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  pdftitle = {A Handbook of Statistical Analyses Using R},
  pdfsubject = {Book},
  pdfauthor = {Brian S. Everitt and Torsten Hothorn},
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\begin{document}

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\title{A Handbook of Statistical Analyses Using \R{} --- 2nd Edition}

\author{Brian S. Everitt and Torsten Hothorn}

\maketitle
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                "DE", "RP", "GAM", "SA", "ALDI", "ALDII", "SIMC", "MA", "PCA", 
                "MDS", "CA"), 1:18)
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@

\chapter[Data Analysis Using Graphical Displays]{Data Analysis 
         Using Graphical Displays: Malignant Melanoma in the USA and
         Chinese Health and \\ Family Life \label{DAGD}}

\section{Introduction}


\section{Initial Data Analysis}


\section{Analysis Using \R{}}

\subsection{Malignant Melanoma}

\index{Boxplot|(}
\index{Histogram|(}
\index{Scatterplot|(}
We might begin to examine the malignant melanoma data in Table~\ref{DAGD-USmelanoma-tab} 
by constructing a histogram or boxplot for \stress{all} the mortality rates
in Figure~\ref{DAGD-USmelanoma-histbox}.
The \Rcmd{plot}, \Rcmd{hist} and \Rcmd{boxplot} functions have already been introduced in 
\Sexpr{ch("AItR")} and we want to produce a plot where both techniques
are applied at once. The \Rcmd{layout} function organises two independent plots
on one plotting device, for example on top of each other. Using this
relatively simple technique (more advanced methods will be introduced later)
we have to make sure that the $x$-axis is the same in both graphs. This can be done 
by computing a plausible range of the data, later to be specified in a plot via the \Rcmd{xlim}
argument:
<<DAGD-USmelanoma-histbox, echo = TRUE>>=
xr <- range(USmelanoma$mortality) * c(0.9, 1.1)
xr
@
Now, plotting both the histogram and the boxplot requires setting up the
plotting device with equal space for two independent plots on top 
of each other. Calling the \Rcmd{layout} function on a matrix with
two cells in two rows, containing the numbers one and two, leads to such
a partitioning. The \Rcmd{boxplot} function is called first on the mortality
data and then the \Rcmd{hist} function, where the range of the
$x$-axis in both plots is defined by $(\Sexpr{xr[1]}, \Sexpr{xr[2]})$. One
tiny problem to solve is the size of the margins; their
defaults are too large for such a plot. As with many other graphical
parameters, one can adjust their value for a specific
plot using function \Rcmd{par}. The
\R{} code and the resulting display are given in 
Figure~\ref{DAGD-USmelanoma-histbox}.

\begin{figure}
\begin{center}
<<DAGD-USmelanoma-histbox, echo = TRUE, fig = TRUE, width = 8>>=
layout(matrix(1:2, nrow = 2))
par(mar = par("mar") * c(0.8, 1, 1, 1))
boxplot(USmelanoma$mortality, ylim = xr, horizontal = TRUE, 
        xlab = "Mortality")
hist(USmelanoma$mortality, xlim = xr, xlab = "", main = "", 
     axes = FALSE, ylab = "")
axis(1)
@
\caption{Histogram (top) and boxplot (bottom) of malignant melanoma mortality rates.
         \label{DAGD-USmelanoma-histbox}}
\end{center}
\end{figure}



Both the histogram and the boxplot in Figure~\ref{DAGD-USmelanoma-histbox} indicate
a certain skewness of the mortality distribution.
Looking at the characteristics of all the mortality rates is a useful 
beginning but for these data we might be more interested in comparing 
mortality rates for ocean and non-ocean states. So we might construct 
two histograms or two boxplots. Such a \stress{parallel boxplot}, visualising
the conditional distribution of a numeric variable in groups as given by
a categorical variable, are easily computed using the \Rcmd{boxplot} function.
The continuous response variable and the categorical independent variable
are specified via a \Rclass{formula} as described in \Sexpr{ch("AItR")}.
Figure~\ref{DAGD-USmelanoma-boxocean} shows such parallel boxplots,
as by default produced the \Rcmd{plot} function for such data,
for the mortality in ocean and non-ocean states and leads to the 
impression that the mortality is increased in east or west coast
states compared to the rest of the country.
\begin{figure}
\begin{center}
<<DAGD-USmelanoma-boxocean, echo = TRUE, fig = TRUE, width = 8>>=
plot(mortality ~ ocean, data = USmelanoma, 
     xlab = "Contiguity to an ocean", ylab = "Mortality")
@
\caption{Parallel boxplots of malignant melanoma mortality rates by contiguity to an ocean.
         \label{DAGD-USmelanoma-boxocean}}
\end{center}
\end{figure}

