Sta-230 R Shiny Project (F23)

Description

The focus of this project is on creating interactive data exploration application. The primary product is an R Shiny application that allows a user to thoughtfully explore a data set. You will be required to submit a short proposal and an interim progress update to keep your project on track.

In addition to creating your app, you will give a 5-minute in-class presentation of your app’s features, being sure to highlight at least one interesting finding it reveals about your data.

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Timeline

• Tuesday 10/24 at 11:59pm - proposed data source, research question/motivation, and partner
• Thursday 10/26 at 11:59pm - data cleaning and/or sketch visualization(s)
• Tuesday 11/7 at 1:00pm - in-class presentations of your app
• Thursday 11/9 at 1:00pm - final code, data, and difficulty justification are due

*This timeline was revised on 10/24

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App Expectations

Your finished Shiny app is expected to include:

• Data visualization
  • Your app should use either ggplot2, plotly, or leaflet in some capacity. You may choose to use more than one of these packages.
• User options
  • A user of your app should be able to manipulate or explore your data in at least three ways. Some possibilities include:
    • selecting a specific variable for a particular visual aesthetic (x, y, color, fill, etc.)
    • choosing a scale transformation (log2, log10, scale reversal, color palate changes, etc.)
    • selecting faceting variables and/or manipulating facet formatting
    • hoverable or clickable content that displays additional information (markers, map regions, etc.)
    • different app pages or panels that a user can select to display different content

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Presentation Expectations

Your presentation is expected to include:

• Introduction to your data and research motivation
  • A brief description of your data source and the background of your project.
• Explanations of app features
  • An overview of what your app is capable of doing and how it handles those tasks.
• Illustration of one relevant finding
  • A demonstration and discussion of at least one interesting trend or relationship that your app helps reveal

Your target audience should be our class, so you may assume some working knowledge of R Shiny and various types of graphics/statistics; but you should not assume any familiarity with your data source or research question.

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Groups

You may work either individually or with one classmate (of your choosing) on this project.

Note: You may not work with the same person on both this project and the final project. So, if you really want to work with a certain classmate on the final you might choose to work with someone else (or independently) on this project.

Additionally, by choosing to work with someone you are consenting to receiving the same score on the project based. If you are not comfortable receiving the same score as your partner, you might opt to work alone.

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Proposal

By the end of the day on Tuesday 10/24, one member of your group should send a brief proposal (via email) that addresses the following:

1. Where your data will come from.
2. Who (if anyone) you’ll be partnering with.
3. What is it you seek to answer or explore using your R Shiny app.

For #3, a good proposal might be something like “I want users of the app to be able to explore whether there are spatial patterns in the incidences of different types of crimes that were reported in the city of Chicago”. A bad proposal might be something like “I want display all crimes in Chicago on a map”. The first example is good because it involves something that is best achieved using Shiny (ie: a user option to change or filter by crime), while the second is bad because Shiny isn’t necessary to make a map.

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Intermediate Progress

By the end of the day on Thursday 10/26 you are expected to have code that cleans/manipulates your data to the point where you can create a sketch version of some type of graphic that you intend for your Shiny app to display. You should submit a compiled R Markdown file documenting this progress via P-web. The sketch graphic you share does not need to ultimately be used in your app.

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Presentation Day

• By 1:00pm on Tuesday 11/7 you must have a working version of your app that runs on your local PC.
• You will be asked to share app via the AirTame screen sharing platform that is present in our classroom

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Assessment Details (100 pts total)

App Code - 10 pts

• Code should be neatly formatted with appropriate comments
• Code should run on another PC without issues (assuming all packages are installed and all data files are moved to the proper location)
• Any substantive data cleaning is done as a preliminary step using a separate script, with the cleaned output (exported using write.csv()) being used in the final app
• Code should be reasonably clean and efficient.

Aesthetics - 20 pts

• The app’s user interface should be professional in appearance. This includes aesthetically pleasing labels, colors, and other design elements
• Figures and output generated by the app should also be of professional quality. This includes colors, labels, themes, visual choices, etc.

Function - 30 pts

• Your app should include at least 3 features that the user can manipulate
• Certain combinations of feature inputs should not be able to break your app
• Proper choices should be made to accommodate issues related to reactivity, challenges in graphing large amounts of data, etc.

Presentation - 15 pts

• Presentation should adhere to the 5-minute time limit
• Presentation includes all three components mentioned under “general details”

Misc - 5 pts

• You are expected to peer review a certain number of your classmates’ apps. Completion of these reviews will be a small part of your project grade.
• You are expected to adhere to the proposal timeline stated earlier in this assignment. Not submitting a proposal on time will lead to a small penalty.
• You are expected to meet and discuss progress on your app on Wednesday/Friday prior to in-class presentations. Insufficient progress at the time of your scheduled meeting will lead to a small penalty.

Difficulty - 20 pts

• To account for the substantial variability of work required to effectively utilize different data sources you will be asked to argue for the level of difficult of your project.
• Your score in this category will be determined by the instructor, but your written narrative will be taken into consideration when determining that score.
• Additional details are provided in the section below

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Level of Difficulty

One goal of this project is to afford you the opportunity to work with a topic that you find interesting. Unfortunately, real-world projects rarely utilize all areas of the data science workflow/life cycle equally. For example, some projects will require you to devote 90% of your time to data cleaning and manipulation in order produce a few relatively simple visualizations or models. Other projects might involve data come in a relatively clean format, and the majority of your time is spent making highly detailed visualizations or sophisticated models.

To address these differences, you will be asked to submit a \(\leq1\)-page written argument describing your project’s level of difficult. More specifically, you should argue that your project had “A-level”, “B-level”, or “C-level” difficulty, providing clear reasons and justification for your rating.

Hallmarks of an A-level project:

• Extensive data manipulation - substantial use of several distinct functions/methods covered in the tidyr, dplyr, merging and joining, stringr, and lubridate labs.
• Professional quality visualizations - thoughtfully constructed graphics that use customized labels, colors, and design choices
• Great functionality - options that are truly useful to an app user for gaining novel insights into your data. These generally go beyond simple filtering or variable selection options (though some of those are good too!)
• Code efficiency - legible and efficient code.
• Self-study - going beyond the contents of our in-class labs and researching methods and tools to accomplish something new. For example, you might learn about advanced UI features, or new types of graphs (including radar charts, treemaps, stacked area charts, sankey diagrams, etc.)

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Finding a Data Source

You are expected to use a challenging data source of your choosing. I encourage you to find something that aligns with your interests, and you are welcome to use data from other courses/internships/etc. provided it is sufficiently complex.

If you’re having trouble finding data, I encourage you to look at this page containing data curated by Grinnell College libraries.

You can consult with the “Data Sources” section of this page, which is a creation of mine that could use updating.

Additionally, for this project, you may use data from Kaggle.com, provided the data set contains a satisfactory amount of documentation describing where it came from. You may not use a data set from a textbook, an R package, or any other source directly relating to R Shiny.

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Additional Comments

R Shiny is a great technology to share and display your data science skills. I encourage you to consider hosting your finished app on shinyapps.io and storing your app’s code on github. If relevant, this allows you to include links to your project in a resume or cover letter to an internship or job opportunity. You can also embed a hosted R Shiny app directly into a personal webpage (if you have one).