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January 9, 2023

#MakeoverMonday 2023 Week 2 - The Spartacus Gay Travel Index

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I found the subject of this week's data set on the Instagram account of The Map Zone. It's a simple map of what's called the "Gay Travel Index". 


The annually updated SPARTACUS Gay Travel Index informs travellers about the situation of lesbians, gays, bisexuals and transgender (LGBT) in a total of 202 countries and regions. The US-Index provides information about each of the 50 federal states of the US.

The index attempts at finding a balance between measuring the rights of the local LGBT community and considering the demands of queer holidaymakers. Our aim is to monitor the safety of queer people in each country and also increase the awareness on grievances. 

When I saw that the data was a ranking over time, I thought I'd give a curvy bump chart a try. I used this blog by Kevin Flerlage. He makes it super simple to follow along. During WatchMeViz, I showed how to use one of Kevin's visualizations from an old Makeover Monday as a template. I showed how to take the data we had for this week, transform it to the write shape needed for the template, then how to swap out the data source with the new one we created.

Honestly, when I swapped out the data sources, I was convinced that I would have messed things up along the way, but alas, I didn't and I was basically done. I spent time formatting, adding in more categories, and a bit more. Check out the viz below or here and follow along with Watch Me Viz to see how to build one yourself here.

November 22, 2022

#MakeoverMonday Week 47 - Worldwide Railroad Infrastructure Quality Rankings

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This week was a fairly simple data set. Rankings by year by country for a single measure. One thing I found tough to get my head around was the 1-7 scale of the quality ratings. I converted the values to a 1-10 scale instead.

I had limited time for #WatchMeViz, so I quickly went through a few techniques you will find interesting:

  1. Recreating the original bar chart
  2. How to create a bump chart
  3. How to use a diverging color palette and alias country names
  4. How to create a ranked dot plot
  5. How to use parameter actions to sort the view
  6. How to use a filter action to remove the highlighting

This definitely ended up being the largest viz I have ever created (1300x3200). You can view the dashboard below the video. Click on the image to view the interactive version (or click here).

I hope you found it useful. If you did, please give the video a like and either share this post or share the video so that you can help others...pass it along. :-)


November 14, 2022

#MakeoverMonday Week 46 - The Cost of Cocaine & Heroin

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Quite the interesting topic this week. I get an email from The Economist regularly and there was an article about the wholesale price of cocaine. That led me down a rabbit whole and I found the data, but also for heroin and also for retail prices.

On Watch Me Viz, I stuck with simple lines charts that compared the price for a country to the overall price for all of the countries in the data set. I allow the user to choose a drug and a country.

I then wanted to show how to Dynamic Zone Visibility feature works (if you haven't seen it, be sure to watch it back). In the end, a simple dashboard that compares the measures in two ways, allows for some filtering, looks good, and that's it.

Check out the dashboard below the video.



October 11, 2022

#MakeoverMonday 2022 Week 41 - UNDP Human Development Index

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From UNDP:

The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

This week, I really liked the original visualization, so during #WatchMeViz,  I spend the time recreating the visual. I got most of the way there in an hour and finished it off in the evening. The questions on the live stream are super helpful...thank you!

A couple things I learned:

  1. Hover action interactivity is very, very slow in Tableau. I ended up changing it to a select action.
  2. Labeling is overly complicated.
  3. Tables in a tooltip can look really good. And they are very responsive.

The final visualization is below the video. Thanks for watching!


March 22, 2021

#WorkoutWednesday 2021 Week 11 - Gapminder: Income vs. Life Expectancy

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As Lorna mentions in the week 11 challenge, the key is in the data prep. Once you have that, the visualization is really simple.

I did not use the new relationships model; I stuck with the traditional method of unions and a join as that's the most straightforward way to ensure you get the data in the correct shape.

First, you want to union together the three CSV files: life expectancy, population, and income. When you do that, you'll get this strange looking view that is super wide and doesn't have headers that mean anything. 