Histograms are generally used for two purposes: counting and displaying the 
distribution of a variable; according to \cite{HSAUR:Wilkinson1992}, 
``they are effective for neither''. Histograms can often be misleading for 
displaying distributions because of their dependence on the number of classes chosen. 
An alternative is to formally estimate the density function of a variable and 
then plot the resulting estimate; details of density estimation are 
given in \Sexpr{ch("DE")} but for the ocean and non-ocean states the 
two density estimates can be produced and plotted as shown in 
Figure~\ref{DAGD-USmelanoma-dens} which supports the impression from 
Figure~\ref{DAGD-USmelanoma-boxocean}. For more details on
such density estimates we refer to \Sexpr{ch("DE")}.
\begin{figure}
\begin{center}
<<DAGD-USmelanoma-dens, echo = TRUE, fig = TRUE, width = 8>>=
dyes <- with(USmelanoma, density(mortality[ocean == "yes"]))
dno <- with(USmelanoma, density(mortality[ocean == "no"]))
plot(dyes, lty = 1, xlim = xr, main = "", ylim = c(0, 0.018))
lines(dno, lty = 2)
legend("topleft", lty = 1:2, legend = c("Coastal State", 
       "Land State"), bty = "n")
@
\caption{Estimated densities of malignant melanoma mortality rates by contiguity to an ocean.
         \label{DAGD-USmelanoma-dens}}
\end{center}
\end{figure}

Now we might move on to look at how mortality rates are related to the 
geographic location of a state as represented by the latitude and longitude 
of the centre of the state. Here the main graphic will be the scatterplot.
The simple $xy$ scatterplot has been in use since at least the eighteenth 
century and has many virtues -- indeed according to \cite{HSAUR:Tufte1983}:
\begin{quote}
The relational graphic -- in its barest form the scatterplot and its 
variants -- is the greatest of all graphical designs. It links at least two 
variables, encouraging and even imploring the viewer to assess the 
possible causal relationship between the plotted variables. It confronts 
causal theories that $x$  causes $y$ with empirical evidence as to the actual 
relationship between $x$ and $y$.
\end{quote}

Let's begin with simple scatterplots of mortality rate against longitude %%'
and mortality rate against latitude which can be produced by the code
preceding Figure~\ref{DAGD-USmelanoma-xy}. Again, the 
\Rcmd{layout} function is used for partitioning the plotting device, now
resulting in two side by-side-plots. The argument to \Rcmd{layout}
is now a matrix with only one row but two columns containing the 
numbers one and two. In each cell, the \Rcmd{plot} function
is called for producing a scatterplot of the variables given
in the \Rclass{formula}.
\begin{figure}  
\begin{center}
<<DAGD-USmelanoma-xy, echo = TRUE, fig = TRUE, height = 5, width = 8>>=
layout(matrix(1:2, ncol = 2))
plot(mortality ~ longitude, data = USmelanoma)
plot(mortality ~ latitude, data = USmelanoma)
@
\caption{Scatterplot of malignant melanoma mortality rates by geographical location.
         \label{DAGD-USmelanoma-xy}}
\end{center}
\end{figure} 

