Heat maps are not just used for alternate representations of geographical maps. They can be used to map out sectors of a hard drive, or performance of various companies or industries in the stock market. In this post I’ll show a simple heat map showing relative values within a 2D worksheet grid, a crosstab of days of the week and times of day.

In Columbus Web Analytics Wednesday — Feedback Analysis, Tim Wilson of the Gilligan on Data blog surveyed attendees at a Web Analytics meeting to find out what times are good for holding future meetings. It was a simple enough survey, and there were 21 respondents. Tim broke out the results in a bubble-style chart, which I reproduce below. Obviously the respondents were not limited to one choice.

From Tim’s chart, it is evident that Happy Hour and Dinner time slots work best, and Wednesday is the best night of the week, with Monday, Tuesday, and Thursday all at nearly the same distant second place.

A well known problem with bubble charts is that we don’t perceive areas proportionally to the values they encode. Tim even said:

Compare the size of a “1″ to the size of the “14″ — does it look to you like the larger circle is fourteen times as big as the smaller one? It doesn’t to me.

It doesn’t to me, either. We seem to underestimate the differences when areas are used to encode values. It’s the opposite when diameters are used. I think we overestimate the differences to an even greater degree.

I didn’t even label the smallest data points, because the labels would have obliterated the circles.

The purpose of this chart is to get a quick idea of the survey responses, so the bubble approach isn’t really inappropriate (even the bubble chart based on diameter). Its point is readily understood, and since there is a clear winner, that winner stands out. But there are alternatives, and I decided to see how a heat map would work.

Heat Maps

Heat maps are pretty easy to generate in Excel 2007 using a worksheet range and conditional formatting. Start with a simple worksheet range.

The Conditional Formatting command is accessed from the Home tab on the Excel 2007 ribbon. Hovering the cursor over the button shows the fancy Super Tooltip below the ribbon.

Clicking on the Conditional Formatting button shows a series of built-in formatting styles plus some style management commands at the bottom. Color Scales has the built-in color gradients. For this data, I selected the first one, which goes from red at the low end, through yellow in the midrange, to green at the high end.

The result is much as expected, but I thought the red and yellow were both a bit overstated. In fact, it would seem better to use a single color that ranges from a low saturation at the low value to a high saturation at the high value. A one-color display is simpler to understand, and does not discriminate against those with color-deficient vision.

I went to Manage Rules on the Conditional Formatting dropdown, which opened the Rules manager.

I selected the Graded Color Scale rule, which I just applied, and clicked Edit Rule.

I switched to a two-color scale, kept the green shade at the high end, but selected a very lightly saturated shade of the same hue for the minimum. (I could have chosen white, but the faintest shade of green makes it obvious that the rule is being applied to the entire range.

The result isn’t too bad. Like the bubble chart, it’s reasonably self-explanatory. With little conscious thought, it is apparent light for low values and darker for higher values. The winning value stands out clearly. Another nice feature of this graphic is that is is very compact.

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For these examples, let’s use this simple dataset. Each value in the grid is the product of the corresponding row and column headers.

Excel 2003 Contour and Surface Charts

Select the data and insert a contour chart, and you will get something like this. I have removed the horizontal and vertical axis features (actually, the “category” and “series” axes), and selected a simple color scheme for the data bands. To format the bands, you format the legend key for that band, the small square in front of the label.

Scale

That scale is a bit course, and symmetrical features like the saddle point and the shapes of the colored bands in the corners are not symmetrically rendered. You can’t do much about the asymmetry; I used to insert rows and columns in the source data, and interpolate values, to double the resolution. This didn’t cure the strange rendering, but at least improved it a bit.

To change the scale of the bands, format the legend. The Format Legend dialog has a Scale tab just like in a typical Format Axis dialog. You can’t see the “value” axis, but you can select it in the Chart toolbar’s Chart Element dropdown, and format its scale directly.

Here is the contour chart with a finer scale and more shades of blue and red.

