For the first day of the Dashboard Week in Data School, we were given a website that contains data about almost every Broadway shows since 1985. The requirement is to get the data from the website and construct an story in Tableau.
Getting Started: Web Scraping
Since we had to do a bit of web scrapping to get desired dataset, I used Alteryx to take advantage of it’s web-scraping capability. I had a session on web scraping with Alteryx, but it was a few weeks ago so my knowledge was a little rustic.
The first challenge was to use Regex to extract the right data. Already, I encountered many issues in regard to Regex and how to configure the tools properly. Few hours spent and I finally got what I need, time to get on with the next step !
Narrow The Focus
I do not watch Broadway, nor I care about it. I simply picked a random show with a catchy name then move on. Since the dataset scrapped is very small – 15 rows, 9 columns – I spent sometimes looking for complementary dataset. Since I’m clueless about the show, looking at other people comments or reviews might be interesting, unfortunately, I could not find a good websites and have to move on.
To ensure no more time is lost, I decided to focus solely on 1 singe element – ticket price – and tried to extract as much insight from it as possible.
What I was interested in is:
- How ticket price fluctuate overtime ?
- Does the rising of ticket price affect attendance to the show ?
This should be a good enough starting point, and hopefully, one thing lead to the other.
Start Building
Using Tableau, I explore how can I answer these two questions
Final Thought
While the dataset was small and the context unfamiliar, diving deep into a single metric like ticket price proved to be surprisingly rich. By combining Tableau’s flexibility with just a little storytelling structure, I was able to uncover a narrative that reflects real-world Broadway pricing strategies.