Since mortality rate is clearly related only to latitude we can now produce 
scatterplots of mortality rate against latitude separately for ocean and non-ocean 
states. Instead of producing two displays, one can choose different plotting
symbols for either states. This can be achieved by specifying a vector
of integers or characters to the \Rcmd{pch}, where the $i$th element of this
vector defines the plot symbol of the $i$th observation in the data to be plotted.
For the sake of simplicity, we convert the \Robject{ocean} factor to
an \Rclass{integer} vector containing the numbers one for land states and two for 
ocean states. As a consequence, land states can be identified by the dot symbol
and ocean states by triangles. It is useful to add a legend to such a plot,
most conveniently by using the \Rcmd{legend} function. This function takes
three arguments: a string indicating the position of the legend in the plot,
a character vector of labels to be printed and the corresponding plotting
symbols (referred to by integers). In addition, the display of a bounding box
is anticipated (\Rcmd{bty = "n"}).
\begin{figure}  
\begin{center}
<<DAGD-USmelanoma-lat, echo = TRUE, fig = TRUE>>=
plot(mortality ~ latitude, data = USmelanoma, 
     pch = as.integer(USmelanoma$ocean))
legend("topright", legend = c("Land state", "Coast state"), 
       pch = 1:2, bty = "n")
@
\caption{Scatterplot of malignant melanoma mortality rates against latitude.
         \label{DAGD-USmelanoma-lat}}
\end{center}
\end{figure}
The scatterplot in Figure~\ref{DAGD-USmelanoma-lat} highlights that
the mortality is lowest in the northern land states. Coastal states show
a higher mortality than land states at roughly the same latitude. The highest
mortalities can be observed for the south coastal states with latitude less than
$32^\circ$, say, that is
<<DAGD-USmelanoma-south, echo = TRUE>>=
subset(USmelanoma, latitude < 32)
@

Up to now we have primarily focused on the visualisation of 
continuous variables. We now extend our focus to the visualisation
of categorical variables.

\index{Boxplot|)}
\index{Histogram|)}
\index{Scatterplot|)}


\subsection{Chinese Health and Family Life}


\index{Barchart|(}
\index{Spineplot|(}
\index{Spinogram|(}
One part of the questionnaire the Chinese Health and Family Life
Survey focuses on is the self-reported health status. Two
questions are interesting for us. The first one is ``Generally speaking,
do you consider the condition of your health to be excellent,
good, fair, not good, or poor?''.
The second question is ``Generally speaking, in the past twelve months,
how happy were you?''. The distribution of such variables is commonly
visualised using barcharts where for each category the
total or relative number of observations is displayed. 
Such a barchart can conveniently be produced by applying the
\Rcmd{barplot} function to a tabulation of the data. The
empirical density of the variable \Robject{R\_happy}
is computed by the \Rcmd{xtabs} function for producing
(contingency) tables; the resulting barchart is given
in Figure~\ref{DAGD-CHFLS-happy}.

\begin{figure}
<<DAGD-CHFLS-happy, echo = TRUE, fig = TRUE, width = 8>>=
barplot(xtabs(~ R_happy, data = CHFLS))
@
\caption{Bar chart of happiness. \label{DAGD-CHFLS-happy}}
\end{figure}

The visualisation of two categorical variables could be 
done by conditional barcharts, i.e., barcharts of the
first variable within the categories of the second variable.
An attractive alternative for displaying such two-way tables
are \stress{spineplots} \citep{HSAUR:Friendly1994,HSAUR:HofmannTheus2005,HSAUR:Chenetal2008};
the meaning of the name will become clear
when looking at such a plot in Figure~\ref{DAGD-CHFLS-health_happy}.

Before constructing such a plot, we produce a two-way table of the health status
and self-reported happiness using the \Rcmd{xtabs} function:
<<DAGD-CHFLS-health_happy_xtabs, echo = TRUE>>=
xtabs(~ R_happy + R_health, data = CHFLS)
@
<<DAGD-CHFLS-health_happy_xtabs2, echo = FALSE>>=
hh <- xtabs(~ R_health + R_happy, data = CHFLS)
@

A \stress{spineplot} is a group of rectangles, each representing
one cell in the two-way contingency table. The area of the
rectangle is proportional with the number of observations 
in the cell. Here, we produce a mosaic plot of health status and happiness
in Figure~\ref{DAGD-CHFLS-health_happy}.
\begin{figure}
<<DAGD-CHFLS-health_happy, echo = TRUE, fig = TRUE, width = 8, height = 8>>=
plot(R_happy ~ R_health, data = CHFLS)
@
\caption{Spineplot of health status and happiness. 
         \label{DAGD-CHFLS-health_happy}}
\end{figure}

Consider the right upper cell in Figure~\ref{DAGD-CHFLS-health_happy}, i.e.,
the $\Sexpr{hh["Excellent", "Very happy"]}$ very happy women with
excellent health status. The width of the right-most bar corresponds
to the frequency of women with excellent health status. The length 
of the top-right rectangle corresponds 
to the conditional frequency of very happy women given their health status is
excellent. Multiplying these two quantities gives the area of this
cell which corresponds to the frequency of women who are both
very happy and enjoy an excellent health status. The conditional
frequency of very happy women increases with increasing health status, whereas
the conditional frequency of very unhappy or not too happy women decreases.

