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April 15, 2019

#MakeoverMonday: Info We Trust - Word by Word Analysis

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UPDATE (19-Apr)

Based on feedback from Eva and Jeffrey Shaffer during Viz Review, I've made the following changes:
  1. Removed the sorting from the table on the right. When you click on a word, it now stays in its position rather than moving to the top.
  2. Made the titles of the bar charts on the right more succinct.

Thank you Jeff & Eva for your feedback!!



Week 16 is here and in collaboration with RJ Andrews, author of Info We Trust, we are making over a word cloud he created based on the frequency of the 270 most popular words in the book.


What works well?

  • The most frequent words stand out because of their size.
  • The word cloud looks interesting, meaning it captures your attention.

What could be improved?

  • There are too many colors.
  • The words are rotated in different directions.
  • Sizing the words make it difficult to compare them and rank them.
  • It's a book about data and the biggest word is data...go figure!

What I did

  • We had been practicing set actions last week at The Data School, so I thought I'd replicate this dashboard by Lindsey Poulter.
  • I wanted to rank the words and also rank the words within each section of the book.
  • Create a mobile version based on the template Tableau builds for you automatically.

With that, here's my Makeover Monday week 16. 

April 9, 2019

#TableauTipTuesday: How to Create a Hub & Spoke Diagram with a Union

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In this week's tip, I show you how to use the Union feature in the connection pane to union a data set to itself in order to create paths between origins and destinations. This example uses airline routes and it could also have many other use cases, e.g., where are bike picked up and dropped off in London.

April 8, 2019

#MakeoverMonday: Cash Solvency of US States

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For Makeover Monday week 15, we are looking at data about the fiscal conditions of US States. According to Mercatus:
States face many fiscal problems, but these problems are not insurmountable. Studying how each state is performing with regard to a variety of fiscal indicators can help state policymakers address persistent issues and anticipate potential problems. 

Mercatus produces this simple map to visualize the results:


What works well?

  • By using a map, people instantly know this is about geographical information.
  • Using a clear legend with distinct colors to indicate good vs. bad
  • Including the top 5 and bottom 5 as a summary/key finding
  • Overall, a very nice layout with the map on the left and the additional context on the right.
  • Including the numbers on the States for context.

What could be improved?

  • Do the colors work for the color-blind? I'd recommend running it through a color blind checker.
  • The States need equal size weighting to ensure they can all be visible equally. This would also help with some of the labels needing to be lines that point to the respective States.
  • There's no definition for fiscal ranking.

What I did

  • I wanted to look at the data over time, but also look at all of the States at the same time. For this I used a tile map. I based it on a similar viz I created.
  • I wanted to give the user an option to compare years to a year they select so that they can see the change compared to a point in time.
  • Use color to indicate the positive or negative change vs. the year selected.
  • I created the calculations for each of the rankings and found cash solvency to be the most interesting, so I focused on that.

And here's my viz for Makeover Monday week 15.

April 1, 2019

#MakeoverMonday: How much plastic waste has been found on UK beaches?

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I've been particularly aware of the amount of plastic used and wasted. Keep in mind that plastic cannot biodegrade, therefore any plastic EVER created is still on Earth. Think about that for a minute. The plastic is washing up on the most remote islands.

Don't believe me? Watch Drowning in Plastic on the BBC. If this documentary doesn't change you mind about the amount of plastic you waste and the impact its having, then you need to have a deeper look into your soul.

This week, Eva chose a data set about the waste found on UK beaches.

SOURCE: BBC

WHAT WORKS WELL?

  • Including the raw numbers, and how big they are, provides great impact.
  • They sort going down the page.
  • The title is clear, concise,  and tells you what you are about to see.

WHAT COULD BE IMPROVED?

  • The infographic makes it appear as though this is ALL of the waste found on the beaches. However, it's only the top 10. You can see that if you read the original article Eva linked to.
  • The icons are cute, but are the necessary?
  • A simpler visualization, like a bar chart, would make the impact of the plastic more apparent.

WHAT I DID

As I did last week, I wanted to try out another tool. This week, I played around with infogram
  1. Infogram is great for building simple infographics very quickly. 
  2. The customization options help you create a good looking visual.
  3. The interactions on the charts are super responsive.
  4. You can change the theme or chart type with one or two mouse clicks.
  5. There's no "publishing" required. It's already live to everyone once you create your graphic.
  6. The chart types are limited, but I suspect 90% or more of what you need is available. 
  7. If you want a chart to display the graphic a slightly different way, you may need to edit the data and either crosstab or transpose the data.

Overall, using infogram was a pretty fun experience. I haven't used it for a while and it seems to have come a long way since then. With that, here's my Makeover Monday for week 14.

