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## Outline

• The following topics will be covered in this lecture:
• ggplot2 basics
• Graphics layers
• Transformations and statistics

• There are three main plotting systems in R,
1. the base plotting system which we have seen already;
2. the lattice package;
3. and the ggplot2 package.
• For the rest of the session, we’ll learn about the ggplot2 package, because it is the common plotting library in R for creating publication quality graphics.
• ggplot2 is built on the idea that any plot can be expressed from the same set of components:
1. a data set,
2. a coordinate system, and
3. a set of geoms – the visual representation of data points.
• The key to understanding ggplot2 is thinking about a figure in layers.
• This idea may be familiar to you if you have used image editing programs like Photoshop, Illustrator, or Inkscape.
• We will begin by loading the gapminder data again along with ggplot2:
require(gapminder)
require(ggplot2)


### ggplot2 basics

• Let's start off with an example:
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point() • The first thing we do is call the ggplot function.

• This function lets R know that we're creating a new plot, and any of the arguments we give the ggplot function are the global options for the plot:

• i.e., they apply to all layers on the plot.

### ggplot2 basics

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point() • We've passed in two arguments to ggplot.

• First, we tell ggplot what data we want to show on our figure, in this example the gapminder data we read in earlier.

• For the second argument, we passed in the aes function, which tells ggplot how variables in the data map to aesthetic properties of the figure;

• in this case the aesthetic properties are the x and y locations.
• Here we told ggplot we want to plot the “gdpPercap” column on the x-axis and the “lifeExp” column on the y-axis.

### ggplot2 basics

ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point() • Notice that we didn't need to explicitly pass aes these columns (e.g. x = gapminder[, "gdpPercap"]);

• this is because ggplot will look in the dataframe for that column.

### ggplot2 basics

• By itself, the call to ggplot isn't enough to draw a figure:
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) • We need to tell ggplot how we want to visually represent the data, which we do by adding a new geom layer.

• In our example, we used geom_point, which tells ggplot we want to visually represent the relationship between x and y as a scatterplot of points.

### Layers

• Using a scatterplot probably isn't the best for visualizing change over time.

• Instead, let's tell ggplot to visualize the data as a line plot:

ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, by=country, color=continent)) +
geom_line()