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.
I had fun with this one, deciding to do a connected scatterplot. There is much debate about the efficacy of connected scatterplots. I agree they require an extra mental leap to comprehend, but once the meaning is grasped, I think they are powerful.
As Hans Rosling proved, they are especially effective when animated, hence the GIF. Following the animation is like following a story, the up/down and left/right movements adding drama and anticipation for the viewer. A static version of the chart is below.
The original this week, on first glance, wasn’t too bad. There’s nothing inherently wrong with a dual axis line chart. However, once you try and work out which axis is which and which colour is which, you realise the creator of this didn’t think through the positioning of the labels!
A simple makeover would have been to fix the labelling and make things much clearer using colour to help the reader. Here’s what that would look like:
I’m happy to concede even that the simpler approach may be better than my connected scatterplot version!
Boy, the Americans need to get saving. That’s what I discovered this week. The source chart was a stacked bar. This is an interesting choice by Andy: the chart wasn’t a total disaster, but there’s certainly room to play around with it.
Also, stacked bars have been much debated recently. Stephen Few has argued that, if a time series is involved, you should always use lines to represent the category instead of bars. Cole Nussbaumer and others have made the case that while the lines allow for clearer comparisons, they don’t show “part-to-whole” relationships as well as stacked bars. You should go and read the fascinating debate on Stephanie’s and Steve’s blogs.
Here’s this week’s original chart:
What do I like about the original?
It asks a clear question
You can look up every single value, even if it takes a while
What could be improved?
Double-labelling the category is overloading. I chose to just use the category name (Baby Boomers) and not show the ages. I acknowledge that means that if you don’t know the ages of those categories you’re stuck. However, the message is still clear, I believe
The colour scheme is arbitrary
Moving your eyes between the legend and back again is difficult.
So how did I go about my makeover?
I first tried a diverging stacked bar, a method developed by Steve Wexler. He places the “negative” categories to the left of the axis and the “positive” categories to the right. This allows you to see more easily the overall positive/negative leanings.
However, my diverging stacks didn’t thrill me, so I thought I’d try a unit chart instead. I like the way they simply represent percentages. I was always struck by the brilliant simplicity of the ONS’ “How well do you know your area project” and wanted to do something similar.
I confess I had to construct a separate dataset to make the unit, because in Tableau every dot needs a separate record (although I suspect Joe Mako could make it work with just two rows)
Once I had the data, I played with shapes and settled on blank/filled circles to show the threshold between adequare and inadequate savings. I chose a colour scheme which still showed the different inadequate saving levels (e.g. “no savings account” and “under $1.000”), but all within a red palette. I also added a parameter to allow you to set the level of “enough savings”. You can play with that by opening the interactive version.
I also created a horizontal and vertical version. The horizontal one works well in tweets, the vertical one is better in a blog. Font choice this week was inspired by Kelly Martin’s amazing Font Choice blog post.
This week’s source chart is a shocker. There’s very little right with this chart. The title is good, because it asks a question. After that it pretty much falls apart. The actual percentage value is very hard to see. The donut doesn’t help. The percent change circle is totally confusing. They also used a negative scale to size the circles which requires mental hurdles to overcome.
My makeover is at the top.
Comparing growth and decline between two time periods immediately cried out “Slope Chart”. It could have been a side-by-side bar chart, but I am not a fan of those. A dumbbell chart might have worked too, but showing the direction of time is a challenge. I tried Comet Charts to show this, unsuccessfully, a few years ago.
I really wanted a pure slope to work, with all lines within one pane.
However, this didn’t work for me. Too many of the lines were too close. I had to do callout labels for Television. The four lines along the bottom are all a little too close to each other for my liking.
Instead I resorted to a pane for each item. Here’s the version without shapes:
It’s still easy to compare the slopes of each line but not quite as easy. The cognitive load is a little higher this way. As you can see at the top, I chose to add icons. I did this to make it easier to identify each line without the requirement to move your eyes to the top of each pane. Icons are more appealing than labels. However, using icons can be a challenge: what’s a universally identifiable icon for “smart thermostat”? How do you tell the difference between a Smartwatch and Fitness Monitor icon?
We chose this week to stick to greyscale. Remaking this chart was straightforward. Because there are multiple panes, I don’t need to use colour to identify each product.
I added the annotations to help users read the slopes, if they’re not familiar with them. Being in greyscale this week was a good reason to soften the text to a very light grey. This helps the annotation disappear when you want to focus on the marks. If the labels were as black as the lines, it all gets too much:
In fact, I constructed the whole of this post about the chart above. As I wrote the final comment “but I didn’t actually think the original was too bad” I thought, maybe I’d just go back and build the original one in Tableau. Soon I came up with this:
I prefer it to my original makeover. So this week’s submission. is really nothing more than a format change. Why do I like the reformatted one over the redesigned version?
There’s lots of detail in the histogram behind the obvious annotation and colour messaging
The shading highlights the Dreaded-Twos distances. The point of the article the chart is from was about the dreaded-twos, but the original chart didn’t call out those distances well enough.
Regarding the first makeover (the one at the top of this post), here’s the notes I made:
Let me confess: I’m not too happy with the outcome of this one. However, here’s what I was trying to acheive in this week’s makeover:
The original story focussed on the fact that Stephen Curry shoots from close or far, not from “the dreaded twos” distance of 7-21ft.
The original chart didn’t group his shots into the three categories, which was what I felt was wrong. If the article’s about those three distances, then why not emphasise that aspect and group things together.
I also wanted to emphasise the points value. It’s not just about shooting from 18 feet or 22 feet, for example. The decision is also about points. The original chart doesn’t show the points value.
I tried to incorporate a basketball arc into the diagram to visually show where each group sits.
I probably messed around with colour more than anything else. I settled on the purple for the dreaded-twos and found that incorporating that colour into the title and subtitle helps decode the chart.
I did consider pie charts, as they do show part-to-whole very well but with this data, they didn’t come out well enough.
The original chart on Business Insider wasn’t fundamentally flawed this week, it only needed a few small tweaks to make it better. [hence the final version at the top – Andy]
With more time, I would have more fully incorporated the metaphor of the baseball pitch graphic.
What does this prove about makeovers?
Don’t reinvent the wheel if the wheel wasn’t too bad in the first place.
Iteration is vital. Iteration is always vital. Sometimes you need to go off on a tangent to realise you were in a good place to begin with.
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?