hourly sales totals and merge the sales data together with the temperature data to provide insight into the relationship between temperature and car wash sales.

  1. In a R markdown file, read in the car wash data https://byuistats.github.io/M335/data/carwash.csv and format it for the needs of this task.
    • Convert the times from UTC time to the business’ local time using the right function out of library(lubridate).
    • Get to know your data. Investigate/look for unusual data points and decide what you should do about them.
    • Create a new hourly grouping variable using ceiling_date() from library(lubridate).
    • Aggregate the point of sale data into hour sales totals.
  2. Use riem_measures(station = "RXE", date_start = , date_end = ) for station RXE from library(riem) to get the Rexburg temperatures for the relevant dates.
    • Create a new hourly variable that matches your car wash hourly variable, this will allow the join to take place.
  3. Merge/join the two datasets together.
  4. Create a visualization (or two) that provides insight into the relationship between sales, temperature, and hour of the day.
  5. In a couple sentences explain a couple of the key take-aways about the relationship between sales, temperature, and hour of the day.
  6. Render the .qmd. Push the .qmd, .md, and .html files to your GitHub repository.

Submit

In I-learn submit a link to the .md file on GitHub.