Chapter 6. Summarized Data Distributions

library(ggplot2)
library(gcookbook)
library(MASS)
library(plyr)

Section 6.1. Making a Basic Histogram

# Figure 6-1
ggplot(faithful, aes(waiting)) +
  geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Figure 6-2
ggplot(faithful, aes(waiting)) +
  geom_histogram(binwidth = 5, fill = "white", colour = "black")

binsize <- diff(range(faithful$waiting)) / 15
ggplot(faithful, aes(waiting)) +
  geom_histogram(binwidth=binsize, fill = "white", color = "black")

# Figure 6-3
h <- ggplot(faithful, aes(waiting))
h + geom_histogram(binwidth = 8, fill = "white", color = "black", origin = 31)
## Warning: `origin` is deprecated. Please use `boundary` instead.

h + geom_histogram(binwidth = 8, fill = "white", color = "black", origin = 35)
## Warning: `origin` is deprecated. Please use `boundary` instead.

Section 6.2. Making Multiple Histograms from Grouped Data

# Figure 6-4
ggplot(birthwt, aes(bwt)) +
  geom_histogram(fill = "white", color = "black") +
  facet_grid(smoke ~ .)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

bw <- birthwt
bw$smoke <- factor(bw$smoke)
bw$smoke <- revalue(bw$smoke, c("0"="No Smoke","1" = "Smoke"))
ggplot(bw, aes(bwt)) +
  geom_histogram(fill = "white", color = "black") +
  facet_grid(smoke ~ .)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Figure 6-5
ggplot(bw, aes(bwt))  +
  geom_histogram(fill = "white", color = "black") +
  facet_grid(race ~ .)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(bw, aes(bwt))  +
  geom_histogram(fill = "white", color = "black") +
  facet_grid(race ~ ., scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Figure 6-6
ggplot(bw, aes(bwt, fill = smoke)) +
  geom_histogram(position = "identity", alpha = 0.4)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Section 6.3. Making a Density Curve

# Figure 6-7
ggplot(faithful, aes(waiting)) +
  geom_density()

ggplot(faithful, aes(waiting)) +
  geom_line(stat = "density") +
  expand_limits(y=0)

# Figure 6-8
ggplot(faithful, aes(waiting)) +
  geom_line(stat = "density", adjust = 0.25, color = "red") +
  geom_line(stat = "density") +
  geom_line(stat = "density", adjust = 2, color = "blue")

# Figure 6-9
ggplot(faithful, aes(waiting)) +
  geom_density(fill = "blue", alpha = 0.2) +
  xlim(35, 105)

ggplot(faithful, aes(waiting)) +
  geom_density(fill = "blue", color = NA, alpha = 0.2) +
  geom_line(stat = "density") +
  xlim(35, 105)

# Figure 6-10
ggplot(faithful, aes(x = waiting, y = ..density..)) +
  geom_histogram(fill = "cornsilk", color = "grey60", size = 0.2) +
  geom_density() +
  xlim(35, 105)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Section 6.4. Making Multiple Density Curves from Grouped Data

# Figure 6-11
ggplot(bw, aes(bwt, color = smoke)) +
  geom_density()

ggplot(bw, aes(bwt, fill = smoke)) +
  geom_density(alpha = 0.3)

# Figure 6-12
ggplot(bw, aes(bwt)) +
  geom_density() +
  facet_grid(smoke ~ .)

# Figure 6-13
ggplot(bw, aes(bwt, y = ..density..)) +
  geom_histogram(binwidth = 200, fill = "cornsilk", color = "grey60", size = 0.2) +
  geom_density() +
  facet_grid(smoke ~ .)

