## Not run: 
# First, we do steps that create or manipulate the data
# frame in its entirety.  For S-Plus, these are done with
# .Data in search position one (the default at the
# start of the session).
#
# -----------------------------------------------------------------------
# Step 1: Create initial draft of data frame
# 
# We usually begin by importing a dataset from
# # another application.  ASCII files may be imported
# using the scan and read.table functions.  SAS
# datasets may be imported using the Hmisc sas.get
# function (which will carry more attributes from
# SAS than using File \dots  Import) from the GUI
# menus.  But for most applications (especially
# Excel), File \dots Import will suffice.  If using
# the GUI, it is often best to provide variable
# names during the import process, using the Options
# tab, rather than renaming all fields later Of
# course, if the data to be imported already have
# field names (e.g., in Excel), let S use those
# automatically.  If using S-Plus, you can use a
# command to execute File \dots  Import, e.g.:


import.data(FileName = "/windows/temp/fev.asc",
            FileType = "ASCII", DataFrame = "FEV")


# Here we name the new data frame FEV rather than
# fev, because we wanted to distinguish a variable
# in the data frame named fev from the data frame
# name.  For S-Plus the command will look
# instead like the following:


FEV <- importData("/tmp/fev.asc")




# -----------------------------------------------------------------------
# Step 2: Clean up data frame / make it be more
# efficiently stored
# 
# Unless using sas.get to import your dataset
# (sas.get already stores data efficiently), it is
# usually a good idea to run the data frame through
# the Hmisc cleanup.import function to change
# numeric variables that are always whole numbers to
# be stored as integers, the remaining numerics to
# single precision, strange values from Excel to
# NAs, and character variables that always contain
# legal numeric values to numeric variables.
# cleanup.import typically halves the size of the
# data frame.  If you do not specify any parameters
# to cleanup.import, the function assumes that no
# numeric variable needs more than 7 significant
# digits of precision, so all non-integer-valued
# variables will be converted to single precision.


FEV <- cleanup.import(FEV)




# -----------------------------------------------------------------------
# Step 3: Make global changes to the data frame
# 
# A data frame has attributes that are "external" to
# its variables.  There are the vector of its
# variable names ("names" attribute), the
# observation identifiers ("row.names"), and the
# "class" (whose value is "data.frame").  The
# "names" attribute is the one most commonly in need
# of modification.  If we had wanted to change all
# the variable names to lower case, we could have
# specified lowernames=TRUE to the cleanup.import
# invocation above, or type


names(FEV) <- casefold(names(FEV))


# The upData function can also be used to change
# variable names in two ways (see below).
# To change names in a non-systematic way we use
# other options.  Under Windows/NT the most
# straigtforward approach is to change the names
# interactively.  Click on the data frame in the
# left panel of the Object Browser, then in the
# right pane click twice (slowly) on a variable.
# Use the left arrow and other keys to edit the
# name.  Click outside that name field to commit the
# change.  You can also rename columns while in a
# Data Sheet.  To instead use programming commands
# to change names, use something like:


names(FEV)[6] <- 'smoke'   # assumes you know the positions!  
names(FEV)[names(FEV)=='smoking'] <- 'smoke' 
names(FEV) <- edit(names(FEV))


# The last example is useful if you are changing
# many names.  But none of the interactive
# approaches such as edit() are handy if you will be
# re-importing the dataset after it is updated in
# its original application.  This problem can be
# addressed by saving the new names in a permanent
# vector in .Data:


new.names <- names(FEV)


# Then if the data are re-imported, you can type


names(FEV) <- new.names


# to rename the variables.




# -----------------------------------------------------------------------
# Step 4: Delete unneeded variables
# 
# To delete some of the variables, you can
# right-click on variable names in the Object
# Browser's right pane, then select Delete.  You can
# also set variables to have NULL values, which
# causes the system to delete them.  We don't need
# to delete any variables from FEV but suppose we
# did need to delete some from mydframe.


mydframe$x1 <- NULL 
mydframe$x2 <- NULL
mydframe[c('age','sex')] <- NULL   # delete 2 variables 
mydframe[Cs(age,sex)]    <- NULL   # same thing


# The last example uses the Hmisc short-cut quoting
# function Cs.  See also the drop parameter to upData.




# -----------------------------------------------------------------------
# Step 5: Make changes to individual variables
#         within the data frame
# 
# After importing data, the resulting variables are
# seldom self - documenting, so we commonly need to
# change or enhance attributes of individual
# variables within the data frame.
# 
# If you are only changing a few variables, it is
# efficient to change them directly without
# attaching the entire data frame.


