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Introduction to Visualisation
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Visualisation is valuable
There are common elements that link data to visual properties
By mapping data attributes to visual attributes clearer visualisation is possible
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Designing effective visualisations
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Don’t use rainbow colour scales!
Colour scales must not confuse the data or add artefacts
Visual components that do not aid understanding should be removed
Overplotting reduces the clarity of communication through visualisation
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Creating Graphics with ggplot2
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Use ggplot2 to create plots.
Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.
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Preparing plots for display
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Make sure your plots have useful titles, labels and legends
Use theme() to modify how your plot looks
The cowplot package lets you create a multi-plot figure
Use ggsave() to save a plot to a file
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Producing Reports With knitr
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Mix reporting written in R Markdown with software written in R.
Specify chunk options to control formatting.
Use knitr to convert these documents into PDF and other formats.
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