For this project, I used data from Crit Role Stats.  They don't currently have an API but they do house most of their data in Google Sheets and what they don't have in a sheet lives in Google Docs which are embedded on their site.  The Google Sheets data was easy to pull into the dashboard using Tableau's built-in Sheets connector, and the rest presented a fun challenge.

To begin, for each set of non-Sheets data that I wanted, I built a Python-powered web scraper in Morph.io, each of which is housed in my own Github in the repositories ("repos") labeled with "critrolestats".  I then created a Google Sheet of my own, where each tab points to one of my Morph.io APIs, like so:

The "importdata()" function in Sheets was instrumental to making this all work!  From here, I was able to again use Tableau's built-in Sheets connector to pull the data into the dashboard.  The API call is made each time the extract is refreshed, which is daily on Tableau Public, so the dashboard stayed up to date until the end of the campaign in 2021.

As for the details about the inner workings of the Tableau workbook, I leave it to the reader to download and sniff around. :) 

In a very cool (for me) turn of events, the Wildemount Dashboard was part of the Long List for the 2019 Information is Beautiful Awards.  Click here to see the entry.

​As of this writing, the viz has almost 10,000 views on Tableau Public.

Also I'm pretty sure this dashboard helped me land a job in early 2020, as I joined a team of D&D nerds with whom I still play on a quasi-regular basis even though I've since moved on from that company!

There is a business in the small town of Logan, nestled in the hills of rural southeastern Ohio, called The Artbreak.  It is truly a unique place, in that in one building and owned by one couple, it houses a real estate brokerage, a piano repair shop / showroom, and an art gallery.  I worked there as an administrative assistant for 4 years right out of high school, their first and sometimes only employee, while I put myself through my first round of college at nearby Ohio University.

In spring of 2018, I received a message from my former boss, the owner of this distinguished and artistic establishment.  She told me that she was putting on a gallery exhibit that fall about alumni of the local high school who'd had "particularly creative career paths" and wondered if I might like to submit one or more data visualizations to the exhibit.

So it was that I embarked on a journey to make a new visualization for this exhibit, one which would be relevant and hopefully interesting to the good people of rural southeastern Ohio.  On the State of Ohio's website, I found in CSV form W2 data ranging from 2011 through 2017; this data included name, job title, department, total wages earned for the year, and hourly wage.  

What a treasure trove!  After some data wrangling and cleaning in Tableau Prep, I realized that I would be safest using the hourly wage, as some positions might have overtime and some people might not have been employed the entire year; hourly wage was the best way to treat all positions equally.  I then took the names and ran them through an API which would tell me the likely gender of each, and then I excluded from all further analysis the surprisingly few which returned as Unknown Gender.  I should point out that this would likely give an incorrect result for some names which may be either female or male, but I have assumed that this does not cause a large enough effect to make a significant difference on the results.

My original intention had been to show the gender pay gap over time, but I found that more interesting story to be in the most recent full year: 2017.  As you can see in the visualization, at a high level using both the mean and median, the pay gap is nearly non-existent.  The median is higher for women than for men, while the mean is higher for men - this demonstrates that there are more men in the higher-paying roles than women, which is shown in the lower left corner of the visualization.

While I would have liked to break down by seniority and job experience, that was not possible, so I settled for breaking down by department (those having at least 100 employees) and looking at the difference between the average (or median) hourly wage between men and women therein.  This result you can see on the right hand side of the visualization.  The $1.09 (average) and the $1.10 (median) are the result of averaging each department's average or median hourly wage of women and dividing by that of the men, as long as that department had at least 100 employees.

This was an incredibly rewarding project, which I proudly stood beside during the exhibit opening in Fall 2018.