FEV$sex   <- factor(FEV$sex,   0:1, c('female','male')) 
FEV$smoke <- factor(FEV$smoke, 0:1, 
                    c('non-current smoker','current smoker')) 
units(FEV$age)    <- 'years'
units(FEV$fev)    <- 'L' 
label(FEV$fev)    <- 'Forced Expiratory Volume' 
units(FEV$height) <- 'inches'


# When changing more than one or two variables it is
# more convenient change the data frame using the
# Hmisc upData function.


FEV2 <- upData(FEV,
  rename=c(smoking='smoke'), 
  # omit if renamed above
  drop=c('var1','var2'),
  levels=list(sex  =list(female=0,male=1),
              smoke=list('non-current smoker'=0,
                         'current smoker'=1)),
  units=list(age='years', fev='L', height='inches'),
  labels=list(fev='Forced Expiratory Volume'))


# An alternative to levels=list(\dots) is for example
# upData(FEV, sex=factor(sex,0:1,c('female','male'))).
# 
# Note that we saved the changed data frame into a
# new data frame FEV2.  If we were confident of the
# correctness of our changes we could have stored
# the new data frame on top of the old one, under
# the original name FEV.


# -----------------------------------------------------------------------
# Step 6:  Check the data frame
# 
# The Hmisc describe function is perhaps the first
# function that should be used on the new data
# frame.  It provides documentation of all the
# variables and the frequency tabulation, counts of
# NAs,  and 5 largest and smallest values are
# helpful in detecting data errors.  Typing
# describe(FEV) will write the results to the
# current output window.  To put the results in a
# new window that can persist, even upon exiting
# S, we use the page function.  The describe
# output can be minimized to an icon but kept ready
# for guiding later steps of the analysis.


page(describe(FEV2), multi=TRUE) 
# multi=TRUE allows that window to persist while
# control is returned to other windows


# The new data frame is OK.  Store it on top of the
# old FEV and then use the graphical user interface
# to delete FEV2 (click on it and hit the Delete
# key) or type rm(FEV2) after the next statement.


FEV <- FEV2


# Next, we can use a variety of other functions to
# check and describe all of the variables.  As we
# are analyzing all or almost all of the variables,
# this is best done without attaching the data
# frame.  Note that plot.data.frame plots inverted
# CDFs for continuous variables and dot plots
# showing frequency distributions of categorical
# ones.


summary(FEV)
# basic summary function (summary.data.frame) 


plot(FEV)                # plot.data.frame 
datadensity(FEV)         
# rug plots and freq. bar charts for all var.


hist.data.frame(FEV)     
# for variables having > 2 values 


by(FEV, FEV$smoke, summary)  
# use basic summary function with stratification




# -----------------------------------------------------------------------
# Step 7:  Do detailed analyses involving individual
#          variables
# 
# Analyses based on the formula language can use
# data= so attaching the data frame may not be
# required.  This saves memory.  Here we use the
# Hmisc summary.formula function to compute 5
# statistics on height, stratified separately by age
# quartile and by sex.


options(width=80) 
summary(height ~ age + sex, data=FEV,
        fun=function(y)c(smean.sd(y),
                         smedian.hilow(y,conf.int=.5)))
# This computes mean height, S.D., median, outer quartiles


fit <- lm(height ~ age*sex, data=FEV) 
summary(fit)


# For this analysis we could also have attached the
# data frame in search position 2.  For other
# analyses, it is mandatory to attach the data frame
# unless FEV$ prefixes each variable name.
# Important: DO NOT USE attach(FEV, 1) or
# attach(FEV, pos=1, \dots) if you are only analyzing
# and not changing the variables, unless you really
# need to avoid conflicts with variables in search
# position 1 that have the same names as the
# variables in FEV.  Attaching into search position
# 1 will cause S-Plus to be more of a memory hog.


