Lecture Note 1: R code for Examples

Setup

library(R.matlab)

## Warning: package 'R.matlab' was built under R version 4.0.3

## R.matlab v3.6.2 (2018-09-26) successfully loaded. See ?R.matlab for help.

## 
## Attaching package: 'R.matlab'

## The following objects are masked from 'package:base':
## 
##     getOption, isOpen

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.0.3

library(ggExtra)

## Warning: package 'ggExtra' was built under R version 4.0.3

library(GGally)

## Warning: package 'GGally' was built under R version 4.0.3

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(gridExtra)

## Warning: package 'gridExtra' was built under R version 4.0.3

library(rgl)

## Warning: package 'rgl' was built under R version 4.0.3

knitr::knit_hooks$set(webgl = hook_webgl)
library(fmsb)

## Warning: package 'fmsb' was built under R version 4.0.3

library(aplpack)

## Warning: package 'aplpack' was built under R version 4.0.3

library(TeachingDemos)

## Warning: package 'TeachingDemos' was built under R version 4.0.3

## 
## Attaching package: 'TeachingDemos'

## The following objects are masked from 'package:aplpack':
## 
##     faces, slider

Example 1 and Example 2

dollar_sales<-c(42,52,48,58)

number_books <-c(4,5,4,3)

Bookstore_sale<-cbind(dollar_sales, number_books)

colMeans(Bookstore_sale)

## dollar_sales number_books 
##           50            4

cov(Bookstore_sale)

##              dollar_sales number_books
## dollar_sales     45.33333   -2.0000000
## number_books     -2.00000    0.6666667

cor(Bookstore_sale)

##              dollar_sales number_books
## dollar_sales    1.0000000   -0.3638034
## number_books   -0.3638034    1.0000000

Upload Datasets of Examples 1.4, 1.5, 1.6, 1.9, 1.10, 1.11

Bear_data<-readMat("Bear.mat")

## Warning in readMat("Bear.mat"): strings not representable in native encoding
## will be translated to UTF-8

attributes(Bear_data)

## $names
## [1] "Bear"
## 
## $header
## $header$description
## [1] "MATLAB 5.0 MAT-file, Platform: PCWIN, Created on: Thu Jan 17 13:03:48 2008                                                  "
## 
## $header$version
## [1] "5"
## 
## $header$endian
## [1] "little"

Bear<-Bear_data$Bear

Bear

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,]    1   48   59   95   82  141  157  168  183
## [2,]    2   59   68  102  102  140  168  174  170
## [3,]    3   61   77   93  107  145  162  172  177
## [4,]    4   54   43  104  104  146  159  176  171
## [5,]    5  100  145  185  247  150  158  168  175
## [6,]    6   68   82   95  118  142  140  178  189
## [7,]    7   68   95  109  111  139  171  176  175

write.csv(Bear, file="Bear.csv") # save the data as csv format in the working directory

Lizard.size<-readMat("Lizard_Size.mat")

attributes(Lizard.size)

## $names
## [1] "Data.all"
## 
## $header
## $header$description
## [1] "MATLAB 5.0 MAT-file, Platform: PCWIN, Created on: Mon Jan 14 10:39:42 2008                                                  "
## 
## $header$version
## [1] "5"
## 
## $header$endian
## [1] "little"

Lizard.size<-as.data.frame(Lizard.size$Data.all)

Lizard.size

##    V1     V2   V3    V4 V5
## 1   1  5.526 59.0 113.5  0
## 2   2 10.401 75.0 142.0  1
## 3   3  9.213 69.0 124.0  0
## 4   4  8.953 67.5 124.0  0
## 5   5  7.063 62.0 129.5  1
## 6   6  6.610 62.0 123.0  0
## 7   7 11.273 74.0 140.0  1
## 8   8  2.447 47.0  97.0  0
## 9   9 15.493 86.5 162.0  1
## 10 10  9.004 69.0 126.5  0
## 11 11  8.199 70.5 136.0  1
## 12 12  6.601 64.5 116.0  0
## 13 13  7.622 67.5 135.0  1
## 14 14 10.067 73.0 136.5  1
## 15 15 10.091 73.0 135.5  1
## 16 16 10.999 77.0 139.0  1
## 17 17  7.610 61.5 118.0  0
## 18 18  7.733 66.5 133.5  1
## 19 19 12.015 79.5 150.0  1
## 20 20 10.049 74.0 137.0  1
## 21 21  5.149 59.5 116.0  0
## 22 22  9.158 68.0 123.0  0
## 23 23 12.132 75.0 141.0  1
## 24 24  6.978 66.5 117.0  0
## 25 25  6.890 63.0 117.0  0

