Tutorial 3 - Visualise data with ggplot2

library(data.table)
library(ggplot2)
rm(list=ls())

d <- as.data.table(diamonds)

## 1.
ggplot(d[cut %in% c('Ideal', 'Premium')], aes(carat, fill=as.factor(cut))) +
  geom_histogram(alpha=0.75)

d[cut == 'Ideal', .(mean_hwy=mean(carat),
        range_hwy=diff(range(carat))),
    .(cut)]

## 2.
ggplot(d[clarity %in% c('SI2', 'SI1', 'VS1')], 
       aes(cut, fill=clarity)) +
  geom_bar(position=position_dodge())

d[, .N, .(cut, clarity)]

## 3.
ggplot(d[price < 10000], aes(cut, price, fill=clarity)) +
  geom_boxplot()

d[price < 1000, .(median_cty=as.numeric(median(price)),
        range_cty=diff(range(price))),
    .(cut, clarity)]

## 4.
ggplot(d[color == 'E'], aes(carat, price, colour=cut, fill=color)) +
  geom_point()