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Showing posts with label united kingdom. Show all posts

September 12, 2023

#MakeoverMonday Week 37 - First time home buyers in the UK are being shut out of the market

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Have you ever heard of the "sunken cost fallacy"? If you haven't, it is where you continue a task based on previously invested resources, such as time, despite new evidence suggesting that the cost, beginning immediately, outweighs the expected benefit.

This is what nearly happened to me during Watch Me Viz. I was convinced I had created the perfect chart. That is, until comments started coming in to the contrary. I had gotten so fixated on this one chart that I thought all other ideas were inferior.

I snapped out of it eventually and turned my attention to another chart that told the story much better.

The lesson learned: be careful of, and attuned to, sunken cost.

With that, here's Watch Me Viz for you to follow along and learn lots about Tableau.


October 3, 2022

#MakeoverMonday 2022 Week 40 - Income Inequality Around the World

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Of course, as soon as I posted the data set this week, the UK PM decided to backtrack on the tax cuts she has promised before. In my opinion, this is a good. But it kinda made that part of the analysis irrelevant.

Still, though, I was curious to see how the UK compared to the rest of the World. During Watch Me Viz (below), I started by rebuilding the original chart, which I quite liked. I then looked at the data over time, but it was quite sparse and difficult to do any meaningful analysis of.

So I decided to stick with a single chart that looked like the original, but it includes all countries and some filtering options.

To learn how I approached the analysis and built the charts, watch the video below. My final dashboard is below the video.

Enjoy!


June 24, 2022

My Sabbatical with Maggie

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Having been with The Information Lab for more than 5 years, I had the opportunity to take a month-long sabbatical. I went to Siam Park with my son Henry in Tenerife, a week long trip to Eckington in the UK, plus every possible moment spent with Maggie.

Of course, I tracked everything I did with my beloved companion. And we did A LOT. 

We covered 254 kilometers (158 miles) in 60 hours over the course of 38 days. 

While I do have the routes for every activity, I thought that was too personal, so I stuck with a simple KPI dashboard. It tells me lots of stories I'll never forget...from gun dog training to long hikes across the English countryside to cuddles on the couch. I love her so much!

August 19, 2021

#MakeoverMonday 2021 Week 33 - UCAS Daily Placements

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UCAS is a service that helps people find open placements universities in the UK (and hopefully place them). I found this particular data set quite confusing until I looked at this viz from UCAS. I then understood that we were basically looking at the number of applications from UCAS based on the time that has elapsed since exam results days. That probably took me a couple hours to wrap my head around.

I decided to create an exploratory dashboard, focusing on the number of applicants that were placed for women vs. men. Women use UCAS's services way more than men do.

June 8, 2020

#MakeoverMonday Week 23 - Frequency of Meat-Free Consumption by Brits in 2019

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Week 23 was a pretty straight forward data set. It focuses on the frequency with which Brits of different diet types have consumed meat-free foods. Somehow meat-eaters have NEVER eaten meat-free food. No fruits or veggies? Ever? 

And what about the vegans? This data says they most frequently eat meat-free food weekly. Huh? They're vegan; they eat meat-free food every meal. 

I'm skeptical of this data. Anyway, here's my viz.

December 22, 2019

#MakeoverMonday: How much are Brits & Europeans expected to spend on Christmas?

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Two weeks to go and then I'm done helping co-lead Makeover Monday. It's an interesting combination of relief, reduced stress and anxiety, mixed with sadness. Either way, it's been amazing adventure.

This week, Eva picked Christmas-themed data...a simple survey from Deloitte about expected Christmas spending and the UK and Europe.


What works well?

  • It a simple table that is easy to understand without doing much thinking.
  • My eyes were immediately drawn to the two red declining arrows, which makes it seem to be the focus on the visualization.
  • The table is neatly organized from highest to lowest spending categories.
  • Everything is clearly labeled.
  • The highlight box on the right provides a nice summary.

What could be improved?

  • Remove the shading from 2018
  • Removed the shading from the background of the Total cell
  • Align the text labels either left of right, but not center
  • Remove the borders between the rows, but keep them to separate the headers and totals from the rest of the table
  • Change the font color of the categories to black; green could give the impression that they are increasing
  • Align the arrows on the second table with the rows they correspond to
  • Why is spending less red? I would think spending less is good

Taking all of this into account, here's my Makeover Monday week 52. Enjoy!

October 7, 2019

#MakeoverMonday: Bearwood Corporate Services - The Money Behind David Cameron's Conservative Party

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What a fascinating data set! More on that in a minute. First, let's have a look at this week's viz to makeover. It comes from The Electoral Commission and focuses on political donations in the UK, which are required to be reported.

SOURCE: THE ELECTORAL COMMISSION

WHAT WORKS WELL?
  • Placing the filters on the upper right let me know immediately that I can interact with the data to find my own story.
  • The bar chart is sorted in descending order.
  • The summary numbers provide some context, but not much.

