## clear the workspace
rm(list=ls())

mm <- read.csv('https://www.sfu.ca/~lmgonigl/materials-qm/data/mammals.csv',
               stringsAsFactors=TRUE,
               na.strings=c(''),
               strip.white=TRUE)

## --------------------------------------------------
## Data set 1: Body mass of late Quaternary mammals

## Read and examine the data

## 1.
head(mm)

## 2.
table(mm$continent)

## 3.
mm$continent[mm$continent=='Af'] <- 'AF'
mm$continent <- droplevels(mm$continent)

## 4.
mm$log.mass <- log10(mm$mass.grams)

## Visualizing frequency distributions

## 1.
tab.contintent <- table(mm$continent)
barplot(tab.contintent)
## and with roated y-labels
barplot(tab.contintent, las=1)

## 2.
barplot(tab.contintent, las=1, cex.names=0.7)

## 3.
barplot(tab.contintent, las=1, cex.names=0.7,
        col='red', xlab='Continent', ylab='Number')

## 4.
barplot(tab.contintent, las=1, cex.names=0.7,
        col='red', xlab='Continent', ylab='Number',
        ylim=c(0,1500))

## 5.
barplot(sort(tab.contintent, decreasing=TRUE),
        las=1, cex.names=0.7,
        col='red', xlab='Continent', ylab='Number',
        ylim=c(0,1500))

## 10.
qqnorm(mm$log.mass[!is.na(mm$log.mass)])

## 11.
qqnorm(mm$log.mass[!is.na(mm$log.mass)], pch=16, cex=0.5)

## 12.
hist(mm$log.mass, col='red', xlab='Log(Mass)', freq=FALSE)
## generate normal distribution with same mean and sd
x <- seq(0, 8, length=1000)
y <- dnorm(x,
           mean=mean(mm$log.mass, na.rm=TRUE),
           sd=sd(mm$log.mass, na.rm=TRUE))
lines(x, y, type="l", lwd=2, col='blue')

## Visualizing associations between variables

## 1.
boxplot(log.mass ~ status, data=mm, cex.axis=0.7)

## 3.
boxplot(log.mass ~ status, data=mm, cex.axis=0.7,
        width=sqrt(table(mm$status)),
        main='Mammalian body size')

## 4.
tapply(mm$log.mass, mm$status, median, na.rm=TRUE)

## 5.
tapply(mm$log.mass, mm$status, mean, na.rm=TRUE)

## 6.
tab <- table(mm$status, mm$continent)
## to calculate which continent has the greatest number of
## extinctions relative to the number of extant species, you could do:
tab['extinct',]/tab['extant',]

## 7.
mosaicplot(table(mm$status, mm$continent), main='Mosaic plot')
mosaicplot(table(mm$continent, mm$status), main='Mosaic plot')

## you could also try a stacked barplot (in this case, its not
## particularly helpful)
barplot(table(mm$status, mm$continent))
barplot(table(mm$continent, mm$status))
## taking the log of the values helps a bit but this is 
barplot(log(table(mm$status, mm$continent)+1))
barplot(log(table(mm$continent, mm$status)+1))


## --------------------------------------------------
## Data set 2: Fly sex and longevity
rm(list=ls())

## read directly from URL
ff <- read.csv(url("https://www.sfu.ca/~lmgonigl/materials-qm/data/fruitflies.csv"),
               stringsAsFactors=TRUE,
               strip.white=TRUE)

## 1.
head(ff)

## 2.
boxplot(longevity.days ~ treatment, data=ff, xaxt='n', las=1,
        main='Longevity')
## add fancy x-labels
x.labs <- sort(unique(ff$treatment))
text(x=seq_along(x.labs)+0.3,
     y=par('usr')[3],
     labels=x.labs,
     srt=35,
     adj=c(1.1,1.1),
     xpd=NA,
     cex=1)

## 3.
stripchart(ff$longevity.days ~ ff$treatment,
           xaxt='n', xlab='', ylab='',
           pch=16, cex=1, las=1, vertical=TRUE)
## add x-labels
x.labs <- sort(unique(ff$treatment))
text(x=seq_along(x.labs)+0.3,
     y=par('usr')[3],
     labels=x.labs,
     srt=35,
     adj=c(1.1,1.1),
     xpd=NA,
     cex=1)
axis(2, las=1)

## now, with some jitter
stripchart(ff$longevity.days ~ ff$treatment, method='jitter',
           xaxt='n', xlab='', ylab='',
           pch=16, cex=1, jitter=0.1, las=1, vertical=TRUE)
## add x-labels
x.labs <- sort(unique(ff$treatment))
text(x=seq_along(x.labs)+0.3,
     y=par('usr')[3],
     labels=x.labs,
     srt=35,
     adj=c(1.1,1.1),
     xpd=NA,
     cex=1)
axis(2, las=1)

## ggplot: try using geom_jitter()

## 4.
plot(x=ff$thorax, y=ff$longevity,
     xlab='Thorax length', ylab='Longevity',
     pch=16, las=1)

## 5.
plot(x=ff$thorax, y=ff$longevity,
     xlab='Thorax length', ylab='Longevity',
     pch=16, las=1)
lines(lowess(x=ff$thorax, y=ff$longevity), col='red')

## 6.
cols <- as.numeric(ff$treatment)
plot(x=ff$thorax, y=ff$longevity,
     xlab='Thorax length', ylab='Longevity',
     pch=16, las=1, col=cols)
legend('bottomright',
       legend=sort(unique(ff$treatment)),
       col=as.numeric(sort(unique(ff$treatment))),
       pch=16,
       bty='n')

## Multipanel plots

## 1.
make.hist <- function(case) {
  ff.sub <- ff[ff$treatment==case,]
  hist(ff.sub$longevity,
       xlab='Thorax length', ylab='Longevity',
       pch=16, las=1, col='red', main=case)
  lines(lowess(x=ff.sub$longevity, y=ff.sub$thorax), col='red')
}

layout(matrix(1:5, ncol=5, nrow=1))
lapply(levels(ff$treatment), make.hist)

## 2.
layout(matrix(1:5, ncol=1, nrow=5))
lapply(levels(ff$treatment), make.hist)

## 3.
make.scatter <- function(case) {
  ff.sub <- ff[ff$treatment==case,]
  plot(x=ff.sub$longevity, y=ff.sub$thorax,
       xlab='Thorax length', ylab='Longevity',
     pch=16, las=1)
  lines(lowess(x=ff.sub$longevity, y=ff.sub$thorax), col='red')
}

layout(matrix(1:5, ncol=1, nrow=5))
lapply(levels(ff$treatment), make.scatter)