Last time: Data frames and apply

• Data frames are a representation of the “classic” data table in R: rows are observations/cases, columns are variables/features
• Each column can be a different data type (but must be the same length)
• subset(): function for extracting rows of a data frame meeting a condition
• split(): function for splitting up rows of a data frame, according to a factor variable
• apply(): function for applying a given routine to rows or columns of a matrix or data frame
• lapply(): similar, but used for applying a routine to elements of a vector or list
• sapply(): similar, but will try to simplify the return type, in comparison to lapply()
• tapply(): function for applying a given routine to groups of elements in a vector or list, according to a factor variable

Part I

Plot basics: ggplot

Plotting in R

There’s 2 major styles of plotting in R. You’ve already seen examples of base R plots include

• plot(): generic plotting function
• hist(): histogram
• and there are many more

But… the R community has come to embrace a new approach to visualization from the ggplot2 package.

Extremely popular graphics library

• 165,050 downloads two weeks ago

Base graphics

• Ugly, laborious, and verbose
• There are better ways to describe statistical visualizations.

Why ggplot2?

• Follows a common grammar, just like any language.
• It defines basic components that make up a sentence. In this case, the grammar defines components in a plot.
• moreover, treats graphics like objects, mapping properties of your dataset to attributes of the graphic.

• Supports a continuum of expertise.
• encourages iterative creation of graphics
• Get started right away but with practice you can effortless build complex, publication quality figures.

Basics: some terminology

• ggplot: The main function where you specify the dataset and variables to plot
• geoms: geometric objects
  • geom_point(), geom_bar(), geom_density(), geom_line(), geom_area()
• aes: aesthetics
  • shape, transparency (alpha), color, fill, linetype.
• scales: Define how your data will be plotted
  • continuous, discrete, log

Introduction to dataset

library(gapminder)
data(gapminder)

ggplot intro: scatter plot

library(ggplot2)
ggplot(gapminder, aes(y = lifeExp, x = gdpPercap)) + 
  geom_point()

ggplot intro: basic structure

ggplot(data = gapminder, aes(y = lifeExp, x = gdpPercap)) + 
  geom_point()
p <- ggplot(data = gapminder, aes(y = lifeExp, x = gdpPercap))
p + geom_point()

• specific all “global” structure in the ggplot function (in this case data and variable mappings)
• add layers of geometric objects, statistical models, and panels.

ggplot intro: change size

ggplot(gapminder, aes(y = lifeExp, x = gdpPercap)) + 
  geom_point(size = .1)

ggplot intro: add color

ggplot(gapminder, aes(y = lifeExp, x = gdpPercap, color = continent)) + 
  geom_point(size = .1)