Brickey's Bionic Belly Button Biodiversity Dashboard

In this assignment, you will build an interactive dashboard to explore the Belly Button Biodiversity DataSet.

Step 1 - Plotly.js

Use Plotly.js to build interactive charts for your dashboard.

• Create a PIE chart that uses data from your samples route (/samples/<sample>) to display the top 10 samples.

  • Use sample_values as the values for the PIE chart

  • Use otu_ids as the labels for the pie chart

  • Use otu_labels as the hovertext for the chart

  

• Create a Bubble Chart that uses data from your samples route (/samples/<sample>) to display each sample.

  • Use otu_ids for the x values

  • Use sample_values for the y values

  • Use sample_values for the marker size

  • Use otu_ids for the marker colors

  • Use otu_labels for the text values

  

• Display the sample metadata from the route /metadata/<sample>

  • Display each key/value pair from the metadata JSON object somewhere on the page

• Update all of the plots any time that a new sample is selected.

• You are welcome to create any layout that you would like for your dashboard. An example dashboard page might look something like the following.

Step 2 - Heroku

Deploy your Flask app to Heroku.

• You can use the provided sqlite file for the database.

• Ask your Instructor and TAs for help!

Deployed app: https://mighty-ravine-11914.herokuapp.com/

Preview:

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Advanced Challenge Assignment (Optional)

The following task is completely optional and is very advanced.

• Adapt the Gauge Chart from https://plot.ly/javascript/gauge-charts/ to plot the Weekly Washing Frequency obtained from the route /wfreq/<sample>

• You will need to modify the example gauge code to account for values ranging from 0 - 9.

• Update the chart whenever a new sample is selected

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Flask API

Use Flask API starter code to serve the data needed for your plots.

• Test your routes by visiting each one in the browser.

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Hints

• Don't forget to pip install -r requirements.txt before you start your server.

• Use console.log inside of your JavaScript code to see what your data looks like at each step.

• Refer to the Plotly.js Documentation when building the plots.

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Copyright

• Project guidelines and starter code © Data Boot Camp 2018. All Rights Reserved.

• Solution code and visualizations © Peter Brickey LeQuire 2019-2020.