Section 6.5. Making a Frequency Polygon

# Figure 6-14
ggplot(faithful, aes(waiting)) +
  geom_freqpoly(binwidth = 4)

binsize <- diff(range(faithful$waiting))/15
ggplot(faithful, aes(waiting)) +
  geom_freqpoly(binwidth = binsize)

Section 6.6. Making a Basic Box Plot

# Figure 6-15
ggplot(birthwt, aes(factor(race), bwt)) +
  geom_boxplot()

# Figure 6-17
ggplot(birthwt, aes(factor(race), bwt)) +
  geom_boxplot(width = 0.5)

# Figure 6-18
ggplot(birthwt, aes(factor(race), bwt)) +
  geom_boxplot(outliser.size = 1.5, outlier.shape = 21)
## Warning: Ignoring unknown parameters: outliser.size

Section 6.7. Adding Notches to a Box Plot

# Figure 6-19
ggplot(birthwt, aes(factor(race), bwt)) +
  geom_boxplot(notch = TRUE)
## notch went outside hinges. Try setting notch=FALSE.

Section 6.8. Adding Means to a Box Plot

# Figure 6-20
ggplot(birthwt, aes(factor(race), bwt)) +
  geom_boxplot() +
  stat_summary(fun.y = "mean", geom = "point", shape = 23, size = 3, fill = "white")

Section 3.9. Making a Violin Plot

# Figure 6-21
p <- ggplot(heightweight, aes(sex, heightIn))
p + geom_violin()

# Figure 6-22
p + geom_violin() + geom_boxplot(width = 0.1, fill = "black", outlier.color = NA) +
  stat_summary(fun.y = "median", geom = "point", fill = "white", shape = 21, size = 2.5)

# Figure 6-23
p + geom_violin(trim = FALSE)

# Figure 6-24
p + geom_violin(scale = "count")

# Figure 6-25
p + geom_violin(adjust = 2)

p + geom_violin(adjust = 0.5)

Section 6.10. Making a Dot Plot

# Figure 6-26
countries2009 <- subset(countries, Year == 2009 & healthexp > 2000)
p <- ggplot(countries2009, aes(infmortality))
p + geom_dotplot()
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.

# Figure 6-27
p + geom_dotplot(binwidth = 0.25) + geom_rug() + 
  scale_y_continuous(breaks = NULL) +
  theme(axis.title.y = element_blank())

# Figure 6-28
p + geom_dotplot(method = "histodot", binwidth = 0.25) + geom_rug() + 
  scale_y_continuous(breaks = NULL) +
  theme(axis.title.y = element_blank())

# Figure 6-29
p + geom_dotplot(binwidth = 0.25, stackdir = "center") + geom_rug() + 
  scale_y_continuous(breaks = NULL) +
  theme(axis.title.y = element_blank())

p + geom_dotplot(binwidth = 0.25, stackdir = "centerwhole") + geom_rug() + 
  scale_y_continuous(breaks = NULL) +
  theme(axis.title.y = element_blank())

Section 6.11. Making Multiple Dot Plots for Grouped Data

# Figure 6-30
ggplot(heightweight, aes(sex, heightIn)) +
  geom_dotplot(binaxis = "y", binwidth = 0.5, stackdir = "center")

# Figure 6-31
ggplot(heightweight, aes(sex, heightIn)) +
  geom_boxplot(outlier.color = NA, width = 0.4) +
  geom_dotplot(binaxis = "y", binwidth = 0.5, stackdir = "center", fill = NA)

# Figure 6-32
ggplot(heightweight, aes(sex, heightIn)) +
  geom_boxplot(aes(as.numeric(sex) + .2, group = sex), width = 0.25) +
  geom_dotplot(aes(as.numeric(sex) - .2, group = sex), binaxis = "y", binwidth = 0.5, stackdir = "center") +
    scale_x_continuous(breaks = 1:nlevels(heightweight$sex),
                       labels = levels(heightweight$sex))

Section 6.12. Making a Density Plot of Two-Dimensional Data

# Figure 6-33
p <- ggplot(faithful, aes(eruptions, waiting))
p + geom_point() + stat_density2d()

p + stat_density2d(aes(color = ..level..))

# Figure 6-34
p + stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE)

p + geom_point() + 
  stat_density2d(aes(alpha = ..density..), geom = "tile", contour = FALSE)

# Figure 6-35
p + stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE, h = c(.5, 5))

END!