Category: Makeover Monday

This week’s Makeover Monday looked at survey results of the top benefits for remote working:

The interesting thing about this week’s makeover, is that it’s actually a makeover of a makeover.  Here’s the original chart from the Buffer survey report:

When you look at it this way, the chart given for the makeover is much improved of this non-aligned, not-quite-sorted, confetti donut chart.

Given this clarification, here’s what I like about the chart:

• Clearly labelled, easy to see which section of the bar is what thanks to alignment and color matching
• It adds up to 100%. So often it seems a donut or stacked bar won’t add up to 100%, which means it was definitely not the right choice. But this one does, so that’s a positive.

What could be improved:

• Even though the stacked bar can give you the proportion, it’s hard to see how those top three items really compare with each other. How much more of a benefit is the flexible schedule than the lack of commute?
• If we take it out of the stacked bar, we don’t need all the colors.

In our MM get together this afternoon, my colleague Yu Dong was trying to brainstorm what was even possible with such a small data set. And she said, “I don’t want to just do simple bars.” Challenge accepted!  So I set out to do “simple” bars, but with a little more pop than just some blue bars on the white background.

Here’s what I built:

Click to view in Tableau Public

This week’s Makeover Monday looked at consumption of animal-free products consumption in Great Britain:

What I like:

• You can see clearly over half of meat-eaters don’t eat animal-free substitutes
• Stacked bars build on the timing group (daily fits in weekly, etc)

What could be improved:

• Why does a share of population axis need to go to 125%?
• Lots of colors make it kind of difficult to keep track of the categories

I wanted to do something that created more focus on the consumption of alternative products. I played around with a few combinations, but settled on looking at weekly consumption (which included the daily group) by those who are self-declared not meat eaters. I put the color focus on that, and left the rest in grays.

Here’s what I built:

Click to view in Tableau Public

This week’s Makeover Monday looked at the World Happiness Report:

What I like:

• It’s pretty easy to see the relative happiness between countries, thanks to the bar length
• Stacked bars show that the different categories are contributing to the whole

What could be improved:

• In the report, there are three pages of this in order to show all 150 countries
• Hard to find a particular country in the list.
• Hard to compare categories between countries, like generosity or perceptions of corruption.

As I started to think about how to view this data, I thought it would be interesting to see where each country stood in each of the categories, as well as the overall life evaluation score. I remembered a viz from Lindsey Poulter when she built a bunch of Set Actions examples that looked at comparable cities with a similar sales level. So I decided to use that kind of a layout, and even realized I could update it with a Set Control that allows the user to select a country from the dropdown rather than scrolling through the list (one of the things I said could be improved above).

Here’s what I built:

Click to view in Tableau Public

 

This week’s MakeoverMonday looked at Greenhouse Gas Emissions across supply chain:

What I like:

• Legend is clear and provides brief explanation for each stage in the supply chain
• Annotations provide additional insight
• Bar lengths allow to clearly see which products create the most emissions through their full supply chain

What could be improved:

• Seven different colors in the view makes it difficult to follow/compare
• The point the source article was trying to make is that emissions from transportation of the food product make up such a small percentage of the total emissions that ‘Buying Local’ doesn’t really do much to help curb emissions

With so many dimensions for each product, I chose to hone in on the transport emissions and how small of a role it plays compared to which foods you choose to eat. While I think ‘Buying Local’ is great for many reasons (supports local farms, often fresher, etc), if one claims to be helping the environment by buying local meat, there really isn’t much of a claim to be had.

Here’s what I built:

At first I showed stacked bars with percent of total, sorted by largest percentage of transport emissions. But I felt like that hid which foods actually have the largest amount of emissions. So I switched to showing the overall bar, but only showing the transport highlighted, with the percentage of total on the label. The color coded subtitle helps to clarify the highlighted portion of the bar and the label.

Click to view in Tableau Public

This week’s Makover Monday looked at goals per game by Lionel Messi and Cristiano Ronaldo:

What I like:

• Clear legends/axis labels

What could be improved:

• Rounded lines make it difficult to identify where the actual points are
• I.Don’t.Like.Grid.Lines

What I built:

Although these two players are known most for their goal scoring, when I looked at the dataset I wanted to look additionally at assists and how many games they played. But now I have five metrics (games, goals, assists, goals/game, assists/game) that I want to look at, but don’t want to clutter things. So I pretty quickly thought of Lindsey Poulter‘s Choose a Metric view in her Set Actions workbook. Once I got everything built I played around with the layout a bit, and finally settled on putting the metrics across the top rather than down the side, which allowed the large chart to be shorter/wider than a square. For me, seeing the assists in addition to the goals actually makes me lean more toward Messi as he’s been more productive overall. This thought helped cement the idea of showing the additional metrics in my mind.

Click to view on Tableau Public

This week’s Makeover Monday looked at allocation of time-use in cooperation with Operation Fistula:

What I like:

• Easy to see how much of total work is composed of unpaid work

What could be improved:

• Hard to see how countries compare to each other
• Hard to see how women and men compare to each other

What I built:

I wanted to put something together that would allow for easy viewing of all the countries without scrolling up or down. I also wanted to see how different men and women were in both their paid and unpaid work. As I experimented with some different combinations, I settled upon this one with paid work on the left, unpaid on the right, and a slope chart showing the gender breakdown. I found it very interesting to see the different patterns in each country and similarities among regional areas. Benin jumped out, as women work more paid and unpaid hours than men!

Click to view on Tableau Public

This week’s MakeoverMonday looked at pizza preferences in the UK:

What I like:

• Looks tasty (except for the corn…who on earth puts corn on their pizza?!?!)
• Numbers are clear

What could be improved:

• The #2 item (onions) didn’t even make the picture! (Neither did #5 chicken)
• Initial view of the (pizza) pie chart infers part to whole, but I’ve never heard of a 485% whole pizza…
• Hard to really compare level of popularity amongst the toppings

What I built:

My first impression was to do a dumbbell chart showing male and female, but I felt like I’ve leaned on those fairly heavily in my MMs of late, so I pushed myself to think of something else. So I settled upon a bikini chart variation, which would show which toppings leaned male or female, while still showing overall popularity for each topping. After I was done, I realized it’s like a slice of pizza!

I went through a couple different variations as I built. I started with a dual axis of Male and Female ratings, but I wanted the overall and topping label to be in the middle, and the only way I could think to do that was to have that label be on a circle or line mark in a dual axis, which would mean I would need my Male/Female measures on the same axis using Measure Values. But using Measure Values made it so I couldn’t have the Female label on the left end of the bar and Male label on the right end. So I went back to the dual axis, which allowed me to label Male/Female on each end, and created a separate sheet for the labels. Then I used transparent sheets to float the label sheet on top of the bar sheet.

To show which toppings were highly preferred by men over women, or women over men, I created a calc to identify which toppings had a difference in gender percentage greater than 5%:

I dropped that on color, picked some from my custom palette, and pulled together the headers and formatting.

Click here to view on Tableau Public

 

This week’s #MakeoverMonday challenge put a twist on this viz about Brits’ preference for the character James Bond:

The twist was that the data was broken down by whether they voted to leave or remain in the EU during Brexit voting, rather than the population dichotomy above.

What works:

• I actually think the stacked bars work fairly well in this case, since Acceptable and Unacceptable both start with a common baseline on each end of the bar. You can see the difference between the two groups fairly easily.
• Sections are clearly labeled so you know what’s being measured

What could be better:

• The population labels (which are fairly wordy) get kind of repetitive
• By the time I get to the bottom, I’ve forgotten which side is Acceptable/Unacceptable

For my viz, I played around for about 30 minutes with different column/row combinations with the category, response, measures, etc. For a while I was starting to think the stacked bars were the best option (and a case could probably be made that they work just fine in this case). Eventually, I decided to just focus on the Acceptable responses, since the don’t knows were similar for both sides and fairly small, so Unacceptable was just a mirror image. Doing so allowed me to reduce the noise and really hone in on a comparison between REMAIN and LEAVE groups for each of the four questions. Here’s what I finished with:

Here’s the link on Tableau Public

This week’s Makeover Monday looked at the use of pesticides in US agriculture.

What works:

• Bar chart makes it easy to see the large number of pesticides banned in the EU
• Bar labels make for easy reference

What could be better:

• Took a minute to figure out what the chart is showing
• Y axis not as necessary with the bar labels

It seems like small datasets are some of the most difficult for MakeoverMonday. They don’t leave many options for analysis, so the question becomes how to focus the visualization. In this case there was a slight difference in the data provided and the original visualization, where the data contained the pounds of pesticide used rather than the count of pesticides. I actually prefer this, as it identifies what percentage of pesticides used is banned in the other locations, rather than just a count of them.

I started out with some bars, grouped them by the country or count, and used horizontal bars to allow for aligned headers. I decided to focus on the amount of pesticides used that are banned in the EU, so made that bar red and the others gray. I felt like there was a little something missing, so I tried a brushed bar to show the relative size of the EU banned pesticides to the overall pesticides used. I liked the look, so I ended up adding it to my full set of bars. Added a color-coded title and subtitle calling out the high percentage of pesticides used that are banned in the EU.

Here’s how it turned out:

Click here to view the interactive on Tableau Public

I work pretty hard to NOT look at any Makeover Monday submissions before I dig into the data on Monday afternoon with my Ancestry crew. I found early last year that if I saw the data presented in a given way, I couldn’t get it out of my head. Well, this week, I didn’t even have to see it. Desiree just said “lots of small multiples this week” as we got started and I couldn’t get it out of my head! I may have landed on it anyway, but we’ll never know.

(Sidenote: VERY intimidated to do a makeover of something from FiveThirtyEight. I hold their work in the highest regard both statistically and visually.)

Anyway, so I started down the path of small multiples, and started a love/hate (more hate than love) relationship with the Charlotte/New Orleans franchises. For those not familiar with the NBA, the Charlotte Hornets turned into the New Orleans Hornets, who were then the Oklahoma City Hornets for a couple years after Hurrican Katrina, then back to the New Orleans Hornets, then the New Orleans Pelicans. Meanwhile, after the Hornets left for New Orleans, the Charlotte Bobcats became the 30th team in the NBA. When the Hornets became the Pelicans, Charlotte wanted the Hornets name back, so they changed from the Bobcats back to the Hornets. So in the data set, the Charlotte Hornets go from 96-97 to 01-02, then pick back up from 14-15 to present. However, those are actually two different teams, each with the same name. So getting that all sorted was a project in and of itself.

But then as I started working on the small multiples, the fact that the Bobcats didn’t start play until 2004 messed me up again. Because everything would go along fine until the Bobcats/Hornets organization showed up, then I would get eight years of Cleveland followed by the rest of Charlotte, and in the next one I had eight years of Milwaukee followed by the rest of Cleveland. So then I began to wonder, how did all of these other people do it?  Well, some people made a different sheet for each team and placed them all on the dashboard, others would filter 5 at a time for a row, then placed six rows on, and others that did it like me but unfortunately didn’t catch that the latter half of teams were split halfway through the season list.

After trying to figure out some magic way to make the row/column table calcs to work without data points for the first 8 Charlotte seasons, I finally just opened the data in Excel and added the first 8 seasons with a rating of 1 so it would show the bars at 0 (or league avg).

Often, if I don’t finish in my scheduled hour, I just don’t get it finished. But this was sports data (I love sports) and I had identified a gotcha that I could blog about. So, I poked at it throughout this week and finally finished it tonight. I wanted to do a bit of branding, so I added the team colors.  They could be slightly overwhelming, but for people who know the NBA, I think it also helps to identify each team, rather than just scanning through all the names. This is an issue since I sorted by overall defense for the time period, but I wanted to show which teams have been the best/worst over that time.

That said, here are my key takeaways this week:

• Make sure you understand what’s going on with the data. The Charlotte/New Orleans fiasco muddied up those waters a bit, but it’s definitely not accurate from an NBA historical perspective to have the original Hornets (pre-2002) in the same grouping as the Bobcats and Hornets (post-2014)
• Make sure your table calcs are doing what you expect them to. One of the reasons I noticed the issue was I added Team to the color shelf, because I wanted to make sure they were all together. That made it very apparent when I had half Cleveland, half Charlotte. Sometimes there are missing pieces in the data, so we need to make sure we’re accounting for them properly, and that’s particularly important when doing table calcs.
• Transparent sheets are awesome!  While I was looking through people who had done small multiples, I came across Mohit Panse‘s viz where he used three different sheets on top of each other that made the labeling for each team much easier than the workarounds I’ve used in the past. (Incidently, while I was looking for this viz on Twitter, I came upon this thread where they discussed the exact issue I had noticed. They were able to solve it with a calc, so I wanted to share that here as well.)

Here’s the final product:

Click here to view on Tableau Public