Welcome to #DuoDare. The Data Duo is back at it with a little friendly competition. Each month, one of us will pick a data source and dare the other to viz it better. We will pass our visualizations to each other for some feedback and present them to the community (you) to be the judge. Please fill out the Google Form to vote for your favorite duo viz. Our bragging rights are on the line so we need you more than ever!
Don’t forget, your monthly vote gets you an entry (up to 12) in our drawing at the end of the year for some free swag! We are doing a mid-project swag drawing after this month, so make sure you vote !
This month I decided to find data on an unusual topic. I found a dataset on Kaggle about suicides in India and realized after sending the data to Adam that the data had quite a bit of flaws as Adam describes in his reaction of the data below. The data has fields like type codes (causes, education status, means adopted to commit suicides and social status of the person) broken down by gender and states in India. I thought the data can be used to tell an emotive story.
I have waffles dynamically change on hover over a state on the map to show distribution of male/female deaths by state. I had to manually add X/Y co-ordinates of the waffle for each state on the waffle template I had so I can get the UI to work the way it does. Also, I must add that I have been extremely busy at work this month because I got to work on some awesome high intensity projects! To even complete this viz in time was a bit of a challenge.
Adam’s Reaction to the Data
I was kinda bummed out to see the morbid data in my email. I wanted to design a dashboard that didn’t just display the data, but conveyed some emotion on this important and very sad topic.
The data itself was messy. It took some time to understand it. States were abbreviated and I was unfamiliar with their names. There was a hierarchical aspect to the data and it appeared to be unioned five times for every year. There were five members in a type code dimension that broke down the suicides in different ways. There was a detailed type field that only made sense when filtered by type code, but it was a mess and needed some serious grouping. The data ranged from 2001-2012, but 2012 seemed incomplete.
I brought in the big guns and cleaned this data in Alteryx. While I was at it, I found 2011 Census populations by State with rural and urban breakdowns. I joined this data to the original data set so I could do some per capita analysis by State.
I shared this cleaned/enhanced version of the data with Poo. I’m thinking this should count as one of my months to find data? HAHAHA!
The Away Team: Adam’s viz
Pooja’s feedback: Adam rocked the design this month. He has a nice flow of the story with when, who, where, how and why? Great way to tell the story of a rather sensitive subject. He used viz in tooltips smartly to convey more about the point hovered and to limit objects on the dashboard.
My favorite parts of the viz are viz-in-viz tooltips, images and the scatter plot. The only thing I can add is, that I wish the titles of the ‘how’ chart and the ‘who’ charts were a bit brighter as they get lost in the background colors.
Overall great story telling, use of colors and emotion by the newly appointed Tableau Zen Master! 👏
The Home Team: Pooja’s viz
Adam’s feedback: Wow, Pooja knocked it out of the park again. At first glance, it seems so simple, but when you dive in you realize she brought out all the stops this month. She’s got some nice dynamic waffle charts that are in hidden in her title as you hover over the map to see the female/male breakout accompanied by an informative viz in tooltip.. There is also a dynamic hover filter to show the trend of deaths by type. These little things are classic Pooja design! I am not sure how she keeps coming up with them.
Great job Pooja! Also, congrats on being re-selected as a 2018 zen master. I’m really proud of you!
It’s up to you now, vote for your favorite viz below.
Voting for this month remains open until we get around to posting month 6.