I agree with the problem but disagree with the solution. I'd much prefer removing the stack completely and using overlapping plots, like this:<p><a href="http://i.imgur.com/zO7CuQG.png" rel="nofollow">http://i.imgur.com/zO7CuQG.png</a><p>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?"
I was going to leave this as a comment on his blog but apparently comments are closed now.<p>Stacked bar charts are better, but only a little better.<p>Line charts overlaid on top of each other are most clear, to me.<p>Like this: <a href="http://i.imgur.com/kyvpiax.png" rel="nofollow">http://i.imgur.com/kyvpiax.png</a>
While I agree to some of the points made in the post, I don't think the author provided a better alternative. Stacked bars are not any better for looking at how the percentage change on the green one.<p><a href="http://i.imgur.com/jfuJGu5.gif" rel="nofollow">http://i.imgur.com/jfuJGu5.gif</a><p>Here is my solution:<p>* It retains the line aspect of it, which is essential as we are talking about a trend here.<p>* 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.<p>* 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.
The stacked bar chart is no better. Yes, it doesn't have the same distortion of making green look smaller towards the right, but it is very hard to see the slow growth.<p>Just use a normal line chart.
Here's a recent paper about how to compensate for this very illusion, by Heike Hoffman and Marie Vendettuoli: <a href="http://users.soe.ucsc.edu/~pang/visweek/2013/infovis/papers/hofmann.pdf" rel="nofollow">http://users.soe.ucsc.edu/~pang/visweek/2013/infovis/papers/...</a>
Rabble rabble rabble.<p>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.<p>Some examples:<p>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.<p>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.<p>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.
One of the things I dislike about stacked area charts (or stacked bar charts) is that are in many cases are used to show percentage breakdown over time.<p>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.<p>Take these two charts representing the breakdown of smartphone shipments by manufacturer: <a href="http://s831.us/1kmFK28" rel="nofollow">http://s831.us/1kmFK28</a> and <a href="http://s831.us/1kmFrEz" rel="nofollow">http://s831.us/1kmFrEz</a><p>The first displays the percentage of market by manufacturer over time. In this chart, Apple's performance looks mediocre.<p>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.
There are things stacked charts can show you much more clearly than other things. For a particular process I'm working on, I have a timing breakdown rendered as an area chart (not normalized, ordered by total time) that showed something I'm not sure would have been visible any other way.<p>My ascii art skills are failing me, and this is going to be hard without visual aids, but I'll try...<p>Overall, there was a significant and stable jump from best case to worse case (not <i>worst</i> 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).
I've always felt like I had some deficiency when it came to viewing stacked area charts. I rarely liked them but couldn't put my finger on why (not that I really gave it much thought). This article's explanation of our eye's tendency to see thickness rather than the vertical distance is spot on. I find the stacked columns way easier to read, although if you had a lot of data points I agree with several of the other posters that a boring old line chart is going to be the easiest to read.
Gauges and 3d area charts are other examples of misleading visualizations that take up a lot of space without deriving much meaning. Why do we keep seeing them? They're flashy and make my dashboards look like the one my competitor is using! That's just the world we live in.<p>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.
we use them on internal dashboards and they can be extremely useful. One use case is keeping track of bad orders, we work on the dropship model so the top line is our total bad order percentage and each area is the bad orders from a certain supplier. When the goal is to lower the top line we focus all our efforts on the largest areas.
Hahaha. The solution described works if you have t=6. Try having t>>25.<p>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.<p>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.
I think this more illustrates a problem with trying to generalize solutions. The final stacked area chart with green at the bottom works perfectly for this data set. Even the author's proposed solution fails to emphasize the growth of the green section.
To be fair, its 'mis-use' that is the problem here. Like any tool, the right one should be selected for the job. There are people out there that generalize this same logic to "charts" without qualification (they prefer tables).
Not meta enough. When was the last time you designed the chart for a book or other static media?<p>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.
Talking about fancy visualisations, you should definitely have a look at horizon graphs<p><a href="http://square.github.io/cubism/" rel="nofollow">http://square.github.io/cubism/</a>
Making a stacked graph interactive can help like this one I prepared earlier:<p><a href="http://theopenindex.org/" rel="nofollow">http://theopenindex.org/</a>
You don't have to stack it like that:<p><a href="http://bl.ocks.org/mbostock/582915" rel="nofollow">http://bl.ocks.org/mbostock/582915</a>
Yeah, that's a fair point. These charts are misleading. Then again, if you generally trust charts for an easy interpretation of data, you're probably going to be misled anyway. Perhaps I express an unpopular opinion, but the only way to really get a handle on the data is to <i>read the data</i>. (I find it to be a rare occurrence that a chart exposes --- rather than oversimplifies --- relationships in the data. They're mostly okay for a cursory glance, but not for more. In this example, they're not even okay for a cursory glance.)