Data Visualization

Using ggplot2

BUILDING PLOTS

What is a plot?

ggplot2 uses the idea that you can build every graph from the same components:

  1. a data set

  2. a coordinate system

  3. geoms - visual marks that represent data

  • to display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations

How to build a graph

Complete the template below to build a graph

How to build a graph

ggplot(data = mpg, aes(x = class, y = hwy))

  • This will begin a plot that you can finish by adding layers to.

  • You can add one geom per layer

Your Turn!

Change the code below to have the points on top of the boxplots.

ggplot(data = mpg, aes(x = class, y = hwy)) + 
  geom_jitter() + 
  geom_boxplot()

What is a geom?

In ggplot2, we use a geom function to represent data points, and use the geom’s aesthetic properties to represent variables.

Once our data is formatted and we know what type of variables we are working with, we can select the correct geom for our visualization.

Available Geoms

What is a layer?

  • it determines the physical representation of the data

  • Together, the data, mappings, statistical transformation, and geometric object form a layer

  • A plot may have multiple layers

Alternative method of building layers: Stats

A stat builds a new variable to plot (e.g., count and proportion)

Faceting

A way to extract subsets of data and place them side-by-side in graphics

ggplot(data = mpg, aes(x = cty, y = hwy, colour = class)) + 
  geom_point()
ggplot(data = mpg, aes(x = cty, y = hwy, colour = class)) + 
  geom_point() +
  facet_grid(~class) #<<

Faceting Options

  • facet_grid(. ~ b): facet into columns based on b
  • facet_grid(a ~ .): facet into rows based on a
  • facet_grid(a ~ b): facet into both rows and columns
  • facet_wrap( ~ fl): wrap facets into a rectangular layout

You can set scales to let axis limits vary across facets:

  • facet_grid(y ~ x, scales = "free"): x and y axis limits adjust to individual facets
    • “free_x” - x axis limits adjust
    • “free_y” - y axis limits adjust

You can also set a labeller to adjust facet labels: - facet_grid(. ~ fl, labeller = label_both) - facet_grid(. ~ fl, labeller = label_bquote(alpha ^ .(x))) - facet_grid(. ~ fl, labeller = label_parsed)

Position Adjustments

Position adjustments determine how to arrange geoms that would otherwise occupy the same space - Dodge: Arrange elements side by side - Fill: Stack elements on top of one another, normalize height - Stack: Stack elements on top of one another

ggplot(mpg, aes(fl, fill = drv)) + 
  geom_bar(position = "")` #<<

Position Adjustments: Jitter

  • Jitter: Add random noise to X & Y position of each element to avoid overplotting
  • There is also a jitter geom

Coordinate Systems

  • coord_cartesian(): The default cartesian coordinate system
  • coord_fixed(): Cartesian with fixed aspect ratio between x & y units
  • coord_flip(): Flipped Cartesian coordinates
  • coord_polar(): Polar coordinates
  • coord_trans(): Transformed cartesian coordinates.
  • coord_map(): Map projections from the mapproj package (mercator (default), azequalarea, lagrange, etc.)

Maps

Maps

Plotting geospatial data is a common visualization task, and one that requires specialized tools.

Typically the problem can be decomposed into two problems:

  1. using one data source to draw a map

  2. adding metadata from another information source to the map

resource

Example: Nebraska Population

Map Source Data

ne_counties <- map_data("county", "nebraska")
long lat group order region subregion
-98.72636 40.69719 1 1 nebraska adams
-98.29664 40.69719 1 2 nebraska adams

Population Data

ne_population <- read.csv("https://raw.githubusercontent.com/unl-statistics/R-workshops/main/r-graphics/data/nebraska-population.csv")
subregion population
adams 31205
antelope 6295

Example: Nebraska Population

ggplot(ne_data) +
  geom_polygon(aes(x = long, y = lat, group = group, fill = log(population)), color = "black") +
  coord_map() +
  theme_void() +
  scale_fill_gradient(low = "white", high = "green4")

ggplot2 extenstions

library(ggforce)

library(ggforce)
ggplot(iris, aes(Petal.Length, Petal.Width, colour = Species)) +
  geom_point() +
  facet_zoom(x = Species == 'versicolor') #<<

library(ggvoronoi)

library(ggridges)

By Claus O. Wilke

library(ggrepel)

The ggrepel library, as illustrated by Allison Horst

library(patchwork)

Patchwork, as illustrated by Allison Horst

ggplot2 extensions

Check out slides for some frequently used extensions by Ashirwad Barnwal HERE.

UNL R Workshops