Makeover Monday Week 12 – UK Pet Population

This week’s Makeover Monday showed the amount of pets being kept in the UK.

What Works Well

  • The message is clear, easy to what’s going on at a glance.
  • A bar chart is probably the easiest way to do direct comparisons
  • The text in the bars is an efficient use of space.

What could improved

  • Too many colours for my liking
  • The title could be better, one might think this has something to do with pet food, but that’s just the name of the source.

What I did

I didn’t have a lot of time to spend on this week’s makeover, so I set aside an hour during my lunch break to see what I could come up with.

I remember seeing a video a few years ago about what if the world’s population was represented by 100 people (how many would men, women, children, asian, etc) and I decided to try something similar.

Makeover Monday Week 11 – Irish Whiskey Sales

This week’s #MakeoverMonday was themed in honour of St Patrick’s day.  The data provided showed the sales of Irish whiskey in countries throughout the world.

What works well

  • The countries have been sorted into groups, making it easy to see trends by regions
  • There’s not much unnecessary clutter
  • Colours are distinct and stand out from one another

What could be improved

  • No sense of context of the sales.  I can see that sales in North America have doubled, but are there more sales in North America overall?
  • Title is somewhat irrelevant
  • A lot of empty space on the graph, could be used for legend or annotations

What I did

(Link to interactive version)

  • I focused my Viz on the huge sales increase in the United States versus sales everywhere else
  • I decided to use a graph of sales by year, rather than growth, as I think it’s easy to see what the trend in growth is (at least in the U.S.)
  • Used a clear (but lighthearted) title to set the tone of the viz.
  • Included a few lines underneath the title, highlighting a few numbers and emphasising the point I was trying to make
  • Made a custom colour palette of greens, in honour of St Patrick’s day

Fairly happy with this week’s effort, my only concern is that using an area graph instead of a line graph makes it look like cumulative sales, rather than sales by year.

Makeover Monday Week 10 – International Women’s Day

This week’s data set came from the Equal Measures 2030 and was themed in honour of international women’s day.  It showed policymakers from different countries estimating statistics concerning women and girls in their own country.

What works well

  • The reference line and actual value makes it easy to see how many data points are close to the correct answer

What could be improved

  • It’s not immediately obvious that each mark represents a policymaker
  • Correct answer for Maternal mortality rate is quoted as a percentage (over 100%), it’s normally given as number of moralities per 100,000 births
  • If the correct answer is given as a percentage then I think the axis should be in percentages too.
  • It seems like the reference line isn’t centred within the reference band.

What I did (1st Attempt)

  • I focused on showing one graph at a time and using parameters to switch country and statistic.
  • I elaborated a bit in the title to make it clear what the graph was showing
  • I used colour to show the difference between policymakers who claimed they knew the statistic and those who had guessed.
  • I used a jitter plot instead of sorting the policymakers in any way because x-axis conveys no information (and I wanted to learn how to do a jitter plot)
  • Excluded maternal mortality rates for the time being, as I need to think of a way to include it while the other statistics are all given as percentages.

What I did (Second Attempt)

Wasn’t entirely happy with my first attempt, so I spent an hour or so making improvements.

  • Played around the formatting so it doesn’t look so weird now
  • Scrapped one of the filters and displayed country data side by side, to allow for some comparison
  • Tried to re-add maternal mortality rate.  In the end I had to add a second parameter that would filter out the main sheet and swap in a sheet specific to maternal mortality rate.  It’s not an ideal solution but it’s the best I could come up with in an hour.

Makeover Monday Week 9 – World Economic Freedom

This week’s dataset is an interactive Viz from the Fraser Institute, it showed the worldwide economic freedom (and other metric) scores for every country of the world from 1970 – 2015.

What works well

  • I like the ranking table on the left, the arrows showing if they’ve gained a place is a nice touch.
  • Colours are easy simple, it’s easy differentiate the good from the bad
  • High level of interactivity, can compare countries and look at different metrics

What could be improved

I only have two gripes with this viz and they are:

  • A worldwide map doesn’t lend itself well to showcasing smaller countries (which is especially bad in this viz, as the top two countries are actually city-states)
  • The scroll bar makes it a bit hard to compare how the countries have changed over time

What did I do

For the past few Makeover Mondays, I’ve been picking relatively simple charts and trying to perfect them.  I feel this is often the best approach, but I wanted to force myself to learn some new things so this week I tried out something different this week.  Check it out here.

  • The first thing I did was hit the play button a few times and looked for any countries/regions that changed dramatically.  I noticed South America changed colour quite a bit, so I decided to make that the focus of my Viz.
  • Since I was focusing on a smaller area, I felt like a map would now might be appropriate to use (really, I just wanted an excuse to try out
  • I kept the rank table, but split it into two, to show the differences between 1980 and 2015 (I picked these dates because some countries did not have data prior to 1980, I also excluded French Guyana and Suriname because they only had data from the mid 2000’s onwards)
  • I included a line graph at the bottom to show a the countries change over time, if I was keeping this simple, I would focus only on this graph.
  • I added a parameter to allow the user to change the metric they wanted to compare.  I’ve used parameters in the past, but it was only this week that I felt like I really understood how they function.
  • I normally try to use neutral colours, and use red to highlight anything I want point out.  This week I tried out some different colours.
  • I didn’t mention this in the text, but the map tooltips also display another line graph.  This was mainly because I found out that you could put sheets in tooltips and I wanted to try it out.

I think the way I’ve laid everything out could definitely be improved, but overall I’m happy with the way it turned out and can’t wait for next week to show off everything I’ve learned!

Makeover Monday Week 8 – Where Does Your Medicine Come From?

Over the past few months I’ve been getting involved with #MakeoverMonday, a weekly social data project that takes a look at some less than perfect data visualizations and looks to redesign them, either improving on the original, or by exploring new stories hidden within the dataset.

Starting with Week 8, I’ll be posting a blog post to accompany my submission, so I can write down my thought process and rationalise the steps I took.

The original viz

By just glancing at this viz we can immediately see who the big exporters of medicines and drugs are.   Including the values within the bubbles helps to add context, so that works well.

However, as the bubbles get smaller, the user has rely on text to read and compare, so any bubbles smaller than Hungary don’t really add anything.

I’m not sure if overlaying the bubbles over the map adds anything to the viz, to me it’s just more clutter.  The use of colour isn’t bad, but it really only serves to reinforce the bubbles.

My Viz

Normally my approach to makeover monday is to look for interesting stories, or cool ways to present the data.  This week’s dataset was fairly straightforward though, so I decided to stick with a simple bar chart but to supplement the medicine exports with medicine imports.  This revealed some really cool information.  Germany, for example, has barely any medicine imports, while a country like Ghana imports more medicine than any other country barring the US.

For a while I couldn’t decide whether to display the bars side-by-side or overlapping.  Ultimately I went with the latter option, partly because it saves space and allowed me to include more countries on my list and also because I kinda think they look thermometers or hypodermic needles?  Anyway, I’m happy with my choice.

I used consistently to make it clear which bar represents imports and which represents exports, I picked a black background because I normally just leave it white and fancied a change.  Red and white for the bars seemed to stand out the most on a black background, so I chose them for the bars (plus the whole thermometer/needle thing).

Lastly, I included a few annotations on some of the more interesting bars, to add a bit more context.  I like using text alongside the data instead of above or below, I think it’s a more efficient use of space although it can get cluttered if you use more than two or three annotations.