http://i.imgur.com/zO7CuQG.png
In a chart like this no data is lost in presentation. You can easily answer questions like "when did Android overtake iOS in marketshare?" and "is Windows Phone marketshare growing or shrinking?"
Off the top of my head I can't think of an example where a time axis wouldn't normally be used to visualize variation over time...? Perhaps quarter earnings as contributing to yearly earnings?
[1] http://www.fusioncharts.com/chart-primers/marimekko-chart/?M...
A solution, when the designer needs or wants to reflect total volume of that whole, is to underlay the lines (in this case) with an area plot representing the total volume over the same time interval. In this way, the two pieces of information (individual performance and total size) are encoded in distinct and easily understandable ways.
4 small groups of students (pairs or triples) repeated enough times to cover whole class. Each group gets one of these three charts or the table. Plenary at end: the ones who got the % bar chart often have totally different sentences from the other three groups.
I tend to give the % bar chart to the more confident students.
Personally, show me the numbers. Wins every time.
If they aim is to communicate a true representation of reality then you need to take that into account.
A lot of these new 3d type charts just come off looking like a flat 2d chart with layers and end up confusing the heck out of me.
Stacked charts are not very good at presenting information where there are more than two entities. Overlapping line charts do a much better job of presenting trends over time.
http://muyueh.com/see/nuk/efficiency.html (Better turn down the sound)
Using the button group in the left, you can zoom to different scale.
One could still depict totals as separate lines on a stacked chart, but for subset sums, a stacked area chart helps show how the sum is composed.
[1] https://en.wikipedia.org/wiki/Color_blindness#Design_implica...
Stacked bar charts are better, but only a little better.
Line charts overlaid on top of each other are most clear, to me.
Like this: http://i.imgur.com/kyvpiax.png
Usually, I'm looking for rate of change of one item at a time, not all of them at the same time. After that, I'll compare them.
Stacked bar charts, IMO, seem skewed in who gained the most over a period, but not the individual gains. I have no idea what green really ended up with by the end of it because it just kept getting pushed towards the top. I have to constantly go to the left and compare ranges with the previous stack.
It would of cource help you, if the chart lines have different line patterns (dottet lines).
I forgot to link to this picture on Wikipedia: http://en.wikipedia.org/wiki/Color_blind#mediaviewer/File:Sa...
With stacks it's much easier to track along based on contrast alone.
A tiny bit ad hominem, but I take it you are not a statistician. The first thing we do is to graphically illustrate the data, because human brains are so good at recognising patterns.
It is important to experiment with several variations of display, also including and excluding outliers and different series, to see if there are biases that mislead you.
Only then do we come up with a mathematical model to fit.
Actually reading the data line by line can be extremely misleading - because you can only compare a few numbers at a time, which fails to give you an insight into the overall variation.
I have seen good charts. Yes, they exist, and I actually see them quite often. It just so happens that the vast majority of charts and graphical illustrations I see are omitting details at best and dangerously misleading at worst.
Reading Tufte carefully, one realizes that (a) he's read more Derrida than he's willing to admit (there are entire unattributed quotations, probably by accident) and (b) great charting is an art form, perilously predicated on someone being both quantitatively educated and visually gifted.
There is no silver bullet but the table, and even then, Simpson's paradox is always ready to bite you.
How do you feel about waterfall charts? (presumably with the table attached)
How do you
But charts are way easier to grasp than numbers. If you want to see the difference between the pace of adoption between two systems, it's much more telling to see it graphically than through numbers.
http://i.imgur.com/jfuJGu5.gif
Here is my solution:
* It retains the line aspect of it, which is essential as we are talking about a trend here.
* It easily allows you to see it both in stacked and overlapped lines. Each one of them is good at communicating clearly a certain point about the data.
* It also mitigates the problem in nostromo's comment, where a whole bunch of lines could overlap without a clear view on a single point of interest.
Those are both issues you could address with small multiples, a Tufte method.
Just use a normal line chart.
The solution described is incorrect. Area charts and column charts are used to display different types of information. Area charts (and line charts, similarly) are used to display continuous data - data that must pass through point B to get from point A to point C. Column charts are used for non-continuous data.
Some examples:
You should use an area chart (or just a line chart) when tracking your weight. If you weigh yourself on Tuesday and you weigh 150 lbs, and then weigh yourself on Thursday and you weigh 155 lbs, because of how weight works, you can assume that between Tuesday and Thursday your weight traveled through all the points required to get from 150 lbs to 155 lbs.
If you're tracking the amount of hours you sleep every night, you should use a column chart. Just because you get 6 hours of sleep on Tuesday and 8 hours of sleep on Thursday doesn't mean you got 7 hours of sleep on Wednesday. The data isn't continuous.
For market share, that data is continuous - you can't get from 10% market share to 15% market share without passing through all the percentages in-between. Therefore, a column chart is very much the wrong way to display that information.
Data continuity is a secondary concern; there's no reason not to have a stepped area chart, which is the union of a set of adjacent bars representing discrete measurements. And sleep hours per day do accumulate nicely over time, so an area-emphasizing plot does make sense (sleep hours per day * days = sleep hours). It lets you look at two parts of the chart and assess 'did I get more sleep in this period or that period'.
I'd like to think that in this age there's no need to force a static, unexplorable view of the data to the user at all.
The problem with representing percentage breakdown over time this way is that it visually eliminates the size of the sample, be it size of market, number of users, page visits, etc. It is visually implying that the same size has stayed the same over time.
Take these two charts representing the breakdown of smartphone shipments by manufacturer: http://s831.us/1kmFK28 and http://s831.us/1kmFrEz
The first displays the percentage of market by manufacturer over time. In this chart, Apple's performance looks mediocre.
Then look at the second graph that displays the number of units shipped by manufacturer. Here the amazing growth of the smartphone market is visually captured along with the breakdown of which manufacturers are driving or benefiting from the growth.
My ascii art skills are failing me, and this is going to be hard without visual aids, but I'll try...
Overall, there was a significant and stable jump from best case to worse case (not worst case). What was interesting was that a chunk of time the size of that delta always fell in a single region but it was not always the same region but always roughly the same amount of time from the start. Since the process is small scale and kicked off at varying times, this means it was something asynchronous but triggered by our activity (or activity we were responding to).
After studying data visualization, it's surprising how most popular dashboard widgets/visualizations are relatively bad at encoding data versus simple things like sorted tables.
The perfect use case for stacked area charts (but not pinned to 100%) is when there are many categories, but one is very predominant, and you're interested in (a) the sum of all quantities and (b) the participation of the principal category.
Example: a country is mainly reliant on hydropower for domestic electricity; there are years where it doesn't need any other source (it's still cheapest....). So we want to track (1) the evolution of domestic consumption and (2) the share of hydropower (3) which years it hasn't been enough.
