(de)Faults in Excel Charting

I recently spoke at SQLBitsIII, and an aspect which went down well was a simple overview on how to make the most important aspect of a graph, namely the underlying data, the prime focus and clear and easy to read. I also had the opportunity to attend Stephen Few’s Information Visualisation Workshops in London, which I’d thoroughly recommend. Stephen also spent some time, as part of a much more detailed overall agenda, on how a typical default chart can be morphed into an effective display.

So it’s back to basics this week, and how to improve the standard, out of the box Excel chart. Unfortunately, despite it’s pervasiveness, the default chart settings, which many users will never stray from, are in the case of Office 2007 not ideal, and in earlier versions, pretty awful. In this piece I’ll outline a few simple steps which can turn the default visual delights of the Excel graph into something you need not be embarrassed to put on the projector.

I’m using ‘classic’ Excel as my start point, because it’s still the incumbent in most organisations (and also because it’s worse). The example is for column charts, but the majority of the tips are valid for any chart type. As our start point we have the unit sales data for 3 products across 6 countries, as a default Excel Column chart, below.

BadChart

 

 

 

 

 

Nice. It’s wrong in a lot of ways, but how many hundred times have you seen this or a version of this? It’s well trodden ground if you have read Tufte, Few at al, but the key recommendations to improve things are surprisingly simple, and quick to implement.

1) Remove the Clutter and noise

The purpose of the chart is to display the data of interest clearly and concisely. It’s not to distract the user with pretty shading or 3D effects etc. Although the default chart is no-frills, there are a number of items which are adding nothing, or have undue prominence, and in doing so detract from the overall goal.

  • The Plot Area
    • The grey background to the plot area adds nothing, so we remove it
    • The border on the plot area – remove it also (numerous studies have shown we only need two axes to effectively group and visualise data)
  • Gridlines
    • The default gridlines are black, too visually intense. They are there for reference when required, not the prime focus, so are best muted – set them to a light grey.

2) Axes and Legend

The axes frame the chart, and are a key point of reference; however they should not draw the focus from the chart itself. As with the gridlines, they should be toned down.

  • Change the default black font colour to charcoal / dark grey
  • Change the default axis colour from black to charcoal / dark grey
  • Typically reduce the font size to 8

Rules for the legend are similar to those for the axis

  • Change font from black to grey
  • Remove border or change it’s colour to very light grey
  • Typically reduce font size to 8
  • For a clustered column, my preference is for the Legend positioned at the bottom, and reading across in the display order of the columns.

3) Columns and colour

The black column borders add nothing, and as such should be removed, they are another form of Tufte’s ‘non-data ink’.

On to colour, and unfortunately Excel’s default chart fills are heavily saturated plum and wine with a light cream..  So I’d strongly suggest changing the chart colour palette. For column charts, there is typically a reasonable block of colour for each series, so the colour scheme shouldn’t be too bold, or it becomes an eyesore. You should aim for mid-intensity colours of similar saturation (unless one is intended to stand out), pastels tend to work well.

 

All the steps above are simple and fairly fast to action, with one exception, the colour scheme. Unless you already have pre-prepared palettes it’s possible to spend an age trying to get the ideal combination – remember the 80/20 rule!

GoodChart2

 

 

 

 

 

 

In my example above, which hopefully you’ll agree is an improvement, I’ve used the colour palette from our upcoming ‘Chart Tamer’ product. Chart Tamer is a lot more than just a colour palette, but that aspect has benefited from minds with much more expertise in colour than mine, and I’ll go with their choice over mine every time!

Household Income Distribution 1967 – 2005 As Small Multiples Chart

In my last post I tied to fix an overloaded line chart Jorge presented in a recent post about loss aversion:

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Jorge asked "does it make any sense to add those nine series to a single chart?
My attempt to fix the chart by using some color coding, has its shortcomings that caused quite some discussion.

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So again, how can you give the users all the data they expect while keeping the chart clean and readable?

D Kelly O’Day pointed out "More data or better colors won’t help a poor chart type selection" and presented a dot plot

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Lets try to select the right chart type. In Chart Rules, As Simple as Possible, But Not Any Simpler! I presented an easy to learn set of rules to determine the best chart type .


1. Determine the relationship you want to display

In our case a we have a Distribution Relationship, we want to show the Distribution of the Income Levels


2. Determine if you want to emphasize individual values or the overall pattern and

emphasize individual values or the overall pattern  and Determine the chart type

As we want to emphasize individual values a column chart works best.

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This chart already gives us a good feel for the income distribution in 1967- Looks like a almost perfect bell distribution with a belly for the mid income levels. But how did things change from 1967 to 2005? Lets create a set of small multiples to show the situation in 1967, 2005 and the increase from 1967 to 2005.

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Using Color to Group and Label in Charts

Jorge wrote in a recent post about loss aversion, the fact that “people strongly prefer avoiding losses than acquiring gains”.

Loss aversion […]: Translated to chart-making, it means that there is a “tendency to avoid losing data at any cost”. The chart below shows you the Money Income Of Households as published.

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Take the above chart, for instance: does it make any sense to add those nine series to a single chart?

Remove irrelevant data series and you risk a mutiny on the Bounty, even if relevant trends are easier to detect. It is absurd, but very human.

So, how can you give the users all the data they expect while keeping the chart clean and readable?

Continue reading Using Color to Group and Label in Charts

Scott MacCloud Pesents Google Chrome

Scott MacCloud comic writer and expert in visual communication created a set of wonderful, information dense comic pages for Google’s new web browser Google Chrome. He presents on 38 pages the technical concepts behind Google Chrome so that even a layman can understand them.

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Regarding Google Chrome I found an interesting article over at the Cooper Journal, where Tim wrote that Google Chrome exists, contrary to the believe of the computer press, for one reason: To provide a framework for web-based applications to look, feel, and act like desktop applications. I can’t wait to see the end of the area of crippled web interfaces.

I really can recommend you to add Scott MacCloud’s excellent book Understanding Comics to you data visualization book shelf. A very inspiring book that explains the inner workings of comics and visual communication in general, and most of the insights apply for the design of visual interfaces, usability and data visualization:

Continue reading Scott MacCloud Pesents Google Chrome