r course 2014: lecture 8

8/10/2019 R Course 2014: Lecture 8

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Lecture 8: graphing in Rand intro to ggplot

Ben FansonSimeon Lisovski


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Lecture Outline1) introduction to R graphics

2) introduction to ggplot

Helpful references

- http://www.cookbook-r.com/Graphs/

- ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham


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R graphicsPros

1) You can make almost any graph that you can think of

2) Graphics are publishable quality

3) Combined with the previous programming learned, you can 'every complex graphs to visualize your data and statistical mod

4) You can make lots of graphs easily [e.g. plot for each individua

Cons

1) it takes some effort to learn the language and quirks of the graapproach


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Overview of R maingraphics

R graphics

base plot[original R graphics]

- plot()

- hist()

- barplot()

- pairs()


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plot(...) image(...) barplot

persp(...) pairs(...)

and


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Some advantages of base plot

1) I find it the easiest to build very customized plot since youplots one element at a time


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#--- example code to build a plot by each element ---#

plot.new()

points(seq(0,1,0.1),seq(0,1,0.1), pch=1:10)

axis(1,at=c(0.2,0.7))axis(2,at=c(0.1,0.8))

mtext('xlab',1,line=2)

mtext('ylab',2,line=2)

box()

abline(0,1, col='red')mtitle('Title',lr='')


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Some advantages of base plot

1) I find it the easiest way to build very customized plot, sincbuild the plots one element at a time

2) being the original, it is the most integrated with packages


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base plot and methods

ds


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ds


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base plot and methodshow can plot() give you very different results?????????????

ds


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plot() is not a single function

How does plot() work?1) plot() looks at the class of the object(s) and then choose a

functione.g. plot( y ~ x )

plot asks what is class(y) and class(x) and since both are numericmakes a scatterplot


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plot() is not a single function

How does plot() work?2) plot() looks at the class of the object(s) and then choose

functione.g. plot( lm_mod )

plot asks what is class(lm_mod), and since it is a'lm' class, it runs function plot.lm()which makes

four graphs by default


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methods(plot)


O i f R i


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R graphics


- plot()

- hist()- barplot()

- pairs()

O i f R i


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R graphics

grid graphics[ alternative framework]


- plot()

- hist()- barplot()

- pairs()

O i f R i


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R graphics



lat

- plot()

- hist()- barplot()

- pairs()

- xyp

- bar

- wir

( k ll )


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xyplot(...)

Faceting (aka Trellising)


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barchart(...)

Lattice can also do most things are bas

wireframe(...)

Overview of R main


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R graphics



lat

- plot()

- hist()- barplot()

- pairs()

- xyp

- bar

- wir

Overview of R main


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R graphics



latggplot2

- plot()

- hist()- barplot()

- pairs()

- ggplot() + geom_line()

- ggplot() + geom_point()

- xyp

- bar

- wir


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http://mandymejia.wordpress.com/2013/11/13/10-reasons-to-switch-to-ggplot-7/


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ggplot(...) + geom_point(...) + facet_wrap(...)

ggmap(...) + geom_tiles()


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why I use ggplot?

1) I like the faceting and grouping...makes it easy to make qucomplex graphs for data exploration

2) I found it easier to add a new layer

3) I liked the grouping options and colour schemes in ggplot

4) You can make up your own 'theme' that you can use over again

5) Lots of active development in the area


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cons of ggplot...

1) I find working with grid graphics more difficult than base pmakes it harder to do some of those final touches on the g[Note- ggplot2 community is active, so can often find the aget help easy enough]

2) no 3d plotting

3) Customising axis labels for facetted graphs can be annoyin

4) cannot do double axesa) Hadley Wickham refuses to add this feature due to philosophica

b) though I have heard of a workaround for it


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Saving a graph

jpeg(filename, height=, width=, units=,res= )

jpeg('figures/test1.jpg', height=6, width=6, units='cm', plot(....)

dev.off()

pdf(filename, height=, width=)pdf('figures/test1.pdf', height=6, width=6)

plot(....)

dev.off()


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Raster vs. vector graphics

Raster images- method: based on a grid of dots (pixels). Each pixel is assig

colour.