March 30, 2019

Groundwater Contamination and Cow Poo: A Major Contributor to Global Warming

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This is a project I've been working on for a while now, mostly because time has not permitted me to finish it and I've had other "issues" to deal with. I've been doing lots of research about global warming, water contamination and whether or not the two are related.

While watching a documentary, they mentioned how methane from cows (i.e., cow farts) are a major contributor to the greenhouse gasses and how cow manure is a major source of nitrate released into groundwater used for drinking. Fortunately, there is tons of data available, the primary source being the Environmental Protection Agency (EPA).

I wanted to understand the geographical distribution of three factors:

  1. The percentage of each State with high groundwater nitrate concentrations.
  2. The total area (square miles) of each State with high groundwater nitrate concentrations.
  3. Where the cow crap comes from that pollutes groundwater used for drinking.

I decided to create a map for each of these topics, as a scrolling story, with three actions you can take to help reduce the impact of cow manure pollution. We all want safe drinking water after all.

March 26, 2019

#TableauTipTuesday: Create a Region to State Drill Down Map with Set Actions

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In this tip, I show you how to use set actions to create a map that allows the user to click on a region and show the states for the region, but keeping all other areas at the region level.

March 25, 2019

#MakeoverMonday: Consumer Spending by Generation

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For week 13, we're making over this viz from Business Insider:


What works well?

  • The generations are sorted from youngest to oldest.
  • The title is clear.
  • The gridlines help guide the eye across the viz.
  • It's easy to compare the general/misc category and the restaurants across generations.
  • A stacked bar chart is easy to understand.

What could be improved?

  • The story in the data, from the article, is about how millennials are spending more on restaurants. It would be good to make that a more obvious focus of the viz. 
  • There are too many colors.
  • While the title is clear, if you don't read the article, you could miss the purpose for the chart.

What I did

I really enjoyed using Google Data Studio last week, so I thought I'd give it another try to continue my learning. Since this was a simple stacked bar chart, I wanted to create a "set" for restaurants vs. all others. I needed to create a calculated field using a case statement that checks the category field. That's it!

From there, it was formatting, which is pretty intuitive as well. I'd highly recommend you give Data Studio a try, especially if you know exactly what you want to build; it's not a data exploration tool.

 

March 20, 2019

#MakeoverMonday Data Studio Edition: Reykjavik Index for Leadership in G7 Countries

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Yesterday I posted a Power BI version of Data Schooler Hanna Nykowska's Makeover Monday viz. Today, I recreated her viz in Google Data Studio.

DATA PREP REQUIRED

  1. Add values for the remainder (100 - index)
  2. Add a sort column
  3. Pivot the data so that the index and the remainder were in the same column

WHAT WORKED WELL

  • To create the stacked bar chart, all you need to do is select the chart type and drop the fields on the appropriate shelves.
  • Customizing the split of the colors for the index and the remainder was easy.
  • I was able to customize the size of the viz.
  • You can choose any font that Google supports!!
  • The tooltips are super responsive.
  • Everything looks very crisp.
  • Hiding the gridlines leaves a nice thin black line on the y-axis without me needing to fiddle around with a few different settings.
  • The overall UX is quite intuitive. I see they have a data explorer now too.

WHAT I COULDN'T OVERCOME

  • I couldn't find a way to show only the mark labels for the purple bars.
  • I couldn't add a reference line for the G7 average so I had to leave it in the view.
  • I couldn't hide the x-axis only. When you do, the y-axis gets hidden as well.

With that, here's my third Makeover Monday for week 12 2019.

March 19, 2019

#MakeoverMonday Power BI Edition: Reykjavik Index for Leadership in G7 Countries

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Data Schooler Hanna Nykowska create a viz this week for Makeover Monday that was quite similar to my first idea. I didn't publish mine, so I thought instead of creating my version again, I would try to recreate her viz in Power BI.

DATA PREP REQUIRED

  1. Add values for the remainder (100 - index)
  2. Add a sort column
  3. Pivot the data so that the index and the remainder were in the same column

WHAT WORKED WELL

  • Creating a stacked bar chart in Power BI was quite simple.
  • Customizing the split of the colors for the index and the remainder was easy.
  • The viz layout is super intuitive and automatically adjust to the size of the screen while maintaining the original chart ratio.
  • The fonts look super crisp.
  • Simple to add a constant reference line for the G7 average.

WHAT I COULDN'T OVERCOME

  • I couldn't find a way to show only the mark labels for the purple bars.
  • I had to change the mark labels so that the values of the grey bars wouldn't be visible by making the text the same color as the grey bars.
  • I couldn't copy/paste into a text box.
  • I couldn't customize the font size for the reference line.
  • I'm sure there's a way, but I couldn't figure out how to color code the bars based on whether they were above or below the G7 average. For example, I wanted to make those countries below the G7 average a lighter shade of purple.

