Plotting 3d in R

8/2/2019 Plotting 3d in R

1/19

R graph gallery

Compilation by Eric Lecoutre

December 12, 2003

Contents

3D Bivariate Normal Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23D Scatterplot - Cloud, regression plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43D Wireframe - For surfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Agreement plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Conditional Plot 1 - coplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Fourfold Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Hexagon Binning Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Mosaicplot - Associations in a contingency table . . . . . . . . . . . . . . . . . . . . . . . . . . 14Parallel Plot - Comparing groups with few subjects . . . . . . . . . . . . . . . . . . . . . . . . . 15Ternary Plot - Biplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Tukeys Hanging Rootogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Violin Plot - Boxplot showing density, aka vase boxplot . . . . . . . . . . . . . . . . . . . . . . 19

1


2/19


3/19

term1*exp(term2*(term3+term4-term5))

} # setting up the function of the multivariate normal density

#

z


4/19

3D Scatterplot - Cloud, regression plan

variables: 3 QTlibrary: lattice or scatterplot3dfunction: cloud scatterplot3d,

Sample graph

o

o

oo

o

o

o

o

o

o

o

oo

o

oo

o

o

o

o

o

o

o

o

o

oooo

oo

o

o

o

oo

o

o

o

oo

o

o

o o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

oo

o

o

oo

o

o

o

o

oo

o

ooo

o

o

o

o

o

o

oo o

o

o

o

ooo

o

o

o

o

o

o

o

o

o

o

o

o

o

oo

o

o

o

oo

oo

o

o

o

o

o

o

o

oo

o

o

o

o

o

o

o

o

o

o

o

ooo

o

ooo

o

oo

o

Petal.Length

Petal.Width

Sepal.Length

Iris Dataooo

setosaversicolorvirginica

Sample code

data(iris)

cloud(Sepal.Length ~ Petal.Length * Petal.Width, data = iris,

groups = Species, screen = list(z = 20, x = -70),

perspective = FALSE,

key = list(title = "Iris Data", x = .15, y=.85, corner = c(0,1),

border = TRUE, points = Rows(trellis.par.get("superpose.symbol"), 1:3),

text = list(levels(iris$Species))))

%$%

4


5/19

Sample graph

scatterplot3d 5

8 10 12 14 16 18 20 22

10

20

30

40

50

60

70

80

60

65

70

75

80

85

90

Girth

Height

Volume

Sample code

data(trees)

s3d


6/19

3D Wireframe - For surfaces

variables: 3 QTlibrary: latticefunction: wireframe

Sample graph

x

y

z

1

0.5

0

0.5

1

Sample code

x


7/19

Agreement plot

variables: one confusion matrixlibrary: vcdfunction: agreementplot

Description

Representation of a k k confusion matrix, where the observed and expected diagonal elements arerepresented by superposed black and white rectangles, respectively.

Agreement chart allows to quickly see where two judges do disagree.

Sample graph

Agreement Chart

Husband

Wife

Never FunN

everFun

Fairly Often

FairlyOften

Very Often

VeryOfte

n

Always fun

Alwaysfun

Sample code

library(vcd)

7


8/19

data(SexualFun)

agreementplot(t(SexualFun))

8


9/19

Conditional Plot 1 - coplot

variables: 3 QL or QTlibrary: -function: coplot

Description

Sample graph

10

30

50

70

0 10 20 30 40 50

10

30

50

70

0 10 20 30 40 50

10

30

50

70

1:54

breaks

A

B

Given : wool

L

M

H

Given:tension

Sample code

data(warpbreaks)

## given two factors

coplot(breaks ~ 1:54 | wool * tension, data = warpbreaks,

col = "red", bg = "pink", pch = 21,

bar.bg = c(fac = "light blue"))

9


10/19

Fourfold Display

data: 2x2xk contingency tableslibrary: vcdfunction: fourfoldplot

Description

The fourfold display depicts frequencies by quarter circles, whose radius is proportional to nij , so thearea is proportional to the cell count . The cell frequencies are usually scaled to equate the marginaltotals, and so that the ratio of diagonally opposite segments depicts the odds ratio. Confidence rings forthe observed odd ratio allow a visual test of the hypothesis H0 : = 1 corresponding to no association.They have the property that the rings for adjacent quadrants overlap iff the observed counts are consistentwith the null hypothesis.

Sample graph

Sex: Male

Admit?:Yes

Sex: Female

Admit?:No

Department: A

512

89

313

19

Sex: Male

Admit?:

Yes

Sex: Female

Admit?:No

Department: B

353

17

207

8

Sex: Male

Admit?:Yes

Sex: Female

Admit?:No

Department: C

120

202

205

391

Sex: Male

Admit?:

Yes

Sex: Female

Admit?:No

Department: D

138

131

279

244

10


11/19

Sample code

library("vcd")

# Load data

data(UCBAdmissions)

x


12/19


13/19

ry


14/19

Mosaicplot - Associations in a contingency table

variables: QL ( 2)library: -function: mosaicplot

Description

Mosaicplot graph represents a contingency table, each cell corresponding to a piece of the plot, whichsize is proportional to cell entry.

