1 Het begint met een idee

Data Visualization in R

Giuseppe Procaccianti

Vrije Universiteit Amsterdam

2 Giuseppe Procaccianti / S2 group / The Green Lab

Data Visualization in R

● Goals for this lab session:

○ learn the basics of the ggplot2 package

■ understand the basic concepts behind ggplot2

■ perform a simple tutorial

Vrije Universiteit Amsterdam

3 Giuseppe Procaccianti / S2 group / The Green Lab

A ggplot2 graph

Vrije Universiteit Amsterdam

4 Giuseppe Procaccianti / S2 group / The Green Lab

A ggplot2 graph

Vrije Universiteit Amsterdam

5 Giuseppe Procaccianti / S2 group / The Green Lab

A ggplot2 graph

● The ggplot2 package is based upon the graphics grammar (gg)

concept [1]

● Complex statistical graphs can be constructed by composing

elementary elements or changing their properties

[1] Wilkinson, Leland. The grammar of graphics. Springer Science & Business Media, 2006.

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6 Giuseppe Procaccianti / S2 group / The Green Lab

Example: a pie chart

pie <- ggplot(mtcars, aes(x = factor(1), fill = factor(cyl))) +

geom_bar(width = 1, position = "fill", color = "black")

Vrije Universiteit Amsterdam

7 Giuseppe Procaccianti / S2 group / The Green Lab

Example: a pie chart

pie + coord_polar(theta = "y")

Vrije Universiteit Amsterdam

8 Giuseppe Procaccianti / S2 group / The Green Lab

The qplot() function

● The qplot() function allows to create quick graphs

● Simple 1-line syntax, but not very powerful - similar to plot()

qplot(CPUusr, Watts, data=run)

Vrije Universiteit Amsterdam

9 Giuseppe Procaccianti / S2 group / The Green Lab

The ggplot() function

● The ggplot() function is the main function of ggplot

● It allows to create graphs by adding subsequent layers

● Three main layers: aesthetics, geometrics, statistics

Vrije Universiteit Amsterdam

10 Giuseppe Procaccianti / S2 group / The Green Lab

The ggplot() function

● The aesthetics layer contains the data you want to encode as

graphical properties

● The geometrics layer contains the actual graphical elements

a_graph <- ggplot(data = run,

aes(x = Watts, y = CPUusr))

a_graph <- a_graph + geom_point()

● The statistics layer contains statistics computed from the data

a_graph <- a_graph + stat_smooth()

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11 Giuseppe Procaccianti / S2 group / The Green Lab

Example: the violin plot

● From a boxplot….

a_graph <- ggplot(data = run, aes(x = Watts, y = CPUusr))

+ geom_boxplot()

+

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12 Giuseppe Procaccianti / S2 group / The Green Lab

Example: the violin plot

● ...to a violin plot

a_graph <- ggplot(data = run, aes(x = Watts, y = CPUusr))

+ geom_violin()

+

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13 Giuseppe Procaccianti / S2 group / The Green Lab

Why violin plots are great

● More informative (full data distribution is shown)

● You can draw boxplots on them!

Vrije Universiteit Amsterdam

14 Giuseppe Procaccianti / S2 group / The Green Lab

Tutorial: qplot() + ggplot()

● Perform a basic exercise using both qplot() and ggplot()...

● ...using real experimental data (similar to yours)

Vrije Universiteit Amsterdam

15 Giuseppe Procaccianti / S2 group / The Green Lab

Further links and references

● Official ggplot2 website

● qplot() tutorial - Edwin Chen

● ggplot2() tutorial - Josef Fruehwald

● Wilkinson, Leland. The grammar of graphics. Springer Science & Business Media, 2006. (Amazon)

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16 Giuseppe Procaccianti / S2 group / The Green Lab

Thank you!

g.procaccianti@vu.nl

i.malavolta@vu.nl