With that, here's my second Makeover Monday for week 12 2019.

March 17, 2019

#MakeoverMonday: To what extent are women and men viewed equally in leadership positions?

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The chart Eva chose this week shows that, not surprisingly, people are less likely to view women and men equally in leadership positions. Scores of 100 mean that people view women and men are equally suited to leadership roles. Sadly, women still have a long way to go in their seemingly never ending fight for equality.

Let's have a look at the chart:

Source: World Economic Forum

WHAT WORKS WELL?


  • Ordering the countries from highest to lowest in terms of people that view women and men equally in leadership positions
  • Including the G7 average for context
  • Assigning a different color to the G7 average
  • Labeling the end of the lines

WHAT COULD BE IMPROVED?

  • Circular bar charts are horrible for comparisons.
  • The title is meaningless.
  • The lines start thin, get thicker, then get thin again. Why?
  • The title and the center of the chart are the same. That's certainly unnecessary redundancy.

WHAT I DID

I started by creating a simple bar chart and that was fine. I also added a grey bar to have it as a stacked bar for each country that goes up to 100%. I then thought about doing a waffle chart (with circles) and then I remembered this viz from Andy Cotgreave back in Makeover Monday week 4 2016. I decided to replicate Andy's work since it looks great and gives lots of context. I created a mobile version like Andy did too.

With that in mind, here's my makeover for week 12.

March 12, 2019

#TableauTipTuesday: How to add a one pixel line to a dashboard

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In this week's tip, I show you how to use text boxes combined with containers to add divider lines in your dashboards. You can download the workbook here.

March 11, 2019

#MakeoverMonday: Has Philadelphia recovered from the Great Recession?

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For this week's Makeover Monday, we're looking at this dashboard from OpenDataPhilly.


What works well?

  • Consistency of colors
  • Simple design
  • Using an area chart with a bold line at the top
  • Bar chart is sorted
  • Interactive actions
  • Automatic proportional brushing

What could be improved?

  • Reduce the outline of the zip codes on the map
  • Remove the background from the map
  • Add a dashboard title
  • Change the chart titles to be more meaningful

And here's my makeover. Click to interact.


March 5, 2019

Makeover Monday Power BI Edition: Births Attended by Skilled Health Staff vs. Female Life Expectancy as a Motion Chart

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One of the things I couldn't do with my Makeover Monday this week was animate the visualization. The animations is what makes the story unfold and neither Tableau Public nor Tableau Server support animation.

What's one to do? Try a tool that does support animation. In this case, Power BI. Scatter plots in Power BI support animation natively and it took less than five minutes to create this.

  1. Upload the data.
  2. Choose the measure for the x-axis and place it on the X Axis shelf.
  3. Choose the measure for the y-axis and place it on the Y Axis shelf.
  4. Add a dimension to the Details shelf to draw more dots.
  5. Place the dimension to animation across, i.e., years, on the Play Axis shelf.
  6. Add a title.
  7. Add a text box as a footer.

BOOM! Done! Easy peasy. Check it out below.

March 4, 2019

Makeover Monday: Are skilled health staff an indicator of female life expectancy in fistula countries?

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For Makeover Monday week 10, Eva presented us with this map of adolescent fertility rates on Data Wrapper:


What work well?

  • Using a continuous color palette
  • There are no exceptionally large countries compared to the others, so a filled map is a good choice.
  • Normalizing the data to make comparisons across countries more relevant.
  • Using grey for countries with no data.
  • Good title and subtitle

What could be improved?

  • If there is data across years, it would provide additional context to the data. In other words, is the situation improving?
  • Make the title bigger; it's too small compared to the large map.

My Goals

  • Compared the metrics between fistula and non-fistula countries
  • Look at change over time
  • Figure out how to deal with all of the nulls
  • Be done

February 26, 2019

Tableau Tip Tuesday: How to Convert a Reference Line into a Level of Detail Expression

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This week's tip builds upon my tip from two weeks ago: How to Convert a Reference Line into a Table Calculation. In this tip, instead of using table calcs, I'll show you how to convert a reference line into a LOD.

Enjoy!

February 25, 2019

Makeover Monday: The Economic Value of the Bicycle Industry in the UK

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Spring is trying to peek out here in London. I saw so many people cycling in Richmond Park yesterday that I thought it was a good time to look at the state of cycling in the UK, focusing on the economic value.

Here's the original chart:


What works well?

  • Using a line chart for representing data over time
  • Minimal use of color
  • Y-axis is properly labeled
  • Including the sources

What could be improved?