Below is the corresponding surface chart. The value axis is now evident, and the saddle-like shape of the surface is perhaps easier to envision than in the contour plot. The downside is that part of the surface, the far dark blue corner, drops far enough that it is obscured by the saddle point in the middle of the chart.

Orientation

You can adjust the orientation of the surface chart using the 3-D View dialog, available by right-clicking on the chart.

In Excel 2003, you can also click on a corner of the box enclosing the chart…

… and drag the corner to reorient the chart. While dragging, just the outline of the box is visible, showing how the orientation is changing.

Excel 2007 Contour and Surface Charts

To make a contour chart in Excel 2007, like 2003, you select the data, and insert the chart.

Scale

You can’t format the legend to adjust the scale of the colored bands, but you can select the value axis using the dropdown on the Chart Tools > Layout or Format tab.

To change the colors of each band, select the legend entry (the legend keys in 2007 cannot be selected) and open the formatting dialog. The tabs for fill and border color refer to the formats of the legend key and the corresponding band.

Here is the contour plot with the same scale as the 2003 example above.

And the corresponding surface chart. Asymmetric rendering of the saddle point and other features has not changed since Excel 2003.

Orientation

The Format Chart Area dialog in Excel 2007 includes a 3-D Rotation pane to allow adjustment of the surface chart’s orientation. Like many 2007 dialogs, it is much larger than the corresponding 2003 dialog without much difference in actual function.

You can also select the corners in the Excel 2007 surface chart, but you cannot drag them to adjust the orientation of the chart.

Formatting

Like much other formatting in Excel 2007, especially in Excel 2007 charts, the shading of the contour and surface charts is rather over-the-top. The charts have excessive color gradients, apparently to represent the reflections and the brighter and darker illumination caused by a light source located at some oblique angle.

The gradients are not too severe in a surface chart, but in a contour chart, they interfere with interpretation of the values in the bands. You’d think since the contour chart is “flat” it would not be affected by an offset light source. But the contour plot is really just a top view of the surface chart, so all shading appears in both charts.

The variations in color would not be half bad if there were a way to tone down the differences. I searched for a while, though, and could find no way to reduce the variation within a color band. Nor could I find any kind of texture or other feature that improved the appearance of the charts.

It is possible to remove the light and dark regions in the 2007 charts, rendering them with flat colors as in their 2003 counterparts. You have to dig pretty deep to find the trick, so I am pleased to share it with you here.

In the Format Band dialog, click on 3-D Format, then click on Material, and under Special Effect, select the Flat option.

This removes all of the textures within the band. Repeat for every band in the contour plot…

… or surface chart.

While the default texture with all of the shading and gradients was excessive, it provided a bit of detail missing in the flat-colored charts. In 2003, this detail was provided by borders on the bands. Add a border using the Format Bands dialog.

The border seems to help the surface chart more than the contour plot.

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A Pareto Chart is a horizontal or vertical bar chart with its data sorted in descending order. The largest items (categories) in the chart are listed first for emphasis. Often a line is overlaid on the bar chart, showing cumulative sums or percentages of the total.

The Pareto Principle, named for Italian economist Vilfredo Pareto, is based on the observation that most of the effects of an action come from a small amount of the causes. This is often called the 80-20 rule, implying that 80% of the failures come from 20% of the types of defect, or that 80% of one’s sales come from 20% of one’s customers, or pretty much any 80-20 metaphor you can come up with.

The simplest Pareto chart is a plain column chart.

The percentages of each value are used as data labels. In this case, obviously the first three bars are substantially taller than any of the rest.The last four bars aren’t even as tall as the axis line is thick, so the bottom five categories can be combined into a category called “Other”.

To show the cumulative proportion of items (in this case, apps you can buy for your iPhone), a line can be added to show the percentage on a secondary axis. In Excel this is a simple column-line combination chart on two axes.

This shows that we didn’t reach 80% of the effect until about 50% of the categories were accounted for. It’s still disproportionate, but not as severely as the “80-20″ nickname would indicate.

