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Data Visualization Using R

Sameer Bamnote

Cytel Statistical Software & Services Pvt. Ltd., India

July 19, 2014

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Disclaimer

Any views or opinions presented in this presentation are solely those of theauthor and do not necessarily represent those of the company.

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Outline

1 Introduction

2

About R3 Graphics in R

Base graphicsGrid graphicsLattice graphicsggplot2 graphics

4 Illustrations

Mutiple figures in Single PanelErrorbar plotKaplan Meier Survival CurveForest plot using R package metaforForest plot using R package ggplot2

5 Summary

6 Conclusion

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Introduction

Data visualization is the study of the visual representation of data.

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Introduction

Data visualization is the study of the visual representation of data.

Presentation of data in a pictorial or graphical format.

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Introduction

Data visualization is the study of the visual representation of data.

Presentation of data in a pictorial or graphical format.

Data in spreadsheets is hard to visualize.

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Introduction

Data visualization is the study of the visual representation of data.

Presentation of data in a pictorial or graphical format.

Data in spreadsheets is hard to visualize.

It helps to see analytical results presented visually, find relevanceamong the millions of variables, communicate concepts andhypotheses to others, and even predict the future.

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Introduction

Data visualization is the study of the visual representation of data.

Presentation of data in a pictorial or graphical format.

Data in spreadsheets is hard to visualize.

It helps to see analytical results presented visually, find relevanceamong the millions of variables, communicate concepts andhypotheses to others, and even predict the future.

Data visualization makes interpretation easier.

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About R

R is a opensource software.

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About R

R is a opensource software.

Available for free and supported by strong user community.

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About R

R is a opensource software.

Available for free and supported by strong user community.

The current R is the result of a collaborative effort with contributionsfrom all over the world.

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About R

R is a opensource software.

Available for free and supported by strong user community.

The current R is the result of a collaborative effort with contributionsfrom all over the world.

Developed by Ross Ihaka and Robert Gentelman at University ofAuckland.

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About R

R is a opensource software.

Available for free and supported by strong user community.

The current R is the result of a collaborative effort with contributionsfrom all over the world.

Developed by Ross Ihaka and Robert Gentelman at University ofAuckland.

Highly extensible and flexible.

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About R

R is a opensource software.

Available for free and supported by strong user community.

The current R is the result of a collaborative effort with contributionsfrom all over the world.

Developed by Ross Ihaka and Robert Gentelman at University ofAuckland.

Highly extensible and flexible.

Implementation of modern statistical methods.

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Graphics in R

R is a powerful environment for data visualization

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

Full control over graphics

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

Full control over graphics

Can be reproduced

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

Full control over graphics

Can be reproduced

Huge number of R packages for graphics are available

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

Full control over graphics

Can be reproduced

Huge number of R packages for graphics are availableR Graphs can be viewed on screen

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Graphics in R

R is a powerful environment for data visualization

Integrated graphics

Good quality of graphics

Full control over graphics

Can be reproduced

Huge number of R packages for graphics are availableR Graphs can be viewed on screen

R graphs can be saved in various formats likepdf/png/jpg/wmf/tiff/ps etc.

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Graphical Environments in R

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Graphical Environments in R

Low-Level Graphics

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Graphical Environments in R

Low-Level Graphics

Base graphics

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Graphical Environments in R

Low-Level Graphics

Base graphics

Grid graphics

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Graphical Environments in R

Low-Level Graphics

Base graphics

Grid graphics

High-Level Graphics

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Graphical Environments in R

Low-Level Graphics

Base graphics

Grid graphics

High-Level Graphics

lattice

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Graphical Environments in R

Low-Level Graphics

Base graphics

Grid graphics

High-Level Graphics

lattice

ggplot2

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Base graphics

Plotting functions:plot - x-y plotting

barplot - bar plots

boxplot - box & whisker plot

hist - histogram

pie - pie charts

dotchart - dot plotsimage, heatmap, contour, persp - functions to generate image likeplots

qqnorm, qqline, qqplot - distribution comparison plots

pairs, coplot - display of multivariate data

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Scatter plot: Basic

R code> set.seed(1234)

> y plot(y[,1], y[,2])

0.0 0.2 0.4 0.6 0.8

0.2

0.

