For the next two weeks, we’re making over dashboards from our past. Andy and I have been using Tableau Public for 6 years: we thought we’d go back into the vault and improve on some of our earlier work.
What have we learnt in all this time and how would apply the learning?
Here’s the original, and below that, some notes on my makeover.
The most important not to do is scoff at your early work, or anyone’s work that looks like this. Why? When I built this chart, and added it to the comments in the original Guardian article, it felt amazing. I felt empowered and excited at the ability to take some data and make my own sense of it.
I decided this week to go for maximum simplicity. For all the detail, the story across all criteria was the same: injuries are going down.
Given that that’s the case, a line chart with some filters seemed like an effective approach.
I pared back as much formatting as possible. In this week’s makeover I was trying to produce something you’d see in a print newspaper; something with a very simple, easy to interpret message.
The declining line reminded me of a roller coaster. I considered putting a roller coaster icon on one of the downslopes. However, this is one of those cases where you need to remember that this is real data about real people, some of whom lost their lives.
But, oh dear, this week’s chart is committing many crimes against dataviz. I wonder sometimes what the motivation behind a chart like this is. Do the editors just expect people to glance at it and think, “Oh, a chart. This story must be backed by data. Therefore it must be valid”? That must be the reason because this chart collapses under serious scrutiny.
If you’re going to make a donut at least use data that is part-to-whole. Unfortunately, this one isn’t. First of all, one of the segments is the overall average. The others are the average wage for each division. Therefore, they’ve drawn a donut with five segments when there are only four members of the dimension.
The colour scheme they chose is blue. Division is ordinal. But they didn’t make the sequence relate to order of the levels.
Did they get anything right in this chart? A few things:
The table below the chart is fine. I’d rather they’d just used the table
I quite like the title and subtitle: it tells me what I’m seeing
What did I do for my remake? I created a dumbell chart, with one line for each level. I really wanted to make a line chart, with one line for each season. But connecting the ordinal data with lines isn’t something we should do, so I abandoned this approach.
My prime goal in the remake was to focus the eye on the absurdly high wages of Premier League Players. To do this I dropped the title to below the mark for the premier league. I hoped to show that Premier League wages had “gone off the scale.” What do you think? Did that work?
It was an interesting journey to this chart this week, too. Initially this seems to lend itself to a slope chart: it’s a comparison of one dimension over two time periods. However, the slopes didn’t appeal to me aesthetically.
The default scale slope (on the left) makes all the lines appear as if they start from the same point. I tried a log scale which fixes it, but I think log scales are misleading for many viewers, who either don’t understand what they show, or don’t notice that it’s a log scale.
This week’s source chart was a donut chart. Donuts are rarely a good idea. At least the labels are clear and it’s sorted, but really, why make me move my eyes all over the place just to identify labels and colours? The choice to sort it in descending order, except for the Other category is visually confusing, even though I understand why that decision might have been taken.
Here’s my alternative:
A sorted bar chart provides the information much more easily and quickly. You only need to make one scan downards to discover all the information you need.
I used logos not text headers because it easier to identify with the brand.
Did I struggle with anything in this makeover? My main design challenge in a straightforward bar chart is where to put the labels. You’ve probably notice I tend to label the bars rather than rely on the axis labels. I think it’s better to see the values on the bars rather than have to look up and down to the axis and back in order to decode the value.
But should they be left, right, or centre-aligned? I’m always torn. Which do you think is best?
Two more offerings this week.
Finally, I did want to try a stacked bar. Sorted bars break the visual “part-to-whole” relationship a little, which has been debated at length recently. So as an attempt to draw something which keeps the part-to-whole, here is a stacked bar version. I kinda like it. What do you think?
I have a confession. The source for this week’s makeover left me confused. I tried to understand it but couldn’t. This is an example of an infographic put together to generate clickbait with almost no thought to meaning. Read on to see why, but stick around for the real shocker at the end.
Let’s look at the original.
What’s a “gamer”? I play the occasional game on my iPad and cell-phone. Does that make me a gamer? Or is a gamer someone who is more hardcore and dedicated? I have no idea: I am not told what the definition is.