What you should see, though, is that the headers are in the first row. To fix that, click on the drop down triangle next to the unioned data sources and choose Field names are in first row.


The years are nicely in the headers now. The next step is to select all of the columns with the years and pivot the data. Be sure to ONLY select the years.

I then renamed Pivot Field Names to "Year" and changed the data type to Number (whole) and also renamed Pivot Field Values to "Values".

Next, add the data source with the list of countries and drag it into the data prep area to create a join. You want to join "country" to "name". And now everything should look good. That's it for the data prep.


Now that the data is pivoted, in order to build the view, you need to create a calculated field for each measure: life expectancy, population, and income



All three calculations are the same. All you need to do is swap out the name of the csv. Lastly, build the view.


Note that the x-axis is a logarithmic scale and both axes have the option to start at 0 turned off.  That's it! I hope you found this helpful.

November 2, 2020

#MakeoverMonday Week 44 - Where do women have more access to the internet and mobile phones than men?

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#MakeoverMonday week 44 is another #Viz5 initiative. The topic this week is access to the internet and mobile phones by gender and country.

First, sorry about the video cutting out at the very end. My mistake.

In this video, I first review the initial visualization and talk about what works and what does. In the end, I went with a quadrant chart, which is a scatter plot with broken up into four quadrants. The viz focuses on only two of the quadrants to highlight the significant difference in the number of countries where women have more access to men for both technologies vs. the opposite.

I showed several methods for visualizing the data:

  1. Side-by-Side Bar
  2. Bar in bar
  3. Bar Graph vs. Reference Line
  4. Barbell
  5. Peas in a pod
  6. Floating bar chart
  7. Slope graph (terrible choice)
  8. Ranked slope graph (even worse choice)
  9. Histograms
  10. Box plot
  11. Scatter plot

Resources:

  1. Final workbook - LINK
  2. Data set - https://data.world/makeovermonday/2020w44
  3. Country and region information (Be careful joining this as some country names don't match. You'll want to using data blending and alias the country names to match.) - https://data.world/vizwiz/country-region-codes
  4. Chart Guide - https://chart.guide/
  5. Interactive chart chooser - https://depictdatastudio.com/charts/ 


November 12, 2019

#MakeoverMonday: Literacy Rates Around the World

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It's TC19 week and Eva has provided a data set about literacy rates around the World for the 1200+ people in Vegas to viz live plus the hundreds more of you that aren't live with us.

Here's the original viz:


What works well?

  • The data by region is ordered alphabetically, making it easy to find each region.
  • The bar chart is sorted by largest to smallest.
  • Nice filtering options

What could be improved?

  • A diverging color palette should only be used when there is a logical midpoint or goal. I don't see those in this viz.
  • The squares are hard to understand.
  • I don't find the map very useful. It would be more useful if it zoomed in when a region is selected.
  • There's no title.
  • There's too much text.
  • The bar chart seems to go out past the edge, or at least visually it appears that way.

What I did

  • I created a KPI scorecard so that I could understand the patterns for the overall or an individual country. Are literacy rates improving or regressing?
  • Show the distribution of the rates of the countries within each region
  • Within each region, which countries are above or below the median for that region?
  • How has the literacy rate changed over time?
  • Allow simple filtering options.

I drew inspiration from Workout Wednesday week 51 2018: Container Fun from Rody Zakovich. I love finding reasons to practice techniques I've tried before and want to master. Consider challenging yourself to learn something new each week.

Enjoy!

June 10, 2018

Makeover Monday: Tourism Density Index

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For week 24, Eva presented us with something called the tourism density index, which basically means how many tourists come into a country compared to that country's population. Here's the original viz:


What works well?

  • Really good explanations for how they define overtourism and undertourism and examples for each
  • Providing the exact figures for each country
  • Colors are easy enough to distinguish
  • Sorting the countries from lowest to highest
  • Splitting the view between the highest 9 and the lowest 9

What could be improved?