4

0.

6

0.

8

y[, 1]

y[,

2]

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Scatter plot: Pairs

R code> pairs(y)

A

0.2 0.4 0.6 0.8

0.0

0.2

0.4

0.6

0.8

0.2

0.4

0.6

0.8

B

0.0 0.2 0.4 0.6 0.8

0.2 0.4 0.6 0.8

0.2

0.4

0.6

0.8

C

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Scatter plot: with label

R code> plot(y[,1], y[,2], pch=20, col="red", main="Symbols and Labels")

> text(y[,1]+0.03, y[,2], rownames(y))

0.0 0.2 0.4 0.6 0.8

0.

2

0.

4

0.

6

0.

8

Symbols and Labels

y[, 1]

y[,

2]

a

b

c

d

e

f

gh

i

j

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Scatter plot: More Examples

Using important plotting parameters

> grid(5, 5, lwd = 2)

> op plot(y[,1], y[,2], type="p", col="red", cex.lab=1.2, cex.axis=1.2,

+ cex.main=1.2, cex.sub=1, lwd=4, pch=20, xlab="x label",

+ ylab="y label", main="My Main", sub="My Sub")

> par(op)

Used argumentsmar- species the margin sizes around the plotting area: c(bottom,left, top, right)

col- color of symbols

pch- type of symbols, samples: example(points)

lwd- size of symbols

cex.- control font sizes

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Scatter plot: More examples

Adding a regression line

> plot(y[,1], y[,2])

> myline summary(myline)

Plot on log scale> plot(y[,1], y[,2], log="xy")

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Line plot

R code> plot(y[,1], type="l", lwd=2, col="blue")

2 4 6 8 10

0.

0

0.2

0.

4

0.

6

0.

8

Index

y[,

1]

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Line plot with multiple variables

R code> plot(y[,1], ylim=c(0,1),xlab="Measurement",ylab="Intensity",type="l",lwd=2,col=1)

> for(i in 2:length(y[1,])) {+ screen(1, new=FALSE)

+ plot(y[,i], ylim=c(0,1), type="l", lwd=2, col=i, xaxt="n", yaxt="n", ylab="",

+ xlab="", main="", bty="n")

+ }

2 4 6 8 10

0.

0

0.2

0.

4

0.6

0.

8

1.0

Measurement

Intensity

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More Functions in Base graphics

Graph Type R function

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More Functions in Base graphics

Graph Type

Barplot

R function

barplot

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More Functions in Base graphics

Graph Type

Barplot

Histogram

R function

barplot

hist

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More Functions in Base graphics

Graph Type

Barplot

Histogram

Density plot

R function

barplot

hist

density

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More Functions in Base graphics

Graph Type

Barplot

Histogram

Density plotPie Chart

R function

barplot

hist

densitypie

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Grid Graphics Environment

What is grid?

Low-level graphics system

Highly exible and controllable system

Does not provide high-level functions

Intended as development environment for custom plotting functions

Pre-installed on new R distributions

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Lattice

What is lattice?

High-level graphics system

Developed by Deepayan Sarkar

Implements Trellis graphics system from S-Plus

Simplies high-level plotting tasks, arranging complex graphical features

Syntax similar to Rs base graphics

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Lattice plot example

R code> library(lattice)

> p1 plot(p1)

1:8

1:8

2

4

6

8

A

2 4 6 8

B

2 4 6 8

C

2

4

6

8

D

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Lattice plot: Density plot

R code> data(Chem97, package = "mlmRev")

> densityplot(~ gcsescore | factor(score),data= Chem97, groups = gender,+ plot.points = FALSE, auto.key = TRUE)

gcsescore

Density

0.0

0.2

0.4

0.6

0.8

0 2 4 6 8

0 2

0 2 4 6 8

4

6

0 2 4 6 8

8

0.0

0.2

0.4

0.6

0.8

10

MF

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ggplot2

What is ggplot2?

