• Developed by Hadley Wickham in 2005.

• Implements the graphics scheme described in the book The Grammar of Graphics by Leland Wilkinson.

• Uses a standardized system of syntax that makes it easy(-ish) to learn.

• It does not do 3D or interactive graphics.

The Grammar of Graphics boiled down to 5 bullets, courtesy of Wickham (2016, p. 4):

• a statistical graphic is a mapping from data to aesthetic attributes (location, color, shape, size) of geometric objects (points, lines, bars).

• the geometric objects are drawn in a specific coordinate system.

• scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).

• the plot may also contain statistical transformations of the data (means, medians, bins of data, trend lines).

• faceting can be used to generate the same plot for different subsets of the data.

Specify data, aesthetics and geometric shapes

ggplot(data, aes(x=, y=, color=, shape=, size=)) +
geom_point(), or geom_histogram(), or geom_boxplot(), etc.

• This combination is very effective for exploratory graphs.

• The data must be a data frame.

• The aes() function maps columns of the data frame to aesthetic properties of geometric shapes to be plotted.

• ggplot() defines the plot; the geoms show the data; each component is added with +

• Some examples should make this clear

We’ll demonstrate ggplot2 using the Albemarle County real estate data, which was downloaded from Office of Geographic Data Services.

Some variables of interest:

• total value of home (totalvalue)
• finished square feet (finsqft)
• high school district in which house is located (hsdistrict)

Note: the following examples use a sample of the homes data.

library(ggplot2) # or library(tidyverse)
ggplot(homes, aes(x=finsqft, y=totalvalue, 
                 color=hsdistrict)) + geom_point() 

ggplot(homes, aes(x=finsqft, y=totalvalue, 
                  color=hsdistrict)) + geom_point() + 
  geom_smooth()

• A natural next step in exploratory graphing is to create plots of subsets of data. These are called facets in ggplot2.

• Use facet_wrap() if you want to facet by one variable and have ggplot2 control the layout. Example:

  • + facet_wrap( ~ var)

• Use facet_grid() if you want to facet by one and/or two variables and control layout yourself.

Examples:
+ facet_grid(. ~ var1) - facets in columns
+ facet_grid(var1 ~ .) - facets in rows
+ facet_grid(var1 ~ var2) - facets in rows and columns

ggplot(homes, aes(x=finsqft, y=totalvalue)) + 
  geom_point() + facet_wrap(~ hsdistrict)

ggplot(homes, aes(x=finsqft, y = stat(density))) +
  geom_histogram() + facet_grid(hsdistrict ~ .) 

• coord_cartesian allows us to zoom in on a plot, as if using a magnifying glass
• coord_fixed allows us to control “aspect ratio”
• coord_flip allows us to flip the x and y axis

ggplot(homes, aes(x=finsqft, y=totalvalue, 
                 color=hsdistrict)) + geom_point() +
  coord_cartesian(ylim = c(1e5, 3e5))

• Scales control the mapping from data to aesthetics and provide tools to read the plot (ie, axes and legends).

• Every aesthetic has a default scale. To modify a scale, use a scale function.

• All scale functions have a common naming scheme:
  scale _ name of aesthetic _ name of scale

• Examples: scale_y_continuous, scale_color_discrete, scale_fill_manual

• Heads up: The documentation for ggplot2 scale functions will frequently use functions from the scales package (also by Wickham)!

ggplot(homes, aes(x=finsqft, y=totalvalue, 
                 color=hsdistrict)) + geom_point() +
  scale_y_continuous(labels = scales::dollar) +
  scale_x_continuous(labels = scales::comma)

• The default ggplot2 theme is excellent. It follows the advice of several landmark papers regarding statistics and visual perception. (Wickham 2016, p. 176)

• However you can change the theme using ggplot2’s themeing system. To date, there are seven built-in themes: theme_gray (default), theme_bw, theme_linedraw, theme_light, theme_dark, theme_minimal, theme_classic

• You can also update axis labels and titles using the labs function.

ggplot(homes, aes(x=finsqft, y=totalvalue, 
                 color = hsdistrict)) + geom_point() +
  theme_minimal()

ggplot(homes, aes(x=finsqft, y=totalvalue, 
                 color = hsdistrict)) + geom_point() +
  labs(title="Total Value versus Finished Square Feet", 
       x="Finished Square Feet", y="Total Value (USD)") 

• Can do a lot with ggplot(data, aes()) + geom!
• Data must be a data frame (not a matrix or collection of vectors)
• The ggplot2 documentation has many good examples
• Prepare to invest some time if you want master ggplot2; the RStudio ggplot2 cheat sheet can help.

Let’s go to R!

• Chang, W. (2013), R Graphics Cookbook, O’Reilly.

• Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis (2nd ed), Springer.

• Wickham, H. and Grolemund G. (2017), R for Data Science. O’Reilly. http://r4ds.had.co.nz/

ggplot2 cheat sheet
https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf

Cookbook for R - Graphs
http://www.cookbook-r.com/Graphs/

Official ggplot2 web site
https://ggplot2.tidyverse.org/

More on plotly
https://plotly-r.com