##                            Introduction 
##                          Commentary on R 
##                              General 
##                            Getting help 
##             Use of an editor as a run-time environment 
##         The development model, and development strategies 
##                           Unifying ideas 
##             Retrospect, prospect and alternatives to R 
##                    Data set size, and databases 
##                 The statistics of data collection 
##                           Documentation 

## Chapter 1: Preliminaries 

## Sec 1.1: Installation of R and of R Packages 
## ss 1.1.1: Installation of packages from the command line 
install.packages("Rcmdr", dependencies=TRUE) 


## Sec 1.2: The R Commander GUI 
library(Rcmdr) 


## Chapter 2: An Overview of R 

## Sec 2.1: Use of the console (i.e., command line) window 
2+2 
 
q() 

##                      Practice with R commands 
1:5         # The numbers 1, 2, 3, 4, 5 
mean(1:5) 
sum(1:5)    # Apply the sum function to the vector 
            # of numbers 1, 2, 3, 4, 5 
(1:5) > 2   # Returns FALSE FALSE  TRUE  TRUE  TRUE 
            # Other relational operators are: >=, <, <=, ==, != 
(2:5)^10    # 2 to the power of 10, 3 to the power of 10, ... 
log2(c(0.5, 1, 2, 4, 8)) # Values that differ by a factor of 2 
                         # are, on this scale, one unit apart. 
help()         # help on use of the help function 
help(plot)     # the help page for the plot function 
example(plot)  # Runs the examples from the help page for plot() 
par(ask=FALSE) # Do not now ask, before displaying a new plot. 


## Sec 2.2: A Short R Session 
##           Entry of vector elements from the command line 
volume <- c(351, 955, 662, 1203, 557, 460) 
weight <- c(250, 840, 550, 1360, 640, 420) 
description <- c("Aird's Guide to Sydney", "Moon's Australia handbook",  
 "Explore Australia Road Atlas", "Australian Motoring Guide",  
 "Penguin Touring Atlas", "Canberra - The Guide") 

##                     Operations with {vectors} 
volume 
 
# Final element of volume 
volume[6] 
## Ratio of weight to volume, i.e., density 
round(weight/volume,2) 

##                           A simple plot 
## Code 
plot(weight ~ volume, pch=16, cex=1.5)   
  # pch=16: use solid blob as plot symbol 
  # cex=1.5: point size is 1.5 times default 
## Alternative 
plot(volume, weight, pch=16, cex=1.5) 
plot(weight ~ volume, pch=16, cex=1.5, xlab="Volume (cubic mm)", 
         ylab="Weight (g)") 
identify(weight ~ volume, labels=description) 

##                   Formatting and layout of plots 

## Sec 2.3: Data frames -- Grouping together columns of data 
## NB, the row names will now be shortened 
travelbooks <- data.frame( 
   thickness = c(1.3, 3.9, 1.2, 2, 0.6, 1.5),  
   width = c(11.3, 13.1, 20, 21.1, 25.8, 13.1),  
   height = c(23.9, 18.7, 27.6, 28.5, 36, 23.4),  
   weight = weight,  # Include values of weight, entered earlier 
   volume = volume,  # Include values of volume, entered earlier 
   type = c("Guide", "Guide", "Roadmaps", "Roadmaps", "Roadmaps", "Guide"), 
   row.names = description 
) 
## Remove objects that are not now needed. 
rm(volume, weight, description)    

##                Accessing the columns of data frames 
travelbooks[, 4] 
travelbooks[, "weight"] 
travelbooks$weight 
travelbooks[["weight"]]   # This treats the data frame as a list. 
## 1: Use the data parameter in the function call 
plot( weight ~ volume, data=travelbooks) 
# 
## 2: Use with(); take columns from the specified data frame 
with(travelbooks, plot(weight ~ volume)) 
# 
## 3: Use attach() to include the column names in the search list 
attach(travelbooks) 
plot( weight ~ volume) 
detach(travelbooks)  # Detach when no longer required 


## Sec 2.4: Input of Data from a File 
## Place the file in the working directory 
library(DAAGxtras)             # DAAGxtras has the needed function 
datafile("travelbooks")        # Place file in directory 
dir()                          # List contents of the working directory 
file.show("travelbooks.txt")   # Display travelbooks.txt 
## Now input the file, to the data frame travelbooks 
travelbooks <- read.table("travelbooks.txt", header=TRUE, row.names=1)  
  # Row 1 of the file gives column names. Column 1 gives row names 
travelbooks <- read.table("travelbooks.txt")  
  # Less explicit alternative; requires file to have a suitable format 


## Sec 2.5: Summary 

## Sec 2.6: Exercise 


## Chapter 3: The Working Environment of an R Session 

## Sec 3.1: The Working Directory and the Workspace 
##                     Listing Workspace Contents 
ls() 
ls(pattern="^w") 

##                   Setting the Working Directory 

## Sec 3.2: Saving and retrieving R objects 
save.image(file="archive.RData")   
save(volume, weight, file="books.RData")  
  # Can save many objects in the same file 
load("books.RData")         # Recover the saved objects 