An alternative approach plots the accumulated value line on the same scale as the individual bars, with data labels showing the cumulative percentages. In Excel it’s even simpler: a column-line combination chart on one axis.

The heights of all bars is reduced substantially, but lowering the resolution also stresses that none of the bars is a huge amount of the total.

Vertical bars seem to be more common in Pareto charts, but there are problems with this orientation. The category labels have to be listed vertically, making them hard to read. Data labels may overlap; the labels were placed in a distracting staggered layout to make them legible.

To remedy these legibility problems, the chart can be rotated. Categories and percentage labels in the following chart are perfectly legible.

There is no difference in our ability to interpret the data in this chart, compared to the first vertically oriented chart above.

As before, a line can be added to show the percentage on a secondary axis. In Excel this requires a bit of a trick, using a bar-XY combination chart on two axes. For the XY series, X is the percentage and Y indicates the order of the categories.

Again, the readability is the same for horizontally and vertically aligned charts.

Anything done vertically can also be done horizontally.

I prefer the horizontal bar charts for improved readability of the labels. In most cases I also prefer the one-axis version, where the line shows the accumulated totals on the same scale as the individual values.

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Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

Question

In the chart below, rank the following in decreasing order of value:

• Red wedge
• Orange wedge
• Pink wedge
• Sum of four smaller wedges

The real answer may surprise you (or maybe not).

. . .

Don’t peek until you’ve ranked the data points!

. . .

No cheating!

. . .

Answer

Whichever way you have ranked the options, if you tried to eyeball their sizes, you were wrong. Maybe the slight 3D tilt was enough to distort the truth.

Here is the answer key.

It looked to my bleary eye like the sum of the small slices was largest, followed by the orange slice close behind, then the red, and finally the pink. Or maybe the orange is slightly larger than the sum of the four small ones. Either way is wrong. Here are the actual values, in order:

• 301 – Red wedge
• 284 – Orange wedge
• 276 – Sum of four smaller wedges
• 229 – Pink wedge

Interactivity

Another interesting “feature” of this display is its interactivity. When you mouse over a pie segment, it moves away from the other pieces, a leader line is drawn from the pie slice to its legend entry, and a box pops up with some information about the data point.

It’s pretty awkward how the rightmost data point has to reach all the way across the other pie slices to connect to its legend key.

Alternative Chart

In the bar chart below, rank the following in decreasing order of value:

• Supermarkets
• Medical Services
• Services
• Sum of four smaller bars

You can display this data more clearly in less space if you use a bar chart. The only difficulty is in judging the relative sizes of the three larger bars and the sum of the four smaller bars, but the numbers are there if you need to do some mental math.

You could make this chart interactive by popping up the same info box as is shown in the pie chart, and it would feel just as cool. If you wanted to, you could even highlighted the targeted bar with a darker shade and make its category label bold.

The colorful interactive pie chart above was provided by Discover Card’s Spend Analyzer Demo.

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An extreme example? Judging from what I see around the web, this is not at all unusual. In fact, 12 points isn’t all that many for a lot of pie charts. They could use a readability improvement.

Chandoo’s technique is to convert the chart to a Pie-of-Pie type, which lets you move some slices to a second pie, leaving a bland “Other” wedge in their place. This can be a deceptive chart. Not only don’t two wrongs make a right, the secondary pie is 75% of the diameter and 56% of the area of the main pie, while having an actual value of only 5% of the main pie.

Chandoo cleans up the chart by moving most of the slices to the secondary pie, then shrinking and hiding the secondary pie by formatting its borders and fills to None.

True, this chart has improved readability. But it has left out a lot of data simply because (a) it didn’t fit nicely into the chosen chart type, and (b) the data made the chosen chart type too cluttered.

For those of us with data loss aversion, the above chart is even worse than a pie chart.

At the risk of becoming a caricature of myself, I’m going to answer Chandoo’s question “Can I use an alternative to a pie chart?” My answer, if you couldn’t guess, or couldn’t at least see below this paragraph, is a bar chart.

