Full Stack Web Application

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Table of Contents

Project Challenge Description

About Our Data

Challenges With Our Data

Walkthrough Video With Jesse

Challenge Description

Bold challenge descriptions with responses below.

1. Your visualization must include a Python Flask–powered RESTful API, HTML/CSS, JavaScript, and at least one database (SQL, MongoDB, SQLite, etc.).
   • Since our data was proprietary, we did not integrate a DB. Though, if we had, we would have used PostgreSQL.
2. Your project should fall into one of the below four tracks:
   • A custom “creative” D3.js project (i.e., a nonstandard graph or chart)
     • Used Tableau API integration
   • A combination of web scraping and Leaflet or Plotly
     • Tableau used to visualize data
   • A dashboard page with multiple charts that update from the same data
     • We have multiple pages with different dashboards
   • A “thick” server that performs multiple manipulations on data in a database prior to visualization (must be approved)
     • <script src="https://public.tableau.com/javascripts/api/tableau-2.min.js"></script>
3. Your project should include at least one JS library that we did not cover.
   • We chose to use the Tableau API library
4. Your project must be powered by a data set with at least 100 records.
   Ours has over 100,000 records.
5. Your project must include some level of user-driven interaction (e.g., menus, dropdowns, textboxes).
   Tableau makes our viz interactive.
6. Your final visualization should ideally include at least three views.
   We each focused on creating visualizations after thorough dialogue about our data results.

Data Sources

Data Name	Description	Reference Link
ICO Time Series	Data provided by the ICO for prices paid to producers and retail prices of coffee	Historical Data
Web Scraped CQI	Data scraped by James LeDoux located on public repository web scraped from CQI website	Repo Link

Data Challenges

ICO Time Series

• Data was received in XLSX report-style format and in batches of time due to query restrictions. This required meticulous cleaning before loading into Python/Pandas Dataframes.
• Each country's price was represented it its original currency (requiring the use of exchange rate conversions)
• Units in weights sometimes varied cauing some meticulous cleaning and iterating of the data.

Web Scraped CQI

• This data was very messy and difficult to clean.

Tech

• Python - The coding language of champions
• Flask - Extensible web microframework for building web applications with Python
• Heroku - Cloud application platform used to launch our application
• Gunicorn - WSGI HTTP Server for UNIX

Video Walkthrough

Coming soon... (Recording 07/14/2020)