During Watch Me Viz, I covered set actions, parameter actions, highlighting, boolean calculations, table calculation, dashboard layout, and I failed at a starburst chart (I couldn't get the years to sort properly; I'll post it when I figure it out).

Enjoy! Check out the viz below the video.

 

Makeover Monday week 30 looked at this viz from Visual Capitalist showing the percentage of each race in each State in America.

In the video below, you'll see my recreate the tiled treemap before creating a tiled bar chart. Thanks for watching!

Click here to view the interactive version on Tableau Public.

How can ethnic groups be treated differently when it comes to the COVID vaccine? It's terrible! And this data from NHS England clearly demonstrates the racial divisions that exist in this country.

As this week is election week in the United States, I thought it would be fun to give everyone a data set about population projections by race, sex, origin, age and year. It's a fascinating data set to explore.

The original visualization is from the US Census Bureau:

What works well?

What could be improved?

What I did

Tough data set for me this week as I know nothing about cycling. In addition, I raced this clock this week and completed Makeover Monday in an hour with the Data School. We then all presented our work back to Eva.

If you're particularly bored, here's a recording of my screen for the whole hour.



Ok, still awake? Hopefully you enjoyed my Spotify playlist at least. This week's viz that we're reviewing is knomea.

What works well?

What could be improved?

Just about three weeks ago I ran my second ever marathon, and it sure was a big one! In fact, the New York City Marathon set a world record for the most marathon finishers: 51,388 finishers, 51,995 people started, a 98.8% completion rate.

Like most runners, I love my running data! My watch syncs to Tom Tom, Runkeeper, Strava and Nike+. Why all of them? Well, why not? Naturally, I wanted to see how my training went. Was it effective? How'd I do in my long runs? How often did I run? What was my average pace? The questions are endless.

I'm also in the middle of testing a new Web Data Connector for Strava that brings back A TON of information about each run. Mix all of this together and you get a dashboard of my marathon training.

Click on the image for the interactive version (it's too wide for my blog). Once you're there, you can click on any activity and see the map update with the route of each run. The activity will also be highlighted across all of the charts.

And yes, I got the data from Strava yet I'm using Runkeeper colors. I simply like their colors better. Enjoy!

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This week's Makeover Monday was much harder than I anticipated and I must note that it took me way, way over an hour to create something I was happy with (more about that in a bit). The website that we reviewed had a series of three charts about police killings in the United States. I'll focus on the first two:


What works:

• Good title and subtitle
• Bars are sorted properly
• Using a rate is good practice because it normalizes the data
• Using a different color of the U.S. average

What doesn't work:

• I hate charts that make me turn my head sideways to read the labels.
• I would have the U.S. average as a reference line.
• There's no sense for the rest of the population.
• It feels like there's more to the story.

Ok, so how about the second chart:


What works:

• Good title and subtitle
• Ranking the police departments
• Using a rate is good practice because it normalizes the data
• Calling out those that police departments that have only killed blacks

What doesn't work:

• I almost didn't notice the U.S. average (it's above the first police department).
• The column headers should wrap so they fit better.
• Again, it feels like there's more to the story.
• The table makes comparing police departments harder than necessary.

A quick bit of background before we get to my viz. Last week, we brought Caroline Beavon to the Data School to teach an infographics and information visualisation course. I would highly, highly recommend the course. It was a perfect blend of the courses that Andy Kirk and Cole Nussbaumer teach if you've ever been to their classes. We learned a ton about knowing your audience, choosing the aim for your visualisation and picking out the proper story in the data. In addition, we designed several infographics, which is something I was particularly excited to put into practice this week. With that being said, here's is my makeover of the two original charts, but really, it's completely different and delves much deeper into the story the data is trying to tell.

You can download the workbook from Tableau Public here.

This past Saturday, 1,589 runners/walkers participated in the Summer Breeze Half/10K/5K, put on by Brazen Racing.  At the end of the race and on their website, when you want to find your time, you look it up on a table like this:

This is great when you simply want to find yourself and see where you placed, but it doesn’t give you any insight into the entire race.  Being the data nerd/runner that I am, I wanted to know more.  Who are the runners?  What do we know about them?  Where are they from?  How do different demographics perform?  Did certain areas in California produce faster average times?

To answer these questions, I downloaded the data and created this race results visualization.  Go ahead…click around, look for patterns, get a feel for the race.