This chart takes up about the same space as either pie chart. Readability has been improved compared to both of Chandoo’s pie charts. The text in the labels is large enough for my eyes, which are twice as old as Chandoo’s, to see clearly. Also, there’s no risk of the color scheme troubling a colorblind viewer.

The bar chart improves on the original pie chart by clearly showing all of the data. No points are hidden by their small size, and the labels are next to the data points, not hanging by a thread.

The bar chart is better than the “improved” pie chart because it keeps all of the data in view, not bundled into a mysterious “Other” category.

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Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

This was intriguing, so I downloaded the original data (pdf), which after cleaning up and pivoting looks like this:

The data is the result of a telephone poll conducted last month, and shows the favorable and unfavorable ratings of four potential candidates for the 2012 Republican presidential nomination. The Fox report showed only the top three candidates in their pie chart, reproduced below. The intrigue of this chart rests in the sum of 193%, which makes it 1.93 times more effective than your typical pie chart, which adds up to only 100%.

Candidate 4 was not far below, so in fairness, he should be included. This pie chart is even better, since the segments add to 251%.

Obviously the results for these candidates are not exclusive: people are free to favor multiple candidates, so the sums are not expected to add to 100%. Not only is the pie chart a poor data visualization choice in general, even with a data set that shows parts of a whole, in this specific case it’s not even relevant to the data.

So, class, what’s the proper chart type for this kind of data?

Right, a bar chart. This simple shart shows the relative approval ratings of the candidates very clearly. For a typical Fox news audience, this is about as much data as you can afford to show in a half hour of news. As we’ve seen, it’s too much data for a Fox News broadcast team.

For completeness, let’s include poor Newt.

These charts so far only show the results among Republican survey respondents. We get a broader view if we include the responses from Democrats. The data labels were removed in favor of the vertical axis to reduce complexity.

We can even display results from independent respondents without sacrificing any clarity.

Let’s add a weighted average of all responses, using a set of horizontal lines. Using another bar for the averages would be confusing. Note that none of the candidates has an approval greater than 50%.

This chart is too much for a television audience, but it would probably not be too confusing for most readers of national “news” magazines.

The published survey results also included ratings for Oprah Winfrey, who is definitely not a candidate for nomination for any position in the Republican party. I suspect her inclusion is an indication that many people were included in the ratings, but only the top four Republicans were included, and some overworked intern has now been fired because Oprah wasn’t deleted.

The unfavorable and other ratings could also be incorporated into a set of charts, but this is more than enough for the purpose of the newscast.

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Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

In Moving a Pie Slice, Patricia shows how to explode one pie segment out of the pie chart. I’ll review the protocol, not because I want all you Chart-Busters-in-Training to start making these charts, but just for completeness. Then I’ll present an alternative way to show the data. Here’s Patricia’s chart, which she claims describes sales of different pies in the days leading up to Thanksgiving, but which I think indicates the relative risk of me exploding from eating too much of each type of pie.

For our non-US friends, I’ll simply say that Thanksgiving is a celebration of abundance, and often celebrants overindulge themselves.

Explode a Pie

I used Patricia’s numbers and created a 3D pie chart in Excel 2003.

The tilt on that sucker is ridiculous, so I backed it off some. It’s still 3D, so there’s some distortion (note how much larger the 16% Blueberry s than the 18% Pecan slice). If you right click on a 3D chart, there’s an item called 3D View which opens a dialog that lets you adjust the vantage point.

That’s a bit better.

To explode just one individual slice of the pie, click once on the slice (not the label) to select the pie, then click on it once more to select just the slice. Now click and drag the segment away from the center.

If desired you can now select another individual slice of pie and drag it away from the center of the pie.

To explode all slices by the same amount, just click once on any slice, and drag outward.

Let’s step back for a moment and let this chart remind us why we don’t like 3D Pie Charts. See the brown slice? It’s a lot bigger than the blue slice, isn’t it? But the values are very close, 18% vs. 16%. The chart exaggerates this difference.

