Chapter 11. Intermediate graphs

Notebook of Reading Books: R in Action_Chapter 11.

This chapter covers

Visualizing bivariate and multivariate relationships
Working with scatter and line plots
Understanding correlograms
Using mosaic and association plots

11.1. Scatter plots

with abline() and lowess()
id.method option indicates that points will be identified interactively by mouse clicks, until the user selects Stop (via the Graphics or context-sensitive menu) or the Esc key.
Figure 11.1. Scatter plot of car mileage versus weight, with superimposed linear and lowess fit lines.
Figure 11.2. Scatter plot with subgroups and separately estimated fit lines.

11.1.1. Scatter plot matrices

Figure 11.3. Scatter plot matrix created by the pairs() function.
Figure 11.3-1. Scatter plot matrix created by the pairs() function with lower.panel = NULL.
Figure 11.3-2. Scatter plot matrix created by the pairs() function with upper.panel = NULL.
Figure 11.4. Scatter plot matrix created with the scatterplotMatrix() function.
Figure 11.4-2. Scatter plot matrix produced with the cpairs() function in the gclus package.
- Variables closer to the principal diagonal are more highly correlated.

11.1.2. High-density scatter plots

Figure 11.5. Scatter plot with 10,000 observations and significant overlap of data points.
- Note that the overlap of data points makes it difficult to discern where the concentration of data is greatest.
Figure 11.6. Scatterplot using smoothScatter() to plot smoothed density estimates.
- Densities are easy to read from the graph.
Figure 11.7. Scatter plot using hexagonal binning to display the number of observations at each point.
- Data concentrations are easy to see and counts can be read from the legend.
Figure 11.7-1. Scatter plot of 10,000 observations, where density is indicated by color.
- The data concentrations are easily discernable.

11.1.3. 3D scatter plots

scatterplot3d(x, y, z)
Figure 11.8. 3D scatter plot of miles per gallon, auto weight, and displacement.
Figure 11.9. 3D scatter plot with vertical lines and shading.
Figure 11.10. 3D scatter plot with vertical lines, shading, and overlaid regression plane.

11.1.4 Spinning 3D Scatter Plots

plot3d(x, y, z)
Figure 11.11(up) and figure 11.11-1(down). Rotating 3D scatter plot produced by the plot3d() function in the rgl package.
Figure 11.12 and figure 11.12-2. Spinning 3D scatter plot produced by the scatter3d() function in the Rcmdr package.

11.1.5. Bubble plots

Figure 11.13. Bubble plot of car weight versus mpg where point size is proportional to engine displacement.

11.2. Line charts

Figure 11.14. Comparison of a scatter plot and a line plot.
Table 11.1. Line chart options.
Figure 11.15. type=options in the plot() and lines() functions.
Figure 11.16. Line chart displaying the growth of five orange trees.

11.3. Correlograms

with corrgram()
Figure 11.17. Correlogram of the correlations among the variables in the mtcars data frame.
- Rows and columns have been reordered using principal components analysis.
Figure 11.18. Correlogram of the correlations among the variables in the mtcars data frame.
- The lower triangle contains smoothed best fit lines and confidence ellipses;
- And the upper triangle contains scatter plots.
- The diagonal panel contains minimum and maximum values.
- Rows and columns have been reordered using principal components analysis.
Figure 11.19. Correlogram of the correlations among the variables in the mtcars data frame.
- The lower triangle is shaded to represent the magnitude and direction of the correlations.
- The variables are plotted in their original order.
figure 11.20. A Corrgram (or Horse) of a Different Color.

11.4. Mosaic plots

Figure 11.21. Mosaic plot describing Titanic survivors by class, sex, and age.

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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)

# 11.1. Scatter plots
# code listing 11.1. A scatter plot with best fit lines
attach(mtcars)
plot(wt, mpg,
     main = "Basic Scatter plot of MPG vs. Weight",
     xlab="Car Weight (lbs/1000)",
     ylab="Miles Per Gallon ", pch=19)

abline(lm(mpg ~ wt), col="red", lwd=2, lty=1)
lines(lowess(wt, mpg), col="blue", lwd=2, lty=2) #figure 11.1

# figure 11.2
library(car)
scatterplot(mpg ~ wt | cyl, data=mtcars, lwd=2, span=0.75,
            main="Scatter Plot of MPG vs. Weight by # Cylinders",
            xlab="Weight of Car (lbs/1000)",
            ylab="Miles Per Gallon",
            legend.plot=TRUE,
            id=list(method="identify"),
            #labels=row.names(mtcars),
            boxplots="xy"
)


# 11.1.1. Scatter plot matrices
# figure 11.3
pairs(~mpg+disp+drat+wt, data = mtcars,
      main="Basic Scatter Plot Matrix")

# figure 11.3-1
pairs(~mpg+disp+drat+wt, data = mtcars,
      lower.panel = NULL,
      main="Basic Scatter Plot Matrix")

