Post on 14-Dec-2020
Data visualisation techniques using R
****************Osama Mahmoud
***Website: http://osmahmoud.comE-mail: o.mahmoud@bristol.ac.uk
*****30 April 2019
Goals
In this session, we aim to cover:What the R base graphics and their types are.How to produce base plots in R.How to save your generated graphics.How to use ggplot2 in R to produce advanced graphics.What the building-blocks of ggplot2 graphs are.
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Contents
1 Introduction to data visualisation
2 Overview of base graphics
3 Advanced graphics
4 Plot building-blocks
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Types of R graphics
Base graphics.Grid graphics.Lattice graphics.ggplot2 graphics.
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Base graphics
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Installing the R package: BristolVis
> install.packages("drat")
> drat::addRepo("statcourses")
> install.packages("BristolVis", type="source")
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Overview of base graphics
Graphics can be easily created in R and subsequently included inWord, Power Point, LATEX, etc.Let’s plot a simple graphic:
> plot(rate ~ conc, data = Puromycin)This implicitly opens a new graphics window.
0.0 0.2 0.4 0.6 0.8 1.0
5010
015
020
0
conc
rate
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Overview of base graphics
Change settings of active graphic window> par(mfrow = c(1,2), mar = c(4,4,0,0) + 0.1)> x <- seq(-3,3, 0.1)> plot(x, x^2, type = ’l’)> plot(x, x^3, type = ’l’)
−3 −2 −1 0 1 2 3
02
46
8
x
x^2
−3 −2 −1 0 1 2 3
−20
−10
010
20
x
x^3
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Overview of base graphics
Close active graphics window:
> dev.off()
Close all graphics windows:> graphics.off()
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Histograms
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Histograms
Simple histogram (next slide left)
> data(birthweight, package = “BristolVis”)> hist(birthweight$weightgain)
with a fixed set of “bars”: (breaks give number of cut-points)
> hist(birthweight$weightgain, breaks = 3)with a defined scale for the y-axis:
> hist(birthweight$weightgain, breaks = 3, ylim = c(0,10))with colored bars:
> hist(birthweight$weightgain, breaks = 3, ylim = c(0,10), col = gray(4:8 / 8))
with arbitrary text, vertical and double text size (next slide right)
> text(1500, 5, "some text", srt = 90, cex = 2)
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Histograms
Histogram of birthweight$weightgain
birthweight$weightgain
Fre
qu
en
cy
500 1000 1500 2000 2500 3000 3500
01
23
45
Histogram of the weight gain
WG
Fre
qu
en
cy
0 1000 2000 3000 4000
02
46
81
0
so
me
text
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Bar plots
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Bar plots
Simple bar plot (next slide left)
> data(med, package = “BristolVis”)> treatment = table(med$treatment)> barplot(treatment, ylim = c(0, 500))
stacked bar plot:
> health_treat = table(med$health, med$treatment)> barplot(health_treat, ylim = c(0, 500))
with juxtaposed bars:barplot(health_treat, ylim = c(0, 300), beside = TRUE)
with legend (next slide right)
> barplot(health_treat, ylim = c(0, 300), beside = TRUE,legend.text = rownames(health_treat))
Osama Mahmoud Data visualisation techniques using R 13 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Bar plots
Existing New
01
00
20
03
00
40
05
00
Existing New
Poor
Fair
Good
nu
mb
er
of
pa
tie
nts
05
01
00
15
02
00
25
03
00
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Box plots
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Box plots
Simple box plot (next slide left)
> boxplot(birthweight$weight)
grouped by sex:
> boxplot(weight ~ sex, data = birthweight)
with new labels for sex, main title and a defined y-scale (next slide right)
> boxplot(weight ~ sex, data = birthweight, names =c("girls", "boys"), ylab = "birth weight [g]", ylim =c(2000, 4000), main = "Distribution of birth weight")
with additional line:> abline(h = 3000, col = "gray", lty = 4)
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Box plots2
40
02
60
02
80
03
00
03
20
03
40
03
60
0
girls boys
20
00
25
00
30
00
35
00
40
00
Distribution of birth weight
bir
th w
eig
ht
[g]
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Scatter plots
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Scatter plots
Simple scatter plot (next slide left)
> plot(weightgain ~ weight, data = birthweight)
for a subset:
> plot(weightgain ~ weight, data = birthweight, subset =(sex == "F"))
with colored solid points by sex and add a legend (next slide right)
> plot(weightgain ~ weight, data = birthweight, subset =(sex == "M"), col = "blue", pch = 20)> points(weightgain ~ weight, data = birthweight, subset =(sex == "F"), col = "red", pch = 20) # add girls data> legend("bottomleft", title = "sex", legend = c("girls","boys"), col = c("red","blue"), pch = 20) # add legend
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Scatter plots
2400 2600 2800 3000 3200 3400 3600
10
00
15
00
20
00
25
00
30
00
weight
we
igh
tga
in
3000 3200 3400 3600
15
00
20
00
25
00
30
00
Weight gain (for boys and girls)
weight [g]
we
igh
t g
ain
(g
)
sex
girls
boys
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Examples of the graphic library
An overview of available demos for the built-in R library (graphics):> demo(graphics)
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Saving plots
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Saving plots
Graphics can be saved directly from within R using different devices,e.g. postscript(), pdf(), bmp(), (see ?Devices)For publications vector graphics such as PDFs or postscript files arepreferable to pixel graphics such as jpg, bmp, etc.
