Are they?

Let me tell you about a little experiment. I call it the scatterplot pie just for fun, and the idea is to display proportions using a scatterplot.

A traditional pie chart with two data points can be reduced to an angle:

The same message, no fat. And because there are no textures and arcs to deal with we can now superimpose many pies.

These are percentages of the age group 65+ in 1996 (left) and 2050 (right) for 220 countries (data from the US Census Bureau). This comparison clearly shows that the World is getting older.

One of the problems with pie charts is that you can compare proportions but you can’t compare wholes. In the images above we are comparing very different country sizes (Tuvalu and China?). With the scatterplot pie we can add this dimension:

China and India are not helping resolution, but it would happen to any other chart. We can focus on a detail:

Other things we could do:

• Group series (in this example, color-coding by continent would show us the significant differences between Europe and Africa);
• Add axis and circular grid lines to improve readability;
• Set line transparency to 50%;
• Remove the vertical line or make it look like a grid line;
• Label the more significant data points.

I actually like this idea and I’ll test it a bit further. I’ll try to decide if it is a good alternative to stacked bar charts. It should also work with three or more slices, but just because it works it doesn’t mean we should use it (like most chart options in Excel…).

So, what do you think? Would you use it?

This is not entirely mine: I was inspired by this comment. And I’m sure someone must have though about this first. If it rings a bell please let me know…)

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Post from: Excel Charts Blog.

The best of two worlds: the scatterplot pie

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The original post is titled The best of two worlds: the scatterplot pie , and it came from The Excel Charts Blog .

Enjoy and comment, please…

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Post from: Excel Charts Blog.

The Unbearable Lightness of Pie Charts [Data Visualization for Excel Users]

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The original post is titled The Unbearable Lightness of Pie Charts [Data Visualization for Excel Users] , and it came from The Excel Charts Blog .

There are several objects in Excel you can make a map with. If you want to use shapes, you can follow my my tutorial. You can also use a scatter plot. The map above uses cells and conditional formatting.

So, here is how to do it (this example used population data):

1. Get a table from the US Census Bureau with latitude, longitude and population data by county.
2. In a new sheet, calculate longitude intervals and use them as column headers (I’m using a 0.25 interval: lower means more resolution but more columns);
3. Calculate latitude intervals and use them as row headers;
4. With the SUMPRODUCT() function calculate total population, total area and density for each latitude and longitude class;
5. Set column width to 5 or 6 pixels and do the same with row height;
6. Select the entire table and add conditional formatting as you like;

Since you can’t add visible text to such small cells, you can merge them and add titles, legend, source, etc. A better option is to enter them in a separate sheet and use the camera tool to add an image to the map.

When you select a cell a small macro identifies the county and displays the data in the table below. At this resolution level, a cell usually contains a single county, but in densely populated areas there may be more than one county in a cell, as shown in the table (I’m also using the camera tool to display the table below the map).

I actually prefer to use a scatter plot but this is fun, and you can always ask your mother to cross stitch the map into your pillow!

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Post from: Excel Charts Blog.

Making maps in Excel: cross-stitching edition

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The original post is titled Making maps in Excel: cross-stitching edition , and it came from The Excel Charts Blog .

Gregor published last Friday an interesting post and I wanted to play with the same data, percentage of population living in poverty (and use my Excel map…). Let me show you some of the variations you get when selecting different class limits.

Same number of states

You assign the same (or approximate) number of data points to each class:

Equal intervals

Divide the range by the number of classes (pro tip: don’t ever do it when you have on outlier at the top or at the bottom, like the District of Columbia for population density).

Positive / Negative

This one has a class limit exactly at the national average (four classes: two above and two below the average).

Emphasis

Here we define a large middle class to focus our attention on the extreme values:

Around national average

Now we are assuming that there aren’t much differences in the data points around the national average:

And here is the data displayed in a bar chart:

As you can see, for each definition of class limits you get a different message. Most people just use equal intervals, but that’s lazy, IMHO. Using equal intervals in a choropleth map is like sorting a bar chart alphabetically. The only thing that is worse than equal intervals is equal intervals plus round numbers.

