Exploratory analysis

In [1]:

    

source("https://raw.githubusercontent.com/eogasawara/mylibrary/master/myGraphics.R")

loadlibrary("MASS")
loadlibrary("gclus")
loadlibrary("RColorBrewer")
loadlibrary("gridExtra")
loadlibrary("GGally")
loadlibrary("reshape")
loadlibrary("dplyr")
loadlibrary("WVPlots")
loadlibrary("aplpack")

col.set <- brewer.pal(11, 'Spectral')
mycolors <- col.set[c(1,3,5,7,9)]

plot_size(4, 3)


    

In [2]:

    

library("MASS")
data(iris)
t(sapply(iris, class))
iris[c(1:3,51:53,101:103),]


    

    






Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Species

	
numeric
numeric
numeric
numeric
factor 









    






Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Species

	
1
5.1       
3.5       
1.4       
0.2       
setosa    
	
2
4.9       
3.0       
1.4       
0.2       
setosa    
	
3
4.7       
3.2       
1.3       
0.2       
setosa    
	
51
7.0       
3.2       
4.7       
1.4       
versicolor
	
52
6.4       
3.2       
4.5       
1.5       
versicolor
	
53
6.9       
3.1       
4.9       
1.5       
versicolor
	
101
6.3       
3.3       
6.0       
2.5       
virginica 
	
102
5.8       
2.7       
5.1       
1.9       
virginica 
	
103
7.1       
3.0       
5.9       
2.1       
virginica 

In [3]:

    

sum <-summary(iris$Sepal.Length)
exp_table(t(sum), c("Statistics", "Freq") , proj=1)
IQR <- sum["3rd Qu."]-sum["1st Qu."]
print(sprintf("IQR=%.1f", IQR))


    

    






Statistics
Freq

	
Min.    
4.300000
	
1st Qu. 
5.100000
	
Median  
5.800000
	
Mean    
5.843333
	
3rd Qu. 
6.400000
	
Max.    
7.900000









    




[1] "IQR=1.3"

In [4]:

    

grfA <- plot.hist(iris %>% select(variable="Sepal.Length", value=Sepal.Length), label_x ="Sepal.Length", color=mycolors[1])
grfB <- plot.hist(iris %>% select(variable="Sepal.Width", value=Sepal.Width), label_x ="Sepal.Width", color=mycolors[1])
grfC <- plot.hist(iris %>% select(variable="Petal.Length", value=Petal.Length), label_x ="Petal.Length", color=mycolors[1])
grfD <- plot.hist(iris %>% select(variable="Petal.Width", value=Petal.Width), label_x ="Petal.Width", color=mycolors[1])

plot_size(7, 2)
grid.arrange(grfA, grfB, grfC, grfD, ncol=4, nrow=1)
plot_size(4, 3)


    

In [5]:

    

data <- melt(iris)
grfA <- plot.density(data, colors=mycolors[1:4])
plot(grfA)


    

    




Using Species as id variables






    

In [6]:

    

grfA <- plot.density(iris %>% select(variable=Species, value=Sepal.Length), label_x = "Sepal.Length", color=mycolors[c(1:3)])
grfB <- plot.density(iris %>% select(variable=Species, value=Sepal.Width), label_x = "Sepal.Width", color=mycolors[c(1:3)])
grfC <- plot.density(iris %>% select(variable=Species, value=Petal.Length), label_x = "Petal.Length", color=mycolors[c(1:3)])
grfD <- plot.density(iris %>% select(variable=Species, value=Petal.Width), label_x = "Petal.Width", color=mycolors[c(1:3)])

plot_size(7, 4)
grid.arrange(grfA, grfB, grfC, grfD, ncol=2, nrow=2)
plot_size(4, 3)


    

In [7]:

    

data <- melt(iris)
grfA <- plot.boxplot(data, colors=mycolors[1:4])
plot(grfA)


    

    




Using Species as id variables






    

In [8]:

    

grfA <- plot.boxplot(iris %>% select(variable=Species, value=Sepal.Length), label_x = "Sepal.Length", color=mycolors[c(1:3)])
grfB <- plot.boxplot(iris %>% select(variable=Species, value=Sepal.Width), label_x = "Sepal.Width", color=mycolors[c(1:3)])
grfC <- plot.boxplot(iris %>% select(variable=Species, value=Petal.Length), label_x = "Petal.Length", color=mycolors[c(1:3)])
grfD <- plot.boxplot(iris %>% select(variable=Species, value=Petal.Width), label_x = "Petal.Width", color=mycolors[c(1:3)])

plot_size(7, 2)
grid.arrange(grfA, grfB, grfC, grfD, ncol=4, nrow=1)
plot_size(4, 3)


    

In [9]:

    

grfA <- exp_norm_dist(iris$Sepal.Length, label_y = "Sepal.Length", color=mycolors[1])
grfB <- exp_norm_dist(iris$Sepal.Width, label_y = "Sepal.Width", color=mycolors[1])
grfC <- exp_norm_dist(iris$Petal.Length, label_y = "Petal.Length", color=mycolors[1])
grfD <- exp_norm_dist(iris$Petal.Width, label_y = "Petal.Width", color=mycolors[1])

plot_size(7, 2)
grid.arrange(grfA, grfB, grfC, grfD, ncol=4, nrow=1)
plot_size(4, 3)


    

In [10]:

    

grf <- exp_correlation(iris[,1:4], color = mycolors[1:3]) 

plot(grf)


    

In [11]:

    

grf <- ggparcoord(data = iris, columns = c(1:4), group=5) + theme_bw(base_size = 10) + scale_color_manual(values=mycolors[1:3])

plot_size(5, 3)
plot(grf)
plot_size(4, 3)


    

In [12]:

    

grf <- exp_pair_plot(data=iris, cnames=colnames(iris)[1:4], title="Iris", colors=mycolors[1])

plot_size(7, 5)
grf
plot_size(4, 3)


    

In [13]:

    

grf <- exp_pair_plot(data=iris, cnames=colnames(iris)[1:4], clabel='Species', title="Iris", colors=mycolors[1:3])

plot_size(8, 5)
grf
plot_size(4, 3)


    

In [14]:

    

grf <- exp_advpair_plot(data=iris, cnames=colnames(iris)[1:4], title="Iris", colors=mycolors[1])

plot_size(7, 5)
grf
plot_size(4, 3)


    

In [15]:

    

grf <- exp_advpair_plot(data=iris, cnames=colnames(iris)[1:4], title="Iris", clabel='Species', colors=mycolors[1:3])

plot_size(8, 5)
grf
plot_size(4, 3)


    

    




`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.






    

In [16]:

    

mat <- as.matrix(iris[,1:4])
x <- (1:nrow(mat))
y <- (1:ncol(mat))
image(x, y, mat, col = col.set, axes = FALSE,  main = "Iris", xlab="sample", ylab="Attributes")
axis(2, at = seq(0, ncol(mat), by = 1))
axis(1, at = seq(0, nrow(mat), by = 10))


    

In [17]:

    

set.seed(1)
sample_rows = sample(1:nrow(iris), 25)

isample = iris[sample_rows,]
labels = as.character(rownames(isample))
isample$Species <- NULL


plot_size(8, 6)
faces(isample, labels = labels, print.info=F, cex=1)
plot_size(4, 3)


    

In [18]:

    

set.seed(1)
sample_rows = sample(1:nrow(iris), 25)

isample = iris[sample_rows,]
labels = as.character(isample$Species)
isample$Species <- NULL

plot_size(8, 6)
faces(isample, labels = labels, print.info=F, cex=1)
plot_size(4, 3)


    

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