Data Science: Data Visualization Boot Camp 3D R's rgl packageccartled/Teaching/2020-Spring/DataVisualization/Presentations/...delFromSubscene3d(obj, subscene = root) 24/28 Type Sample

Data Science: Data Visualization Boot Camp3D

R’s rgl package

Chuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhDChuck Cartledge, PhD

26 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 202026 January 2020

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Type Sample data Hands on Q & A Conclusion References Files

Table of contents (1 of 1)

1 TypeUsesGeneral considerations

2 Sample data

3 Hands on

4 Q & A

5 Conclusion6 References7 Files

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A definition

“The phrase three-dimensional (some-times abbreviated 3-D) graph is used in twosignificantly different ways, which sometimescauses confusion. In one case the phrase isused to mean that a graph has the appear-ance of having depth which makes it lookthree-dimensional. This frequently is donefor cosmetic purposes. In the other case,the phrase three-dimensional graph refers toa graph that has three axes. This may bedone for cosmetic purposes or to display thedata in a way that simplifies an overall anal-ysis or provides insights not possible in otherformats.”

R. L. Harris [2]

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Motor Trend R supplied data set (1 of 2)

Included in the R package datasets.

“The data was extracted from the 1974 Motor TrendUS magazine, and comprises fuel consumption and 10 as-pects of automobile design and performance for 32 auto-mobiles (1973-74 models).”

R Core Team [4]

?mtcars

head(mtcars)

Resulting in:

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Motor Trend R supplied data set (2 of 2)

mpg cyl disp hp drat wt qsec vs am gear carb col

Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 red

Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 red

Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 green

Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 red

Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 blue

Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 red

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Volcano R supplied data set (1 of 2)


“Maunga Whau (Mt Eden) is one of about 50 volcanosin the Auckland volcanic field. This data set gives topo-graphic information for Maunga Whau on a 10m by 10mgrid.”

R Core Team [4]

?volcano

head(volcano)

Resulting in:

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Volcano R supplied data set (2 of 2)

[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]

[1,] 100 100 101 101 101 101 101 100 100 100 101 101 102 102

[2,] 101 101 102 102 102 102 102 101 101 101 102 102 103 103

[3,] 102 102 103 103 103 103 103 102 102 102 103 103 104 104

[4,] 103 103 104 104 104 104 104 103 103 103 103 104 104 104

[5,] 104 104 105 105 105 105 105 104 104 103 104 104 105 105

[6,] 105 105 105 106 106 106 106 105 105 104 104 105 105 106

(Output truncated.)

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Norfolk Island elevation data (1 of 2)


“. . . lets look at a digital elevation model of NorfolkIsland. This model was built as part of a Master’s studentproject to study rainfall on the island.”

R. Ihaka [3]

source(‘‘http://www.stat.auckland.ac.nz/~haka/

120/Data/ni-smoothed.R’’)

head(smoothed)

Resulting in:

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Norfolk Island elevation data (2 of 2)

[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]

[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[3,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0

[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]

[1,] 0 0 0 0 0 0 0 0 0 0 0 0

[2,] 0 0 0 0 0 0 0 0 0 0 0 0

[3,] 0 0 0 0 0 0 0 0 0 0 0 0

[4,] 0 0 0 0 0 0 0 0 0 0 0 0

[5,] 0 0 0 0 0 0 0 0 0 0 0 0

[6,] 0 0 0 0 0 0 0 0 0 0 0 0

Output truncated.

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The first codes. (1 of 6)

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rm(list=ls())

library(rgl)

library(ggplot2)

library(knitr)

library(rglwidget)

library(scales)

library(modeest)

library(clusterSim)

plot3d(mtcars$wt, mtcars$disp, mtcars$mpg,

type="s",

size=1,

lit=TRUE,

main = "Car Weight Vs Engine Displacement Vs Mileage",

sub="",

xlab = "Weight (1,000 lbs)",

ylab="Engine disp (cu in.)",

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zlab = "Mileage"

)

plot3d(mtcars$wt, mtcars$disp, mtcars$mpg,

type="p",

size=5,

lit=FALSE,

box=FALSE,

col = c("red","blue","green"),

expand = 1,


sub="",



zlab = "Mileage"

)

mtcars$col

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mtcars$col[which(mtcars$cyl == 4)]

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rgl.close()

rgl.open()

rgl.init()

rgl.bg(color="white")

rgl.spheres(

x=mtcars$wt,

y=mtcars$disp,

z=mtcars$mpg,

r=10,

color=mtcars$col

)

aspect3d(1,1,1)

axes3d(edges="bbox")

## axes3d(edges=c(’x’,’y’,’z’))

title3d(


sub="",


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zlab = "Mileage",

col="blue"

)

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The second codes. (1 of 4)

Idea taken from [3].

