Project One


What a fun Journey it has been!

To access the visualization, tab on the Link

You can hover over to walk through the cases and percentage of each disease verses ethnicity and gender.  As of now, I have not build it to work on mobile phones.

I decided to work on the Be More Dataset to represent Causes of Death with Ethnicity and race.  I wanted to begin working on the dataset from Tableau and then look for a charting tool such as D3 and C3.

I was a bit overwhelmed by the D3 learning curve, so I decided to work on library that are build on top of D3.  I found dimplejs with many options.  I then choose the image below.

Screenshot-2017-10-5 Pie Matrix

However, the library has no flexibility to adjust for my many variables dataset.  Thus, I pushed myself into D3 and watched “D3.js Essential Training for Data Scientists”  by Emma Saunders.  The class was super helpful to understand and work with D3 library.

Afterwards, I choose the style of visualization below.


I spend a day and half to recreate it, see below.

Screenshot-2017-10-5 Be More Project

However, the final visualization will have many problems with scaling.  For example, if the visualization is opened from a mobile phone, the visualization will either be very small or loose the axes.

Finally, I found what I think the best way to represent my many variable dataset.  The chart uses a hierarchy to go from disease to ethnicity to gender using pies.  The final visualization is below and hosted on the cloud.

Screenshot-2017-10-5 Be More Data


To access the visualization, tab on the Link



Working with a Dataset

My goal is to work with a community partner.  Julia and I exchanged emails for using the dataset from the Library of Congress.  Now, I am about to examine the data using the XML editor.  Another option is to work on Be More Dataset on Death Disparities for project, and then work on the Library of Congress data for project III.


I will be using Tableau to get a sense of the data, and then work on a charing Javascript library such as D3 and C3.

If I use the Be More Data, I will divide the visualization based on ethnicity first.  Then, I will divide the data based on gender, then based on diseases etc.

Link for the Be More data: