Category: Makeover Monday

Makeover Monday – Success

This week’s Makeover Monday challenge was a very pretty infographic drawing a very bad conclusion. The original is really, really not okay and I’m not posting it here. IllinoisPixels has a fantastic write up of the issues that is 150% better than what I was planning to write! The only point I would add to their critique is that the order of the reasons for success seemed random. In my makeover I ordered them by the gap between rich and poor, which highlights the differing perspectives.


Baader-Meinhof alert! This data is supported by the headwinds/tailwinds asymmetry theory that I learned about on the Freakonomics podcast last week! It’s a good episode, and March is #trypod month, so I’m inviting you to go check it out!

Dashboard 2 - Copy

This week’s makeover isn’t very interactive, but you can still play with it at Tableau Public.

Makeover Monday – Credit Card

For this week’s Makeover Monday we’re working with credit card transactions!

Here’s our starting point:

AMEX Category Summary - CopyAMEX Monthly Summary - Copy

My goal was to create a visual that would help me understand my own credit card statement. For my own needs I would like to see my overall spending, categorical spending, and vendor level spending month by month. I also want to see a part-to-whole view of categories annually and by month. Ideally there would be several views that flow really nicely using intuitive actions and filters. That’s a pretty lofty goal, and my final product definitely falls short for now. I use Mint to track my spending, and working on this makeover made me appreciate their nice visuals a lot more! In fact, I think the Trends section of the website (not mobile!) is pretty much perfect.

Here’s the makeover:

Amex Makeover 1 - CopyAmex Makeover 2 - Copy

Go here for the interactive version.

After starting work, I realized that my preferred views could be grouped into categorical and monthly. By creating the two views, and then linking them with a bunch of dashboard actions I was able to show most of the desired views. I added a map with date on page because I wanted to work with the page feature. I kept it because it’s fun to use Andy’s purchases to watch his travels!

Continuous vs Discrete gave me a real headache this week! I started using discrete months, which make more sense for this data. The labels and size/spacing were perfect.

Amex - Discrete 1 - Copy

…But when filtered, months went missing.

Amex - Discrete Filtered - Copy

We might be able to correct this with a cross join (null or zero spend default for each category/month combination) but it feels strange that I couldn’t find a “include null axis values” setting. So, we’re stuck with continuous for now. Maybe I’ll revisit this if I have time in the future.



Makeover Monday – Potatoes

This week we’re working on a report of European potato production.


My biggest gripe with the original is that the colors are set by rank not by country. Poland is blue in figure one, orange in figure two, green in figure three, and red in figure four! It makes transitioning between charts more difficult than necessary. I was also frustrated with the alignment between the visuals and the sections they related to. Because of the sidebar format and number of charts and visuals included, the reader has to do a lot of scrolling to check the visuals while reading.

This week my goal was to make something prettier and easier to read, that would fit nicely in the sidebar and align with the article’s sections. I’m not really sure what the audience for this report is – maybe some poor sap is trying to research Iceland’s potato production – so I also tried to avoid removing data in my makeover.

In my makeover I assigned each country a color using the official national colors for inspiration. I’m not very experienced at building palettes, but practicing is fun! I used an area chart to show tonnes of potatoes because it combines change over time with part-to-whole. It’s not quite as effective as a stacked bar or line chart, but can show the information in a single chart, which would really help the visuals line up with the article.

For the Farms and Areas section of the report I was planning to use a scatterplot comparing number of farms with average farm size, but the MM dataset only included a few key measures so I created a tree plot instead. I think it does a better job of portraying area than a pie chart, and it kinda looks like farm fields too! When creating the makeovers I totally missed the first tab! So, I only have makeovers of the first two sections, but that’s enough for this exercise.

After creating my two visuals, I also built the first two tables in Tableau as dashboards. Then I used this guide to create actions that let the user go back and forth between the visuals and the tables.


Check out the interactive version here!

Makeover Monday – New Zealand Tourism

Makeover & Original

Just a simple makeover this week! I wanted to include a map-as-filter section, but the file with the geographic coordinates couldn’t be opened by my personal freebie account, or by my work’s paid but version 9.3 account. I turned the bar chart into a line chart, and included each year instead of limiting it to the last three. I think this makes it easier to compare years and see the overall trend.

This viz is really very simple, but the underlying data has a lot going on. The region column includes “Total (all TLAs)” representing the entire country. Because the regional values are weighted for population, simply aggregating them would under represent larger, more populous regions, and over represent the smaller ones. Use the Total (all TLAs) rows to represent the entire country, and avoid aggregating the regions.

Makeover Monday – iPhone Sales

iPhone Original - Copy.PNG

This week’s original makes a bad first impression. The gradient background, bevel effect on (most) bars, and extra characters (space before question mark, orphaned asterisk ) make it look like an old Powerpoint slide. Despite the design, I actually like the simplicity of this visualization. It’s clear, easy to read, and enhances the article.

A few more critiques:

  • The gradient background draws attention toward the center of the chart and away from 2016
  • The labels on the bars are unnecessary; the number of iPhones sold is less important than the yearly trend
  •  Indicating numbers in million units as a part of the Y axis would make the chart easier to understand

Because this original had a solid base with poor visuals, I decided to focus on formatting this week. Formatting in Tableau is something I’ve struggled with. I feel like there are multiple (unintuitive) ways to access slightly different menus, and I’m never quite sure what the different options actually control. My goal was to learn as much as I could about formatting while aiming for classic Apple minimalism. I even used the official iOS7 red (255, 59, 48) for the line chart! By intentionally adjusting every color, line, and label in my makeover, I’ve become much more comfortable navigating the formatting options.


Makeover Monday – Australia’s Income Gender Gap

Eva Murray of Tri My Data has taken over for Andy Cotgreave and will be running Makeover Monday with Andy Kriebel (VizWiz) this year.

This week’s original was an article about Australia’s wage gap that concluded with two ordered lists – one for the top 50 jobs for Australian women, and another for men. It’s easy to make a visualization that’s more engaging than an ordered list, so I focused on creating something that would enhance the article’s thesis.


The diverging bar chart in third section of my viz was my starting point. It includes jobs that appear on either top 50 list, sorted by average occupational income, and the sides are colored by % of average occupational income. If only one chart made it into the article, I would pick this one because it’s closest to the original ordered lists. This chart made me curious about jobs that look like very promising careers, but aren’t great for women once the gender gap is accounted for. Are people accounting for these differences when giving young women career advice?

The first section is a straight-up comparison of average male incomes to average female incomes by occupation, highlighting differences that are especially egregious. It’s a bit obvious, but gives a nice summary of the situation. After creating this chart, I wanted to see what the pay gaps looked like when controlling for income level. (a $1000 difference is a lot smaller to someone making 100k than someone making 10k.) That led to the middle section, which does the best job of supporting the author’s argument. Gender gaps do increase with income, even when controlling for scale.

Overall, some of the skills that went into this were: diverging bar charts, LOD calcs, sets, custom colors, and setting up a 45 degree reference line.