# figure 11.3-2
pairs(~mpg+disp+drat+wt, data = mtcars,
      upper.panel = NULL,
      main="Basic Scatter Plot Matrix")

# figure 11.4
library(car)
scatterplotMatrix(~ mpg + disp + drat + wt, data = mtcars, spead=F,
                  smoother.args=list(lty=2),
                  main="Scatter Plot Matrix via car Package")


cor(mtcars[c("mpg", "wt", "disp", "drat")])
#             mpg         wt       disp       drat
# mpg   1.0000000 -0.8676594 -0.8475514  0.6811719
# wt   -0.8676594  1.0000000  0.8879799 -0.7124406
# disp -0.8475514  0.8879799  1.0000000 -0.7102139
# drat  0.6811719 -0.7124406 -0.7102139  1.0000000
# There were 50 or more warnings (use warnings() to see the first 50)

# code listing 11.2. Scatter plot matrix produced with the gclus package
# install.packages("gclus")
library(gclus)
colnames(mtcars)
# [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"

str(mtcars)
mydata <- mtcars[c(1, 3, 5, 6)]
mydata.corr <- abs(cor(mydata))

mycolors <- dmat.color(mydata.corr)

myorder <- order.single(mydata.corr)

# figure 11.4-2
cpairs(mydata,
       myorder,
       panel.colors=mycolors,
       gap=.5,
       main="Variables Ordered and Colored by Correlation"
)

# 11.1.2. High-density scatter plots

set.seed(1234)

n <- 10000
c1 <- matrix(rnorm(n, mean=0, sd=.5), ncol=2)
c2 <- matrix(rnorm(n, mean=3, sd=2), ncol=2)
mydata <- rbind(c1, c2)
mydata <- as.data.frame(mydata)
names(mydata) <- c("x", "y")

# figure 11.5
with(mydata,
     plot(x, y, pch=19, main="Scatter Plot with 10,000 Observations"))

# figure 11.6
with(mydata,
     smoothScatter(x, y, main="Scatterplot Colored by Smoothed Densities"))

# figure 11.7
# install.packages("hexbin")
library(hexbin)
with(mydata, {
  bin <- hexbin(x, y, xbins=50)
  plot(bin, main="Hexagonal Binning with 10,000 Observations")
})


# figure 11.7-1
# install.packages("IDPmisc")
library(IDPmisc)
with(mydata,
     iplot(x, y, main="Image Scatter Plot with Color Indicating Density"))


# 11.1.3. 3D scatter plots
# figure 11.8
# install.packages("scatterplot3d")
library(scatterplot3d)
attach(mtcars)

scatterplot3d(wt, disp, mpg,
              main="Basic 3D Scatter Plot")


# figure 11.9
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt, disp, mpg,
              pch=16,
              highlight.3d = T,
              type = "h",
              main="3D Scatter Plot with Vertical Lines")

# figure 11.10
library(scatterplot3d)
attach(mtcars)
s3d <- scatterplot3d(wt, disp, mpg,
                     pch=16,
                     highlight.3d = T,
                     type = "h",
                     main="3D Scatter Plot with Vertical Lines")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)

# figure 11.11 and 11.11-1
# continue figure 11.10
# install.packages("rgl")
library(rgl)
attach(mtcars)
plot3d(wt, disp, mpg, col = "red", size=5)

# figure 11.12 and figure 11.12-1
# install.packages("Rcmdr")
library(Rcmdr)
attach(mtcars)
scatter3d(wt, disp, mpg)


# 11.1.5. Bubble plots
attach(mtcars)
r <- sqrt(disp/pi)
symbols(wt, mpg, circles = r, inches = 0.30,
        fg="white", bg="lightblue",
        main="Bubble Plot with point size proportional to displacement",
        ylab = "Miles Per Gallon",
        xlab = "Weight of Car (lbs/1000)")
text(wt, mpg, rownames(mtcars), cex=0.6)
detach(mtcars)

# 11.2. Line charts
# code listing 11.3. Creating side-by-side scatter and line plots
opar <- par(no.readonly = T)
par(mfrow=c(1,2))
table(Orange$Tree)
t1 <- subset(Orange, Tree==1)
plot(t1$age, t1$circumference,
     xlab = "Age (days)",
     ylab = "Circumference (mm)",
     main = "Orange Tree 1 Growth")
plot(t1$age, t1$circumference,
     xlab = "Age (days)",
     ylab = "Circumference (mm)",
     main = "Orange Tree 1 Growth",
     type = "b")
par(opar)

# figure 11.15
opar <- par(no.readonly = T)
par(mfrow=c(2,4))

linetpye <- c("p", "l", "o", "b", "c", "s", "S", "h")

t1 <- subset(Orange, Tree==1)

for (i in linetpye){
  mainstr <- paste("type = ", '"', i, '"')
  plot(t1$age, t1$circumference,
       xlab = "Age (days)",
       ylab = "Circumference (mm)",
       type = "n",
       main = mainstr)
  lines(t1$age, t1$circumference, type = i)
}

par(opar)