You cannot assume that the data is uniformly distributed, so most of the time you shouldn’t use equal intervals.  My personal preference is to create homogeneous classes, using the data itself to define their number and their upper and lower limits. You can do it using complex statistical analysis, or you can do it just by looking at a bar chart or a scatter plot.

The data will tell you which is the best sorting key or class limit. So, like everything else in data visualization, let the data talk first.

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Post from: Excel Charts Blog.

The same data, the same map, different stories

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The original post is titled The same data, the same map, different stories , and it came from The Excel Charts Blog .

It’s commonly accepted that, when displaying a time series, you should use line charts to show trends, and column charts if you want the reader to compare individual data points. It seems that you can choose any one of them, but often you can’t.

In a data set like the one above, there is too much variation, and a line chart is useless. Make a column  chart instead, and try to explain the outliers.

So, use your common sense and don’t use a line chart when monitoring a new habit. Compare data points and if you can’t find a good explanatory variable just annotate the outliers and try to replicate the good ones.

If you want to give yourself a motivational  boost keep repeating “I want to use line charts, I want to use line charts”. It will help you reduce variation and soon you’ll achieve your goals! (Scatter plots are even more powerful motivators, but don’t rush.)

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Post from: Excel Charts Blog.

The health benefits of line charts

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The original post is titled The health benefits of line charts , and it came from The Excel Charts Blog .

Actually, you don’t.

You can oversample that group only, and then weight it down to its known proportion in the population. For example, you may want to increase the number of managers and decrease the number of housewives (because the former are usually more heterogeneous than the latter). Oversampling is a common research method, and a very cost-effective way to get precise estimates for a subgroup.

This is a real-world solution, and if we have finite resources to solve a real-world problem, resource allocation must be part of the equation. Higher variability usually demands for more resources.

Why is this relevant in a blog about charts and information visualization? Glad you ask.

The Great Irregular Interval Debate

Let me give you an example. A while back, Jon Peltier wrote in his blog:

I don’t understand the obsession with an equal date interval. A line chart need not show the trend of only evenly-spaced data. Suppose I am observing temperatures, and I decide for simplicity that where the temperature hasn’t changed, or where it has been changing steadily, I do not need to record every value. Overnight after the temperature has dropped, I can characterize my temperature profile with one point per hour. As the sun rises, I may need more frequent recordings to capture the morning warm up. Then the clouds blow over, it starts to rain, then it clears up again; I may need minute-by-minute data points to track this. When I make my plot, is it any less relevant because the spacing of the data ranges from minutes to hours?

This is oversampling, and a wise resource allocation, too. In a survey, you weight the subgroup down to its right proportion, and that’s also what you do in a chart, when irregular date intervals are displayed proportionally.

Stephen Few disagrees:

Using a line to connect values along unequal intervals of time or to connect intervals that are not adjacent in time is misleading.

Furthermore:

How could we trust graphical representations of time series or frequency distributions if their shapes could have been altered by inconsistently manipulating the sizes of intervals along the scale, either arbitrarily or intentionally to deceive? We can derive meaning from patterns and trends that these graphs display only if the intervals are consistent.

He exemplifies his argument with these two charts (actually, there are three, but we can safely disregard the third one).

The first chart displays the correct annual sales. The second one displays arbitrarily grouped annual sales and, obviously, its pattern is quite different.

Now, the second chart is plain wrong, so I am not sure if you can use it to argue against unequal intervals.

Let’s use a fairer example with the same dataset and the same arbitrary grouping.

Compare the orange line with Few’s first chart. I actually don’t see much difference. Sure you lose a lot of detail, but the basic pattern is there. Instead of sums, I am using averages (you can’t compare a single year with the total sales of three or four years).

The other two lines show the difference between equal and unequal intervals. The brown line displays the data points unequally spaced while the gray one uses equal intervals (Few’s second chart). I had to make some assumptions regarding the reference date, so this is not the best example, but it is good enough to show the potential risk of using equal intervals with unequal intervals of time.

Bottom line, oversampling is a useful method for better resource allocation. We can view irregular time series as some sort of oversampling, provided there are no missing values and irregular intervals in the chart are consistent with intervals in the time series.