WHAT COULD BE IMPROVED?
  • The bar chart would be easier to read if it was horizontal.
  • Why are all of the bars colored? There are way too many colors and they have no meaning.
  • The packed bubbles would be much better as a bar chart or BANs.

WHAT I DID
I started by exploring each field in the data set. Many of them didn't have information I found useful, so I hid all of those fields so that they would not distract from my analysis. 

As I explored the donors and who gave what to whom, I saw that Bearwood Corporate Services was donating A LOT to the Conservative Party over a period of a few years. I hadn't heard of it before so I did some research on Google and it turns out that they were pretty controversial and closely linked to the rise to power of David Cameron.

That's where my story begins...

September 30, 2019

#MakeoverMonday: London's Aging Population

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This week, Eva chose a viz/data from the London Datastore about population projections. Here's the original viz:

SOURCE: London Datastore

What works well?
  • It's a simple bar chart, which is very easy to read.
  • Uses a single color; we often see the bars double encoded with the same value as the length of the bar.
  • The axis starts at zero.
  • Simple, clear axis labels

What could be improved?
  • I think a line chart would be even easier to read.
  • The title says "Population of London" but it's really the projected population plus the past population; some clarification would be good.
  • The legend isn't needed.
  • The y-axis title could either be removed or changed. "Number" doesn't mean a whole lot.

For my makeover, I was interested in comparing the distribution of the population by age for one year compared to the distribution of the population in 2050. For example, what was % of the population for 45 year olds for 2018 and 2050, then compare those two values. This then shows how the population distribution will change.

August 5, 2019

#MakeoverMonday: Britain's Reduced Dependency on Coal

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For week 32, Eva picked a data set about Britain's progress towards zero dependency on coal power. There have been calls to do this in the US, even policies put into place by the Obama administration, only to have Trump rip them all apart to appease his donors. Slimeball!


WHAT WORKS WELL?
  • Great color scheme
  • Easy to understand layout
  • Good color legend
  • Informative title and subtitle

WHAT COULD BE IMPROVED?
  • Not much; it's quite fantastic.
  • Maybe make it more interactive so you can see the specific values when you hover

WHAT I DID
I wanted to see if I could rebuild this viz because it looks so good and it would be a good learning experience for me. Creating the viz was pretty straightforward. The tricky part was getting the colors to work just right. I did that through a calculation that makes 0% go to -20% so that the color range would be close to the original.

Lastly, I wanted to change the title and subtitle to something different that explains the data better. I pulled some text out of the article by The Guardian.


May 1, 2019

The UK's Most Popular Baby Names

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Today DS13 was supposed to have most of the day to work on their client project. However, after a training session where I showed them how I approach a new data set and then design a dashboard, we brought Sophie Sparkes in to throw a surprise dashboard week challenge at the team. After all, they only had three days of dashboard week anyway.

Sophie's Challenge

While Tableau is an amazing tool, when you use it all the time you can fall into data-viz-auto-pilot mode. You build the same kinds of charts; you construct similar kinds of dashboards; you fall back on the same formatting styles. While familiarity with tool, and a workflow, is a good thing, it also narrows your view of what’s possible.

For today’s Dashboard Week challenge, I want you to step outside your data viz comfort zones and try building a viz using Flourish. Flourish is a free tool that lets your build interactive, responsive, and embeddable vizzes and data stories, all within the browser using your own data. Flourish is focused at the communication side of data viz (more than the data exploration side), and I’d like DS13 to really think about communication in today’s challenge.

Why Flourish? I really like their wide (and ever expanding) range of templates and interactivity (transitions, stories and ‘Talkies’ to name a few); also they are based in London – so why not viz-local?

Using any part (years, geographic locations, genders) of the England and Wales baby names data sets, I want DS13 to find and communicate one specific story from this data set.

Here are the rules for today, and what I’d like to see as output:

  1. They must work independently.
  2. Everything must be finished by 5pm.
  3. They must use Tableau and Alteryx for the data prep and exploration.
  4. The final viz must be made in Flourish.


My Approach

First, I had to get some data. I decided to download the data from the ONS for 1996-2016 because it was in a relatively decent format.

Next, I opened the "Plotting Competitors" example because I loved the animation. The great thing about Flourish is you can immediate use the template. All you need to do is upload your own data, assign the columns, and you're done!

This meant I had to do some data prep in Alteryx to get it in the correct shape. I needed the years across the view and the number of births for each name and gender. Then I filtered both the boys and girls to the 25 most common names, giving me 50 names in total.

I absolutely LOVED playing with Flourish and will definitely use it in an upcoming Makeover Monday.

Check it out! The animations are so so good! There were straight line and curved line options. I went for the curves. Enjoy!

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.

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.

