Photo by Isaac Smith on Unsplash.
If you are in the field of data, you are probably familiar with the feeling of having a never ending list of tools you would like to learn, hopefully some day soon. For me, one of the items on my list for a very long time was Observable Plot, a JavaScript library for exploratory data visualization. Finally this year, I decided to use the #30DayChartChallenge to dive into this amazing library. As a first-time participant in this challenge, I want to share my experience and the insights gained as well as provide advice for those contemplating participation in similar upcoming challenges.
What is the #30DayChartChallenge
The #30DayChartChallenge is a community-driven challenge that happens each year during April. There is a daily prompt that provides a theme for that day’s visualization, which is interpretable by the data viz creator. There is no limitation on the data source or tool used to create these visualizations. It is a fascinating event for data enthusiasts, designers, and aspiring data visualization artists.
Why I participated in the #30DayChartChallenge.
My decision to participate in the challenge was driven by several factors:
Exploring the Observable Plot library: This was my primary goal. I wanted to expand my skillset and explore new tools for creating interactive data visualizations by diving into all the goodness of the JS based Observable Plot library. You can find the code and the data sources for all my visualizations, including the ones referred here, in my #30DayChartChallenge collection.Improving my data visualization skills: I was eager to enhance my ability to transform raw data into visually appealing and informative charts and plots.Boosting creativity: The challenge provided a unique opportunity to experiment with various data visualization techniques and ideas, pushing me to think outside the box. It provided a really fun way to be creative with my storytelling.Building connections: The #30DayChartChallenge community comprises of data enthusiasts from various backgrounds, which provided an excellent platform for networking and sharing ideas.
Tips from my first #30DayChartChallenge experience of learning a new tool
1. Planning — Your Trusted Ally:
With prompts available long before the month starts, allocate time to brainstorm ideas, find data sources, and sketch out rough designs beforehand. Planning ahead before the challenge starts allows you to focus on the visualization process and learning without feeling overwhelmed each day. Some of my favorite data sources that I browsed through generously were from Our World in Data and Kaggle.
Here’s a visualization I created using US tornadoes dataset from Kaggle for the prompt “Hazards” on Day 7.
Day 7: Hazards, source: #30DayChartChallenge collection
2. The Art of Data Reusability:
Looking for new data to inspire you every single day for 30 days straight can get draining very quickly. Recycle data to try a new way of telling the same story.
The first plot below is from Day 6 with prompt “Data day: OWID”. I used the data on Cumulative Land Use Change for six countries over a century time period. On Day 28 the prompt was “Trend”, and I thought this data would be perfect for that as well. So instead of a new story, I focused on how to tell the story through animation so I could learn something new using the same data while also saving time from data hunting.
Day 6: Data day — OWID, source: #30DayChartChallenge collectionDay 28: Trend, source: #30DayChartChallenge collection
3. Clarity of Purpose.
Define your objectives for participating in the challenge. For me, learning the Observable Plot library was the primary focus. This clarity helped me stay committed and motivated throughout the process. Sometimes that meant that I had to settle with a good enough plot that took longer even though there are other tools that I am already comfortable with like ggplot2 with which I could have made nicer plots in less duration.
Here’s a plot that I created for the prompt “Slopes” on Day 5 using Zoo animals lifespan data. This simple line and dot plot took me way too long to figure out because I was learning a new tool but felt worth it when I accomplished it.
Day 5: Slopes, source: #30DayChartChallenge collection
4. The pursuit of growth, not perfection!
This is a difficult one because we all would like to ensure each day, we put out the best possible visualization into the world. Embracing the learning process and accepting that not every visualization will be perfect was key to not burning out from the challenge. Focus on improving your skills and understanding of data visualization concepts.
Here’s an example from Day-13 where the prompt was “Pop culture”. After an already long day of work when I sat down for the challenge, I wanted to learn to create a radial plot to see how the Superbowl ad category distribution has changed over time. I had envisioned creating it for all categories and I started with the 4 I was most interested in observing. While that wasn’t the final plan and it was difficult to stop there, I decided to call it a day at 4 categories in order to not burn out and have energy for the next day’s visualization.
Day 13: Pop culture, source: #30DayChartChallenge collection
5. Celebrate the journey!
Celebrate your achievements, no matter how small. Acknowledge your progress and growth, and most importantly don’t fall into the trap of comparing your work to others’ but use it for inspiration. Remember that the challenge is about personal growth and learning. The first few plots I put out are nowhere near the amazing visualizations I saw from others, but reminding myself that I will be bad before I get better was important in those initial days.
It is ok to not be able to finish all thirty days, participating in it can be draining but impactful, so whenever you can participate is good enough and you should celebrate showing up every time.
Here’s a plot I made just for fun in the last week using Bigfoot data because who doesn’t love Bigfoot and by the last week I had pushed myself so hard that I needed a bit of fun to keep going!
, source: #30DayChartChallenge collection
Onward and upward!
Participating in the #30DayChartChallenge has been a transformative experience for me. I’ve developed my data visualization skills, mastered a new library, and connected with and learned from so many amazing creators. Now my repo with all the code and data sources acts as a personal Observable Plot cheat sheet that I have often referred to. I recently shared my experience of learning Observable Plot through #30DayChartChallenge in a panel discussion with two very talented data viz artists, Tanya Shapiro and Allison Horst.
I hope this article encouraged you to participate in your first #30DayChartChallenge and may be also use it to learn a new tool. There is also a #30DayMapChallenge coming up soon in November, and the daily prompts for this year are already available here. So it’s time to start planning for the holiday season.
You can find the code and data to reproduce all visualizations in this article in my Observable collection.
If you’d like, find me on Linkedin.
How I used my first #30DayChartChallenge to learn Observable Plot was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