  • Circles are inherently difficult for comparisons. Are they measure by area or diameter? Either way, the circle in a circle in overkill.
  • Why does the size of the light green circle change once the dark green circle is a larger value? That makes no sense at all.
  • If the exact numbers were not included, it would be impossible to compare countries.
  • Why show the top 9? That seems like an unusual way to select the countries.

My Goals

  • Focus on either the raw values or the percentages. I'll figure this out once I explore the data.
  • Make it easier to compare countries.


February 18, 2018

Makeover Monday: Who gets all the medicine money?

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I had no idea that drug and medicine exports were $318B in 2016 before this week's Makeover Monday. That's crazy! I went in assuming that the US would be the highest since drugs are so stupidly expensive there, but I was clearly wrong; Germany is the clear leader. Apparently there are lots of big pharma companies there.

I love learning something new! Let's take a look at the original viz:


What works well?

  • Resizing the continents by their overall exports makes it obvious that Europe is the largest exporter.
  • I hate packed bubbles, however in this view, the largest countries stand out by double encoding with color.
  • Including the percentages for each continent provides needed context

What could be improved?

  • The packed bubbles make comparisons overly difficult.
  • The color legend uses unequal intervals.
  • Plotting the data on a map doesn't add any context.
  • There's no sense of overall ranking across all countries.
  • If you ask "so what?", there's no answer.

The article that accompanied the viz provided quite a few interesting statistics. It would have been great if these were included in the original viz, but they weren't, so I decided to make something super simple that:

  1. Has an informative title and subtitle
  2. Provides context
  3. Provides insight
  4. Uses color effectively

That's it. Simple, quick. That's how I like my Makeover Mondays.

October 30, 2017

Makeover Monday: Does having a lot of public holidays lead to a happier country?

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Onward we go with another week of Makeover Monday. This week Eva is asking us to review this viz from The Telegraph about public holidays around the world.


What works well?

  • People understand and like maps so this will capture their attention
  • Using a consistent color scale for all countries
  • Ensuring countries with no data are white (kind of like blanking them out)
  • Simple tooltips

What could be improved?

  • Red has a negative connotation, is this the best use for red? I'd say not.
  • It can be difficult to compare countries.
  • Using a filled map can make smaller countries impossible to find. Take Europe as an example; it's nearly impossible to see European countries without zooming in.
  • Using the search and entering a continent zooms you in way too far. This is completely broken.
  • There's no title. If you saw this independently of the article, you would have no idea what it's about.

My goals

  • Make comparing countries easier
  • Consider supplementing the viz with additional data
  • Use story points for a step-by-step makeover
  • Answer a simple question: does more public holidays mean a happier country?

May 29, 2017

Makeover Monday: How Has Internet Access Changed Around the World?

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UPDATE: Thank you to Jamie Briggs for pointing out that I shouldn't have used the average internet users for the worldwide figures. I have changed it to a weighted average by year by including population stats for each country for each year.

For #MakeoverMonday week 22, Eva picked an interactive map from Knoema that shows internet access over time.

Click image for the interactive version

What works well?

  • Nice interactivity
  • Informative tooltips
  • Summary in the subtitle provides some context
  • Including a definition of an internet user
  • Highlighting on the timeline the block for the year you are seeing
  • Providing the context of "per 100 people"
  • Per 100 people makes it easy to understand because you can think of them like percentages

What could be improved?

  • Include a more engaging title.
  • Stoplight colors work ok for me, but not for the color-blind folks.
  • The color scale in this case makes anything below 75% look bad. Is that really the case? Isn't providing more access over the years more important?
  • Why are years without data included on the timeline?
  • Having to flick through the years prevents you from seeing the change over time.
  • Smaller countries get lost on filled maps
  • Needs more context

What were my goals?

