Workshop 4 Data visualisation 2 Learning Objectives By following - - PowerPoint PPT Presentation

Workshop 4

Data visualisation

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Learning Objectives

By following the slides and applying the techniques to select examples from the workbook the successful student will be able to:

• explain what is important in choosing a figure
• determine which variables are best mapped to which elements of a figure
• explain the fundamentals of ggplot and recognise it as a 'tidy' package
• create, with ggplot, appropriate figures to accompany lm and glm analyses of up to

three explanatory variables which show the data, the main statistical model and the results of any post-hoc testing as appropriate.

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Key ideas in data visualisation

• communicate information clearly, efficiently, honestly

(without distortion)

• Should help the reader understand the data, the analysis, and

the results

• Make it as easy as possible to make relevant comparisons
• Minimise ink
• See Edward Tufte

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Key ideas in data visualisation

• Variables to elements

– Response variable almost always on the vertical axis – Explanatory variables: horizontal axis, colour, shape, size,

• facets. Consider the variable type
• Ideally plot all the data and the model
• Or model and additional summary of data

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Data visualisation to enjoy

David McCandless http://www.informationisbeautiful.net/ Hans Rosling’s Gapminder

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ggplot2

• ‘Tidy’ datasets
• are easy to manipulate, model and visualize
• have a specific structure: each variable is a column, each
• bservation is a row, and each type of observational unit is a

table

Wickham, H. (2014), “Tidy Data,” Journal of Statistical Software, 59, available at http://www.jstatsoft.org/article/view/v059i10 “tidy datasets are all alike but every messy dataset is messy in its own way”

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Tidy data

Each variable is in a named column Each row is an

• bservation

Easy to explore, plot, model, report. Easy way to think about data. Several powerful packages exist.

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Keys to understanding ggplot

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ggplot() ggplot(data =clover) ggplot(clover, aes(x = yarrow.s, y = clov.y))

Empty plot

The data

'data.frame':30 obs. of 3 variables: $ cycle : Factor w/ 3 levels "A","B","C": $ clov.y : num 14 50.7 11.4 23.1 32.2 18.5 $ yarrow.s: int 220 20 510 40 120 300 60 10

The aesthetic maps variables to axes ….but it doesn’t know what to plot

Keys to understanding ggplot

9 ggplot(clover, aes(x = yarrow.s, y = clov.y)) + geom_point()

geoms say what what the data should be plotted as

ggplot(clover, aes(x = yarrow.s, y = clov.y)) + geom_bar(stat = "identity") ggplot(clover, aes(x = yarrow.s, y = clov.y)) + geom_line()

...but will plot what you tell it, sensible or not

Keys to understanding ggplot

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• You can have as many geoms as you want
• geoms use the aes() previously defined, or

you can add

• geoms have a default ‘stat’ often count or

identity

ggplot

• Axes: xlim(), xlab()
• Annotations
• Themes
• Code layout
• google

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ggplot

All the figures you’ve ever done The cookbook for R ggplot2 cheatsheet The R Graph Gallery Googling and more googling

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Summary

• Making the data, the analysis and the result easier to

understand is the most important thing

• The response nearly always goes on the vertical; explanatory

variables are mapped to the horizontal axis, colours, shapes, sizes, facets

• ggplot is awesome

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