plot, barplot, hist

• One function per plot style
• Limited ability to modify the plot (eg colours) depending on plot type
• Inconsistent data requirements (eg matrix vs data frame, tall vs wide)

ggplot2

• One package many, many, many plot styles
• Many aspects of plot appearance can be made to depend on the data
• Data should generally be tall rather than wide. Use melt and cast to transform.

ggplot is an implementation of the grammar of graphics

• Data
• Geometric objects (points, lines, areas)
• Aesthetic mappings between data and objects
• Statistical transformations (binning, aggregating)
• Position adjustment (stack, dodge, jitter)
• Coordinate system and scale
• Annotations (title, legend etc)
• Faceting (representing data across multiple plots)

Each layer in ggplot has at least data, geom (geometric object), stat (statistical transform) and aes (aesthetic)

The ggplot function sets up a plot's baseline defaults

p<-ggplot(data,aes(x=someval,y=someval))

Add layers with the + operator

p+geom_point()

This layers inherits data and aes from the base plot and has a default stat

library(ggplot2)
data(diamonds)
p<-ggplot(diamonds)
p+geom_point(aes(x=carat,y=price))

p<-ggplot(diamonds)
p+geom_point(aes(x=carat,y=price,color=clarity))

Aesthetics for geom_point are;

• x
• y
• alpha
• colour
• fill
• shape
• size

Find available aesthetics at http://docs.ggplot2.org/current/.

p<-ggplot(diamonds,aes(x=carat,y=price))
p+geom_point(aes(color=clarity))
p+geom_smooth()

p<-ggplot(data=diamonds,aes(x=carat,y=price))
p<-p+stat_density2d(aes(color=..level..))
p+scale_color_continuous("Density")

p<-ggplot(data=diamonds,aes(x=carat,y=price))
p<-p+geom_point(aes(color=cut))
p+facet_grid(color~cut)