R Notebook

Project 1

Updated by NDG 2/7/24

Description

You have been hired as a consultant by Disney to create a location for a new amusement park. Your job is to analyze weather data from different locations to pick out the best option.

Part 1: Data cleanup (5 points)

Begin by looking at the documentation for the climate normal data.

Then load your ClimateNormalData.csv datafile. You will need to do some clean-up. Be sure to look at the data carefully. Below are a list of suggested dplyr activities.

Suggested tasks:

• Load both csv files as raw data
• Cleanup the data
• Turn all character date fields into integers
• Create a new string with paste or paste0 that has m/d/y (use 2024 for year)
• Use mdy from lubridate to create an actual date column
• Remove any fields you don’t want to use
• Join the two tables together to get the titles for each station.

Part 2: Summary table (5 points)

Now that you have the data, create some basic summary data. Show a table with the average temperature by month and location. Have the stations as rows, and the month as columns.

Hint: you may need to use dplyr pivot.

`summarise()` has grouped output by 'station', 'name'. You can override using the `.groups` argument.

Part 3: Best location for amusement park (5 points)

We want to find the best location for an amusement park that isn’t too hot, or too cold. Define an appropriate temperature range where it is comfortable to be outside. Then, create a graph showing how different locations meet your temperature requirement.

Write a brief 2-3 sentence explanation of your findings.

Answer: I would define the appropriate temperature range for an amusement park to be around 60 - 80 degrees Fahrenheit. Amusement parks should be able to be enjoyed year round and could include water attractions, therefore the temperature shouldn’t be too low. Austin, TX, was identified to be the most suitable location within the specified temperature range.

Part 4: Airport (5 points)

Which of the sites would be best as an airport? Explore the others variables in your dataset. For example, look at cloud cover or average wind speed. This may require some data clean, so carefully look at your dataset for weird values.

Give a chart, and write a 2-3 sentence explanation of your answer.

Answer: For locating the best site for an airport, I looked at the average wind speed, percent of clear skies, percent of overcast, percent of calm winds, and the average temperature. I found that Pittsburgh, PA would be the most suitable location for an airport. This is because the location has the highest percent of clear skies, the lowest percent of overcast, the lowest average wind speed, the second lowest percent of calm winds, and a comfortable average temperature for ground workers.