Finding Meaning in my Data Visualisations
Data visualisation experiments with hand-drawn visualisations
Hi there! I am a freshmen in the National University of Singapore (NUS). Alongside my major (food science and technology), also under the University Scholars Programme (USP) and I’ll be talking about a foundational module I have taken under USP. I draw parallels between my data visualisation journey and the Scientific Method. I start off with experimentation- playing around with various softwares. In the initial weeks of my quantitative reasoning (QR) module, I toggled with applications like excel, tableau, to aid in the creation of ‘data visualisations’. I have to admit I still felt rather lost then. I often rushed to create a chart on excel and tableau (especially tableau; that made creating data visualisations so easy), and based my analysis of what I expected rather than what the visualisations showed me, because I was just so excited to start analysing the data. Hence, I did not always consider the context of my visualisations, or how the audience would view my visualisations, as I already had the ideal visualisation in my mind. As a result, I did not fully understand my own visualisations, and could not drive home an impact. That led to many projects and ideas that I scrapped because I was not satisfied with my work.
I remember that day Dr Charles, my professor for this module, posted something on slack, our class forum, about hand-drawn visualisations. I was intrigued by that and I wanted to venture into it more. After all, creating visualisations by hand meant that I could not just drag and drop down variables and have the visualisations instantly appear in front of me, I had to think through which variable to be put on which axis, did I want to use the count or the average- the list goes on. This was essentially creating meaningful data visualisations. I decided to take this on myself, although I knew it was going to be challenging.
I was mindlessly scrolling through r/dataisbeautiful on reddit, during a QR slump, as I had ran out of ideas and inspiration for what else to take on for another project. I saw someone document their rubik’s cube journey on reddit, and I wanted to try it out too. So I played with my brother’s rubik’s cube for a week and noted down the time it took me each day and my feelings as well. Here is what I managed to come up with after the little experiment:
It’s definitely no big feat, but this time I had to think about how I wanted to portray my feelings and the mini dataset I had collected. Instead of dropping variables into rows or columns, I had to think of every part of the visualisation from scratch, and it took me quite a while to decide on the axis. As a result, the data became more meaningful because I managed to develop and create the visualisation all by myself, and had greater sense of the context. If I did not have the chance to do all of that myself, I definitely would not have thought about the visualisation as critically and would even have produced an entirely different visualisation! Side note: Dr Charles very rightfully pointed out that sometimes in this module (and by extension, life), we may hit a few plateaus, like in my graph, but that’s okay because we can always pick ourselves up and try again!
After I had posted my mini venture onto slack, Makarios, one of my classmates had suggested that I try this out with the 2x2 and 4x4 rubik’s cube. So I decided to try out his suggestion as an adaptation to my previous experiment, and made similar infographics for all three, and ended up with the final data visualisation here:
I represented the different cubes visually, the size of the pink bubble depicted how satisfied I was with myself after conquering the cube, amount of clocks showing the amount of time I devoted to figuring the cube out, and the red and yellow lines depicting whether I felt satisfied or not with the time I took to complete the cube.
It’s not much, but a lot of thought went into deciding what should go into the visualisation, how should I represent my data, how should I represent and clean my data to produce the best visualisation etc. Although the project seems simple, I really think it has taught me the importance of meaningful data visualisations. You can think of data visualisations like creating a new food item to sell to the public. If your product is similar to those already in the market, it is unlikely that consumers will deviate from their go-to brand just to try out a new brand that pretty much is trying to sell the same thing. Instead, a touch must be added: maybe a different texture, or a new flavour, with the consumers’ wants in mind. Likewise, a data visualisation should always aim to tell the audience a story. I’ll be sure to think twice before screen-shotting any visualisation on tableau in which I had mindlessly dragged and dropped the variables just to churn it out.
Apart from this mini project, I was also intrigued by the use of emojis after my project on perceptions of happiness. So I decided to do more experimentation with the project idea and managed to collect some data here:
I decided to produce my own data visualisation again, but this time not hand-drawn but using an infographic editor instead, and came up with this infographic:
I was looking for a new software to help me represent the data I had collected, and was excited when I came across Venngage. To my surprise, this software required me to label all my axis on my own, compared to excel and tableau, so I had to see if my chart made any sense to me before I could even start labelling. This was a new challenge for me, slightly different from the previous one as this time I did not curate the data but only cleaned it myself. But I eventually decided on the chart above and received rather satisfactory responses! Dr Charles suggested I post this on r/dataisbeautiful on reddit, and I was a little nervous because this would mean my visualisation would be open for the world to see and provide feedback on. My post is here:
What I did not expect was to get 22.7k upvotes on reddit! They were a lot of feedback, some nasty but mostly constructive criticism and positive responses to my post. This really taught me a few things: 1) for a visualisation to be well received, I had to understand it in its entirety, and 2) the impact a meaningful visualisation can have, especially on social media! A visualisation on social media has the power to spread and reach to so many people. However, it is important to take note that with great power, comes great responsibility. It is pertinent to be able to discern false information, or else the consequences would be dire.
Amongst the nasty comments, there were very helpful feedback, in which some asked me to use data on emojis related to Covid-19, and show a more obvious change over time rather than using just 2 data points. So I took their feedback into consideration and produced this other infographic. This shows how people’s interest (and by extension, usage) of emojis that are related to the Covid-19 pandemic changed pre and during the pandemic. I had used the same application:
Experimenting with new software was really cool, but in the past few lessons, we have been picking up and improving our tableau game. These lessons are definitely making me more confident in tableau again, because we learnt how to filter the data and represent the data in a more intuitive and organised fashion using dashboards, etc. I decided to do even more experimentation, and managed to branch off and figured out how to make animated data visualisations in tableau, which you can view here:
This leads me to my next point: when should we represent our data in a certain way? Well, it all boils down to the limits of the dataset and what you are trying to show the audience. A large dataset, like the one I used to create that animated visualisation, allows me to show how the life expectancy with respect to GDP per capita would change over a long period of time. Whereas my work on emojis only aimed to capture the increase in usage of emojis during the pandemic, hence there was no need for me to find data beyond the timeframe i was interested in.
Though many may think that data analysis comes after the visualisation of data, I beg to differ. Before one attempts to analyse their data, one should ask themself: What is do I want to show? What is my analyse trying to tell my audience? personally believe that to produce a good visualisation, you have to analyse your data first and see what is relevant too, if not it is highly unlikely that you will end up with a visualisation that does not show anything. Keep in mind that my reflection is in no way trying to discredit excel or tableau or any visualisation software for that matter, but I really prefer going through the entire data visualisation thought process on my own instead of allowing software to do it for me.
