visualizations-and-dashboards | Computer Science Assignment: I need help writing a research paper.

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.