  • The x-axis label is completely wrong.
  • The title needs to be more specific. As a standalone chart, we have no idea if this is about a country, a store, whatever.
  • Don't include all of the axis ticks between each of the quarters.
  • Remove the diamonds as markers for each point.
  • Change the units of measure on the y-axis to thousands.

My goals

  • Create something that's easy to understand.
  • Stick with the minimal use of color.
  • See if the change between periods is important. If so, what is changing and why?
  • Is there seasonality? If so, how is that seasonality changing?
  • Consider other metrics, like value added per bike. How does that change through time? What do the changes mean?

With those goals in mind, here is my Makeover Monday week 9 2019. I made value added per bike the primary focus of my viz to help explain that the downturn in the cycling industry is due to volume, not value added per bike.

February 22, 2019

Where are New York's Parking Meters?

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There are times when you come across a data set that you immediate know will look cool as a visualization. New York City open data had, what I thought, was a pretty obscure dataset: Parking Meters GPS Coordinates and Status.

Their map is impossible to read with some many big dots overlapping each other. This also makes it hard to see the concentration of parking meters. My assumption going in was that you'd see way more in Manhattan.

All I really did was create a map, plot each point, and change the mark type to Density. From there it was formatting:

  1. Using a custom mapbox map, which I customized based on the mapbox template Metropolis.
  2. Play around with lots of colors, then intensity and opacity of each of those colors, before settling on a choice. 

I probably could have done this process for days and days without ever finding a "perfect" solution for the formatting, so I decided it's good enough and wanted to get it published. Enjoy!

February 20, 2019

Improving Upon Strava Activity Summaries: RIDE IT London Osterley Sportive

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A few weeks ago, I voluntold Luke Stoughton (Head of Data School Recruitment and good friend of mine) to join me for a 52 mile cycling sportive hosted by Evans, a UK-based chain of cycling shops. Now, there's one thing to keep in mind...Luke had only ridden up to about 20 miles on a single ride. He WAS NOT happy with me, but he went anyway.

Coach Carl joined us as well, so I met him at a train station on the way and we rode the rest of the way to the start at Osterley House, an incredibly beautiful National Trust park and house. We met Luke there and headed off on our trek, stopping at two feed stops (I ate about eight pieces of cake at the second), peddling up three steep hills (they weren't that bad, but Luke hated it and threatened me), before looping back to the start and hitting the pub for a couple well deserved refreshments.

Along the way, I was thinking about how I could visualize the data. Strava does a decent job (see my ride here) and I thought I'd use that as a basis for my viz. My goals:

  1. Use a Mapbox map background as similar as I could to the Strava map (which is proprietary).
  2. Use the Strava colors (I found the hex codes in their brand guidelines and added them to my preferences file).
  3. Prep the data in Alteryx. There is a web data connector, but I like being able to create my own row level calculations and extract only the data I'm interested in. Plus it gives me an excuse to practice Alteryx more.
  4. I like how the elevation timeline, but for me it's lacking context by not including speed. I decided to keep its wide and short style while creating a dual-axis chart to overlay speed on top of the elevation.
  5. I included key moments as viz in tooltips. Hover over the gold ribbon (fastest speed) to see a picture of Luke and Carl speeding down the hill. That was fun! You can also see a selfie of us at the end.
  6. Include interactivity like Strava. Basically link the map to the chart and vice versa.
  7. Include speed and elevation on each point.
  8. Everyone loves a good BAN! FYI, Strava calculations elevation climb differently.

This isn't anything complicated and I think it's a viz that provides ever so slightly more context than Strava. Click on the image below for the interactive version.

February 18, 2019

Makeover Monday: Which States have invested the most per home in wind energy?

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For week 8, we're making over this chart from HowMuch.net:


What works well?

  • The States are sorted from most installed capacity to least.
  • The title clearly indicates what the chart is about.

What could be improved?

  • The log scale is misleading.
  • The colors are double encoding the data.
  • The windmill symbols are chart junk.
  • The labels for the Stated get smaller and smaller, to the point where you can hardly read them.
  • The investment labels are not consistently formatted, they are aligned on an angle, then vertically, and they get smaller and smaller.

What I did

  • Since this is a simple data set, I wanted to use a tool other than Tableau. I find this useful so that I can speak about other tools from an educated perspective.
  • Create a simple viz
  • Look at the relationship between installed capacity and development
  • Consider metrics that normalize the data

February 13, 2019

Tableau Tip Tuesday: How to Convert a Reference Line into a Table Calculation

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In this tip, I show you how a reference line is merely a table calculation that Tableau makes easy for you. I'll show you how to write any reference line as a table calc for use later.