> pdf("Fig1.pdf", width = 10, height = 7)> boxplot(weight ~ sex, data = birthweight)> dev.off()
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Advanced Graphics Usingggplot2
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Remember: Using base graphics> plot(med[med$health=="Poor",]$age, med[med$health=="Poor",]$time,xlim=c(30,72), ylim=c(0.5,13))> points(med[med$health=="Fair",]$age, med[med$health=="Fair",]$time,col=2)
> points(med[med$health=="Good",]$age, med[med$health=="Good",]$time,
col=4)
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30 40 50 60 70
02
46
810
12
med[med$health == "Poor", ]$age
med
[med
$hea
lth =
= "P
oor"
, ]$t
ime
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Osama Mahmoud Data visualisation techniques using R 25 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Remember: Using base graphics
We had to manually set the scales using the xlim and ylimparameters.
We had not created a legend. We would need to use the legendfunction to create one.
The default axis labels were terrible!
Osama Mahmoud Data visualisation techniques using R 26 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Equivalent graphics using ggplot2
> library(ggplot2)> g = ggplot(data=med, aes(x=age, y=time))> g + geom_point(aes(colour=health))
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0.0
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7.5
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40 50 60 70
age
time
factor(health)
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Poor
Fair
Good
Osama Mahmoud Data visualisation techniques using R 27 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Factor of point sizes
g + geom_point(aes(size=health))
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Osama Mahmoud Data visualisation techniques using R 28 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Example of a line chart
g + geom_line(aes(colour=health, size = health)))
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Osama Mahmoud Data visualisation techniques using R 29 / 44
Base graphics Introduction Base graphics Advanced graphics Building-blocks
Geometric objects
Points, bars and lines are examples of geom’s. Some useful standardgeoms and their equivalent base graphic counter part:
Plot Name Geom Base graphicBar chart bar barplotBox-and-whisker boxplot boxplotHistogram histogram histLine plot line plot and linesScatter plot point plot and points
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
More complicated functions
The idea of graphical layers, enables constructing more complicatedfunctions, e.g.:p = ggplot(mpg, aes(x = displ, y = cty)) +geom_point(aes(colour=factor(cyl))) +stat_smooth(aes(colour=factor(cyl)))
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Understanding ggplot2 philosophy
Each ggplot command adds iteratively layers. A single layer maycomprise of four elements:
an aesthetic and data mapping;a geometric object (geom);a statistical transformation (stat);a position adjustment, i.e. how should overlapped objects be handled.
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Understanding ggplot2 philosophy
For example, the command:g + geom_point(aes(colour=health))
actually calls (in the background) the command:g + layer(data = med, #inheritedmapping = aes(color=health), #x and y are inherited.stat = "identity",geom = "point",position = "identity",params = list(na.rm=FALSE))
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Plot building-blocks
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Initial plot object
An initial ggplot object, can be setup using the ggplot() functionwhich has two arguments:data (takes a data frame)an aesthetic mapping (creates default aesthetic attributes)
g = ggplot(data=mpg, mapping=aes(x=displ, y=cty,colour=factor(cyl)))Or equivelently,g = ggplot(mpg, aes(displ, cty, colour=factor(cyl)))
doesn’t actually produce anything to be displayed, it just sets the intialplot object. We need to add layers for that to happen.
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
The geom_ functions
The geom_ functions perform the actual rendering in a plot, e.g. a linegeom will create a line plot and a point geom creates a scatter plot.
Each geom has a list of aesthetics that it accepts such as x , y ,colour and size.
However, some geoms have unique elements. For example, thegeom_errorbar requires arguments ymax and ymin.
The full list of aesthetics can be displayed by:
> ggplot2:::.all_aesthetics
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
The geom_ functions
This table gives names and descriptions of some commonly usedgeoms:
Name Descriptionabline Line, specified by slope and interceptboxplot Box and whiskers plotdensity Kernel density plothistogram Histogramsjitter Individual points are jittered to avoid overlapstep Connect observations by stairs
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Combining geoms
I will show how to combine more that one geom function to produce abit more complex plots. If we consider the mpg data set, a base ggplotobject:g = ggplot(mpg, aes(x=factor(cyl), y=hwy)) will do nothing.Now we’ll create a boxplot: (g1 = g + geom_boxplot())
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Combining geoms
Previous figure was a boxplot of all the mpg data, a more useful plotwould be to have individual boxplots conditional on number of cylinders:(g2 = g + geom_boxplot(aes(x=factor(cyl), group=cyl)))
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Combining geoms
We are not restricted to a single geom. When data sets are reasonablysmall, it is useful to display the data on top of the boxplots:(g3 = g2 + geom_dotplot(aes(x=factor(cyl), group=cyl),binaxis="y", stackdir="center", binwidth=0.25,stackratio=2))
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Standard plots
There are a few standard geom ’s that are particular useful:
geom_line : a line plot.geom_boxplot : produces a boxplot.geom_point : a scatter plot.geom_dotplot: a dot plot.geom_bar : produces a standard barplot that counts the x values.geom_text : adds labels to specified points (as geom_point but drawlabels rather than points).geom_raster: Similar to levelplot (heatmap).
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Useful links
Choice of graphic coloursNames of colours in various formats:http://colorbrewer2.org/
R Course: Shiny web-pageThe RVis tool for learners of data visualisation using R:http://bristol-medical-stat.bristol.ac.uk:3838/RVis/
R package: BristolVis web-pageThe BristolVis package that associated with these materials:https://github.com/statcourses/BristolVis
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
References
888
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Base graphics Introduction Base graphics Advanced graphics Building-blocks
Thank You
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