You can also explode one or more slices in a 2D pie chart. What you lose in 3D effect, you make up for in easier to compare slices. (Comparisons are still likely to be inaccurate because there’s no common baseline for each data point, but the brown and blue slices look closer in size.)

And here is the 2D version of a pie chart with one piece dragged away.

Alternate to an Exploded Pie

In my role as Chart Buster, it is my duty to show an alternate chart style which makes comparison of the data easier. We’ll use a classic bar chart. Data points (bars) in a bar chart are easier to compare, because they share a common baseline: the left edge of the chart. We can also dispense with the legend, because the category labels are located immediately adjacent to the bars. No need to look back and forth between the legend and the data.

The first thing to do with this chart is to sort its data range, in decreasing order so the largest bar is at the top.

Note the comparison between the brown and blue bars. You can tell that Pecan is slightly larger than Blueberry. The difference is greater than if you’d guessed using the 2D pie chart, but much less than if you’d guessed using the 3D version. Yet here you know the score without having to guess.

How can we highlight the Apple data point? We’re using the same color scheme as in the pie chart, so we can’t just use a different color. Well, we could use a black bar for Apple, of course, but that’s kind of ugly. Instead, I decided to keep red for the Apple bar, and unhighlight the other bars. I did this by keeping the same color, but formatting the fill using a 50% pattern, with white as the second color. (On my good monitor it looks great, but on the monitor that’s slowly dying, the patterned bars give me some interesting moiré effects.)

If you’re using Excel 2007 and you can’t find the fill pattern dialog, don’t fret. Microsoft left them out of the Excel 2007 user interface, but kept them in the VBA object model, so patterns can still be applied. If you don’t feel like programming patterns for all the bars, my colleague Andy Pope has written a Fill Patterns Add-In for Excel 2007. It’s free, so download and install it.

All of this futzing with colors is unnecessary. One benefit of bar charts over pie charts is that we need not rely on colors or patterns to help distinguish one data point from another. I’ve decided to pick a light orange-brown for my bars, and a darker brown for the highlighted data point.

It doesn’t match the pie contents as aesthetically as the pie chart’s color fills, but the light and dark hues will survive photocopying in black and white, and we don’t have to worry about bad video settings or misinterpretation by colorblind viewers.

Yet another benefit of a bar chart is that you can make it rather small without sacrificing detail.

You can construct an information-dense dashboard with arrays of small charts like this.

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The upper two series in the stack are almost constant from year to year, while the bottom series is increasing steadily. This chart may not tell a story as clearly as it could.

Reordered Stacked Charts

To show that the orange and brown series are almost constant and the yellow series accounts for most of the variation in the total, you could rearrange the order of the stacked series.

In many cases, this is all you need to do. This particular data set was uniquely suited for this arrangement of stacked series, and rearranging the series told a nice little story. As Jan said in a follow-up comment, and I’m paraphrasing, when you’re making a presentation for the boss, each chart is a custom experience, and you do what seems to make sense.

But what if there’s some kind of relationship between the three series? Suppose in the original chart they were stacked in A-B-C order. In our rearranged chart, the order is now B-C-A. This may be awkward, but we can reorder the series again into C-B-A order, which is less disruptive.

What if the original series showed first quarter data on the bottom, then second quarter data, then third quarter data? It hardly makes sense to plot second quarter on third and first on second.

And what if you didn’t have two essentially constant series? Rearranging the order of the stacked series may not help.

Line Chart

We can change the chart to a line chart, and add a total series. This shows as well as the reordered stacked charts that the yellow accounts for most of the total growth, and it does not give any meaning to the order of series, which a stacked chart may imply.

An added benefit is that we can compare all data points. It may not always be clear in the stacked charts whether the brown series has greater values than the orange series. In the line chart there’s no mystery: the marker placement makes it obvious, even without the data labels.

Stacked Line Charts

You could show the same data in a stacked line chart (unsorted and sorted).

