visualizations-and-dashboards | Computer Science homework help

 

In this assignment, you will build an interactive dashboard to explore the Belly Button Biodiversity dataset (http://robdunnlab.com/projects/belly-button-biodiversity/),  which catalogs the microbes that colonize human navels.

The dataset reveals that a small handful of microbial species (also called operational taxonomic units, or OTUs, in the study) were present in more than 70% of people, while the rest were relatively rare.

Step 1: Plotly

  1. Use the D3 library to read in samples.json.
  2. Create a horizontal bar chart with a dropdown menu to display the top 10 OTUs found in that individual.
  • Use sample_values as the values for the bar chart.
  • Use otu_ids as the labels for the bar chart.
  • Use otu_labels as the hovertext for the chart.

hw01.png

  1. Create a bubble chart that displays 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.

hm02.png

  1. Display the sample metadata, i.e., an individual’s demographic information.
  2. Display each key-value pair from the metadata JSON object somewhere on the page.

hm03.png

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

Additionally, you are welcome to create any layout that you would like for your dashboard. An example dashboard is shown below:

dashboard_part1

  • Adapt the Gauge Chart from https://plot.ly/javascript/gauge-charts/ to plot the weekly washing frequency of the individual.
  • You will need to modify the example gauge code to account for values ranging from 0 through 9.
  • Update the chart whenever a new sample is selected.

dashboard_part2

Deployment

  • Deploy your app to a free static page hosting service, such as GitHub Pages. Submit the links to your deployment and your GitHub repo.
  • Ensure your repository has regular commits (i.e. 20+ commits) and a thorough README.md file

Hints

  • 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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