Graphics - GitHub Pages · 2017. 10. 27. · Graphics examples Graphics...

Graphics examplesGraphics

Graphics

Thomas Källman (adopted from slides by M. Kierczak

10/4/2017

Thomas Källman (adopted from slides by M. Kierczak Graphics

Graphics examples

World map with data overlayed

−150 −100 −50 0 50 100 150

Figure 1: Climate stability (From: M. Pettersson, UU)Thomas Källman (adopted from slides by M. Kierczak Graphics

Voronoi tesselations on maps

●●

● ●

●●

● ●●

●●

● ●

●●

●●●

●●

Figure 2: Airport coverage poland

Faces of asia

Figure 3: Use figures to represent data

Gapminder type of plots

Figure 4: Economy vs expected life spanThomas Källman (adopted from slides by M. Kierczak Graphics

Circos plots

Figure 5: Human genome with metadataThomas Källman (adopted from slides by M. Kierczak Graphics

Graphics

Graphical devices in R

The concept of a graphical device is crucial for understanding Rgraphics. A device can be a screen (default) or a file. Some Rpackages introduce their own devices, e.g. Cairo device.

Create plots:

opening a graphical device (not necessary for plotting onscreen)plotting to the graphical deviceclosing the graphical device (very important!)

The most common devices

screenbitmap/raster devices: png(), bmp(), tiff(), jpeg()vector devices: svg(), pdf(),Cairo versions of the above devices – for Windows users theyoffer higher quality graphics

for more information visithttp://stat.ethz.ch/R-manual/Rdevel/library/grDevices/html/Devices.html

Plotting on screen

plot(x=c(1,2,7), y=c(2,3,5))

1 2 3 4 5 6 7

c(1, 2, 7)

Plotting to file

png(filename = 'myplot.png',width = 320,height = 320,antialias = TRUE)

plot(x=c(1,2,7), y=c(2,3,5))dev.off()

Base R graphics viewport

Figure 6: R plotting areasThomas Källman (adopted from slides by M. Kierczak Graphics

Changing parameters

For convience we will create a data frame to hold our data

df <- data.frame(x = c(1,2,7),y = c(2,3,5),row.names = c("A","B","C"))

## x y## A 1 2## B 2 3## C 7 5

Changing parameters

plot(df, pch=c(15,17,19),col=c("tomato", "slateblue"),main="Three points")

1 2 3 4 5 6 7

Three points

Plotting altering defaults

There is many parameters one can set in plot().

pch – type of the plotting symbolcol – color of the pointscex – scale for pointsmain – main title of the plotsub – subtitle of the plotxlab – X-axis labelylab – Y-axis labellas – axis labels orientationcex.axis – axis lables scale

Changing parametersGraphical parameters can be set in two different ways:

As plotting function arguments, e.g. plot(dat, cex=0.5)using the function par().

# read current graphical parameterspar()# first, save the current parameters so that you# can set them back if neededopar <- par() # should work in theory, practise varies :-(# now, modify what you wantpar(cex.axis = 0.8, bg='grey')# do your plottingplot(................)# restore the old parameters if you wantpar(opar)

pch - example

# create a grid of coordinatescoords <- expand.grid(1:6,1:6)# make a vector of numerical pch symbolspch.num <- c(0:25)# and a vector of character pch symbolspch.symb <- c('*', '.', 'o', 'O', '0', '-', '+', '|', '%', '#')# plot numerical pchplot(coords[1:26,1], coords[1:26,2], pch=pch.num,bty='n', xaxt='n', yaxt='n', bg='red',xlab='', ylab='')# and character pch'spoints(coords[27:36,1], coords[27:36,2], pch=pch.symb)# label themtext(coords[,1], coords[,2], c(1:26, pch.symb), pos = 1,col='slateblue', cex=.8)

pch - example

0 − + | % #

1 2 3 4 5 6

7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

25 26 * . o O

0 − + | % #

Visualising graph parameters

0 5 10 15 20

Graphic layersElements are added to a plot in the same order you plot them. It islike layers in a graphical program. Think about it! For instance theauxiliary grid lines should be plotted before the actual data points.