When the association of a categorical and a continuous variable
is of interest, say the monthly income and self-reported happiness,
one might use parallel boxplots to visualise the distribution
of the income depending on happiness. If we were studying self-reported
happiness as response and income as independent variable, however, 
this would give a representation of the conditional distribution
of income given happiness, but we are interested in the 
conditional distribution of happiness given income.
One possibility to produce a more appropriate plot
is called \stress{spinogram}. Here, the continuous $x$-variable is
categorised first. Within each of these categories, the conditional
frequencies of the response variable are given by stacked barcharts, 
in a way similar to spineplots. For happiness depending
on log-income (since income is naturally skewed we use a 
log-transformation of the income) 
it seems that the proportion of unhappy and not too
happy women decreases with increasing income whereas the proportion
of very happy women stays rather constant. In contrast
to spinograms, where bins, as in a histogram, are given
on the $x$-axis, a \stress{conditional density plot} uses
the original $x$-axis for a display of the
conditional density of the categorical response given
the independent variable.

\begin{figure}
<<DAGD-CHFLS-happy_income, echo = TRUE, fig = TRUE, width = 8, height = 5>>=
layout(matrix(1:2, ncol = 2))
plot(R_happy ~ log(R_income + 1), data = CHFLS)
cdplot(R_happy ~ log(R_income + 1), data = CHFLS)
@
\caption{Spinogram (left)
         and conditional density plot (right) of happiness depending on log-income
         \label{DAGD-CHFLS-happy_income}}
\end{figure}

\index{Barchart|)}
\index{Spineplot|)}
\index{Spinogram|)}

\index{Trellis plot|(}
For our last example we return to scatterplots for inspecting
the association between a woman's monthly income and the
income of her partner. Both income variables have been computed
and partially imputed from other self-reported variables and
are only rough assessments of the real income. Moreover, the 
data itself is numeric but heavily tied, making it difficult to
produce `correct' scatterplots because points will overlap.
A relatively easy trick is to jitter the observation
by adding a small random noise to each point in order to
avoid overlapping plotting symbols. In addition, we want to
study the relationship between both monthly incomes conditional
on the woman's education. Such conditioning plots are called
\stress{trellis} plots and are implemented in the package 
\Rpackage{lattice} \citep{PKG:lattice, HSAUR:Sarkar2008}.
We utilise the \Rcmd{xyplot} function from package \Rpackage{lattice}
to produce a scatterplot. The formula reads as already explained with
the exception that a third \stress{conditioning} variable, \Robject{R\_edu}
in our case, is present. For each level of education, a separate 
scatterplot will be produced. The plots are directly comparable since
the axes remain the same for all plots. 
\begin{figure}
<<DAGD-CHFLS-RAincome3, echo = TRUE, eval = FALSE>>=
xyplot(jitter(log(A_income + 0.5)) ~ 
       jitter(log(R_income + 0.5)) | R_edu, data = CHFLS)
@
<<DAGD-CHFLS-RAincome3, echo = FALSE, fig = TRUE, width = 8>>=
library("lattice")
lattice.options(default.theme =
        function()
            standard.theme("pdf", color = FALSE))
print(xyplot(jitter(log(A_income + 0.5)) ~ jitter(log(R_income + 0.5)) | R_edu, data = CHFLS))
@
\end{figure}

The plot reveals several interesting issues. Some observations are
positioned on a straight line with slope one, most probably an artifact
of missing value imputation by linear models (as described
in the data dictionary, see \texttt{?CHFLS}). Four constellations can 
be identified: both partners have zero income, the partner has no income,
the woman has no income or both partners have a positive income.