Extended mosaic displays show the standardized residuals of a loglinear model of the counts from bythe color and outline of the mosaics tiles. (Standardized residuals are often referred to a standard normaldistribution.) Negative residuals are drawn in shaded of red and with broken outlines; positive ones aredrawn in blue with solid outlines.

Thus, mosaicplot are perfect to visualize associations within a table and to detect cells which createdependancies.

Sample graph

Standardized

Residuals:

4

Hair

Ey

e

Black Brown Red Blond

Brown

Blue

Hazel

reen

Male Female Male Female MaleFemale Male Female

Sample code

data(HairEyeColor)

mosaicplot(HairEyeColor, shade = TRUE)

14


15/19

Parallel Plot - Comparing groups with few subjects

variables: 1 QL and at least 2 other variableslibrary: lattice or MASSfunction: parallel

Description

Sample graph

SepalLength

SepalWidth

PetalLength

Min Max

setosa versicolor

SepalLength

SepalWidth

PetalLength

virginica

Three

Varieties

of

Iris

Sample code

data(iris)

parallel(~iris[1:3]|Species, data = iris,

layout=c(2,2), pscales = 0,

varnames = c("Sepal\nLength", "Sepal\nWidth", "Petal\nLength"),

page = function(...) {

grid.text(x = seq(.6, .8, len = 4),

y = seq(.9, .6, len = 4),

label = c("Three", "Varieties", "of", "Iris"),

gp = gpar(fontsize=20))

})

15


16/19

Sample graph

Petal L. Petal W. Sepal W. Sepal L.

Sample code

data(iris3)

ir


17/19

Ternary Plot - Biplot

variables: 3 QT and 1 QLlibrary: ade4function: triangle.plot

Description

Graphs for a dataframe with 3 columns of positive or null values triangle.plot is a scatterplot trian-gle.biplot is a paired scatterplots

Sample graph

0 0.8

pri

0.50.2 sec 0.7

0.3

ter

0.134

0.36

0.506

0 0.8

pri

0.40.2 sec 0.6

0.4

ter

Belgium

Denmark

Spain

France

Greece

Ireland Italy

Luxembourg

Netherlands

Portugal

Germany

United_Kingdom

0 0.8

pri

0.50.2 sec 0.7

0.3

ter

12

3

4

5

6 7

8

9

10

11

12

0 0.8

pri

0.50.2 sec 0.7

0.3

ter

12

3

4

5

6 7

8

9

10

11

12

1314

15

16

17

18 19

20

21

22

23

24

Principal axis

Sample code

data (euro123)

par(mfrow = c(2,2))

triangle.plot(euro123$in78, clab = 0, cpoi = 2, addmean = TRUE,

show = FALSE)triangle.plot(euro123$in86, label = row.names(euro123$in78), clab = 0.8)

triangle.biplot(euro123$in78, euro123$in86)

triangle.plot(rbind.data.frame(euro123$in78, euro123$in86), clab = 1,

addaxes = TRUE, sub = "Principal axis", csub = 2, possub = "topright")

par(mfrow = c(1,1))

17


18/19

Tukeys Hanging Rootogram

variables: 1 QLlibrary: vcdfunction: rootogram

Description

Discrete frequency distributions are often graphed as histograms, with a theoretical fitted distributionsuperimposed. It is hard to compare the observed and fitted frequencies visually, because (a) we mustassess deviations against a curvilinear relation, and (b) the largest frequencies dominate the display.

The hanging rootogram (Tukey, 1977) solves these problems by (a) shifting the histogram bars tocoincide with the fitted curve, so that deviations may be judged by deviations from a horizontal line, and(b) plotting on a square-root scale, so that smaller frequencies are emphasized. Featured example showsmore clearly that the observed frequencies differ systematically from those predicted under a Poissonmodel.

Sample graph

0 1 2 3 4 5 6

Number of Occurrences

sqrt(Frequency)

0

2

4

6

8

10

Sample code

library("vcd")

# create data

madison=table(rep(0:6,c(156,63,29,8,4,1,1)))

# fit a poisson distribution

madisonPoisson=goodfit(madison,"poisson")

rootogram(madisonPoisson)

18


19/19

Violin Plot - Boxplot showing density, aka vase boxplot

variables: 1 QT and optional QL for groupslibrary: simpleR - Using R for Introductory Statisticsauthor: John Verzani, College of Staten Island - http://www.math.csi.cuny.edu/ verzani/featured in: Simple Rfunction: simple.violinplot

Description

Violin plot are similar to boxplot except that they show the density of the data, estimated by kernelmethod.

Sample graph

A B C D E F G H

0

50

100

15

0

Sample code

# library(Simple) - required library which comes with Simple R# http://www.math.csi.cuny.edu/Statistics/R/simpleR

data(OrchardSprays)

simple.violinplot(decrease ~ treatment, data = OrchardSprays,col="bisque",border="black")

19

Documents

Plotting 3d in R