• Layers
• ggplot() vs. qplot()

We will be using the NBA draft data set.

nba <- read.csv("NBA Draft Class.csv")

This data has the same context - a common time and common place

• Want to aggregate information from different sources onto a common plot
• Start with a common background the lat/long grid
• With ggplot2 we will superimpose data onto this grid in layers

To give you an idea…

library(ggplot2)
p <- ggplot() # Empty canvas
p

Now we add some points

p <- p + geom_point(data = nba, 
                    aes(x = Points.Per.Game, y = Win.Share, colour = Year),
                    show.legend = T)
p

Now we change the color scale of the points

p <- p + scale_colour_gradient(high = c("blue","green"))
p

Now we add a title

p <- p + ggtitle("Win Shares vs Points Per Game")
p

Now we add axes labels

p <- p + labs(x = "Points Per Game", y = " Win Shares")
p

Now we edit some ascthetics

p <- p + theme(plot.title = 
                 element_text(hjust = .5, face = "bold", colour = "blue", size = 25))
p

There are a lot of parameters and many, many, many more things that can be added or done differently. https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf provides excellent information and is well documented.

• Most maps (and many plots) have multiple layers of data. The layers may be from the same or different datasets.
• ggplot2 builds around this same idea. Very easy to add additional layers to the plot. To do this we need to understand a little more about the underlying theory…

• A default dataset
• A coordinate system
• layers of geometric objects (geoms)
• A set of aesthetic mappings (taking information from the data and converting into an attribute of the plot)
• A scale for each aesthetic
• A facetting specification (multiple plots based on subsetting the data)

qplot() stands for "quickplot":

• Automatically chooses default settings to make life easier
• Less control over plot construction

ggplot() stands for "grammar of graphics plot"

• Contructs the plot using components listed in previous slides
• Very flexible

Different ways to construct the same plot:

qplot(Points.Per.Game, Win.Share, colour = Year, data = nba,
      main = "Win Shares vs. Points Per Game") 

or:

ggplot() + 
  geom_point(data = nba, 
                      aes(x = Points.Per.Game, 
                          y = Win.Share, colour = Year),
             show.legend = T) +
  ggtitle("Win Shares vs. Points Per Game")

even this works:

ggplot(data = nba, 
       aes(x = Points.Per.Game, y = Win.Share, colour = Year)) +
  geom_point()+
  ggtitle("Win Shares vs. Points Per Game")

A layer added ggplot() can be a geom…

• The type of geometric object
• The statistic mapped to that object
• The data set from which to obtain the statistic

… or a position adjustment to the scales

• Changing the axes scale
• Changing the color gradient

More geoms described at http://docs.ggplot2.org/current/

1. Find the ggplot() statement that creates this plot:

1. Edit the plot to add a centered titled and labeled axes without the periods.

2. Change the shape of each point with respect to groups (Lookup documentation if needed).

1.

# One of many that will produce the same plot
ggplot(
  aes(x = Rebounds.Per.Game, y = Win.Share, colour = Position), data = nba) + 
  geom_point()

2.

ggplot(
  aes(x = Rebounds.Per.Game, y = Win.Share, colour = Position), data = nba) + 
  geom_point()+
  ggtitle("Win Shares vs. Rebounds Per Game") +
  labs(x = " Rebounds Per Game", y = "Win Shares") +
  theme(plot.title = element_text(hjust = .5))

3.

ggplot(
  aes(x = Rebounds.Per.Game, y = Win.Share, colour = Position), data = nba) + 
  geom_point(aes(shape = Position)) +
  ggtitle("Win Shares vs. Rebounds Per Game") +
  labs(x = " Rebounds Per Game", y = "Win Shares") +
  theme(plot.title = element_text(hjust = .5))