Welcome to the 10th month of our Dear Duo project. #DearDuo has been a fun way to get to know each other and to keep practicing visualizing foreign data sets. In 2017, we started publishing a single post each month written together rather than 2 separate ones. The biggest reason is we want this project to be more collaborative.
We’ve had a lot of fun this year vizzing each other’s datasets to learn more about one another. This project has also helped us stay consistent and visible in the community. We have tried different styles and have learnt some techniques in Tableau as well. This month we decided to look at how soon The Data Duo publish their #MakeoverMonday vizzes after the data is made available (usually the data is available on a Sunday).
I believe someone from the Tableau community suggested that we track this because on most weeks we seem to publish our vizzes the same day the data is made available.
Adam – What a pain in the ass it was to collect this data. Pooja was faster than I this month and already described the data below so I will just complain in this space. We had to go back and manually grab data tweet dates from Andy K’s profile and viz publication dates from Pooja’s and my tweets. We also collected the number of replies, likes and retweets. For more current visualizations, we waited a week to collect those stats. Usually Poo forgot to track hers and I like a good friend took care of it for her. Occasionally, she helped me out as well. I remember being pretty under the weather one week and she took care of all the blog/tracking stuff. It is so great working with your best friend!
Pooja – So as always, the oh-so hard working that Adam is, he created a google sheet for us to track this data. We were asked to start tracking sometime this year I believe, so Adam went back to Andy Kriebel’s twitter profile to see when he tweeted the datasets in 2016 🤦♀️. I obviously denied having to go back and look at (the very active on twitter) Andy’s profile 😂. But I looked at at least my profile to see when I published mine and helped him a little, I am nice like that 😂. And then he of course kept updating it as and when we published. And again, I helped when I *remembered* that we are tracking this. We collected fields like year, week, data tweeted date, viz published date, dataset, author and some twitter stats such as likes, retweets and replies we got on our submissions. So the data looks like this:
Adam – So we always viz each other’s data, but I decided to break the mold a little and viz both of our data. This is the benefit of being the self-proclaimed CTO, you can do whatever you want. 🙂 I began by talking about how much we love #MakeoverMonday and some of the many reasons why. I used a custom font in PowerPoint for the title and subtitle using Gujarati MT font. Why? Gujarat is the State in India where Pooja is from. 🙂
We were asked to viz the time between when a data set was published and when we published our vizzes. I created two parameters. One was to switch the measure between likes/retweets and the other to display each dataset by either the date the data was published or the date the viz was published. The viz published date is interesting because it shows when we went back to complete a data set or when we were slacking. The main visualization is a DNA chart with a donut chart floated over to show the breakdown between both of our stats. Also, I wrote a ton of calcs to display dynamic text/colors in the tooltips. Tooltips are kind of my thing so I spend way too long on them. Also, a note here that #MakeoverMonday is not about competition and Pooja and I are not trying to compete with anyone. However, I added some calcs to determine which one of us received a better twitter engagement from the community.
I tried to incorporate some of what has become known in ‘the’ Twitter world as #Poojatastic style throughout. Poo always does a great job a incorporating circles and other elements to group important elements of a viz together. I decided to create a Pooja and Adam breakdown of likes, retweets and the time it takes us to publish a visualization once the data is posted.
The circles are packed with stats, sparklines and icons for identification. Also, funny thing I noticed…Pooja is missing a week and is not in the 100% club right now. She missed a week when she visited her sister in Austn a few weeks ago. I borrowed some of Curtis Harris’s magic and created a tweet button so you can all remind her #DearDuo style to catch up already. PLEASE use the button to provide her some encouragement. Finally, we have an inside joke regarding using ‘the’ in front of Twitter. It’s pretty funny how long this has been a joke. Find us in Vegas and ask us the story, but until then, I added ‘The’ in the source tag just for Pooja. 😂 Also, calcs, calcs, and more calcs. I wrote some nice calcs to determine the total minutes between to the two publishing dates and turned them into dynamic text to display everything in hours and minutes in the tooltips.
Pooja – I wanted to use a playful font that adds a personal touch to this viz. I decided to use ‘Wawati SC’ because it looks like its hand written and looked really nice on the viz. I used it recently on my ‘National Parks’ #MakeoverMonday viz and everyone seemed to like it as well.
