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Grammatical fix for Visualization intro paragraph (#174)

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Christian G. Warden 2016-07-24 07:03:35 -07:00 committed by Hadley Wickham
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@ -18,7 +18,7 @@ Once you have tidy data, a common first step is to __transform__ it. You may zer
Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualisation and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times.
__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you're asking the wrong question, or you need to collect different data. Visualisations can surprise you, don't scale particularly well, because they require a human to interpret them.
__Visualisation__ is a fundamentally human activity. A good visualisation will show you things that you did not expect, or raise new questions about the data. A good visualisation might also hint that you're asking the wrong question, or you need to collect different data. Visualisations can surprise you, but don't scale particularly well because they require a human to interpret them.
__Models__ are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them. Models are a fundamentally mathematical or computational tool, so they generally scale well. Even when they don't, it's usually cheaper to buy more computers than it is to buy more brains! But every model makes assumptions, and by its very nature a model can not question its own assumptions. That means a model cannot fundamentally surprise you.