Scatter plot

Overview

Teaching: 20 min
Exercises: 0 min

Questions

Objectives

• Learn how to plot a nice scatter plot in ggplot2

Basic component of ggplot

• A data frame
• aes: aesthetic mappings showing how data are mapped to color, size
• geoms: geometric objects like points, lines, shapes.
• facets: for conditional plots.
• stats: statistical transformations like binning, quanti les, smoothing.
• scales: what scale an aesthetic map uses
• coordinate system

Layers of ggplot

gp <- ggplot(mpg, aes(hwy, cty))

gp+geom_point(aes(color=cyl))
gp+geom_point(aes(color=factor(cyl)))
gp+geom_point(aes(color=factor(cyl)))+geom_smooth(method="lm")
gp+geom_point(aes(color=factor(cyl)))+geom_smooth(method="lm")
  +facet_grid(.~cyl)
# Save plot to file
ggsave("plot.png",width=5,height=5)

Scatter plot with full annotation

• Labels: xlab(), ylab(), labs(), ggtitle()
• global annotation: use theme()
• Standard appearance: theme_bw()

  gp+geom_point(aes(color=factor(cyl),
              size=factor(cyl)))+
  geom_smooth(method="lm")+
  xlab("Highway miles per gallon")+
  ylab("city miles per gallon")+
  ggtitle("Scatter plot for cty & hwy")+
  xlim(10,40)+ylim(10,40)+
  theme_bw(base_size = 15)
  

  

Another type of data:

data("midwest")
gg <- ggplot(midwest, aes(x=area, y=poptotal)) + 
  geom_point(aes(col=state, size=popdensity)) + 
  geom_smooth(method="loess", se=F) + 
  xlim(c(0, 0.1)) + 
  ylim(c(0, 500000)) + 
  labs(subtitle="Area Vs Population", 
       y="Population", 
       x="Area", 
       title="Scatterplot", 
       caption = "Source: midwest")
plot(gg)

Key Points

• ggplot2, scatter plot