write.csv(Lizard.size, file="Lizard_size.csv") # save the data as csv format in the working directory

playroll<-read.table("playroll.DAT")

playroll

##        V1    V2
## 1 3497900 0.623
## 2 2485475 0.593
## 3 1782875 0.512
## 4 1725450 0.500
## 5 1645575 0.463
## 6 1469800 0.395

lumber <- read.table("lumber.DAT")

lumber

##      V1   V2   V3   V4
## 1  1889 1651 1561 1778
## 2  2403 2048 2087 2197
## 3  2119 1700 1815 2222
## 4  1645 1627 1110 1533
## 5  1976 1916 1614 1883
## 6  1712 1712 1439 1546
## 7  1943 1685 1271 1671
## 8  2104 1820 1717 1874
## 9  2983 2794 2412 2581
## 10 1745 1600 1384 1508
## 11 1710 1591 1518 1667
## 12 2046 1907 1627 1898
## 13 1840 1841 1595 1741
## 14 1867 1685 1493 1678
## 15 1859 1649 1389 1714
## 16 1954 2149 1180 1281
## 17 1325 1170 1002 1176
## 18 1419 1371 1252 1308
## 19 1828 1634 1602 1755
## 20 1725 1594 1313 1646
## 21 2276 2189 1547 2111
## 22 1899 1614 1422 1477
## 23 1633 1513 1290 1516
## 24 2061 1867 1646 2037
## 25 1856 1493 1356 1533
## 26 1727 1412 1238 1469
## 27 2168 1896 1701 1834
## 28 1655 1675 1414 1597
## 29 2326 2301 2065 2234
## 30 1490 1382 1214 1284

paper_quality <- read.table("paper_quality.DAT")

paper_quality

##       V1     V2    V3
## 1  0.801 121.41 70.42
## 2  0.824 127.70 72.47
## 3  0.841 129.20 78.20
## 4  0.816 131.80 74.89
## 5  0.840 135.10 71.21
## 6  0.842 131.50 78.39
## 7  0.820 126.70 69.02
## 8  0.802 115.10 73.10
## 9  0.828 130.80 79.28
## 10 0.819 124.60 76.48
## 11 0.826 118.31 70.25
## 12 0.802 114.20 72.88
## 13 0.810 120.30 68.23
## 14 0.802 115.70 68.12
## 15 0.832 117.51 71.62
## 16 0.796 109.81 53.10
## 17 0.759 109.10 50.85
## 18 0.770 115.10 51.68
## 19 0.759 118.31 50.60
## 20 0.772 112.60 53.51
## 21 0.806 116.20 56.53
## 22 0.803 118.00 70.70
## 23 0.845 131.00 74.35
## 24 0.822 125.70 68.29
## 25 0.971 126.10 72.10
## 26 0.816 125.80 70.64
## 27 0.836 125.50 76.33
## 28 0.815 127.80 76.75
## 29 0.822 130.50 80.33
## 30 0.822 127.90 75.68
## 31 0.843 123.90 78.54
## 32 0.824 124.10 71.91
## 33 0.788 120.80 68.22
## 34 0.782 107.40 54.42
## 35 0.795 120.70 70.41
## 36 0.805 121.91 73.68
## 37 0.836 122.31 74.93
## 38 0.788 110.60 53.52
## 39 0.772 103.51 48.93
## 40 0.776 110.71 53.67
## 41 0.758 113.80 52.42

Utility <- read.table("Utility.txt")

