datasci_4_web_viz

HHA507 / Data Science / Assignment 4 / Web-based Data Visualization Using Shiny and Flask

This repo focuses on web-based data visualization using Shiny and Flask. The dataset used in this repo is a subset taken from the CDC Health Places dataset and visualized using Shiny on R, shinyapps.io, and Python. It is also visualized with Matplotlib using Flask.

This repo contains the following:

• datasets folder containing the datset used in the repo.
• python_flask folder containing the code for visualizing the dataset in a basic Flask application.
• shiny_python folder containing the code for visualizing the dataset using Python's shiny package.
• shiny_r folder containing the codes for visualizing and deploying the dataset using Shiny in R and shinyapps.io
• README.md file containing an overview of the repo.

Assignment Reflections

1. R's Shiny Visualization

shinyapp.io link: https://t9tlf5-susan0chen.shinyapps.io/web-viz/

The challenege I encoutered during this part of the assignment was when I tried to deploy the code on shinyapps.io. An error message was shown when I tried to publish my initial R file on shinyapps.io. I created a new file by going to File>New File>Shiny Web App... that created a new file called app.r. I copied the code over from my original file to the file```app.r``, ran the app, and published.deployed it on shinyapps.io. The following was the error message I encountered when deploying using my original file:

Error in `checkAppLayout()`:
! Cancelling deployment: invalid project layout.
ℹ Expecting one of the following publication types:
  1. A Shiny app with `app.R` or `server.R` + `ui.R`
  2. R Markdown (`.Rmd`) or Quarto (`.qmd`) documents.
  3. A website containing `.html` and/or `.pdf` files.
  4. A plumber API with `plumber.R` or `entrypoint.R`.
Backtrace:
    ▆
 1. └─rsconnect::deployApp(...)
 2.   └─rsconnect:::appMetadata(...)
 3.     └─rsconnect:::checkAppLayout(appDir, appPrimaryDoc)
 4.       └─cli::cli_abort(...)
 5.         └─rlang::abort(...)

2. Python's Shiny Visualization

No significant challenge was encountered in this portion, however, I can to pip install matplotlib in the shell.cloud terminal to run the code.

3. Flask with Matplotlib Visualization

No challenge was encountered in this portion.