- – -

It shows the data fine, and if your three series are first, second, and third quarter, the lines plotted in that order show 1Q YTD, 2Q YTD, and 3Q YTD.

But stacked line charts are difficult to interpret and may be deceiving. A frequent problem in the online help forums is:

My line chart doesn’t plot correctly. The first series has greater values than the second, but the second is plotted higher. It’s almost like the second series plots the sum of the first and second series.

That’s right, because the user clicked on the second line chart icon, not the first. And not just this user. I suspect that half the time the stacked line chart type is used, it is used  unintentionally.

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In Category Axis Tricks for Line and Area Charts – 1 I extended this technique to show how to format parts of a line chart in distinct colors:

A reader asked how to show two years of data on two separate lines and show a trendline for each.

Line Chart Approach

Let’s make up two year’s worth of data:

Here’s the data in a two-series timeline chart:

To add a trendline to a series, right click on it and select Add Trendline. Here’s the chart with a trendline for each series.

These trendlines are too long, each extending across the entire date range. The trendline dialog allows us to extend a trendline beyond the data in the forward or backward direction, but it doesn’t allow us to shorten a trendline (the forward and backward text boxes only accept positive values or zeros).

Well, we can always roll our own trendlines. Here is an updated data range:

The slopes and intercepts are calculated as follows:

B27: =SLOPE(B2:B13,A2:A13)

B28: =INTERCEPT(B2:B13,A2:A13)

C27: =SLOPE(C14:C25,A14:A25)

C28: =INTERCEPT(C14:C25,A14:A25)

Column D has trendline data for 2008 with this formula in D2 and filled down to D13:

=$B$28+$B$27*A2

Column E has trendline data for 2009 with this formula in E14 and filled down to E25:

=$C$28+$C$27*A14

The data in these two columns is added to the chart, and a little formatting gives us two distinct timelines with distinct trendlines:

That’s not too bad, but is there any way to use only two series and the native chart trendline feature?

XY Chart Approach

Why not just make an XY chart? Looks the same as the line chart with the homemade trendlines.

But wait, on closer inspection, the date axis looks wrong. The line chart has a date label for every second month, but the XY chart’s axis shows August and October 2008 instead of September and November 2008.

Line charts allow us to adjust the scale to put a label at the first of each month, regardless of the number of days in the previous month. In the line chart, we can set our min and max as 1-Jan-2008 and 1-Jan-2010, and the major and minor tick spacing as 2 months and 1 month (and don’t forget the base unit should be days).

In the XY chart, we can set the same min and max, 1-Jan-2008 and 1-Jan-2010, but we have to use the inconvenient values of 60.917 and 30.4585 days as our major and minor tick spacing (731 days divided by 12 major and 24 minor divisions). This causes some ticks to occur before the start of a month (31-Aug-2008 and 31-Oct-2008 in the chart below), and some after the first of the month (02-Mar-2009, 02-May-2009, and 02-Jul-2009).

The line chart is needed if we want “nice” date scale axis labeling.

XY-Line Combination Approach

Let’s start with a line chart using only the 2008 data, to give us our preferred date axis format.

Copy the 2009 data (select A14:A25, hold CTRL and select C14:C25, A1, and C1, then press CTRL+C), select the chart, and use Paste Special to add the data as a new series. Excel adds a line chart series, using the same X values (dates) as the first series, even though we copied and pasted the X values as well as the Y values.

We can convert the new series to an XY type (right click the series, choose Chart Type, and pick the style we want).

If Excel moves the XY series to the secondary axis as in the chart above, move it back to the primary axis.

Now we can add a trendline to each series, and it extends only as far as the series itself.

Summary

As usual, Excel provides multiple ways to accomplish our goal. We could not do this directly, but we have our choice of at least two viable workarounds: Line chart with manually calculated trendlines, and Line-XY combination chart with built-in trendlines. What’s more tedious, calculating your own trendlines, or creating a combination chart?