Utility

##      V1   V2  V3   V4   V5    V6   V7    V8
## 1  1.06  9.2 151 54.4  1.6  9077  0.0 0.628
## 2  0.89 10.3 202 57.9  2.2  5088 25.3 1.555
## 3  1.43 15.4 113 53.0  3.4  9212  0.0 1.058
## 4  1.02 11.2 168 56.0  0.3  6423 34.3 0.700
## 5  1.49  8.8 192 51.2  1.0  3300 15.6 2.044
## 6  1.32 13.5 111 60.0 -2.2 11127 22.5 1.241
## 7  1.22 12.2 175 67.6  2.2  7642  0.0 1.652
## 8  1.10  9.2 245 57.0  3.3 13082  0.0 0.309
## 9  1.34 13.0 168 60.4  7.2  8406  0.0 0.862
## 10 1.12 12.4 197 53.0  2.7  6455 39.2 0.623
## 11 0.75  7.5 173 51.5  6.5 17441  0.0 0.768
## 12 1.13 10.9 178 62.0  3.7  6154  0.0 1.897
## 13 1.15 12.7 199 53.7  6.4  7179 50.2 0.527
## 14 1.09 12.0  96 49.8  1.4  9673  0.0 0.588
## 15 0.96  7.6 164 62.2 -0.1  6468  0.9 1.400
## 16 1.16  9.9 252 56.0  9.2 15991  0.0 0.620
## 17 0.76  6.4 136 61.9  9.0  5714  8.3 1.920
## 18 1.05 12.6 150 56.7  2.7 10140  0.0 1.108
## 19 1.16 11.7 104 54.0 -2.1 13507  0.0 0.636
## 20 1.20 11.8 148 59.9  3.5  7287 41.1 0.702
## 21 1.04  8.6 204 61.0  3.5  6650  0.0 2.116
## 22 1.07  9.3 174 54.3  5.9 10093 26.6 1.306

Figure 1.1 and Figure 1.2

x1 <- c(3,4,2,6,8,2,5)
x2 <- c(5,5.5,4,7,10,5,7.5)
 
X1<- rbind(x1,x2)

x1 <- c(5,4,6,2,2,8,3)
x2 <- c(5,5.5,4,7,10,5,7.5)

X2<- rbind(x1,x2)

p <- ggplot(as.data.frame(t(X1)), aes(x=x1, y=x2, color=1, size=1)) +
          geom_point() +
            theme(legend.position="none")
p1 <- ggMarginal(p, type="histogram")


p2 <- ggplot(as.data.frame(t(X2)), aes(x=x1, y=x2, color=1, size=1)) +
  geom_point() +
  theme(legend.position="none")
p3 <- ggMarginal(p2, type="histogram")

grid.arrange(p1, p3, nrow = 1)

Example 1.4

par(mfrow=c(1,1))
plot(playroll[,1]/1000000, playroll[,2], xlab="Player payroll in millions of dollars", ylab="Won-lost percentage", ylim=c(0.35,0.7), col="blue", type="b")

Example 1.5

names(paper_quality)=c("Density", "Strength(MD)", "Strength(CD)" )
ggpairs(paper_quality, upper=list(continuous = "points", combo = "facethist", discrete = "facetbar", na = "na"))

Example 1.6, Example 1.7, and EXample 1.9

names(Lizard.size)<-list("Lizard", "Mass", "SVL", "HLS", "Sex")
plot3d(Lizard.size[,2:4], size=5)

mycolors <- c('royalblue1', 'darkcyan')
Lizard.size$color<-mycolors[as.numeric(Lizard.size$Sex)+1]
plot3d(Lizard.size[,2:4], col=Lizard.size$color,size=5)

plot3d(lumber[,1:3], size=5)

plot3d(lumber[,2:4], size=5)

plot3d(lumber[,c(1:2,4)], size=5)

plot3d(lumber[,c(1,3:4)], size=5)

Example 1.10

plot(x=c(2:5), xlab="Year", ylab="Weight", y=Bear[1,3:6], ylim=c(50,270), type="b", lty=1, pch=1,col=1)

for (i in 2:7){

  lines(x=c(2:5), y=Bear[i,2:5], ylim=c(50,270), type="b", lty=i,pch=i,col=i)
  
}

legend(2,250, c(1:7), cex=0.8, col=c(1:7), pch=1:7)


title(main="Combined growth curves for weight for seven female grizzly bears")

Example 1.11

stars(Utility)

Example 1.12

faces(Utility)

faces2(Utility)

Lecture Note 1: R code for Examples

PENG Heng

1/14/2021

Setup

Example 1 and Example 2

Upload Datasets of Examples 1.4, 1.5, 1.6, 1.9, 1.10, 1.11

Figure 1.1 and Figure 1.2

Example 1.4

Example 1.5

Example 1.6, Example 1.7, and EXample 1.9

Example 1.10

Example 1.11

Example 1.12