For couples where the woman has a university degree, the income of
both partners is relatively high (except for two couples where only
the woman has income). A small number of former junior college students
live in relationships where only the man has income, the income
of both partners seems only slightly positively correlated for the
remaining couples. For lower levels of education, all four constellations
are present. The frequency of couples where only the man has some income
seems larger than the other way around. Ignoring the observations
on the straight line, there is almost no association between the income
of both partners.
\index{Trellis plot|)}
\section{Summary}

Producing publication-quality graphics is one of the major strengths
of the \R{} system and almost anything is possible since graphics
are programmable in \R{}. Naturally, this chapter can be only a very
brief introduction to some commonly used displays and the reader is
referred to specialised books, most important 
\cite{HSAUR:Murrell2005}, \cite{HSAUR:Sarkar2008}, and
\cite{HSAUR:Chenetal2008}. Interactive 3D-graphics are available
from package \Rpackage{rgl} \citep{PKG:rgl}.

\section*{Exercises}

\begin{description}

\exercise 
The data in Table~\ref{DAGD-household-tab} are part of a data set collected 
from a survey of household expenditure and give the expenditure of $20$ single men 
and $20$ single women on four commodity groups. The units of expenditure are 
Hong Kong dollars, and the four commodity groups are
\begin{description}
\item[\Robject{housing}]: housing, including fuel and light,
\item[\Robject{food}]: foodstuffs, including alcohol and tobacco,
\item[\Robject{goods}]: other goods, including clothing, footwear and durable goods,
\item[\Robject{service}]: services, including transport and vehicles.
\end{description}
The aim of the survey was to investigate how the division of household 
expenditure between the four commodity groups depends on total expenditure 
and to find out whether this relationship differs for men and women.
Use appropriate graphical methods to answer these questions
and state your conclusions.

<<DAGD-household-tab, echo = FALSE, results = tex>>=
data("household", package = "HSAUR2")
toLatex(HSAURtable(household), 
  caption = paste("Household expenditure for single men and women."),
  label = "DAGD-household-tab")
@

\exercise 
The data set shown in Table~\ref{DAGD-USstates-tab} contains values 
of seven variables for ten states in the US. The seven variables are
\begin{description}
\item[\Robject{Population}]: population size divided by $1000$,
\item[\Robject{Income}]: average per capita income,
\item[\Robject{Illiteracy}]: illiteracy rate (\% population),
\item[\Robject{Life.Expectancy}]: life expectancy (years),
\item[\Robject{Homicide}]: homicide rate (per $1000$),
\item[\Robject{Graduates}]: percentage of high school graduates,
\item[\Robject{Freezing}]: average number of days per below freezing.
\end{description}
With these data
\begin{enumerate}
\item Construct a scatterplot matrix of the data labelling the 
      points by state name (using function \Rcmd{text}).
\item Construct a plot of life expectancy and homicide rate 
      conditional on average per capita income.
\end{enumerate}

\begin{sidewaystable}
<<DAGD-USstates-tab, echo = FALSE, results = tex>>=
data("USstates", package = "HSAUR2")
toLatex(HSAURtable(USstates), 
  caption = paste("Socio-demographic variables for ten US states."),
  label = "DAGD-USstates-tab")
@
\end{sidewaystable}

\exercise 
Mortality rates per $100,000$ from male suicides for a number of 
age groups and a number of countries are given in Table~\ref{DAGD-suicides2-tab}. 
Construct side-by-side box plots for the data from different age 
groups, and comment on what the graphic tells us about the data.

<<DAGD-suicides2-tab, echo = FALSE, results = tex>>=
data("suicides2", package = "HSAUR2")
toLatex(HSAURtable(suicides2), 
  caption = paste("Mortality rates per $100,000$ from male suicides."),
  label = "DAGD-suicides2-tab", rownames = TRUE)
@

\exercise
\cite{HSAUR:FluryRiedwyl1988} report data that give various length 
measurements on $200$ Swiss bank notes. The data are available
from package \Rpackage{mclust} \citep{PKG:mclust}; a sample of 
ten bank notes is given in Table~\ref{DAGD-banknote-tab}.

<<DAGD-banknote-tab, echo = FALSE, results = tex>>=
data("banknote", package = "mclust")
banknote$Status <- NULL
banknote <- banknote[c(1:5, 101:200),]
toLatex(HSAURtable(banknote, pkg = "mclust", nrow = 10),
  caption = paste("Swiss bank note data."),
  label = "DAGD-banknote-tab", rownames = FALSE)
@       
Use whatever graphical techniques you think are appropriate
to investigate whether there is any `pattern'
or structure in the data. Do you observe something suspicious?

\end{description}

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\end{document}