  • Create something easy to understand
  • Only include 2010-2015 since those were the only reliably consecutive years
  • Eliminate countries without values for 2015
  • Allow the user to pick a country to spotlight
  • Be able to compare that to the worldwide average
  • Label the ends of the lines for context
  • Include a title with a summary of what happened between 2010 and 2015

With that, here's my Makeover Monday week 22 creation. Enjoy!

November 16, 2015

Makeover Monday: What Country Has the Most Unsustainable Debt?

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Consider a scale of 1-10: 1 is a great viz, 10 is terrible. The visualisation I’m choosing to makeover this week is about a 15. It’s THAT bad. Here goes:



Let’s take a look at what they’ve done poorly:

  1. Splitting the countries apart makes absolutely no sense whatsoever. That totally ruins the reason for using a map in the first place.
    To Do: Put the map back together and let the map serve its purpose.
  2. Each country is sized by a combination of its GDP and debt, but I only realized that in the super tiny note at the bottom left.
    To Do: Make the sizing more obvious and easier to understand.
  3. The author used a stop light colour scheme. This would be terrible to read for a colour-blind person.
    To Do: Change the colour scale to something more effective that appeals to everyone.
  4. The colour scale is not even. There’s only one colour on the negative side and three on the positive side.
    To Do: Make the ranges equivalent.
  5. Not every country is included, so is a map even the right choice in the first place?
    To Do: Consider alternative charts, e.g., bars.
  6. There’s no need to label every country and the metric labels are overly precise. Does it make much difference if Germany is 74.70% or 75%?
    To Do: Clean up the clutter.

Given the above challenges, I’ve used Tableau story points to walk you through the makeover. In the story, I take you through a series of visualisations that gradually improve the original.

October 12, 2015

Makeover Monday: State of Connectivity 2014

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I didn’t have much time today for a makeover, so this will be a bit brief. I had tagged this article from the Facebook internet.org team as needing a makeover. It’s the cover of the report that caught my attention.


I guess what bothers me most is that this simple bar chart is completely unreadable. I can’t find any countries unless I use a microscope. I assume this was by design, but I don’t see the value in it.

Of course the data was not provided, so I did a quick Google search and found the data in Wikipedia. I then used the InterWorks Web Data Connector for import.io to extract the data. I blended that with another data set I had of country abbreviations.

I started by recreating the original in Tableau.



Ok, I still can’t read it, but I can hover over a bar at least. I wanted something better, something easier to understand, something people might want to quickly explore. I created this three chart layout which includes a map, bar chart (same as the original, but larger), and a slope graph.

I’m not totally sold that this is complete or great, but it’s definitely better than the original. And remember, I timebox myself on these makeovers, so once I reach my time limit I stop. Rules are rules.


March 2, 2015

Makeover Monday: There Are Only Three Countries in the World Where Your Boss Is More Likely to Be a Woman

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Back in November 2013, the team I was on in Facebook at the time hosted the first VizCup. One of the participants, Mike Evans, flew all the way up from LA just to participate. Mike created this incredible UFO visualization, that got him 2nd place. From there, we invited Mike to have breakfast and talked him into working with us. Mike has become a very good friend of mine and is now writing a great column called Mike’s Advice, which you should follow on Facebook and Instagram.

Mike is actively involved in diversity efforts at Facebook and asked me to share this visualization he created as part of the Makeover Monday series. Mike’s post was triggered by this article on the Washington Post, which included this bar chart.


In Mike’s own words:

Here’s a story just begging for context. Only three countries in the world have more women managers than men. The accompanying bar chart is really just a long list of countries though. You’d have to sift through it and mentally map out where these countries are to get a sense of regional trends.

I created a map with a simple red-blue color scale from 10%-60%. Since the top 3 countries were highlighted in the story, I decided to add labels on the map for these.



The story is much richer with this map. You can see swaths of red in the Middle East. Japan and South Korea jump out as well (at 11%!). I didn’t even notice these countries in the bar chart until I switched it to a map.

You can download the Tableau workbook used to created this map here (requires Tableau 9).