```#--------------------------------------------------
#
# Things to learn:
#   matching order (full names, partial names, position)
#   ... slurps up all the extra arguments
#   anonymous functions

f <- function(x,count=10) {
paste(rep(x,length=count))
}

f('me')            # use the default value for count
f('me',count=3)    # override the default value for count
f(count=3,'me')    # named arguments can be put anywhere in the argument list
f(co=3,'me')       # named arguments can be abbreviated

f <- function(x,count=10,...) {
cat('You passed an extra',length(list(...)),'arguments\n')
paste(rep(x,length=count), ...)
}

f(count=3,'me','and you')     # extra arguments are slurped into ..., and can be passed on
f(count=3,'me',collapse='-')  # extra arguments may be named or unnamed

(function(x) {y <- x+1; y^2})(10)

#----------------------------------------
# 2. Lattice Graphics
#
# Things to learn:
#   xyplot (draws a scatter plot)

diary[1:5,c('date','type','category','civiliankia')]

library(lattice) # load in the graphics library

# A simple scatterplot.
# Here civiliankia and date are columns from the diary data frame,
# so we need to tell xyplot that the data is drawn from diary.
xyplot(cumsum(civiliankia)~date, data=diary)

# The default is to draw circles. To draw lines instead,
xyplot(cumsum(civiliankia)~date, data=diary, type='l')

# Speed it up by only plotting a subset
n <- nrow(diary)
xyplot(cumsum(civiliankia)~date, data=diary,
subset=seq(1,n,length=1000))

# Add in some extra lines for other columns from the data frame
xyplot(cumsum(civiliankia) + cumsum(hostnationkia)
+ cumsum(enemykia) + cumsum(friendlykia) ~ date, data=diary,
subset = seq(1,n,length=1000))

# Add a legend, and make the y axis label more sensible
xyplot(cumsum(civiliankia) + cumsum(hostnationkia)
+ cumsum(enemykia) + cumsum(friendlykia) ~ date, data=diary,
subset = seq(1,n,length=1000),
auto.key=TRUE, ylab='kia')

#----------------------------------------
# 3. Grouping and conditioning
#
# Things to learn:
#   y~x|cond, groups=g

# For more sophisticated graphics, it's helpful
# to work with the data in 'long form'
diarym[1:10,]
diarym\$date <- diarym\$year + (diarym\$mth+1)/12

# This says: split the data frame into pieces, one for each value of count,
# and for each piece draw a separate line. Here count is a column which
# takes values civiliankia, civilianwia, friendlykia, friendlywia, etc.
xyplot(n~date, groups=count, data=diarym,
type='l', auto.key=TRUE)

# There are too many lines. It'd be easier to read if we had two panels,
# one for kia and one for wia, and four lines in each.
# First, we need to add extra columns (who=civilian,friendly,... and how=kia,mia).
nn <- nchar(as.character(diarym\$count))
diarym\$who <- factor(substr(diarym\$count,1,nn-3))
diarym\$how <- factor(substr(diarym\$count,nn-2,nn))
diarym[1:10,]
# Now we can plot it. The syntax n~date|how, groups=who, says:
# split the data into pieces, one for each possible pair of values (how,who), where how and who
# are factors. Draw one panel for each level of how (kia or wia). Within each panel,
# draw one line for each level of who (civilian or friendly or ...).
xyplot(n~date|how, groups=who, data=diarym,
type='l', auto.key=TRUE)

# It's very simple to pivot, e.g. plot one panel for each level of who,
# one line for each level of how.
xyplot(n~date|who, groups=how, data=diarym,
type='l', auto.key=TRUE)

# Anything you want plotted you should plot by setting up an
# appropriate data frame, with columns for x-coord, y-coord, and
# anything you want to split by. For example, two superimpose two EDFs,

samp1 <- runif(100)
samp2 <- rexp(800)
df <- rbind(data.frame(x=sort(samp1),y=length(samp1):1/length(samp1),sample='unif'),
data.frame(x=sort(samp2),y=length(samp2):1/length(samp2),sample='exp'))
xyplot(y~x, groups=sample, data=df,
type='l', auto.key=TRUE)

#--------------------------------------------------
# 4. Customizing what is drawn
#
# Things to learn:
#   panel
#   anonymous functions

# The xyplot command is a wrapper which simply works out what data belongs in each panel.
# It then passes the relevant data to a panel function, which is what actually does the drawing.
# The default panel function for xyplot is called panel.xyplot(x,y,...)
# To customize the drawing, e.g. to add a design to each panel, override the panel function.
xyplot(n~date|who, groups=how, data=diarym,
type='l',
panel = function(x,y,...) {
lrect(2007.1,0,2008.5,5000, col='grey80', border='transparent')  # draw a rectangle
panel.xyplot(x,y,...)  # plot the lines as usual using the default panel function.
})

# Any arguments that xyplot doesn't understand itself, it passes on to the panel function.
# Here, xyplot doesn't know what to do with type='l' or extraarg='fiddlesticks', so it passes
# them both on. panel.xyplot does know what to do with type='l', namely, draw lines rather than points.
xyplot(n~date|who, groups=how, data=diarym,
type='l', extraarg='fiddlesticks',
panel = function(x,y,...) {
lrect(2007.1,0,2008.5,5000, col='grey80', border='transparent')
panel.xyplot(x,y,...)
print(names(list(...)))
})

```