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Intro to ggplot2

Spencer Nystrom

Expectations

  1. Understand basic ggplot syntax
  2. Know how ggplot views factors and how to relevel them for plotting

Install the gapminder dataset

install.packages("gapminder")

Next we will load the sample data and subset it. We will cover how to do this in detail next week, but for now run the following code to generate a data.frame tracking life-expectancy, population, & gdp over time in the United States.

library(gapminder)
# We'll cover dplyr next week!
usa <- dplyr::filter(gapminder, country == "United States")

Don’t forget to load the ggplot2 library

library(ggplot2)
## Registered S3 methods overwritten by 'ggplot2':
##   method         from 
##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

Exercise 1

  • Recall last class’s lesson on ggplot. Try to recreate the following scatterplot of life expectancy over time:

Exercise 2

  • Change the point shape to diamonds
  • increase the point size to 5

Layered grammar of graphics

ggplot adds things in layers. The order of addition matters! Lets add a line to our scatterplot. Observe that the line is plotted over the points.

I’ve increased the line thickness with the size argument so it renders well on a projector.

ggplot(data = usa, mapping = aes(x = year, y = lifeExp)) +
  geom_point(shape = 18, size = 5) +
  geom_line(color = "red", size = 1)

Exercise 3

  • redo the above plot so that the points are on top of the line.

Exercise 4

  • set the point size to scale with the population (pop column)
  • what happens if you set the color to scale with the population?
  • what happens if you set the color to distinguish the continent?

Data Types in ggplot2

Recall the following example:

ggplot(iris, aes(Sepal.Width, Petal.Width)) +
  geom_point(aes(color = Species))

ggplot interprets categorical variables as factors in order to assign their order in the legend, and the color on the plot.

Factors are ordered categorical data with “Levels”

By default the levels are in alphabetical order.

Note how in the below example the ‘levels’ of myFactors are arranged alphabetically.

myFactors <- factor(c("One", "Two", "Two", "Three"))

myFactors
## [1] One   Two   Two   Three
## Levels: One Three Two

In the iris dataset, Species is already a factor

iris$Species
##   [1] setosa     setosa     setosa     setosa     setosa     setosa    
##   [7] setosa     setosa     setosa     setosa     setosa     setosa    
##  [13] setosa     setosa     setosa     setosa     setosa     setosa    
##  [19] setosa     setosa     setosa     setosa     setosa     setosa    
##  [25] setosa     setosa     setosa     setosa     setosa     setosa    
##  [31] setosa     setosa     setosa     setosa     setosa     setosa    
##  [37] setosa     setosa     setosa     setosa     setosa     setosa    
##  [43] setosa     setosa     setosa     setosa     setosa     setosa    
##  [49] setosa     setosa     versicolor versicolor versicolor versicolor
##  [55] versicolor versicolor versicolor versicolor versicolor versicolor
##  [61] versicolor versicolor versicolor versicolor versicolor versicolor
##  [67] versicolor versicolor versicolor versicolor versicolor versicolor
##  [73] versicolor versicolor versicolor versicolor versicolor versicolor
##  [79] versicolor versicolor versicolor versicolor versicolor versicolor
##  [85] versicolor versicolor versicolor versicolor versicolor versicolor
##  [91] versicolor versicolor versicolor versicolor versicolor versicolor
##  [97] versicolor versicolor versicolor versicolor virginica  virginica 
## [103] virginica  virginica  virginica  virginica  virginica  virginica 
## [109] virginica  virginica  virginica  virginica  virginica  virginica 
## [115] virginica  virginica  virginica  virginica  virginica  virginica 
## [121] virginica  virginica  virginica  virginica  virginica  virginica 
## [127] virginica  virginica  virginica  virginica  virginica  virginica 
## [133] virginica  virginica  virginica  virginica  virginica  virginica 
## [139] virginica  virginica  virginica  virginica  virginica  virginica 
## [145] virginica  virginica  virginica  virginica  virginica  virginica 
## Levels: setosa versicolor virginica

Factors can have their order set on assignment

myFactors2 <- factor(c("One", "Two", "Two", "Three"), levels = c("One", "Two", "Three"))

myFactors2
## [1] One   Two   Two   Three
## Levels: One Two Three

Factors can be reordered after the fact

Use the forcats library! It’s built for dealing with factors!

library(forcats)

myFactors
## [1] One   Two   Two   Three
## Levels: One Three Two

all forcats functions start with fct_

fct_relevel lets you reorder factors

fct_relevel(myFactors, c("One", "Two", "Three"))
## [1] One   Two   Two   Three
## Levels: One Two Three

fct_rev reverses the order of levels

myFactors2
## [1] One   Two   Two   Three
## Levels: One Two Three
fct_rev(myFactors2)
## [1] One   Two   Two   Three
## Levels: Three Two One

ggplot customization

ggplot(iris, aes(Sepal.Width, Petal.Width)) +
  geom_point(aes(color = Species))

The order of the factors in the legend (and thus the default color assignment) can be changed by reordering the factor levels!

ggplot(iris, aes(Sepal.Width, Petal.Width)) +
  geom_point(aes(color = fct_rev(Species)))

Setting axis labels & title

For these examples I’m going to use the whole gapminder dataset. To save typing I will save a scatterplot of year vs lifeExp to myPlot.

myPlot <- ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) +
    geom_point()

myPlot

Titles can be added with ggtitle()

myPlot +
  ggtitle("Year vs Life Expectancy")

Axis titles can be customized with xlab() and ylab()

myPlot +
  ggtitle("Year vs Life Expectancy") +
  xlab("Year") +
  ylab("Life Expectancy")

myPlot +
  ggtitle("Year vs Life Expectancy") +
  xlab("Year") +
  ylab("Life Expectancy")

global aesthetics can be added to the whole plot

myPlot +
  aes(color = continent)

Final Exercise

To check your understanding of ggplot and topics covered in this lesson, replicate the plot below of data from the Gapminder dataset. Make use of your resources in this document, the ggplot2 documentation, R for Data Science, and Google.

Hints:

  • this plot uses the following subset of the Gapminder data
  • this plot uses facets to show the different years
  • a linear trendline
  • log10 x-axis
  • altered transparency of the points
  • renamed legend titles
  • axis text is resized to 12
  • axis title text is resized to 14
  • facet titles are resized to 14
finalExampleData <- dplyr::filter(gapminder, year %in% c(1952, 2007))