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The good, the bad, and the ugly.

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This is a recitation for
the visualization week.

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With great power comes
great responsibility.

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There are many ways to
visualize the same data.

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You have just seen how to make
quite attractive visualizations

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with ggplot2, which has
good default settings,

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but judgment is still
required from the user.

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For example, do I decide
to vary the size of a point

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or do I vary the
color of a point?

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It is worth noting at
this point that Excel

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and other similar
programs can also

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be used to make perfectly
acceptable visualizations,

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or terrible ones.

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The tool can help but it's
ultimately up to the user

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it to make decisions.

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So what is the difference
between a good visualization

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and a bad visualization then?

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I would argue that
a good visualization

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clearly and accurately conveys
the key messages in the data.

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A bad visualization will
obfuscate the data either

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through ignorance or malice.

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So what does this mean?

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Visualizations can
be used by an analyst

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for their own consumption to
gain insights into the data.

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Visualizations can also be
used to provide information

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to a decision maker and/or to
convince someone of something.

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Now, a bad
visualization can hide

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patterns that could give insight
or mislead decision makers.

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This is where the
malice part comes in.

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So today, we will look at a
few examples of visualizations

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taken from a variety of sources.

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We'll discuss what is good
and what is bad about them.

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Then we will switch into R to
build better versions of them

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for ourselves.

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But I want you to think for
yourself in this presentation.

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You might not agree with
all the points I make

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or my opinions about
these visualizations.

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Visualization is
inherently subjective

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and the right visualization
will depend on the situation.

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So use your own
judgment and think

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about what I talked about
before with a good visualization

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and a bad visualization.