Peltier Technical Services, Inc., Copyright © 2010.
 
Licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
 

This simple approach uses line charts, and the up-down bars and high-low lines that can be added to line charts. I’ve described these embellishments in Stock Charts and Other Line Chart Tricks. When two or more line chart series are present in a chart, the up-down bars draw vertical bars from the first line series to the last, and the high-low lines are drawn behind the up-down bars (if present) from the lowest to highest line series value. The formatting of up bars (the last series is greater than the first) is distinct from that of down bars (the last series is less than the first).

A stock chart is simply a line chart that uses these embellishments. When data is entered in Open-High-Low-Close order, the up-down bars and high-low lines are used to create candlestick-style stock charts in Excel. Excel 2003 also accepts data in Open-Low-High-Close order, while Excel 2007 insists upon OHLC. Excel 2003 allows the price data to be arranged in columns (as such data is usually provided) or in rows. Excel 2007 insists on price data in columns only. If you start with a line chart and add these graphic features, the arrangement of data is ignored.

Excel 2003 and Earlier

We’ll start with this set of calculations, based on three samples of a larger population that has mean of 25 and standard deviation of 10. The First and Third Quartiles are listed first and last so the up-down bars represent the inner quartiles of the box plot. The Min and Max will be used by the high-low lines, and the intermediate Mean and Median have no effect on the added features.

Here is the line chart produced from the data. (You could instead start with a candlestick stock chart using First Quartile, Min, Max, and Third Quartile data, add the Median and Mean data, and rearrange the series, but I think the way I’ve done it is easier and more straightforward.)

In Excel 2003, the Format Series dialog with any of the lines selected offer Up-Down Bars and High-Low Lines on the Options tab.

Hide all of the series lines, and all markers except for Median and Mean, and we have a serviceable box plot. Here the “Dash” marker is used for median.

If desired, the Min and Max series can also be formatted with a dash marker to provide end caps for the whiskers (high-low lines).

The dash marker is a horizontal line, with a minimum thickness of 2 pixels. The markers above are 14 points wide and still 2 pixels high. At 15 points, the markers expand to 3 pixels. At the size required to match the width of the up-down bars, the dashes are ridiculously thick (and even the two pixel thickness above is too thick).

Particularly ridiculous when used for the end caps too.

Horizontal median lines are relatively easy to obtain. Change the chart type of the median series from Line to XY, and if necessary format it to appear on the primary axes with the line series. Remove its marker, and apply positive and negative horizontal error bars with a value of 0.2.

The default gap width for the up-down bars is 150% of a bar width, the bar is 100% of a bar width, and the gap plus the bar are 250%, so half the bar is 50%/250% or 0.2.

You can add duplicate Max and Min series, convert them to XY series, and format them with no marker and error bars of ± 0.2, to get matching end caps for the whiskers (high-low lines).

Excel 2007 (and Later?)

The protocol in Excel 2007 is similar. Create the line chart.

Add up-down bars and high-low lines using the Chart Tools > Layout tab of the ribbon. Don’t panic if the Mean and Median markers disappear. In Excel 2003 markers always appear in front of the up-down bars, and lines behind them, but in 2007 if the series has lines, then the markers stay behind the bars with the lines.

Remove the series lines, and the markers appear.

Change the markers, and use the dash for Median. The dash gets wider in 2007 than in 2003, so it looks silly sooner.

As in 2003, the median series can be changed to an XY type, moved to the primary axis, and given horizontal error bars. Adding error bars in Excel 2007 is a trick, though. You have to add them using the Chart Tools > layout ribbon tab, and this adds both vertical and horizontal error bars, with the Format Vertical Error Bars dialog showing. Clear the dialog, select the vertical error bars, and press Delete. Then select the horizontal error bars, which have a default length of 1, press Ctrl+1 to open the Format Horizontal Error Bars dialog, change the value to 0.2, and remove the end caps.

Likewise, you can add duplicate Min and Max series with error bars, to provide end caps to the whiskers (high-low lines).

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