High-level graphics system

Implements grammar of graphics from Leland Wilkinson

Streamlines many graphics workows for complex plots

Syntax centered around main ggplot function

Simpler qplot function provides many shortcuts

ggplot function accepts two arguments (1)Data set to be plotted(2)Aesthetic mappings provided by aes function

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qplot function

qplot syntax is similar to Rs basic plot function

Arguments:I x: x-coordinates (e.g. col1)I y: y-coordinates (e.g. col2)I data: data frame with corresponding column namesI xlim, ylim: e.g. xlim=c(0,10)I

log: e.g. log="x" or log="xy"I main: main title; see ?plotmath for mathematical formulaI xlab, ylab: labels for the x- and y-axesI color, shape, sizeI ...: many arguments accepted by plot function

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qplot: Scatterplot

R code> p print(p)

Dot Size and Color Relative to Some Values

x

y

2

4

6

8

10

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2 4 6 8 10

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qplot:Scatterplot with Regression Line

R code> set.seed(1234)

> dsmall p print(p)

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ggplot: Scatterplot

R code> p print(p)

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color

! D

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! F

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! H

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ggplot: Scatterplot with Regression Line

R code> p print(p)

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ggplot: Jitter Plot

R code> p print(p)

color

price/carat

2000

4000

6000

8000

10000

D E F G H I J

color

D

E

F

G

H

I

J

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Multiple Figures in Single Panel

Requirement - Multiple figures in a single panel

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Multiple Figures in Single Panel

Requirement - Multiple figures in a single panel

o

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Plot 1

Treatment

IndividualValues

Trt A Trt B

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Plot 2

Treatment

IndividualValues

Trt A Trt B

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Plot 3

Treatment

IndividualValues

Trt A Trt B

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Plot 4

Treatment

IndividualValues

Trt A Trt B

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Multiple graphs in Single Panel

R Codepdf("D:/Example1.pdf")

par(mfrow=c(2,2)) # Divide the plotting area

#

plot(b,a, xaxt = n, xlab = Treatment,ylab = Individual Values,

xlim=c(1,5),main = "Plot 1",cex=1.5,col=blue, pch = o)

axis(1,at=c(2,4),labels = c(Trt A, Trt B))

#

plot(jitter(b,amount=0.2),a, xaxt = n, xlab = Treatment,ylab =Individual Values, xlim=c(1,5),main = "Plot 2", cex=1.5,col=blue,

pch = o)

axis(1,at=c(2,4),labels = c(Trt A, Trt B))

#

#

dev.off()

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Errorbar Plot

Using an inbuilt data in R named ToothGrowth whose variables arerenamed as rep, trt and time.

Errorbar plot using lineplot.CI from sciplot package in R

5

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Time (Hours)

Value(MEAN

+/!

SE)

!

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0.5 1 2

Errorbar plot using lineplot.CI function in sciplot package

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Errorbar plot using sciplot package

R Codelineplot.CI(time, rep, group = trt, data = tg, cex = 1.5,

xlab = "Time (Hours)", ylab = "Value (MEAN +/- SE)",

cex.lab = 1.3, x.leg = 1,y.leg=30,col = c("red","dark green"),

pch = c(16,16),ylim=c(5,30), err.width = 0.05, xaxt = n,lwd=2)

axis(1,at=c(1,2,3),labels=c(0.5, 1, 2))

title("Errorbar plot using lineplot.CI function in sciplot package")

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Errorbar plot using ggplot2 package

Errorbar plot using ggplot2 package in R

!

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!

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!

