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Project instructions

Important

This is the final project for course 1. This will not be graded, and is intended to provide you with the opportunity to test the skills you’ve acquired throughout the course in a less structured environment. This project is also structured for you to host on a webpage and link as proof of work on your resume or CV.

Rubric items can be found at the end of the README in the Self rubric section to help self-assessment of your project. Do not expand the rubric items until you have finished a draft of your project.

Before you start

Please complete the following steps before getting started if you have not downloaded and configured Git.

Download Git

Note, if you are on Windows or Linux, you will need to install Git. If you do not have git installed, please do so:

Windows -> https://git-scm.com/download/win

Linux -> https://git-scm.com/download/linux

Configure git with RStudio

In the console, run the following:

install.packages("usethis")

usethis::use_git_config(
  user.name = "Your name", 
  user.email = "Email associated with your GitHub account"
 )

Please make sure that you replace "YourName" with your actual name, and "Email associated with your GitHub account" with your email used for your GitHub account.

Create and authenticate your personal access token

In the console, run the following to geneate your personal access token (PAT)

usethis::create_github_token()

Next, run the following and paste your PAT in:

gitcreds::gitcreds_set()

Getting started

Once you restart R, you will be able to clone this project repository and get working! To clone this project:

  • Click the green code button on the repo
  • Copy the HTTPS
  • Open a new project in RStudio
  • Click version control and then Git
  • Paste the copied HTTPS in the Repository URL box.
  • Name the project directory
  • Browse and select where this project will be saved
  • Click create project!

Instructions

This final project consists of an exploratory data analysis and write up of results you discover. The data set you will be working with is a pre-packaged R data set called taxi. Your write up, in index.qmd, will explore (at least) two questions.

Description

A taxi is a type of vehicle for hire with a driver. Often seen as a yellow vehicle, taxi drivers charge passengers money to transport them from one place to another. These data contain information on a subset of taxi trips in the city of Chicago in 2022. You may have some initial hypotheses on how the taxi system works! For example, it may be reasonable to think the longer the taxi ride is, the more likely a passenger is to tip. If someone is considering working for a taxi company, they may wonder if one company tends to get more tips than the others.

A data table is below:

variable description
tip Whether the rider left a tip. A factor with levels "yes" and "no"
distance The trip distance, in odometer miles
company The taxi company, as a factor. Companies that occurred few times were binned as "other"
Time time of requested loan
local Whether the trip's starting and ending locations are in the same community. See the source data for community area values
dow The day of the week in which the trip began, as a factor
month The month in which the trip began, as a factor
hour The hour of the day in which the trip began, as a numeric

Research Questions

You are tasked to use these data to explore the following questions:

  1. Is there there a relationship between the distance (in miles) someone travels in a taxi and if they tip or not?

  2. Do taxi passengers tend to tip more for the company Chicago Independents than the other companies?

Hint: you may need to make a new variable in order to answer the second research question. You may also find the function if_else() useful.

Important

- Version control with GitHub
This project also provides you with the opportunity to practice using version control, often expected to be used in a real-world context. We challenge you to Render; Commit; and Push after each visualization or summary statistics you create. If you are working on a project with more than one contribute, make sure that you Pull before making any progress to ensure your project matches up with the project repository.

Displaying code

Depending on the purpose of the project, it may be advantageous to either show or hide all of your code in your rendered document. To hide all of your code, set echo: false in your YAML. If you want to display your code, set echo: true.

Citations

When working on your project, you may want to include outside sources. When doing so, we need to make sure these sources are properly cited. In order to do so, we need to create a .bib file and specify it in your index.qmd's YAML heading. Your project files include a references.bib txt file. This is where you will put bibtex entries. An example of an entry to be referenced can be seen below:

@article{Cetinkaya2020,
	title        = {A Fresh Look at Introductory Data Science},
	author       = {Cetinkaya, Mine and Ellison, Victoria},
	year         = 2020,
	month        = {08},
	journal      = {Journal of Statistics Education},
	volume       = 29,
	pages        = {1--27},
	doi          = {10.1080/10691898.2020.1804497}
}

In this entry above, Cetinkaya2020 is the citation identifier. The default way to cite an entry in your text is with this syntax: [@citation-identifier].

To automatically generate a references section with your entries, the path to the .bib file needs to be specified in your YAML with bibliography: references.bib. This has been done for you.

Self rubric

Below are a collection of rubric items to self assess your report.

Rubric items - Report should include 1-2 visualizations with accompanying summary statistics per research question.

  • All visualizations should be appropriately labeled, including having a title and customized axes.

  • Only plots and summary statistics that are relevant to the research question(s) should be created.

  • Should come to the conclusion that there is a relationship between longer taxi rides and those who tip (~ 2 miles difference between tip and no tip).

  • Should come to the conclusion that there is some to little evidence of a relationship between those who tip for Chicago Independents (94.8% of the time) and the rest of the companies (91.8%).

Challenge

Using these data, come up with a third research question, add it to your written report, and investigate!

Acknowledgements

This final project was inspired by a tidymodels data set.

Kuhn M (2024). modeldata: Data Sets Useful for Modeling Examples. R package version 1.3.0, https://github.com/tidymodels/modeldata, https://modeldata.tidymodels.org.