Exploratory Data Analysis
8 Exploratory Data Analysis
Experimental Data Analysis (eda) is the process of looking at a data
set to see what are the appropriate statistical inferences that can
possibly be learned. For univariate data, we can ask if the
data is approximately normal, longer tailed, or shorter tailed? Does
it have symmetry, or is it skewed? Is it unimodal, bimodal or
multimodal? The main tool is the proper use of computer graphics.
8.1 Our toolbox
Our toolbox for eda consists of graphical representations of the data
and our interpretation. Here is a summary of graphical methods
covered so far:

barplots
 for categorical data
 histogram, dot plots, stem and leaf plots
 to see the shape of
numerical distributions
 boxplots
 to see summaries of a numerical distribution, useful
in comparing distributions and identifying long and shorttailed
distributions.
 normal probability plots
 To see if data is approximately normal
It is useful to have many of these available with one easy
function. The function
simple.eda does exactly that.
Here are some examples of distributions with different shapes.
8.2 Examples
Example: Homedata
The dataset
homedata
contains assessed values for
Maplewood, NJ for the year 1970 and the year 2000. What is the shape
of the distribution?
> data(homedata) # from simple package
> attach(homedata)
> hist(y1970);hist(y2000) # make two histograms
> detach(homedata) # clean up
On first appearances (figure
35), the 1970
data looks more normal, the year 2000 data has a heavier tail. Let's
see using our
simple.eda function.
> attach(homedata)
> simple.eda(y1970);simple.eda(y2000)
> detach(homedata) # clean up
The 1970 and year 2000 data are shown (figures
36 and
37).
Figure 35: Histograms of Maplewood homes in 1970 and 2000
Figure 36: 1970 Maplewood home data
Figure 37: 2000 Maplewood N.J. home data
Neither looks particularly normal  both are heavy tailed and
skewed. Any analysis will want to consider the medians or a transformation.
Example: CEO salaries
The data set
exec.pay
gives the total direct
compensation for CEO's at 200 large publicly traded companies in the
U.S for the year 2000 (in units of $100,000). What can we say about
this distribution besides it looks like good work if you can get it?
Using
simple.eda yields
> data(exec.pay) # or read in from file
> simple.eda(exec.pay)
Figure 38: Executive pay data
we see a heavily skewed distribution as we might expect. A
transformation is called for, let's try the logarithmic
transformation (base 10). Since some values are 0 (these CEO's are
directly compensated less than $100,000 or perhaps were forced to return
all profits in a plea arrangement to stay out of jail), we ask not to include
these.
> log.exec.pay = log(exec.pay[exec.pay >0])/log(10) # 0 is a problem
> simple.eda(log.exec.pay)
Figure 39: Executive pay after log transform
This is now very symmetric and gives good insight into the actual
distribution. (Almost log normal, which says that after taking a
logarithm, it looks like a normal.) Any analysis will want to use
resistant measures such as the median or a transform prior to analysis.
Example: Taxi time at EWR
The dataset
ewr
contains taxi in and taxi out times at
Newark airport (EWR). Let's see what the trends are.
> data(ewr)
> names(ewr) # only 310 are raw data
[1] "Year" "Month" "AA" "CO" "DL" "HP" "NW"
[8] "TW" "UA" "US" "inorout"
> airnames = names(ewr) # store them for later
> ewr.actual = ewr[,3:10] # get the important columns
> boxplot(ewr.actual)
Figure 40: Taxi in and out times at Newark Airport (EWR)
All of them look skewed. Let's see if there is a
difference between taxi in and out times.
> par(mfrow=c(2,4)) # 2 rows 4 columns
> attach(ewr)
> for(i in 3:10) boxplot(ewr[,i] ~ as.factor(inorout),main=airnames[i])
> detach(ewr)
> par(mfrow=c(1,1)) # return graphics as is (or close window)
Figure 41: Taxi in and taxi out by airline at EWR
(The third line is the only important one. Here we used the
boxplot command with the model notation  of the type
boxplot(y ~ x)  which when
x is a factor, does
separate boxplots for each level. The command
as.factor ensures
that the variable
inorout is a factor. Also note, we used
a
for loop to show all 8 plots.