So 73% of adults use a desktop or laptop. To play games? How often? And is the 53% of adults using a game console the same as the headline 53%? I suspect it maybe, in which case the headline 53% is totally arbitrary.
Let’s move on.
Oh dear. This is terrible, for at least 3 reasons:
Radial bars, even when well implemented, make it hard to compare categories. But this one goes beyond bad by starting each bar at an apparently random position. Also, the lack of alignment doesn’t help. Is this meant to be a visual metaphor to a gaming trope? If so, I don’t know what it is.
I don’t trust the data. Apparently parents use all forms of gaming device more than non-parents. As a parent of two, I simple don’t believe that.
Look again at the infographic’s title: “Adults vs Teens”. If this is about adults and teens why are we even comparing Parents and Non-Parents? What does that have to do with Teens?
Being left with a distrust of this data, I only spent a little while on this week’s makeover. Without an understanding of what the percentages actually represent, I found it impossible to come up with a coherent new approach.
I also turned to the source for this data: Pew Research’s Gaming and Gamers report. That’s the source for this information.
And here’s the real clanger: the numbers used in this infographic are are entirely different to Pew’s. Only 10% of Americans consider themselves gamers:
This video is very informative, and trashes pretty much every number Forbes used:
I initially decided to get join forces with Andy Kriebel on MakeoverMonday because I wanted to refresh my dataviz skills. What I didn’t expect was how quickly the project would evolve.
It’s no longer about just making charts. It’s about using a tool to debate data. It’s about improving people’s data literacy.
We must improve society’s data literacy if we are to debate and understand the sometimes deliberately deceptive charts produced by media, business and political organisations.
Each week, everyone involved brings a new perspective to the data. As the day progresses, people challenge and discuss what each version of the chart reveals.
People getting involved later in the day adapt their entries according to the debate that’s already gone ahead. This one from week 5 was an excellent example. Luke made an intentional choice to do something deceptive, even though it falls in the realms of “good practice”, by changing the measures.
They callout the key departments they are telling the story about (departments which shot only black people last year)
Things I didn’t like:
The bar chart has vertical labels – I don’t like turning my head!
They split the number of deaths and the rate of deaths into 2 separate charts.
The warning symbol for departments that killed only black people didn’t appeal to me as a clean visual indicator.
For my makeover, I wanted to stay true to their intention: a clean list, showing just the numbers. There’s power in these numbers which don’t need elaboration. I turned the rate of killings into a bar, as that’s simply the best way to show magnitude of a measure.
I added the number of deaths as a sized circle at the left of the bar. I toyed with just adding the number, but it was hard to identify what that number meant. I also put the labels inside the circle. I wanted to imply the magnitude of what these big numbers mean: people killed.
I took the same colour scheme as Mapping Police Violence for my own chart.
In the original, the commentary to the chart is placed on the right hand side of the chart. Since I chose to use a sorted bar chart, I have lots of white space on the middle and lower right of the chart. I added the commentary there in order to make good use of this blank space.
In 2016, I’m joining Andy Kriebel on Makeover Mondays. Each Monday, we’ll take a viz, or some data, and create a new perspective. You can join in by checking Andy’s blog. Click here to find the latest data.
I have to admit I struggled with this. What’s the most important thing to show? Is it just salary? Or value? Or surplus value? In the end, I felt that all 3 need to be shown, but finding a good way to show it was difficult. I’m not even sure I succeeded. I wanted the salary bar to stand out, and tried all manner or Gantts/dual axes/stacked bar combinations. Only the one above came close to emphasising total value and surplus value.
What I like about my makeover:
Salary is labelled
Total value is labelled
Surplus value is implied but not labelled
Annotated mark to help comprehension
Sorted bar charts allow you to put the title in the white space, freeing up space for the marks themselves
I enjoyed combining Gantts and Bars on dual axes to make the marks this way
Custom sorting with parameters on the interactive version
What I don’t like:
I’m just not sure how understandable it is!
Formatting is pretty plain. I ran out of time!
I wonder if a simple bar of surplus value would have been enough?