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rm(list=ls())

library(grDevices)

library(rgl)

data(volcano)

z

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open3d()

surface3d(x, y, z, color = col, back = "lines")

source("https://github.com/mamonu/Ropengl/raw/master/ni-smoothed.r")

heights = smoothed

rgl.clear()

rgl.clear("lights")

rgl.light(theta=0,phi=45)

ncolors = 100

colorlut = terrain.colors(ncolors)

colorlut[1] = "steelblue"

xg = seq(xlim[1],xlim[2],length=n)

yg = seq(ylim[1],ylim[2],length=n)

ymax = max(heights)

ymin = min(heights)

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col = colorlut[1 + (ncolors - 1) * (heights - ymin)/(ymax - ymin)]

rgl.surface(yg, xg, heights, color=col,

smooth=TRUE, specular="#000000")

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The third codes. (1 of 4)

Ideas from: “subscene3d: Create, select or modify a subscene”[1].

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rm(list=ls())

library(rgl)

lat

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texture=system.file("textures/world.png",

package="rgl"),

specular="black", axes=FALSE, box=FALSE,

xlab="", ylab="", zlab="",

normal_x=x, normal_y=y, normal_z=z),

spheres3d(matrix(0, 9, 3), col = cols, radius = rs))

root

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viewport = "inherit", copyShapes = TRUE, parent = root)

clipplanes3d(0,0,-1,0)

delFromSubscene3d(obj, subscene = root)

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Hands-on exercises

1 In the first set of slides, your supervisor wants just onechange.

1 He wants to see which of the cars has a “Straight” engine, andwhich have a “V” engine.

2 In the second set of slides:1 Provide a way to “cycle” through the various color pallets to

see which looks best.

3 In the third set of slides:1 Change the world to an orange.

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Q & A time.

Q: How many Zen masters doesit take to screw in a light bulb?A: None. The Universe spins thebulb, and the Zen master staysout of the way.

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What have we covered?

There are a multitude of differentaspects to 3D plots.

They are very good for displayingdata with more than one or twoattributes.They can be very difficult toexplain because of their higherdimesionality.The interaction between differentplotting commands can bedifficult to control.Interactive plots can be veryuseful.

There are times when they are thebest.

Next: Looking at graphs and plots with different eyes.

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References (1 of 1)

[1] Daniel Adler, Duncan Murdoch, and others, subscene3d:Create, select or modify a subscene,https://rdrr.io/rforge/rgl/man/subscene3d.html,2018, R package version 0.99.16.

[2] Robert L. Harris,Information Graphics: A Comprehensive Illustrated Reference,Oxford University Press, 2000.

[3] Ross Ihaka, Three-D Visualisation and Animation,https://www.stat.auckland.ac.nz/~ihaka/120/

Tutorials/lab09.html, 2003.

[4] R Core Team,R: A Language and Environment for Statistical Computing, RFoundation for Statistical Computing, Vienna, Austria, 2018.

https://rdrr.io/rforge/rgl/man/subscene3d.htmlhttps://www.stat.auckland.ac.nz/~ihaka/120/Tutorials/lab09.htmlhttps://www.stat.auckland.ac.nz/~ihaka/120/Tutorials/lab09.html

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Files of interest

1 Code snippet to createimages in this presentation

2 Elevation data from

https:

//github.com/mamonu/

Ropengl/raw/master/

ni-smoothed.r

## First codesrm(list=ls())

library(rgl)library(ggplot2)library(knitr)library(rglwidget)library(scales)library(modeest)library(clusterSim)

plot3d(mtcars$wt, mtcars$disp, mtcars$mpg, type="s", size=1, lit=TRUE, main = "Car Weight Vs Engine Displacement Vs Mileage", sub="", xlab = "Weight (1,000 lbs)", ylab="Engine disp (cu in.)", zlab = "Mileage" )

plot3d(mtcars$wt, mtcars$disp, mtcars$mpg, type="p", size=5, lit=FALSE, box=FALSE, col = c("red","blue","green"), expand = 1, main = "Car Weight Vs Engine Displacement Vs Mileage", sub="", xlab = "Weight (1,000 lbs)", ylab="Engine disp (cu in.)", zlab = "Mileage" )

mtcars$col

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Data Science: Data Visualization Boot Camp 3D R's rgl packageccartled/Teaching/2020-Spring/DataVisualization/Presentations/...delFromSubscene3d(obj, subscene = root) 24/28 Type Sample