Notice the taxi in times are more or less symmetric with little
variation (except for HP  America West  with a 10 minute plus
average). The taxi out times have a heavy tail. At EWR, when the
airport is busy, the planes can really backup and the 30 minute wait
is not unusual. The data for Northwest (NW) seems to be less. We can
compare this using statistical tests. Since the distributions are skewed,
we may wish to compare the medians. (In general, be careful when applying
statistical tests to summarized data.)
Example: Symmetric or skewed, Long or short?
For unimodal data, there are 6 basic possibilities as it is
symmetric or skewed, and the tails are short, regular or long. Here
are some examples with random data from known distributions
(figure
42).
## symmetric: short, regular then long
> X=runif(100);boxplot(X,horizontal=T,bty=n)
> X=rnorm(100);boxplot(X,horizontal=T,bty=n)
> X=rt(100,2);boxplot(X,horizontal=T,bty=n)
## skewed: short, regular then long
# triangle distribution
> X=sample(1:6,100,p=7(1:6),replace=T);boxplot(X,horizontal=T,bty=n)
> X=abs(rnorm(200));boxplot(X,horizontal=T,bty=n)
> X=rexp(200);boxplot(X,horizontal=T,bty=n)
Figure 42: Symmetric or skewed; short, regular or long
8.3 Problems

8.1
 Attach the data set
babies
. Describe the
distributions of the variables birth weight (bwt), gestation,
age, height and weight.
 8.2
 The Simple data set iq contains simulated scores on
a hypothetical IQ test. What analysis is appropriate for measuring
the center of the distribution? Why? (Note: the data reads in as a
list.)
 8.3
 The Simple data set slc contains data on red
blood cell sodiumlithium countertransport activity for 190
individuals. Describe the shape of the distribution, estimate the
center, state what is an appropriate measure of center for this
data.
 8.4
 The t distribution will be important later. It depends on a
parameter called the degrees of freedom. Use the rt(n,df)
function to investigate the tdistribution for n=100 and
df=2, 10 and 25.
 8.5
 The c^{2} distribution also depends on a parameter called the
degrees of freedom. Use the rchisq(n,df) function to
investigate the c^{2} distribution with n=100 and
df=2,10 and 25.
 8.6
 The R dataset trees contains girth (diameter),
height and volume (of boardfeet) measurements for several trees of a
species of cherry tree. Describe the distributions of each of these 3
variables. Are any long tailed, shorttailed, skewed?
 8.7
 The Simple dataset
dowdata
contains the Dow Jones
numbers from January 1999 to October 2000. The BlackScholes theory
is modeled on the assumption that the changes in the data within a
day should be log normal. In particular, if X_{n} is the value on
day n then log(X_{n}/X_{n1}) should be normal. Investigate this
as follows
> data(dowdata)
> x = dowdata[['Close']] # look at daily closes
> n = length(x) # how big is x?
> z = log(x[2:n]/x[1:(n1)) # This does X_n/X_(n1)
Now check if z is normal. What do you see?
 8.8
 The children's game of Chutes and Ladders can be simulated easily
in R. The time it takes for a player to make it to the end has an
interesting distribution. To simulate the game, you can use the
Simple function simple.chutes as follows.
> results=c()
> for(i in 1:200) results[i]=length(simple.chutes(sim=TRUE))
> hist(results)
Describe the resulting distribution in words. What percentage of the
time did it take more than 100 turns? What is the median and compare
it to the mean of your sample.
To view a trajectory (the actual dice rolls), you can just plot as
follows
> plot(simple.chutes(1))
Copyright © John Verzani, 20012. All rights reserved.