Grouping data points is always a tricky issue, and Stephen Few show it clearly, but we shouldn’t infer that “line graphs and irregular intervals is an incompatible partnership.”

(When using time series in Excel, make sure that category axis labels are recognized as dates. Alternatively, use a scatter plot with connected data points.)

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Post from: Excel Charts Blog.

Irregular Time Series? No. Oversampling.

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The original post is titled Irregular Time Series? No. Oversampling. , and it came from The Excel Charts Blog .

Clear signs of a bad chart, right? Right. It is so easy to spot a badly designed chart that you can use a computer to do it. Don’t waste your time.

Let’s stop discussing the obviously wrong and start discussing the useless right. Like this chart here. (I’ve borrowed the dataset Nathan used in one of his visualization challenges – some interesting entries and great discussion there, by the way).

There may not be anything really, really wrong with this chart, but it reflects a bureaucratic way of thinking about data and data presentation where every single data point must be clearly shown and labeled. Just like a table.

Listen, unless you work for a statistics office, you should never create a chart like this. I know, it’s irresistible to check how well my state ranks, but identifying each and every data point in a virtually limitless bar chart makes no sense in most cases.

Do you read the labels between the top five and the bottom five? Charts like this encourage look up of individual data points, and for that a table is probably a better option. If anything, a skyscraper bar chart is a clear sign of loss aversion.

A Flexible Bar Chart: Introducing the Accordion Bar Graph

How do you graph a categorical variable with more than, say, 20 data points without creating a skyscraper? This is what I have in mind:

• You must retain the overall pattern, so you can’t remove data from the chart;
• Create one or more focus area (top five and bottom five, for example);
• Gaps between bars should be larger in these focus areas, so that labels can easily be added.
• Minimize the height of the remaining bars and remove the labels;

The chart should look like this:

I like the accordion metaphor and I’m playing with it. An interactive version could use a simple event to create a focus inside the context area, so when the user moves the mouse the bar is enlarged and the label is shown.

What do you think? Do you agree that skyscraper bar charts are (almost) useless or should we focus on reducing the number of data points instead? How would you improve this design? Please share your comments and charts below.

Update

Well, if you want to know how to do this in Excel and read a great discussion about it, Jon wrote Accordion Chart for Jorge. He not only discusses some of the options but also shares the Excel file with us. Thanks Jon! And Dick, over the Daily Dose of Excel wants to make sure that your state is automatically highlighted (Ego Charts). Nice “quarter step”!

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Post from: Excel Charts Blog.

Focus + Context (a Bar Chart Is Not a Skyscraper)

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The original post is titled Focus + Context (a Bar Chart Is Not a Skyscraper) , and it came from The Excel Charts Blog .

Actually, I am more in the mood to defy current “crowd wisdom” about pie chats.

Search the web for “pie chart” and you’ll get more than one million results, and a depressing picture of human knowledge. Browse the first 100 and what do you get? Some educational(?) sites (poor kids), tutorials (Excel, php, java, Illustrator), humor (here, here), bad (here, here, here, here or here) or just plain stupid examples. You’ll also find them in in court or fighting government (who could ever imagine that?). I’ll leave for another post what the Wikipedia and the pie chart thread in Tufte’s Ask E.T. say about pie charts (Stephen Few’s Save the Pies for Dessert is not listed within the first 100 results).

An old litany

Some of these sites discuss the use of pie graphs, but they usually recite the same old litany: our perception is bad at judging angles, you should use no more than five or six categories, don’t use them to compare series, Cleveland’s findings, etc. (there also is at least one unfair comparison between pie and bar graphs and one very aggressive rant against them).

If there is something that I would like to have written about pie graphs it is this Expert notes at ManyEyes:

Pie charts have a mixed reputation. They are popular in business and the media but many information designers have criticized the technique. Some claim that the pie slice shape communicates numbers less exactly than other possibilities such as line length. But this remains unclear in the context of proportions: for example, we have seen no studies that looked at the task of judging whether an item is more or less than 50%. It’s also unclear whether exact communication of numeric values is the only evaluation criterion; at least one study indicates that use of a pie chart for analyzing a problem as opposed to a bar chart changes the way people think about the problem.