5

10

15

20

25

30

0.5 1.0 1.5 2.0

Time (Hours)

Value(Mean+/!S

E)

trt

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!

Trt A

Trt B

Errorbar plot in ggplot2 package

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Errorbar plot using ggplot2 package

R Codeggplot(summary, aes(x=time, y=rep, colour=trt)) +

geom_errorbar(aes(ymin=rep-se, ymax=rep+se), width=.05, lwd = 0.8) +

geom_line(lwd=0.8) +

geom_point(cex=3) + xlab("Time (Hours)") + ylab ("Value (Mean +/- SE)")+

ggtitle("Errorbar plot in ggplot2 package")+

theme_bw()+

scale_y_continuous(limits=c(5,30), breaks=0:30*5)

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Errorbar plot using ggplot2 package

R Codepd

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Errorbar plot using ggplot2 package

!

!

!

!

! !

5

10

15

20

25

30

0.5 1.0 1.5 2.0

Time (Hours)

V

alue(Mean+/!S

E)

trt

!

!

Trt A

Trt B

Errorbar plot with Jittering in ggplot2 package

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Comparison of Kaplan Meier Survival Curves

R Code> library(ISwR)

> fit.bysex plot(fit.bysex,conf.int=TRUE,col=c("red","blue"),lty=1:2,main =Comparing Sur ...

> legend(1000,0.2, c("Male","Female"),lty=c(2,1),lwd=c(1,1),col=c("blue","red"))

0 1000 2000 3000 4000 5000

0.

0

0.

2

0.

4

0.6

0.

8

1.

0

Comparing Survival Curves by Sex

Male

Female

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Forest plot using R package metafor

RE Model for All Studies

0.05 0.25 1.00 4.00

Relative Risk

Comstock et al, 1976

Comstock & Webster, 1969

Comstock et al, 1974

Rosenthal et al, 1961

Coetzee & Berjak, 1968

TPT Madras, 1980

Vandiviere et al, 1973

Hart & Sutherland, 1977

Rosenthal et al, 1960

Ferguson & Simes, 1949

Aronson, 1948

Stein & Aronson, 1953

Frimodt!Moller et al, 1973

27

5

186

17

29

505

8

62

3

6

4

180

33

16886

2493

50448

1699

7470

87886

2537

13536

228

300

119

1361

5036

29

3

141

65

45

499

10

248

11

29

11

372

47

17825

2338

27197

1600

7232

87892

619

12619

209

274

128

1079

5761

0.98 [ 0.58 , 1.66 ]

1.56 [ 0.37 , 6.53 ]

0.71 [ 0.57 , 0.89 ]

0.25 [ 0.15 , 0.43 ]

0.63 [ 0.39 , 1.00 ]

1.01 [ 0.89 , 1.14 ]

0.20 [ 0.08 , 0.50 ]

0.24 [ 0.18 , 0.31 ]

0.26 [ 0.07 , 0.92 ]

0.20 [ 0.09 , 0.49 ]

0.41 [ 0.13 , 1.26 ]

0.46 [ 0.39 , 0.54 ]

0.80 [ 0.52 , 1.25 ]

0.49 [ 0.34 , 0.70 ]

Systematic Allocation

Random Allocation

Alternate Allocation

TB+ TB! TB+ TB!

Vaccinated Control

Author(s) and Year Relative Risk [95% CI]

0.65 [ 0.32 , 1.32 ]RE Model for Subgroup

0.38 [ 0.22 , 0.65 ]RE Model for Subgroup

0.58 [ 0.34 , 1.01 ]RE Model for Subgroup

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Forest plot using R package ggplot2

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Summary

R is useful in Data visualization

Various graphics environment in R

R can be used to explore data and create various graphics

Various R functions from various packages for same purpose

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Conclusion

From examples we have seen, how graphs generated in R are good interms of appearance, time taken for coding and overall quality of thegraph. So, R can be an ideal choice for data visualization (exploring) andcreating graphs.

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Thanks for your attention!

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