##                 Writing data frames to text files 

## Sec 3.3: Installations, packages and sessions 
## ss 3.3.1: The architecture of an R installation -- Packages 
sessionInfo() 

##                     Installation of R packages 
install.packages(pkgs="D:/DAAG_0.97.zip", repos=NULL) 

## ss 3.3.2: The search path: library() and attach() 
search() 

##                      Attachment of R packages 
##                     Attachment of image files 
attach("books.RData") 
detach("file:books.RData") 


## Sec 3.4: Demonstrations, \& Help Examples 
demo()           # List available demonstrations 
demo(graphics)   # Demonstration of R's graphics abilities 
## 
example(plot)    # Run examples from help page for plot() 
demo() 
demo(image) 
demo(graphics) 
demo(persp) 
demo(plotmath)   # Mathematical symbols can be visually interesting 
library(lattice) 
demo(lattice)    # Demonstrates lattice graphics 
demo(package = .packages(all.available = TRUE)) 
library(vcd)     # The vcd package must of course be installed. 
demo(mosaic) 

##              Examples that are included on help pages 
##           Access to help resources from a browser screen 
##                 Searching for key words or strings 
help.search("bar")  
help.search("str", package="base")  
help.search("char", package="base")  


## Sec 3.5: Summary 

## Sec 3.6: Exercises 
summary(worldRecords) 

## The following is for pages beyond page 26, for anyone who
## wants to explore a bit further.

## Chapter 4: Worked Examples 

## Sec 4.1: World record times for track and field events 
##                          Data exploration 
str(worldRecords) 
library(DAAGxtras)   # Version 0.6-6 or later of DAAGxtras 
## Plot with untransformed scales 
plot(Time ~ Distance, data=worldRecords) 
## Now use log scales 
plot(Time ~ Distance, data=worldRecords, log="xy") 
## Attach lattice package 
library(lattice) 
## Left panel 
xyplot(Time ~ Distance, groups=roadORtrack, data=worldRecords,  
       scales=list(log=10), auto.key=list(columns=2)) 
## Right panel 
xyplot(log(Time) ~ log(Distance), groups=roadORtrack,  
       data=worldRecords, auto.key=list(columns=2)) 

##                     Fitting a regression line 
lm(log(Time) ~ log(Distance), data=worldRecords) 

## ss 4.1.1: Summary information from model objects 
## Store the result in worldrec.lm 
worldrec.lm <- lm(log(Time) ~ log(Distance), worldRecords) 
plot(log(Time) ~ log(Distance), data = worldRecords) 
abline(worldrec.lm) 
print(worldrec.lm)    # Equivalent to typing wtvol.lm at the command line 
summary(worldrec.lm) 

##                          Diagnostic plots 
par(mfrow=c(1,2))  # Subsequent plots appear in a 1 x 2 layout 
plot(worldrec.lm, which=1:2) 
par(mfrow=c(1,1))  # Reset to 1 plot per page, for any later plots  

## ss 4.1.2: The model object 
worldrec.lm$call 
coef(worldrec.lm) 


## Sec 4.2: Time series -- Australian annual climate data 
## Code for Panel A 
library(splines) 
plot(mdRain ~ Year, data=bomregions)  
## Calculate and plot curve showing long-term trend 
hat <- predict(lm(mdRain ~ bs(Year,5), data=bomregions)) 
lines(hat ~ Year, data=bomregions)  
## Calculate and plot curve showing short-term trends 
hat2 <- predict(lm(mdRain ~ bs(Year,36), data=bomregions)) 
lines(hat2 ~ Year, data=bomregions)  
## Panel B: Replace mdRain by mdAVt 
## Panel C: Replace mdRain by SOI 


## Sec 4.3: Regression with two explanatory variables 
##                          Data exploration 
str(nihills) 
## Create scatterplot matrix 
library(lattice) 
splom(~nihills) 
 
## Correlation matrix 
round(cor(nihills), 2) 
## Create a data frame that holds the logged data 
lognihills <- log(nihills) 
names(lognihills) <- c("ldist", "lclimb", "ltime", "ltimef") 
## Scatterplot matrix; logarithmic scales 
splom(~ log(nihills),  
library(lattice) 
 
## Correlation matrix 
round(cor(lognihills), 2) 

## ss 4.3.1: The regression fit 
lognihills <- log(nihills) 
names(lognihills) <- paste("l", names(nihills), sep="") 
lognihills.lm <- lm(ltime ~ ldist + lclimb, data=lognihills) 
 round(coef(lognihills.lm),3) 
nihills$gradient <- with(nihills, climb/dist) 
lognihills <- log(nihills) 
names(lognihills) <- paste("l", names(nihills), sep="") 
lognigrad.lm <- lm(ltime ~ ldist + lgradient, data=lognihills) 
round(coef(lognigrad.lm),3) 
## Plot the terms in the model 
termplot(lognigrad.lm, col.term="gray", partial=TRUE,  
         col.res="black", smooth=panel.smooth)    


## Sec 4.4: Exercises 
huron <- data.frame(year=as(time(LakeHuron), "vector"),  
                    mean.height=LakeHuron) 
lag.plot(huron$mean.height)