This is clearly more constructive than saying that “they are as professional as a pair of assless chaps” (less funny though).

Not all charts are born equal

Current wisdom presumes that bar graphs and pie graphs are equivalent. For that reason, bar graphs should be used, always. After all, they are more efficient, right? But if they are not equivalent, as the above quote suggests? Take a time series, for example. If you want to see trends, you’ll choose a line graph; if you want to compare data points you’ll use a column graph. They are very similar, but by choosing one or the other, the designer is making a choice of how he/she’ll  look at the problem. Bar graphs and pie graphs are very different, so shouldn’t we think twice before selecting a bar graph because of its presumed superior efficiency?

This disdain for pie charts has its roots in Cleveland’s work and in Tufte’s and Few’s writings. Their positivist view towards information visualization may be as relevant as the classic economic theory and its presumption that consumer always take the rational decision, but are we not all predictably irrational? I agree with Robert at EagerEyes when he says:

There is no doubt that we need to be careful about the choice of visual representation, and that we need to encourage the use of good charts and criticize the bad ones. But that doesn’t mean we can get lazy and squeeze everything into a few standard charts types we’ve been using for decades. That is especially true if we want people to actually care about what we’re trying to show – and not bore them to tears.

We should probably try to be more rational and circumspect in a decision-making environment and do not use the media as our role model, otherwise business visualization may become useless. However, ruling pie charts out is not the wisest decision.

Simple rules are made for beginners. Let’s break some. How about this one:  “you should use no more than five or six categories in a pie chart”. Are you sure?

(Before that, we must re-read what Cleveland said and what others said about Cleveland. That’s the next post.)

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Post from: Excel Charts Blog.

Pie Charts – A Devil’s Advocate Point of View

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The original post is titled Pie Charts – A Devil’s Advocate Point of View , and it came from The Excel Charts Blog .

2th Place
(also nice, but too many slices, and I don’t like the title around the pie)

Lateral Pie-Thinking Award
(well, perhaps someone just messed up the template)

Designer’s Pie Charts Award
(data? what data?)

Seth Godin’s Pie Chart Award
(“makes an obvious point, no nuances“)

Consensus Pie Charts: The Venn Pie

Consensus Pie Charts: The Line Pie

Consensus Pie Charts: The Bar Pie

Flash Gordon Pie

We Try Harder Award

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Post from: Excel Charts Blog.

The Best Pie Charts Come From Germany

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The original post is titled The Best Pie Charts Come From Germany , and it came from The Excel Charts Blog .

One of the reasons why people like pie charts is because of its strong and familiar metaphor – it is part of our daily life.

Another good metaphor is the analog clock. You don’t need a legend to know the time. So, why don’t you use it to display hourly data?

Take a look at the radar chart on the left (the roman numerals – neat, hum?). It displays pageviews per hour by hour of the day. There are two series, daytime and nighttime. As you can see, the nighttime pageviews are much lower (I wonder why…).

If you want to compare daytime and nighttime data do everyone a favor: forget about day and night. Don’t assume that those 24 data points should be split in midnight to midday and midday to midnight. Or just because you raise early, the split should be 6:00 a.m. to 6:00 p.m. and 6:00 p.m. to 6 a.m. Look at the data and do what it tells you to do.  A good split creates two series that maximizes variability between them (and each series becomes more internally consistent). In this case, the split was at 8:00 a.m/p.m.

Yes, but what about the Curate’s egg? Glad you asked.

Chandoo, over PointyHairedDilbert, had “an interesting charting idea to show the data around the clock“:

Jon Peltier doesn’t really like the idea and suggests a much more conservative aproach:

Now, shake both charts (shaken, not stirred…) and what do you get? My radar chart, of course! And what a fine mix of both it is!

Ok, where was I? Ah, yes, my soft boiled egg…

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Post from: Excel Charts Blog.

Charting around the clock

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The original post is titled Charting around the clock , and it came from The Excel Charts Blog .