Data Viz Done Right

May 21, 2018

Makeover Monday: How well did The Guardian predict the Premier League table?

Back to sports again this week. With the Premier League season just finishing, we're looking at how well The Guardian predicted the EPL table at the start of the season.

What works well?

  • Sorting the teams by prediction makes sense since this is an evaluation of their performance against their prediction.
  • Including the logos so people can find their favorite team
  • Including the numbers for the table position so that the reader doesn't have to count as they go
  • Shading every other row helps break up the view

What could be improved?

  • If you don't know the team logos, it can be hard to track a team across the table.
  • It's hard to see which team did better and worse than expected.
  • There's no scale for how "well" The Guardian predicted the table.

My Goals

  • Focus on the difference between the predicted and actual results
  • Try to create some sort of unit chart (I didn't have time to figure out the calcs, so I cheated with distribution bands)
  • Make it easier to see if team finished above or below the predictions
  • Finish in under an hour because we did MM live at the Data School and had to present to Eva at the end of the hour 

May 14, 2018

Makeover Monday: Which European commuters spend the most time in traffic jams?

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It's crazy busy times at the Kriebel household, so this week I had to get something done quickly. Eva chose a data set about time people spend in traffic congestion in European cities.

What works well?

  • Bars are ranked in descending order
  • Simple, clear title
  • Axis title tells us what the bars represent
  • Nice tooltips
  • Footnotes that qualify the data

What could be improved?

  • The alternating bar colors add no meaning.
  • The title has a weird shape to it. 

My Goals

  • Change the metric to percent of time spend in congestion during peak hours, which required me to go to the source to get the additional data.
  • I took inspiration from Eva's viz, but wanted to show the congestion as a percentage rather than a raw number. I feel this gives move context to the numbers and lets the audience know their likelihood of being stuck in traffic in these cities.
  • Create the viz as a single worksheet.

May 6, 2018

Makeover Monday: Toughest Sport by Skill

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I've had this data set in the queue for probably two years now and it's finally time that I got to post it. I know Eva loves sports data sets (cue eye roll), so this week we're looking at the toughest sport by skill according to a group of sports science experts surveyed by ESPN.

What works well?

  • The sports are ordered by the rank by default, making it easy to see how the compare to other sports.
  • You can sort by any of the column headers.
  • All of the definitions are provided and thoroughly explained.
  • If you need to look up a value, a table works perfectly.

What could be improved?

  • It needs to be easier to see the relative difference between sports.
  • Comparing more than one metric at a time across two sports takes too much brain power (for me at least).
  • Filtering would help make the list more digestible.

My Goals

  • Allow the user to compare two sports, rather than all sports at once.
  • Make the difference between the sports across all of the skills easier to understand.
  • Show which skill is harder/easier when comparing two sports.

April 30, 2018

Makeover Monday: Annual Change in American Bee Colonies

This week Makeover Monday is collaboration with #VizForSocialGood to analyze bee colonies in America. The data and visualization come from Bee Informed.

What works well?

  • When you hover over a state, the value is indicated on the color legend.
  • Informative tooltips
  • Good filtering capabilities to customize the view
  • Very responsive tooltips
  • Displaying the states and territories outside the continental US separately

What could be improved?

  • A diverging color scale is typically used to indicate positive and negative values. In this case, all values are positive.
  • If you do want to use a diverging palette, the colors should merge at the median.
  • A filled map makes smaller state difficult to compare to larger states.
  • There's no sense of the change over time. Are colonies increasing or decreasing?

My Goals

  • Create small multiple maps that show each year
  • Make the story about the change, rather than the specific values
  • Use highlight actions to make it easy to see a state across all maps
  • Incorporate the total annual loss into the tooltip

April 23, 2018

Makeover Monday: Biocapacity vs. Ecological Footprint

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Makeover Monday week 17 is a collaboration with the Global Footprint Network for Earth Day 2018. They've been fantastic to work with throughout the planning. Here's the viz they volunteered for us to makeover:

What works well?

  • Simple title
  • Nice framing of the legend
  • Clicking on categories to add/remove them from the view
  • Super responsive tooltips

What could be improved?

  • Everything is very compact making it impossible to read
  • Rotate the chart and make it tall vs. wide
  • Reduce the number of categories to reduce the number of colors
  • Provide a total option

For my viz, I wanted to recreate what was in the tooltip of one of their maps. I didn't have much time so I had to get it done quickly and I really like the BANs and how coloring between the lines helps emphasize the difference.

April 16, 2018

Makeover Monday: The Seasonality of Confirmed Malaria Cases in Zambia Southern Province

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For Makeover Monday week 16, Eva and I are hosting a #MakeoverMonday Live at Tableau HQ in London. The data and viz this week were provided by Jonathan Drummey and the Visualize No Malaria project.

What works well?

  • The colors are distinct from each other.
  • The seasonality is very evident.
  • The title is simple and tells us what theviz is about.

What could be improved?

  • Are the colors stacked or is one behind the other?
  • The overall decline is harder to see than necessary.
  • What happened at the spikes? Adding some annotations would be helpful.
  • Why is the data split between health facilities and health workers?

My Goals

  • Can I show the overall decline more effectively?
  • What does the viz look like when I combine the health facilities and health workers?
  • Are there colors that will work more effectively?
  • How can I make the seasonality more evident?

With those goals in mind, here is my Makeover Monday week 16. If this looks somewhat familiar, I created a very similar viz with a very similar data set for Makeover Monday week 34 2016.

April 12, 2018

Workout Wednesday Part 2: Total Products by Sub-Category

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Ok, part 2 of this week's challenge, the Jedi version, really sucked (requirements here). It took me FOREVER! I used table calcs for all of the calculations and getting them just right took a really long time and a lot of experimenting. Surely Tableau can make this easier for us.

Some thoughts:

  1. Getting the subcategories to layout correctly in a trellis plot was easy.
  2. Getting the labels above each grid was easy.
  3. Getting 10 dots across each pane was easy.
  4. Getting the stacking of the dots in rows was a pain!
  5. Luke has an evil side.

But I absolutely loved the challenge. I'm really enjoying these! My advice for everyone is to keep at it until you get it. Even if you're stuck, don't cheat and download the solution. That doesn't help your learning. If it's too hard, then consider skipping it; it might not be the right level for you yet. You can always come back and do it later.

April 11, 2018

Workout Wednesday Part 1: Top 5 Subcategories with the Most Products

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Luke is back and he has posted two challenges this week. First, create a pictogram of the top 5 subcategories that have sold the most products. They should be represented in 100 dot sections and colored depending on the section they are in. Get the requirements here.

There were two requires that I didn't need to use to make it work:

  1. Set the minimum and maximum values on the columns axis (x-axis) to -3 and 12, respectively.
  2. Set the minimum and maximum values on the rows axis (y-axis) to -1 and 32, respectively.

I'm not sure what the purpose of these would be, but I suspect it's some sort of spacing. I didn't need them, so I ignored these requirements. Here's my version and now I'll get to work on part 2.

April 9, 2018

Makeover Monday: Arctic Sea Ice is Disappearing Fastest in Summer Months

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I'm writing this having just finished a bike tour of Rome with my family in an absolute monsoon. Global warming is proven to cause unusual volatility in the weather, including hotter summers, extreme winter storms, and changing warm water patterns around the earth. This warming is most evident near the Arctic, where ice levels are at all time lows and the cycle of melting is accelerating year upon year.

So when I found this visualization by the National Snow & Ice Data Center, it seemed an appropriate topic for Makeover Monday. One of the most fun elements of this data set is that it includes only two columns: date and sea ice extent.

What works well?

  • Without even trying, it tells a compelling story.
  • The interactivity is fabulous. I really like being able to simply click on an item on the legend to have it added or removed as a highlighted line.
  • Including the 1981-2010 median along with the IQR and IDR provides great context.
  • Defaulting the view to show 2012 (the previously worst year for arctic ice) to 2018 helps show how 2018 is looking to surpass 2012 (in a bad way) by a lot.
  • Subtitle explains what sea ice extent means
  • Good use of simple colors
  • Great example of using highlighting for context

What could be improved?

  • The x-axis could be simpler by only showing the month names and removing the word "Date" from the axis title.
  • Make the title more impactful

My Goals

  • First, I wanted to rebuild the original and see if I could make it any better. I couldn't.
  • Second, build a spiral diagram that shows the months around the outside, but this only worked well when it was animated.
  • Finally, I settled on a different take on the metric that swaps the months and year on the original. That is, put the year on the x-axis and month on each line. This gave me only 12 lines which looked less busy and helped me see patterns for each month.
  • Next, I included a line that is the average of each year (black line).
  • I then decided to look at how each year of each month changed compared to 1979. I went with a percent change because I think that provides more context.
  • Lastly, I included a highlighter for the months and included some BANs of the actual values for comparison.

Click on the image for the interactive version.

April 4, 2018

Workout Wednesday: Frequency Matrix

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This week Ann Jackson stepped in to provide everyone the challenge. I think she's been seeking some revenge and she sure dished it out. Find all of the requirements here.

