Overview for Data Visualization

Making Visualizations with `ggformula`

The package ggformula is built to overlay the Grammar of Graphics used in package ggplot2 with a formula based syntax. The formula based syntax means that all ggformula generated graphics will generally take one of the following forms:

One Variable

gf_goal(~ x, data = mydata)

Two Variable - Relationships

gf_goal(y ~ x, data = mydata)

Two or More Variables Split by Groups

gf_goal(~ x | z, data = mydata)
gf_goal(y ~ x | z, data = mydata)

where the

goal will be the specific function for the graph type (e.g. gf_histogram()),
the y and x will be the specific variables/columns in the data mapped to the y-axis and x-axis respectively, with | z the feature that facets into different graphs for group values of z, and
mydata is the object name of your data.

Getting Started

First, be sure you have installed ggformula. Remember, you only need to install the package once on your machine.

Then, be sure to load the package ggformula. Remember, you need to do this with each new Quarto/RMarkdown document or R Session.

library(ggformula) #for graphs

Common Modifications/Arguments

Every visualization type has some modifications that are specific to that type, but there are some universal modifications that should be added to every graph.

Axis Labels

Every visualization should have its axes labelled according to the context of the data. Axis labels should always include

The variables or cases they represent
- Always include units (e.g. inches, %) for variables
The individual/sample/population represented by data
A descriptive title - this should not be “X vs Y”, but something like “As X increases we see that Y decreases”

Here is how that is added to our gf_goal() function:

gf_goal(formula, #takes many different forms 
        data = mydata,
        xlab = "X Variable, Units, and/or Sample",
        ylab = "Y Variable, Units, and/or Sample",
        title = "Title describing relationship")

Adding `ggplot2` Layers

The package ggformula is built on top of another package called ggplot2 and so any ggplot2 function can be added to a ggformula generated graphic.

For example, we can change the theme to a built-in theme or modify other features using + after the ggformula function to add the ggplot2 layers to the graph.

gf_goal(formula, #takes many different forms 
        data = mydata,
        xlab = "X Variable, Units, and/or Sample",
        ylab = "Y Variable, Units, and/or Sample",
        title = "Title describing relationship") +
  theme_light()

Data Structures

In order to make graphs, your data needs to be “tidy”. That means it should have the structure:

Every Column is a Variable
Every Row is an Individual/Case
Every Cell is a Single Value

Here is an example of “tidy” data using data from a package called palmerpenguins (remember to install it!). First, load the package.

library(palmerpenguins)

Here is a snippet of the data:

Palmer Penguins
species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex	year
Gentoo	Biscoe	46.5	14.8	217	5200	female	2008
Chinstrap	Dream	49.0	19.5	210	3950	male	2008
Gentoo	Biscoe	48.8	16.2	222	6000	male	2009
Gentoo	Biscoe	42.7	13.7	208	3950	female	2008
Adelie	Dream	41.5	18.5	201	4000	male	2009
Chinstrap	Dream	49.6	18.2	193	3775	male	2009
Adelie	Torgersen	36.2	16.1	187	3550	female	2008
Adelie	Biscoe	37.8	20.0	190	4250	male	2009
Adelie	Dream	41.3	20.3	194	3550	male	2008
Adelie	Torgersen	40.6	19.0	199	4000	male	2009

We notice that each column is a variable, such as

species is the species of penguin
island is the location/island on which the penguin is found
bill_length_mm is the length of the penguin’s bill in millimeters (mm)

We also notice that each row represents a single penguin and its characteristics. Each cell contains a single value associated with a specific variable measured on a specific penguin.

Making Visualizations with ggformula