In this blog post, I'm going to introduce Chapter 5. Its title is "Practical Applications of Spotfire Visualizations". It's a guide to using various visualization types in Spotfire, with explanations and examples of when to use each of the visualization types and some common pitfalls to avoid. I particularly enjoyed writing this chapter because it gave me the opportunity to work with different visualizations in Spotfire and source examples from all sorts of datasets. It was great fun to deliberately produce some misleading/invalid bar charts to illustrate how NOT to use them. It was also very enjoyable to produce examples of the bar chart and scatter plot visualizations. It reminded me once again, just how powerful and flexible these chart types are.
The introduction to the chapter is:
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Some real-world examples of some common Spotfire visualization types
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What to use each visualization for
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The pros and cons of the visualization types
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Some configuration hints and tips
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Common pitfalls and things to avoid
The chapter then goes on to discuss the different Spotfire visualizations, explaining the pros and cons of each of them in a concise, easy to follow manner. For example, here's the introduction to bar charts:
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Good for visualizing:Any type of data that is split into categories. Examples of categories include the following:
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Product category
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Sales region
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Car make and model
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Don't use for:Generally, visualizing continuous data on the x-axis is not recommended (as you will see in the following example), unless you are interested in the general shape or trend of the data.
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Pros:Really easy to construct, configure, and interpret.
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Cons:If you have lots and lots of categories, there simply isn't enough space on the categorical axis to show all the labels, so you will need to use techniques such as zoom sliders and hierarchical axis selectors. SeeChapter 8, The World is Your Visualization, for more information on constructing hierarchies from axis selectors.
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Summary:Bar charts are the go-to visualizations in Spotfire. Think bar chart first!
So - what are these things to watch out for? Perhaps my favourite is being careful with summing averages! In my opinion, the Avg aggregation is generally far more applicable than the Sum average. In fact, Spotfire X introduced a preference for setting the default aggregation method to use. You can find it under Tools | Options | Visualization:
However, if you do set Avg as the aggregation for the values axis of a bar chart, then using stacked bars leads to an invalid visualization. Here's how the book explains it:
The chapter continues with exploring the bar chart visualization in more detail - showing how to work with continuous and categorical x axes and how an integer represented as a categorical variable may lead to non-contiguous axis scales (with a suggestion as to how to avoid this).
Highlighting the bar-chart examples is just a taster for the rest of the chapter! It goes on to discuss scatter plots, cross tables, box plots, line charts and more. The example of the box plot visualization is particularly interesting to me, as it uses statistics to identify outliers and spot trends in global infant mortality data.
The TIBCO Spotfire: A Comprehensive Primer - Second Edition, by Andrew Berridge is a great starting place for further exploration of topics covered in the book. Another related and very useful page on the community is: training - this is a launchpad for all sorts of great stuff!
This is the first part of a series of blog articles on the book - watch this space for more excerpts and related concepts. I'll be picking various topics and discussing them - providing introductions to those topics in some cases; expanding on the content in the book in others.
Pick up a copy of the book from Amazon.
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