Easiest Parts

  • Use sub-categories (HINT: Since it's a frequency matrix, there's a trick here.)
  • Dashboard size is 1000 x 900; tiled; 1 sheet
  • Distinctly count the number of orders that have purchases from both sub-categories
  • Sort the categories from highest to lowest frequency
  • White out when the sub-category matches and include the number of orders
  • Match formatting & tooltips – special emphasis on tooltip verbiage

Hardest Parts

  • Calculate the average sales per order for each sub-category
  • Identify in the tooltip the highest average spend per sub-category (see Phones & Tables)
  • If it’s the highest average spend for both sub-categories, identify with a dot in the square

I messed around with the average sales per order calc for quite a while. I have every number the same except for a couple in paper and binders. I have had a look at Ann's solution and I think mine is more accurate, so I'm sticking with it. :-)

Excellent challenge this week! Give it a try, but give it a REAL try before looking at someone's solution; you will absolutely learn more. Click on the image for my interactive version.

April 1, 2018

Makeover Monday: World Wine Production

This week Eva provided a simple data set and a simple viz from the International Organisation of Vine and Wine.

What works well?

  • It's a simple line chart, which makes it easy to understand.
  • The red line stands out well against the white background without being too bright.
  • The units on the axis are labeled.
  • The title tells is what the line represents.

What could be improved?

  • The subtitle could be moved to a caption below the image.
  • The axis has a strange scale. Why does it start at 180?
  • Adding the drop lines makes it look like the length of those lines is important, but if you compare the length of the lines, then that could be misleading due to the axis starting at 180. I'd remove those lines.
  • The year labels are diagonal.
  • Each year doesn't need a label.
  • Why doesn't the source document contain data for all of the years?

My Goals

  • It's Easter and I have basically no time to work on this, so do something quick.
  • Mimic what we created for Workout Wednesday week 33.
  • Focus on the relative change from a chosen period instead of the absolute change. For me, this is more meaningful if you want to see how much a country has changed and it normalizes all of the countries.

That's it. All done!

March 28, 2018

Workout Wednesday: Color and Ordering

This week Rody challenged us to create a tabular view that has three columns, only one of which is colored and the user needs to be able to sort by a given year and metric. Find the full requirements here.

I had done something like this previously for the color. What tripped me up was the sorting calculation. It's not overly complicated. The only hint I'll provide is that you can't sort the subcategory by this calculation. Click on the image below for the interactive version.

March 26, 2018

Tableau Tip Tuesday: How to Customize the Tooltip of a Reference Line

Tableau (as of this writing) does not allow you to customizing reference line tooltips. So how can you work around that? Simple! Check out this video and download the workbook to see how it's done.


March 25, 2018

Makeover Monday: What is the UK's Favorite Chocolate Bar?

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This coming Sunday is Easter so I thought we'd look at the UK's favorite chocolate. As someone new to the UK, the Brits can be quite snobby about chocolate, particularly despising American chocolate. I mean, how can someone NOT love Reese's?

The chart we're making over this week is from CDA.

What works well?

  • The bump chart is a very nice visual display for ranked data.
  • Including the rank as a number at each point.
  • The lines are easy to follow.
  • Labeling both the left and right side so that you don't have to trace the line back to the start when you get to the end.
  • Using a different mark type when the chocolate is not ranked.
  • Simple title and subtitle.

What could be improved?

  • This is a LOT of colors and some of them are very close to each other.
  • Why are there age bands missing?

My Objectives

  • Split each of the age groups out rather than connecting them and then include a total, which is the average across the age groups. I'm making the assumption here that the same number of people were surveyed in each age group. 
  • Display the data as a dot plot along a scale from 0-10 for each chocolate bar for each age group
  • Use a brown theme to go with the connotative color of chocolate
  • Color the values using a brown scale

March 21, 2018

Workout Wednesday: Sub-Category Sales Change in the Last Two Periods

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Another Workout Wednesday in the books. This week, Curtis Harris created the challenge in Luke's absence. Why didn't I think of subs last year?

Find all of the requirements on the Workout Wednesday website. Essentially, you have to provide the user with a date filter and use that filter to display a barbell chart of sales up to the date selected for its month compared to the same days in the prior month.

What tripped me up most was creating the tooltips. I'm going to have to look at how Curtis did it, which I suspect was with LODs. I used LODs to get the sales based on the filter table calcs for the tooltips since all of the dimensions I needed were already in the view.

Give this one a go before downloading any of the solutions. You'll learn more by struggling through it.

March 18, 2018

Makeover Monday: What pets do people in the U.K. prefer?

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For Makeover Monday week 12, Eva posted this chart from the Pet Food Manufacturers Association about the pets that people in the UK have in their homes.

What works well?

  • The pets are ranked in descending order.
  • The bar colors match the icons.
  • Including the text in the bars saves room

What could be improved?

  • Include a title that explains the data a bit more
  • While the icons are cute, are they necessary?
  • If you remove the icons, you then don't need as many colors.

My Goals

  • Practice what I learned in last week's Workout Wednesday because reinforcing the learning helps it stick for me
  • Create a better title
  • Simplify the colors
  • Create a mobile version

That's it. Simple, quick and done!