Lec02 - Visualizing Data Devore-Berk Chp 1 Visualizing...

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Visualizing Data Devore-Berk Chp 1 Visualizing Quantitative Data, Tufte E. R., Graphics Press, 2001 Graphical Methods for Data Analysis , Chambers J., Cleveland, B. Kleiner, and P. Tukey, Duxbury Press, Boston, 1983 Exploratory Data Analysis , Tukey J., Addison-Wesley Pub Co., 1977
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Observation and Perspective
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Tufte: Graphics reveal data. X1 Y1 X2 Y2 X3 Y3 X4 Y4 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47 14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04 6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25 4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50 12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56 7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91 5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
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Tufte Script par(mfrow=c(2,2)) plot(tufte$X1,tufte$Y1,  xlim=c(0,20),ylim=c(0,15),cex=1.5) plot(tufte$X2,tufte$Y2,  xlim=c(0,20),ylim=c(0,15),cex=1.5) plot(tufte$X3,tufte$Y3,  xlim=c(0,20),ylim=c(0,15),cex=1.5) plot(tufte$X4,tufte$Y4,  xlim=c(0,20),ylim=c(0,15),cex=1.5) par(mfrow=c(1,1))
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Communicate complex ideas with clarity, precision, and efficiency – Tufte Show the data Substance rather than method Avoid distortion Present many numbers in a small space Make large data sets coherent Encourage eye to make comparisons Reveal data at several levels Purpose: Description, exploration, tabulation, decoration Closely integrated with statistical and verbal descriptions
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Napoleon’s Russian Campaign Minard (1885) Tufte (2001) http://www.math.yorku.ca/SCS/Gallery/re-minard.html
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Leaves data (histogram) hist(leaves)
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N = 132 Median = 68 Quartiles = 58, 79 Decimal point is 1 place to the right of the colon 2 : 7 3 : 3 : 689 4 : 3 4 : 555688 5 : 00022223344 5 : 556777778888899 6 : 00001111122223344 6 : 5556667888888999 7 : 0011222233444 7 : 5555556666788899999 8 : 0000112222344 8 : 555567777999 9 : 01234 stem(leaves)
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Boxplot boxplot(leaves)
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Exploratory Analysis Class Data Sex, Foot size (cm), Head Circumference (cm)
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Class Data Class    Sex Foot Head   1   F   23  9.5  2   F   20 54.0  3   F   23 59.0  4   M   26 57.0  5   F   26 58.0  6   M   26 56.0 . . .   
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dim(Class) [1] 98  3 Class[1:4,1:3]   Sex Foot Head  1   F   23  9.5 2   F   20 54.0 3   F   23 59.0 4   M   26 57.0
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Accessing Elements Class$Foot[1:5]   23 20 23 26 26 Class[1:5,2]   23 20 23 26 26  Class[1:5,"Foot"]   23 20 23 26 26
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hist(Class$Head)
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This note was uploaded on 09/08/2009 for the course BTRY 3010 taught by Professor Sullivan,p. during the Fall '07 term at Cornell.

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Lec02 - Visualizing Data Devore-Berk Chp 1 Visualizing...

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