Posts tagged radial area chart
Episode 08: How to Dare to Use a Radar Chart - Featured Viz by Zak Geis
 
Zak Geis. Image via his  Twitter .

Zak Geis. Image via his Twitter.

 

Welcome to episode 8 of Data Viz Today. How can you use a radar chart in an effective way? This episode features Zak Geis' eye-catching data viz inspired by radar charts. This is a difficult chart type to work with and this episode dives into its limitations and how best to implement it.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

"Which Characteristic is Most Important to Women and Men" by Zak Geis. Image via his  Tableau Public profile .

"Which Characteristic is Most Important to Women and Men" by Zak Geis. Image via his Tableau Public profile.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s featured viz is “Which characteristic is most important to women and men?” by Zak Geis

  • 00:30 - It was a result of #MakeoverMonday.

  • 01:34 - Today’s question “Should we dare to use a radar chart?”

  • 01:40 - Zak is a Business Intelligence Solutions Architect at JP Morgan Chase based in Columbus, Ohio.

  • 02:17 - Since there are six characteristics, he chose to use a radar chart.

  • 02:30 - Radar charts use polar coordinate system with radii representing different variables and the distance from the center represents a metric.

  • 03:50 - Tutorial that Zak used to create a radar chart in Tableau.

  • 06:00 - Zak’s radar charts were success because he grouped them first by similar shapes, and he removed the grid lines to remind the reader that this wasn’t an exercise in precision - just general trends.

  • 06:45 - Why are radar charts hard to read? Graham Odds where he pointed out 3 main difficulties with radar charts:

    1. A radar chart supposedly can give you the advantage of using different scales on each radius, like one variable could be measured in dollars, the next one could be a percent, but showing data in this way is confusing at best.

    2. Where you put your radius labels can be really misleading because it can be arbitrary. If you’re filling in the area underneath the line too, it can exacerbate this problem.

    3. When you have each radius or spoke representing a variable, and then are connecting a line from each one, then you’re implying some sort of connection between these variables, which might not be the case.

  • 08:11 - So when would you want to use a radar chart:

    • Nathan Yau says “if your data is cyclical or seasonal and there are clear differences as time passes, the circular time series chart might be an option.”

    • Stephanie Evergreen says “radar charts are appropriate when the exact values aren’t totally critical for a reader but the overall shape says something.”

    • Alberto Cairo uses an example that showed the voting results of a city and the neighborhoods were the radii which were arranged according to their geographical locations. So it acted as a compass of sorts and accompanied a choropleth map.

  • 11:40 - For my inspired viz, I used Google Trends to get the data for search popularity of frappuccino.

  • 12:18 - Radar chart R tutorial by Nathan Yau that I used. My inspired viz:

frap_GIF.gif

 

  • 13:40 - Final takeaway: radar charts are tricky but just know its limitations and your goals - then experiment away!

  • 14:27 - Ensure you’re not misleading with your radar chart.

  • 14:50 - Email me your radar chart if you want a second pair of eyes! I promise not to throw the book at you. ;)

  • 15:15 - Zak’s advice to new designers: try to absorb as much as you can from those that inspire you. Take the time to deconstruct a viz that you really like. Think about what’s done well? How did they create it? And How can I use what I’ve learned to better myself and my designs moving forward? And most importantly, practice!

  • 15:40 - He’s looking forward to learning more about Flourish.

  • 15:50 - Follow him on Twitter @zaksviz and check out his Tableau Public profile.

  • 16:05 - Nominate a data viz!

  • 16:13 - Follow me on Twitter @DataVizToday!

     

New episodes are released every Tuesday at 6 AM EST. 


Episode 02: How to Find & Represent Seasonality in Data - Featured Viz by Nadieh Bremer & Zan Armstrong
Nadieh Bremer. Image via  Twitter .

Nadieh Bremer. Image via Twitter.

Zan Armstrong. Image via  LinkedIn

Zan Armstrong. Image via LinkedIn

Welcome to episode 2 of Data Viz Today. How can you identify seasonality in your data? How can you best visualize it? I share Nadieh Bremer and Zan Armstrong's data viz about the seasonal trends in U.S. births to show how to find and represent seasonality in data.

Listen on Apple Podcasts, Google Play, Google PodcastsStitcher, SoundCloud & Spotify.

"Baby Spike" by Nadieh Bremer and Zan Armstrong. Image via  VisualCinnamon.com

"Baby Spike" by Nadieh Bremer and Zan Armstrong. Image via VisualCinnamon.com

  • Welcome! I'm Alli Torban.

  • 00:30 - Featured viz by Nadieh Bremer and Zan Armstrong published in the Scientific American.

  • 00:40 - Today's question: How do we find seasonality in data and how can we visualize it in a beautiful way?

  • 00:50 - Nadieh Bremer is a Freelance Data Visualization Designer from Amsterdam, and Zan Armstrong is a Data Visualization Engineer and Data Analyst in San Francisco.

  • 01:10 - What seasonal time trends are visible in data about U.S. births?

  • 01:20 - The viz began with Zan's OpenVis Conference talk called "Everything is Seasonal".

  • 02:05 - Nadieh and Zan began collaborating in person on how to best display the CDC's data on seasonal trends in U.S. births for Scientific American story.

  • 02:25 - Because the data is cyclical, they decided to start with a radial area chart.

  • 02:45 - To make the chart easier to understand, they decided to draw a circle that represents the average number of babies born over a certain period of time, and then show the data rise above and below that circle.

  • 03:25 - To smooth the lines of the data around the circle, Nadieh used a technique called the Loess curve.

  • 04:00 - People tend to like circular things.

  • 04:10 - Study shows that people prefer images with curved edges rather than sharp edges, and the images with curved edges produced increased and stronger activity in the brain.

  • 05:10 - Nadieh and Zan email back and forth to decide on final touches.

  • 05:25 - Nadieh estimated she spent about 25 hours on her parts of this viz. She used R and Adobe Illustrator.

  • 06:00 - In the article, there are three radial area charts showing trends over time at various levels of granularity.

  • 06:15 - Being able to immediately find yourself in the data makes this viz even more compelling.

  • 06:35 - My top 3 takeaways from this viz:

    • Reach out to someone and try a collaboration!

    • If you have data over time, check to see if there is seasonality in your data. You can do this for free with no coding experience with Tableau Public. Here is a short tutorial how to do it. Warning: be mindful of aggregating your data by month. More details in Zan's talk.

    • Think about using curved lines in your data viz, especially if your goal is beauty or art focused.

  • 09:00 - Thanks to Nadieh and Zan for the inspirational data viz!

  • 09:20 - Connect with Nadieh on her website or on twitter.

  • 09:30 - Connect with Zan on her website or on twitter.

  • 09:40 - Next featured viz is by Pierre Massat!

  • 10:00 - Subscribe to my weekly newsletter, which compiles the top tips from the week into a short email.

  • 10:10 - Nominate a data viz to be featured on the show.

  • 10:15 - Contact me on social media @dataviztoday!

  • 10:20 - These shows are a little longer than I initially intended. What do you think? Shoot me a message!

New episodes are released every Tuesday at 6 AM EST.