Starbucks Coffee!!

I found a data set on Starbucks coffee, and got really excited.

I wanted to analyse the data based on calorie and caffeine content.
Here’s my thought process when cleaning the data set:
1. I decided to standardise the data points by size and milk content first so that there would be a common basis for comparing calorie and caffeine content. Thus, all the drinks in the data set are “tall” size and contain non-fat milk. I choose these two categories because there were a few missing data points for the other categories.
2. Removed smoothies because they aren’t sold in Singapore, and I wanted the information/visualisation to cater to Singaporeans! I also removed the data points which had “varies” for caffeine content.

In graphics:

And finally, here’s the data visualisation I posted on Reddit! It came as a surprise to me that “signature espresso drinks” contain more sugar than “frappuccinos”, so I hope that this will help y’all make more informed decisions when ordering drinks!

Update: I posted the above data visualisation on Reddit, and got some comments about the suitability of using a pie chart — user Zero36 summed it up nicely

Thus, I reworked my data visualisation:

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