# make an empty plotplot(1:5, type='n', las=1,bty='n')# plot gridgrid(col='grey', lty=3)# plot the actual datapoints(1:5, pch=19, cex=3)# plot a lineabline(h = 3.1, col='red')# you see, it overlaps the# data point. It is better# to plot it before points()Thomas Källman (adopted from slides by M. Kierczak Graphics

Graphic layers

1 2 3 4 5

General considerations

raster or vector graphics,colors, e.g. color-blind people, warm vs. cool colors and opticalillusionsavoid complications, e.g. 3D plots, pie charts etcuse black ang greys for things you do not need to emphasize,i.e. basically everything except your main result,avoid 3 axis

Multiple plots one figure

par(mfrow=c(2,3))plot(1:5)plot(1:5, pch=19, col='red')plot(1:5, pch=15, col='blue')hist(rnorm(100, mean = 0, sd=1))hist(rnorm(100, mean = 7, sd=3))hist(rnorm(100, mean = 1, sd=0.5))par(mfrow=c(1,1))

1 2 3 4 5

5Index

1 2 3 4 5

Histogram of rnorm(100, mean = 0, sd = 1)

rnorm(100, mean = 0, sd = 1)

−2 −1 0 1 2 3

rnorm(100, mean = 7, sd = 3)

0 5 10 15

Histogram of rnorm(100, mean = 1, sd = 0.5)

rnorm(100, mean = 1, sd = 0.5)

cy0.0 0.5 1.0 1.5 2.0

If more complex compositions the layout function can be used

layout(matrix(c(1, 2, 2, 2, 3, 2, 2, 2), nrow = 2,ncol = 4, byrow = T))

plot(1:5, pch = 15, col = "blue")hist(rnorm(100, mean = 0, sd = 1))plot(1:5, pch = 15, col = "red")

rnorm(100, mean = 0, sd = 1)

−3 −2 −1 0 1 2

Colors

mycol <- c(rgb(0, 0, 1), "olivedrab", "#FF0000")plot(1:3, c(1, 1, 1), col = mycol, pch = 19, cex = 3)points(2, 1, col = rgb(0, 0, 1, 0.5), pch = 15, cex = 5)

1.0 1.5 2.0 2.5 3.0

Colors

There are built-in color palettes

mypal <- heat.colors(10)mypal

## [1] "#FF0000FF" "#FF2400FF" "#FF4900FF" "#FF6D00FF" "#FF9200FF"## [6] "#FFB600FF" "#FFDB00FF" "#FFFF00FF" "#FFFF40FF" "#FFFFBFFF"

Colors

pie(x = rep(1, times = 10), col = mypal)

Colors

mypal <- colorRampPalette(c("red", "green", "blue"))pie(x = rep(1, times = 12), col = mypal(12))

Note that cololRampPalette returns a functionThomas Källman (adopted from slides by M. Kierczak Graphics

Colors

library(RColorBrewer)display.brewer.all()

BrBGPiYG

PRGnPuOrRdBuRdGy

RdYlBuRdYlGnSpectral

AccentDark2Paired

Pastel1Pastel2

Set1Set2Set3

BluesBuGnBuPuGnBu

GreensGreys

OrangesOrRdPuBu

PuBuGnPuRd

PurplesRdPuRedsYlGn

YlGnBuYlOrBrYlOrRd

Histogram

x <- rnorm(1000) # generate sample from normal dist.hist(x)

Histogram of x

−4 −2 0 2

Histogram

hist(x, breaks = 100)

Histogram of x

−4 −2 0 2

Boxplot

boxplot(x)

Hist. vs Box

Histogram of x

−4 −2 0 2

Violinplot

library(vioplot)

## Loading required package: sm

## Package 'sm', version 2.2-5.4: type help(sm) for summary information

vioplot(x)

Plotting categorical data

library(vcd)data(Titanic) # Load the datamosaic(Titanic,

labeling=labeling_border(rot_labels = c(0,0,0,0)))

Survived

No Yes

3rd Adult

2nd AdultChild

Male Female

AdultChild

Heatmaps

heatmap(matrix(rnorm(100, mean = 0, sd = 1), nrow = 10),col=terrain.colors(10))

8 9 1 2 3 10 5 4 7 610694732185

More inspiration

http://www.r-graph-gallery.com

Graphics - GitHub Pages · 2017. 10. 27. · Graphics examples Graphics...

Documents

Transcript of Graphics - GitHub Pages · 2017. 10. 27. · Graphics examples Graphics...