Because the viz is about the data duo, I started by using a photo of us that Emily Chenty took in Austin, TX during #data16 last year. The colors are unlike my typical choice because I wanted to emphasize Adam’s love for #MakeoverMonday and so ended up using white, red and a little bit of black.
I used bubble chart to show the number of days that elapsed between the data publish date and his viz publish date. I could have chosen to do see minutes passed, but I just ended up using elapsed days to measure his fastness. Most of the other design elements are my usual style: lines, text boxes, shades of colors here and there to emphasize important things etc.
I thought it was nice to show that the number of ‘likes’ that he got on his vizzes increased greatly as the months passed. So I used a line chart to show that trend and a status bar showing total vizzes published, likes and retweets that he got on his work.
Lastly I wanted to emphasize for sure that, Adam is one of the very few people in the Tableau community who have a 100% participation rate in #MakeoverMonday. Imagine the practice you get using 74 datasets that are different in nature. He has a pretty good understanding of data types, styles to use, chart types to use for what kind of data sets and still being able to convey the message clearly. He has come a long way and his work has been inspirational for many. Checkout his Tableau Public profile here.
And of course, I leave no chance when it comes to promoting #MakeoverMonday. So I used the last banner to let the users visit the makeovermonday site and to get involved. A project that has become widely popular and has created a platform for people to practice, learn and network. Such a great initiative!
What did we learn?
Adam – I learned that I have some catching up to do to match the quality of work Poo puts out on a regular basis. Her work is so impressive and the engagement with the community reflects the effort she puts into it each week. It was really cool to see the longer you participate, the more people seem to interact with you. We have met so many great people through this project and I suspect some of this is just being friends with a growing group of people. Great job Pooja!
I learned that Pooja is slower than I am when it comes to publishing (in 2017). 😂 Pooja publishes her vizzes on average of 16 hours after the data is published. Not 16 hours and 1 minute, just 16 hours on the dot. Of course she’d have a nice perfect round number. What is really interesting is that she publishes much sooner than that usually. She has had a few dramatic procrastinations. Poo does not get sports data. For awhile, she pretended she wasn’t going to do the visualizations, but I eventually talked her through the data and bridged the gap. 🤦♀️ A few of these data sets come to mind, Bryce Harper (baseball), Steph Curry (basketball) and March Madness (basketball). I only visualized the 2017 timing and if I remove the March Madness viz (errrrr the time she spent complaining) her average drops to 9 hours and 31 minutes and she is faster. Of course, if we are excluding things…I think I had two or three anomalies as well, but I’ll give it to Poo, I’m nice like that. 😉
Also, reminder number two to tweet Poo some encouragement to get back into the 100% club. It’s not her style to be missing one…We should track how many people tweet her using the button…
Finally, it is crazy how Pooja and I don’t talk about this visualizations until they are done, yet there are always many similarities. We have similar ideas on the tooltips and titles in this one.
Pooja – I learnt that Adam is very diligent (as if I didn’t already know this 😂). He rarely procrastinates. All of his work is always ready either in time or before time. I admire this quality of his and it has also motivated me to keep in sync with his work ethic. So being one half of the data duo with him is pretty tough, just saying. 😉
I also learnt that he usually publishes his vizzes before I do and we both seem to finish majority of our vizzes the same day the data is made available. I think I was smart when I made the choice to ask Adam to be one half of the data duo, I couldn’t be more happy about this!
His prescriptions viz(zes) got a 114 likes but I learnt that those likes are divided between 4 vizzes he submitted that week (yeah that week he was going crazy) and I hadn’t even started. I woke up to 67 notifications that morning because he cranked out 4 vizzes when he was just supposed to do one 🤦♀️. I didn’t check which viz publish date did he use to track 😉, maybe the first one he tweeted.
The viz that people engaged with the most was ‘Are Britons Falling Out of Love with Booze?’ For some reason anything to do with booze always gets more attention. It’s his thing, ya know! Checkout the viz here.
The viz that got the most rewteets was the VOTAMATIC: Elections viz which I absolutely love. Checkout the viz here.
Thank you for following along our quantified self journey. Two more to go! Thank you for reading, until next month!
Here are our vizzes: