VizWiz

Data Viz Done Right

September 24, 2018

Makeover Monday: Priorities for Progress on Gender Equality

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This week Makeover Monday HQ is collaborating with the folks at Equal Measures 2030 focusing on issues related to gender equality. Learn more about the background on the introduction webinar Eva ran with them last week here.


What works well?

  • The colors are easy to distinguish.
  • The footnotes help to explain the percentages...sort of.
  • The chart layouts are easy to understand. Bar charts nearly always work well.
  • Labeling the top of each bar removes the need for an axis.
  • Good drill down from the overall to the per region level.

What could be improved?

  • I find the chart titles quite confusing.
  • Comparisons within a single region are difficult.
  • I had no idea how to read these charts without reading the article. A chart should, ideally, be able to stand on its own.

What I did

  • I focused on making over the bottom chart.
  • I kept the original colors.
  • I changed the bars to dots to help show the range of responses better.
  • I didn't convert the responses to percentages as I wasn't confident it was accurate.
  • I included a sorting option to allow sorting by the total responses or the responses within a region.
  • Based on a twitter conversation with Dan Caroli last week, I turned on advanced logging feature which tracks each change you make in a sheet and then I turned them into this gif.



With that, here's my Makeover Monday week 39 for Equal Measures 2030.


September 17, 2018

Makeover Monday: How does the cost of a ticket change as your trip approaches?

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This week we look at viz straightforward viz from DW:


What works well?

  • Simple title and subtitle that explain what the viz is about
  • Line colors are easy to distinguish
  • Good small multiples layout
  • Reversing the time scale so that the larger number is to the last since it represents more days in the past
  • Making the obvious
  • Sorting the routes by distance

What could be improved?

  • Reduce the font size for additional information like the footnote and the source
  • Move the subtitle closer to the title and add space between the subtitle and the first chart
  • Label the ends of the lines

What I did

I don't mind the original too much other than I feel like it's missing some context. I decided to basically recreate the chart, but show the change in price as the days got closer. For me, this helped show how much more expensive tickets will be if you wait until the last minute.

September 13, 2018

Clayton Kershaw & My Learning Process

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What is Learning?

According to UC Berkley, learning is a process that:

  1. is active - process of engaging and manipulating objects, experiences, and conversations in order to build mental models of the world (Dewey, 1938; Piaget, 1964; Vygotsky, 1986). Learners build knowledge as they explore the world around them, observe and interact with phenomena, converse and engage with others, and make connections between new ideas and prior understandings.
  2. builds on prior knowledge - and involves enriching, building on, and changing existing understanding, where “one’s knowledge base is a scaffold that supports the construction of all future learning” (Alexander, 1996, p. 89).  
  3. is situated in an authentic context - provides learners with the opportunity to engage with specific ideas and concepts on a need-to-know or want-to-know basis (Greeno, 2006; Kolodner, 2006).
  4. requires learners’ motivation and cognitive engagement to be sustained when learning complex ideas, because considerable mental effort and persistence are necessary.

I've left a couple bits out that aren't relevant to learning in the context of data visualization, but all of the others should resonate with you if you approach learning with the correct mindset.

As an example, I am actively look for reasons to practice features in the Tableau 2018.3 beta, especially around density mapping. I was reading an article this morning about Clayton Kershaw, whom many consider the best pitcher in Major League Baseball. He also has highest base salary at $33M for 2018.

Most of the density maps I've seen have had a mapping component. In the case of baseball, and pitching in particular, the spatial zone is the strike zone. Data is easily accessible to get the coordinates of every pitch as it crosses home plate.

For this project, the learning process:

  1. is active in that I am building my knowledge as I explore the data set and learn the new features.
  2. builds on my prior knowledge of how the feature works and my knowledge of the game of baseball. However, I had never done a scatterplot of pitching before, so I had to learn new terminology in the data. This knowledge will help me be more productive and learn faster in the future.
  3. is situated in the authentic context of engaging with the ideas and visual concepts that I saw online and drew on paper.
  4. required my motivation and engagement to see the project through to fruition and the persistent to make the display visually accurate.

I hope my thought process helps you focus your learning. I love helping people get better at what they do and if I can help you speed up your learning, then we'll all be better for it.

With that in mind, here are two images I created for this project. The first is all pitches by Kershaw and the second is of his curveballs, which is known to be his most potent pitch. Once Tableau Public supports Tableau 2018.3, I'll publish them and include links on the images.




September 11, 2018

Tableau Tip Tuesday: How to Conduct Market Share Analysis with Level of Detail Expressions

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In this week's tip, I show you how to do market share analysis with Level of Detail expressions. In this video, I show you the calculations, then I expand it to make it more dynamic using a parameter.

Enjoy!

September 10, 2018

Makeover Monday: Spending at Trump Properties in Washington D.C.

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I think it's very safe to say that Donald Trump is the most controversial President ever. This week's data comes from ProPublica, who explain part of the reason why people doubt Trump's commitment to the country over himself:
Since Watergate, presidents have actively sought to avoid conflicts between their public responsibilities and their private interests. Every president since Jimmy Carter sold his companies or moved assets into blind trusts or broadly held investments – until now. Donald Trump never did this, despite his expansive holdings. He stands to gain personally when groups pay his companies. 
Let's start by looking at the chart created by ProPublica:


What works well?

  • Colors are easy to distinguish
  • Good interactivity for additional information
  • Filter options are obvious and easy to use
  • Sizing the blocks gives you relative comparisons
  • Good use of annotations
  • Stacking the blocks makes it obvious there were more records in one month versus another

What could be improved?

  • Using size for the blocks makes exact comparisons difficult
  • Include a title
  • Include a subtitle with additional context
  • Provides the user the ability to ask "How does this affect me?"

What I did

  • I explored the data quite a bit, before focusing on Washington. I did this because I saw a large increase in spending after Trump was elected.
  • Use simple colors like the original
  • Compare spending during the Campaign vs since Trump has been President
  • Use BANs to call out the import information

With that, here's my viz for Makeover Monday week 37:

September 3, 2018

Makeover Monday: Where Nike Products Are Made

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Nike, one of the most recognizable brands in the world, is also being very transparent with its data about their facilities. Hopefully many other companies will follow their lead. Here's the viz Eva tasked us with making over:


What works well?

  • The summary card on the right is packed full of information and as you zoom, you get different levels of detail.
  • Keeping the map a single color allows you to focus on where the factories are located.
  • Excellent interactivity
  • Overall, everything is simple and clean.

What could be improved?

  • Adding some filtering capabilities
  • Make it easier to identify the smaller countries, possibly by outlining the countries with a white border.

What I did

  • Mimic the style of the cards with its BANs
  • Make some of the importion that's buried deeper in the map more evident
  • Use Nike official colors
  • Add some simple interactivity through actions

This is a pretty simple viz this week and I really enjoyed making it. It's almost like a report card.

August 29, 2018

Tableau Tip: How to Create Rounded Bar Charts

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I've used rounded bar charts a few Makeover Mondays. I received an email from a reader yesterday about how to create them, so I decided to make a video in case anyone else has the same question going forward.

The end goal is a chart that looks like this:


You can download the workbook by clicking on the download link on the toolbar below the video.


August 27, 2018

Makeover Monday: Which body parts are we attaching computers to?

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This week we're looking at wearable tech. I'm a big fan of fitness watches because I learn so much about things from my heart rate and sleeping patterns to how effective my training is. So this week, we're making over this viz from Quartz:


What works well?

  • The simple ranked bar chart is easy to understand.
  • Including the sources in the footer
  • Including labels on the ends of the bars
  • Choosing an easy to read font
  • Simple title

What could be improved?

  • I'm not so sure about the color choice. I feel like it's shouting at me too much.
  • Remove the tick marks next to each body part. The label is already next to the bar, so the tick is unnecessary.
  • What are the bars measuring? Is it the number of devices per person? The average price? This should be made more clear.

What I did

  • Since I'm an avid runner that has gone through multiple devices and brands, I wanted to focus on the most popular watch brands.
  • The number of devices didn't help because each company has a different amount of diversity in their product line.
  • When I'm looking for a new watch, I start shopping by price since I know my budget. For this makeover, I focused on average price.
  • I liked the idea of the original bar chart, so I created a bar chart.
  • I sent the viz to Eva because I was struggling with the title and she sent me back the headline WATCH THE PRICE.

With that, here's my Makeover Monday week 35.

August 24, 2018

Makeover Monday: Africa's Deadliest Armed Conflicts - Density Map vs. Heat Maps

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It's Friday night and I'm learning rather than watching TV or wasting time on other activities that don't exercise my brain. Earlier this week, I posted my Makeover Monday viz about armed conflict in Africa using the upcoming density map feature in Tableau. As part of my plan to improve my Alteryx skills, I wanted to recreate Tableau's density maps as heat maps in Alteryx as closely as I could. In Alteryx, it all starts with the workflow:


The workflow is simple and I'm sure there's a more elegant want to create it. The guts of the workflow:

  1. Create spatial points from the latitude and longitude of each record.
  2. Convert the spatial points into a heat map by grouping the points into grids. 
  3. Union the four streams back together.
  4. Export the data as a shapefile.

The reason I have four streams in the workflow is because I didn't see a way of doing a grouping by the event type within the heatmap tool.

The Alteryx heatmaps are easy to adjust; I played with the grid size and maximum distance a few times until I got something close to Tableau's density maps (without fiddling with the settings endlessly). I settled on a grid of 33 miles and a max size of 75 miles.

I also chose to create the shapes as donuts so that they wouldn't stack on top of each other. Here's the result:


Density Maps vs. Heat Maps

Let's start by looking at the difference between the density map and the heat map.


Here are some of the differences I have noted:

  1. Density maps in Tableau are completely dependent upon the number of marks in the view. The more accurate you want the density, the more marks you need to include. That's 9229 marks in this case.
  2. Since the heat maps Alteryx generates are shapefiles, they will render much faster as there are only 28 marks.
  3. The Alteryx heat maps clearly encompass all points, meaning you can see EVERYWHERE that there was an incident.
  4. Tableau's density maps hide the outliers.
  5. You can't have highlight actions on a density map as there no dimension categorizing the heat.
  6. The density maps looks much cleaner than the heat maps.

I like both version and they both have their benefits. The point of me doing this, though, was to improve my skills. The only way I'll get really good at Alteryx is to use it more. Focused time helps me develop. Removing distractions like social media (those things that don't help you learn) are a good way to free up wasted time. Make time to learn and you'll rarely regret it.