Visualise UK’s pizza topping preferences using Tableau
My first viz attempt using Tableau
Last week, while grabbing my favourite Hawaiian pizza with a friend, an idea struck me as I was wondering:
- What is the most favourite pizza topping preference?
- And to its further extent What is the ranking of my favourite but yet controversial pizza topping — Pineapple belongs to in that list?
To answer those questions, I believe there’s nothing better than making a visualization to represent my ideas. For that, I gave a shot to the world-famous BI tool — Tableau.
The Dataset
The Data gathered for this project is from the YouGov website in the UK. The dataset gives a good overview of different topping preferences by gender, age group, location, etc. However, in the scope of this blog, I will focus on analysing the different taste buds by gender and total. Here is a snapshot of how the dataset is like:
Noted that, the provided dataset is fairly clean and optimized for my visualisation purpose, thus, no minimal efforts need to perform data cleaning on this dataset.
Let’s Visualize!
Since my purpose is to find the preferences of different pizza toppings, a bar graph would be a great idea to represent all of the ingredient information and also provide a good snapshot into the comparison between each one. For that, I will create 2 bar graphs, one for total preference, and the other will represent preferences categorised by gender.
Preference by the total graph:
Preference by gender:
After that, to make the graph more interesting, I’ll add them into a dashboard along with some decoration that is tailored to the chosen topic. The one that I chose to highlight is the yummy pizza slice as illustrated below. Feeling hungry yet? Just to let you know, I’m starving while creating this visualisation :D
The Result
My final output is represented as follows:
Now that we got the visualisation. Let’s dig deeper to see what insights that can be drawn from this graph!
As transparent, in terms of what people actually like to have on a pizza, the graph reveals that the nation’s most popular topping is mushroom, which is enjoyed by nearly two-thirds of Brits (65%). Overall, seven pizza toppings are enjoyed by more than half of the country: joining mushrooms are onion (62%), ham (61%), peppers (60%), chicken (56%), pepperoni (56%) and tomato (as a topping, 51%). Meanwhile, the two fish options, tuna (22%) and anchovy (18%), are the least popular on the list. It is really surprising to me that even being a controversial pizza topping, pineapple still be in the top 10 of most favourite pizza toppings with a 42% vote. Another noticeable trend is that men are much more likely than women to like meat on their pizza.
Reflection
I heard and viewed a lot of “bad-ass” viz people made using Tableau but this is the very first time that I had ever used the tool. From my experience, there are a few takeaways that I got:
There are 3 things that I love when using Tableau — Ease of use, Tool learning support, Community support.
- Ease of use: One of the best strengths of Tableau along with other BI tools is the ease of use. Users don’t have to possess practical knowledge of programming to actually use the tool. Also, the interface helps people to create beautiful visualization by just drag-and-drop interactions with the tool.
- Tool learning support: If you already purchased the Tableau subscription, there is many online learning paths/certification provided by Tableau itself for users to learn and master the tool based on their usage purpose.
- Community support: This is one of the best things I love about Tableau. They have a Tableau public section where designers/users can upload their work and review others as well. I myself have been inspired by many other visualizations to create mine because they gave me the ideas of how my work should look when I got stuck, which make the user experiences of this tool so much better than other.
However, there is still room for improvement. From my experience, the tool difficulties mainly lie in the data preparation stage because it seems like Tableau can only deal with a fairly clean dataset due to its calculation limitation.
Therefore, if you’re looking for a tool that can do a full data cycle process then Tableau is not efficient enough for the purpose. On the other hand, for those who first explore the world of data visualisation and don’t want to be overwhelmed by the complexity of programming languages, I highly recommend using Tableau as it offers great flexibility for different users!
Hi friend, I’m Leah and I’m a data enthusiast! 👩💻 Follow me for more data contents or:
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