- file formats: jpg, tiff, bitmap, psd

- use: best for photographs


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Vector images- method: based on mathematical equations to redraw the

- file formats: eps, ps, pdf, ai

- use:best for drawings, logos, graphics. Much easier to doprocessing revisions


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Adobe illustrator for post-process

Illustrator is great for minor little touches to the graphs or colmultiple graphs into a single page.

>


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Short introduction to ggplo


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geomsgeometric objects [think of as plot type]

e.g. scatterplot, line graph, histogram

ggplot jargon

geom_point()geom_line()

geom_bar


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aesaesthetics are the attributes associated with each geomobject

ggplot jargon


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aesthetics

x-value = 2.4

y-value = 0.4

shape = dot

colour = black

transparency = opaque

h i


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aesthetics

x-value = c(1.7,2.4,2.7...)

y-value = c(-0.5, 0.4,0.6...)

line type = solid

colour = black

transparency = opaque

l t j


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scalesattributes of the x-axis and y-axis [and any z-axis]

ggplot jargon

l


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scales

continuous

ranges from -1.5 to 2.1

ticks marks at every 0.5

scales


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scales

co

ra

tic

set.seed=100

ds


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scalesattributes of the x-axis and y-axis [and any z-axis]

facetsmaking separate plots broken up by one or two varia

ggplot jargon

facets


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facets

set.seed=100

ds


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similar to dplyr grammar, think of it as a sentence that you ar

'specify dataset' + # ggplot(ds,...)

ggplot grammar

l t


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'specify dataset' +

'specify x, y, grouping variables' +

ggplot grammar

# aes(x=,y=,col=, sh


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ggplot grammar


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'specify dataset' +


'specify plot layers (e.g. point, line, stat function)' +

'specify if you want faceting' +

ggplot grammar

# facet_grid()

ggplot grammar


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'specify dataset' +


'specify plot layers (e.g. point, line, stat function)' +

'specify if you want faceting' +

'specify minor details/options [labels, position of legen

ggplot grammar

# scale_name(), th


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example dataset

Bird_id Sex Treatment Growth_rate

1 male t1 12.3

2 male t2 10.3

3 male t3 14.5

4 female t1 14.35 female t2 9.3

6 female t3 15.6

scatterplot of data


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ggplot( ds ) + geom_point( aes(x=sex, y= growth_rate ) )

scatterplot of data

scatterplot of data


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ggplot( ds ) + geom_point( aes(x=trt, y= growth_rate ) )

scatterplot of data

scatterplot of data

co


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ggplot( ds ) + geom_point( aes(x=trt, y= growth_rate, col=sex) )

scatte p ot o data

coby sex


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you can move aes() t


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ggplot( ds, aes(x=trt, y= growth_rate, col=sex, group=sex )) +

geom_point( ) +geom_line( )

y ()ggplot()

adding a facet


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ggplot( ds, aes(x=trt, y= growth_rate, col=sex, group=sex ) ) +

geom_point( ) +geom_line( ) +

facet_grid(.~sex)

adding a facet

row ~ column

['.' just means no grouping variable]

key points so far


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1) I have not had to specify the dataset anymore

2) all the geom adopt the same scales (no specifying x-range

3) grouping by colour, shape, fill, etc. is easy

4) faceting is quick

5) a common language to everything (i.e. not a bunch of seppackages for different plot types)

key points so far

What's next


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Learning about base plot

- introducing basics of plot()- overlaying plots and customizing your plots

- discuss some more advanced plotting functions


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Lecture 8: Hands on Sectio

Lecture 8 files


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1) get Lecture8.Rfrom github

2) make sure that you have data/lecture7/ [same files as last w

3) open up Lecture8.Rin Rcourse_proj.Rpoj

4) start working through the example and then try the exercis

r course 2014: lecture 8

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