# code listing 11.4. Line chart displaying the growth of five orange trees over time
# figure 11.16
class(Orange$Tree)
Orange$Tree <- as.numeric(Orange$Tree)
class(Orange$Tree)

ntrees <- max(Orange$Tree)

xrange <- range(Orange$age)
yrange <- range(Orange$circumference)

plot(xrange, yrange,
     type = "n",
     xlab = "Age (days)",
     ylab = "Circumference (mm)"
     )

colors <- rainbow(ntrees)
linetype <- c(1:ntrees)
plotchar <- seq(18, 18+ntrees, 1)

for (i in 1:ntrees){
    tree <- subset(Orange, Tree==i)
    lines(tree$age, tree$circumference,
          type="b",
          lwd=2,
          lty=linetype[i],
          col=colors[i],
          pch=plotchar[i]
    )
}

title("Tree Growth", "example of line plot")

legend(xrange[1], yrange[2],
       1:ntrees,
       cex = 0.8,
       col = colors,
       pch = plotchar,
       lty=linetype,
       title = "Tree"
)

# 11.3. Correlograms
options(digits = 2)
cor(mtcars)
#        mpg   cyl  disp    hp   drat    wt   qsec    vs     am  gear   carb
# mpg   1.00 -0.85 -0.85 -0.78  0.681 -0.87  0.419  0.66  0.600  0.48 -0.551
# cyl  -0.85  1.00  0.90  0.83 -0.700  0.78 -0.591 -0.81 -0.523 -0.49  0.527
# disp -0.85  0.90  1.00  0.79 -0.710  0.89 -0.434 -0.71 -0.591 -0.56  0.395
# hp   -0.78  0.83  0.79  1.00 -0.449  0.66 -0.708 -0.72 -0.243 -0.13  0.750
# drat  0.68 -0.70 -0.71 -0.45  1.000 -0.71  0.091  0.44  0.713  0.70 -0.091
# wt   -0.87  0.78  0.89  0.66 -0.712  1.00 -0.175 -0.55 -0.692 -0.58  0.428
# qsec  0.42 -0.59 -0.43 -0.71  0.091 -0.17  1.000  0.74 -0.230 -0.21 -0.656
# vs    0.66 -0.81 -0.71 -0.72  0.440 -0.55  0.745  1.00  0.168  0.21 -0.570
# am    0.60 -0.52 -0.59 -0.24  0.713 -0.69 -0.230  0.17  1.000  0.79  0.058
# gear  0.48 -0.49 -0.56 -0.13  0.700 -0.58 -0.213  0.21  0.794  1.00  0.274
# carb -0.55  0.53  0.39  0.75 -0.091  0.43 -0.656 -0.57  0.058  0.27  1.000

# install.packages("corrgram")
library(corrgram)
corrgram(mtcars, order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="Correlogram of mtcars intercorrelations")  # figure 11.17



# a second example
# figure 11.18
library(corrgram)
corrgram(mtcars, order=TRUE, lower.panel=panel.ellipse,
         upper.panel=panel.pts, text.panel=panel.txt,
         diag.panel=panel.minmax,
         main="Correlogram of mtcars data using scatter plots and ellipses")

# figure 11.19 with originalß order
library(corrgram)
corrgram(mtcars, lower.panel=panel.shade,
         upper.panel=NULL, text.panel=panel.txt,
         main="Car Mileage Data (unsorted)")

# figure 11.20
library(corrgram)
col.corrgram <- function(ncol){
  colorRampPalette(c("darkgoldenrod4", "burlywood1",
                     "darkkhaki", "darkgreen"))(ncol)}
corrgram(mtcars, order=TRUE, lower.panel=panel.shade,
         upper.panel=panel.pie, text.panel=panel.txt,
         main="A Corrgram (or Horse) of a Different Color")



# 11.4. Mosaic plots
ftable(Titanic)
#                    Survived  No Yes
# Class Sex    Age                   
# 1st   Male   Child            0   5
#              Adult          118  57
#       Female Child            0   1
#              Adult            4 140
# 2nd   Male   Child            0  11
#              Adult          154  14
#       Female Child            0  13
#              Adult           13  80
# 3rd   Male   Child           35  13
#              Adult          387  75
#       Female Child           17  14
#              Adult           89  76
# Crew  Male   Child            0   0
#              Adult          670 192
#       Female Child            0   0
#              Adult            3  20

# figure 11.21
library(vcd)
mosaic(Titanic, shade=TRUE, legend=TRUE)

# altinative code
# mosaic(~Class+Sex+Age+Survived, data=Titanic, shade=TRUE, legend=TRUE)

Table of Contents

This chapter covers

11.1. Scatter plots

11.1.1. Scatter plot matrices

11.1.2. High-density scatter plots

11.1.3. 3D scatter plots

11.1.4 Spinning 3D Scatter Plots

11.1.5. Bubble plots

11.2. Line charts

11.3. Correlograms

11.4. Mosaic plots