Putting it all together

For your final project, I’m asking you to create an exploration of a dataset of your choice. This project should provide people with ways to explore the dataset in a way that will guide them towards learning something about relationships and patterns in the data. The final project will have two graded parts – a Shiny app for end-users and a final paper where you tell us just what you did. Your app and paper should explore two or more particular features of the data you find interesting, and provide an end-user a way to derive new and meaningful inferences from the data within the bounds of the controls you provide them with.

The Shiny App

Your app should present 1) the dataset you are looking at, and a full explanation of what it is (this can be a front page, a tabset, or whatever you would like), 2) provide users at least 3 ways to explore different aspects of the data, 3) provide model fits and statistical tests where it would help the user, 4) useful visual representations that are easy for the user to understand. It should be organized in a way that a user has no problem navigating around, and should not just be one giant pane of overwhelming data and analyses.

The Paper

ORGANIZATIONAL STRUCTURE

The final paper should be broken up into something along the lines of the following sections. You may feel free to adapt this flexibly given your unique data set and set of problems and questions. But this is a general guide, particularly if you are lost.

1) INTRODUCTION

• What is the dataset you are using?
  • Describe the dataset in detail
  • Visualize summary information about the data
• Based on the data, what types of elements did you want people to be able to explore? What questions did you want them to be able to ask and answer?

2) METHODS

• How did you prepare and manupulate the data to get it ready for your app? Write out the workflow(s) step by step.
• How did you decide on the ways you wanted the end-user to be able to explore the data? What information did you chose to give to them and why? What choices did you give them and why?
• What was the setup of your app? Tell us about the layout you chose, why you chose it, and how it created a
• What are the visualizations and analyses that you provided access to?
  • Show us the types of visualizations you wanted to create to help the data tell its story
  • What are the underlying models you let the user fit? Why those models? What is their data generating process and your error generating process?
  • What statistical tests did you return to the user. Why? What did they tell them?
• Include a working link to your app

3) RESULTS

• What are the most obvious things that you can learn from your app about your dataset?
  • Based on what people could do in your app, is there any 2-3 visualizations or analyses that stand out? Show them here.
• What are other visualizations and analyses that come out of your dataset?
  • Anything that your app made you think you should go look at?
  • Any statistical tests of patterns that you can see in your app, but are not formally tested there?

4) DISCUSSION

• What did you learn from your app and your exploration of this data?
• Does the way you setup your app allow you to learn anything you did not expect?
• What feedback did you get about your app?
• Do your results suggest additional visualizations/analyses? Feel free to conduct them here.
• What final conclusion can you draw about your data set?

SOME MARKDOWN NOTES FOR THE PAPER

As a quick note, you might want to massage you code chunks a bit in your presentation so we don’t see code, error messages, warnings, etc. Remember warning=FALSE, message=FALSE, echo=FALSE, and more are all your friends. For more – including how to resize graphs and such – see here.

If you want to output your statistical results as a table, use knitr::kable or kabelExtra. If you want to build tables that you fill in with text, I recommend this markdown table generator as markdown tables can be tricky.

You might want to use the goodpracticeslibrary and the styler library to make sure your code is readable and accords to good style-guide principles.

Last, here’s a markdown cheatsheet – although Rstudio’s help has a good set of materials as well.