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.