”¢品介紹/03... · 2017. 3. 13. · Diamete r(D) (nn) Working Length(W) (nn) 25 45 45 50 100 50 100 50 100 150 200 50 100 150 200 50 100 150 200 100 150 200 300 350 400 100 150

Gilts vs. swaps – what’s going down? · 0 – 5 50 50 6 – 10 60 60 11 – 15 70 70 16 – 20 100 100 21 – 25 150 150 26 – 30 100 100 31 – 35 50 50 ... as proxy, value

Worst-case Exceedance of NML 50 0 50 100 150 m

1 100 pt 150 pt 200 pt 250 pt 50 pt 100 pt 150 pt 200 pt 250 pt 50 pt 100 pt 150 pt 200 pt 250 pt 50 pt 100 pt 150 pt 200 pt 250 pt 50 pt 100 pt 150 pt.

GBC China · 80 100 50 80 100 50 80 100 50 LWmrn) 0.275 0.275 0.275 0.338 0.338 0.338 0.338 0.338 0.338 0.338 50-600C 150 100 100 150 100 100 150 100 100 150 30.3 31.7 39.1 36.6 39.8

50 75 100 25 200 150 50 75 100 200 25 150 50 75 100 200 25 150 50 75 100 200 25 150 50 75 100 200 25 150 Rules.. Tie Breaker Tie Breaker Final Teams Daily.

Microseismic Signatures of Hydraulic Fracture Propagation ......2 -200 -150 -100 -50 0 50 100 150-50 0 50 100 150 200 250 300 East, m North, m Figure 1. Mapview of the hydraulic fracture

SaS-Infographic-v6 - · PDF file10 25 50 75 100 125 150 175 50 100 250 Server Solution Cloud Solution 10 25 50 75 100 125 150 175 50 100 250 500 USERS USERS CO S

Market 150 -100 -150 25 25 20 10 250 200 150 -50 -100 -150 ...

CNP brochure - Rodelta · 100 50 50 65 80 100 125 125 100 125 125 150 200 200 4 5 7 9 DEL 20 25 32 40 50 65 25 32 40 50 65 80 100 65 80 100 125 150 150 Drg. Pump Dimensions Foot Dimensions

Polyurethane BELTS - Elatech · tk10 k13 k6 width (mm) 32 50 75 100 150 atk5 k6 32 50 75 100 width sat10 50 75 100 32 50 75 100 150 atk10 k13 k6 2,5 40° 1, 6 t2.5 0, 7 2, 5 10 40°

RUT950 flyer v1 20 - 4Gon · PDF fileLTE 100/50 Mbps 100/50 Mbps 100/50 Mbps 100/50 Mbps FDD 150/50 Mbps TDD 112/10 Mbps 150/50 Mbps

Eyesight Vision-System – alles ist möglichfiles.vogel.de/vogelonline/vogelonline/companyfiles/5330.pdf · Focal length: 12mm-150-100-50 0 50 100 150 0 100 200 300 400 500 600 700

polynomial reg 150...60 70 80 90 0 50 100 150 x y Polynomial Regression (degree=2) 60 70 80 90 0 50 100 150 x y LOESS 60 70 80 90 0 50 100 150 x y Smoothing Spline 60 70 80 90 0 50

Products Direct FP-50-100-150

Machines Catalogue - OEMME SPA · 2017-05-04 · 2 45° 45° 22,5° 22,5° 90° 90° 90° 45° 22.5° 350 300 250 200 150 100 50 0 50 100 150 200 350 300 250 150 100 50 0 50 100 150

EWH 30, 50, 80, 100, 120, 150 Comfort EWH 30, 50, …...EWH 30, 50, 80, 100, 120, 150 Comfort EWH 30, 50, 80, 100, 120, 150 Comfort N Geschlossene Wandspeicher 30 Liter bis 150 Liter

· B. Tech 3r year(6th Semester) Mechatronics Allotment Marks Sessional Theory Practical 50 25 50 125 TutorialsSpace.com SIMPLE Duration of Exam Total 150 150 150 150 150 150 100

AASHTO NTPEP Rolled Erosion Control Product …08...3:1 Slope 50 100 150 50 100 150 50 100 150 50 100 150 CONTROL 44.8 124.5 237.2 20.743 0.0167 47.7 109.8 252.6 Greensolutions DSC-2

200 150 100 50 d) TRMM- PR Total 200 150 100 50 h ......200 150 100 50 d) TRMM- PR Total 200 150 100 50 h) CloudSat-CPR Total N N N N N N N N Feb Apr Aug Sep Oct Nov Eq 50s loos Jan