January 21, 2019

Makeover Monday: Electricity Use at 10 Downing Street

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For the week 3 makeover, Eva picked his viz about energy usage at 10 Downing Street. For those of you that might not be familiar with the building, it's the headquarters of the U.K. government and home of the Prime Minister. Basically, it's the equivalent of the White House. I go by it quite often on my commute to work. You might not even notice it if not for the throngs of tourists and the guards with really big guns.

Here's the viz Eva chose:


What works well?


  • Really nice BANs that also have context included. I give people feedback quite often that BANs can be great, but they're meaningless without context.
  • Nice filter options with the buttons at the bottom
  • The chart shows the peaks and troughs well.
  • Using different colors for peak usage
  • Data updates as you click on the BANs

What could be improved?

  • Include a legend so you know what the colors signify
  • A better x-axis is needed
  • Remove the buttons that don't have any data, District Heat and Gas in this case

My Plan

  • Hold off on working on my viz until we have our weekly Makeover Monday time at the Data School. I've written this section and the two above Sunday night.
  • Explore the data with line charts to get a sense for the patterns in the data.
  • Keep something similar to the BANs; consider different or additional context.
  • Should the timeline show all of the data? Play about with different filter options.
  • Consider a heatmap that shows usage by hour of the day compared to day of the week or perhaps month.
  • Will reporting energy use, money, and carbon impact in the same dashboard be too crowded?
  • Explore relationships between the metrics with scatterplots. Is a connected scatterplot an option?
  • Would a mobile version be better so that people can look at it on the go?
  • Is there any additional data?

What I Uncovered

  • The data set only included 2017, so I downloaded back to 2008 as well. But data only existed back to 2013, so I had to deleted 2008-2012. Tableau Prep doesn't allow you to skip the first three rows, which is required for 2013-2016, so I used Alteryx instead and then unioned those years with 2017.
  • Only data for electricity usage is consistent across the years; I was expecting to see money and carbon impact as well. I wonder why don't they include those as well. Anyway, this eliminates a scatter plot.
  • Data was missing for December 2015, so I excluded that month from the data set.
  • There were lots of zeros, so I removed those as well.

And here's my viz after working on it for 60 minutes at the Data School.

June 4, 2018

Makeover Monday: The UK Gender Pay Gap Across Salary Bands

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This week's data comes from the UK government and more specifically the Valuation Office Agency. I was alerted of this data set by Aisling Roberts, who had written a great article on LinkedIn that questions whether people will actually take any actions based on the data.

Let's start with this viz from the official report:


What works well?

  • The symbols make it clear this about females and males.
  • The BAN in the middle tells us what the bonus pay gap is.

What could be improved?

  • Both icons are filled to the same level, making it look like there is no bonus pay gap. These should be filled to the actual values for each gender.
  • The icons don't add much value.
  • The title could tell us a whole lot more.
  • There's no source listed nor no timeframe.
  • The gridlines aren't evenly spaced between 0% and 50%.

I must admit, this is a tough data set. Hopefully the explanations I wrote on data.world provide sufficient context. I found it most useful to look at a specific company and look those values up in the data provided to ensure I understood what it means. Given that I found the data overwhelming, I decided to focus on the pay bands since that's what Aisling focused on in her article. 

From there, I started to build lots of charts, but found the number of companies overwhelming. Therefore, I decide to limit the data to those companies located in the City of London (i.e., those with a postcode that starts with EC). I also knew I need to do some data prep so that I could compare females and males in each pay band more easily. I turned to Tableau Prep for this.

The flow works like this:

  1. Remove columns that aren't needed
  2. Splitting the data up into two streams, one for the female columns and one for the male columns.
  3. Pivot the data so that the pay bands are listed down instead of across
  4. Add a column for the gender
  5. Union the data back together
  6. Export to an extract

Pretty straightforward and this short amount spent prepping the data made the gender comparison significantly easier. I first wanted to understand how the median proportion of females and males in each pay band by the size of the company with a City of London total (NOTE: the total only represents companies that reported).

Click on the image for the interactive version.


This simple view makes it incredibly evident that the proportion of females declines as the pay band increases. Males would be the inverse. It's particularly stark in the largest organizations. In the City of London, there are only three employers in that range (British Telecom, Royal Mail, and Sainsbury's Supermarket).

The heat map helped give me an overview of the data and felt ready to create something more detailed. This time I wanted to look at all companies together by gender by pay band compared to the overall median for each gender. I also wanted to provide the user with the option to choose a specific company. When they do, that company gets highlighted.

Click on the image for the interactive version.

What first struck me in this view is the clear, overwhelming patterns down and to the right for women. This gave me a great impression for how big the gender pay gap problem is.

The gender pay gap is not a myth. These are facts, facts that show women are underrepresented at higher salary levels. Don't let this discussion get lost. Check out your own company. How are they performing? Ask them to share the data within your organization. Transparency is a key to fixing this discrepancy.

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

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