attach(FEV)
# Use e.g. attach(FEV[,Cs(age,sex)]) if you only
# want to analyze a small subset of the variables
# Use e.g. attach(FEV[FEV$sex=='male',]) to
# analyze a subset of the observations


summary(height ~ age + sex,
        fun=function(y)c(smean.sd(y),
          smedian.hilow(y,conf.int=.5)))
fit <- lm(height ~ age*sex)


# Run generic summary function on height and fev, 
# stratified by sex
by(data.frame(height,fev), sex, summary)


# Cross-classify into 4 sex x smoke groups
by(FEV, list(sex,smoke), summary)


# Plot 5 quantiles
s <- summary(fev ~ age + sex + height,
              fun=function(y)quantile(y,c(.1,.25,.5,.75,.9)))


plot(s, which=1:5, pch=c(1,2,15,2,1), #pch=c('=','[','o',']','='), 
     main='A Discovery', xlab='FEV')


# Use the nonparametric bootstrap to compute a 
# 0.95 confidence interval for the population mean fev
smean.cl.boot(fev)    # in Hmisc


# Use the Statistics \dots Compare Samples \dots One Sample 
# keys to get a normal-theory-based C.I.  Then do it 
# more manually.  The following method assumes that 
# there are no NAs in fev


sd <- sqrt(var(fev))
xbar <- mean(fev)
xbar
sd
n <- length(fev)
qt(.975,n-1)     
# prints 0.975 critical value of t dist. with n-1 d.f.


xbar + c(-1,1)*sd/sqrt(n)*qt(.975,n-1)   
# prints confidence limits


# Fit a linear model
# fit <- lm(fev ~ other variables \dots)


detach()


# The last command is only needed if you want to
# start operating on another data frame and you want
# to get FEV out of the way.




# -----------------------------------------------------------------------
# Creating data frames from scratch
# 
# Data frames can be created from within S.  To
# create a small data frame containing ordinary
# data, you can use something like


dframe <- data.frame(age=c(10,20,30), 
                     sex=c('male','female','male'),
                     stringsAsFactors=TRUE)


# You can also create a data frame using the Data
# Sheet.  Create an empty data frame with the
# correct variable names and types, then edit in the
# data.


dd <- data.frame(age=numeric(0),sex=character(0),
                 stringsAsFactors=TRUE)


# The sex variable will be stored as a factor, and
# levels will be automatically added to it as you
# define new values for sex in the Data Sheet's sex
# column.
# 
# When the data frame you need to create is defined
# by systematically varying variables (e.g., all
# possible combinations of values of each variable),
# the expand.grid function is useful for quickly
# creating the data.  Then you can add
# non-systematically-varying variables to the object
# created by expand.grid, using programming
# statements or editing the Data Sheet.  This
# process is useful for creating a data frame
# representing all the values in a printed table.
# In what follows we create a data frame
# representing the combinations of values from an 8
# x 2 x 2 x 2 (event x method x sex x what) table,
# and add a non-systematic variable percent to the
# data.


jcetable <- expand.grid(
 event=c('Wheezing at any time',
         'Wheezing and breathless',
         'Wheezing without a cold',
         'Waking with tightness in the chest',
         'Waking with shortness of breath',
         'Waking with an attack of cough',
         'Attack of asthma',
         'Use of medication'),
 method=c('Mail','Telephone'), 
 sex=c('Male','Female'),
 what=c('Sensitivity','Specificity'))


jcetable$percent <- 
c(756,618,706,422,356,578,289,333,
  576,421,789,273,273,212,212,212,
  613,763,713,403,377,541,290,226,
  613,684,632,290,387,613,258,129,
  656,597,438,780,732,679,938,919,
  714,600,494,877,850,703,963,987,
  755,420,480,794,779,647,956,941,
  766,423,500,833,833,604,955,986) / 10


# In jcetable, event varies most rapidly, then
# method, then sex, and what.

## End(Not run)