As calls to the
ggplot() function are often quite long and take many different arguments, it is helpful to understand the function’s generic structure before looking at some concrete examples.
A generic template for creating a graph with the
ggplot() function has the following structure:
# Generic ggplot template: ggplot(data = <DATA>) + # 1. specify data set to use <GEOM_fun>(mapping = aes(<MAPPING>), # 2. specify geom + mappings <arg_1 = val_1, ..., arg_n = val_n>) + # - optional arguments to geom # - additional geoms + mappings ... <FACET_fun> + # - optional facet function <LOOK_GOOD_fun> # - optional themes, colors, labels, etc.
The generic template includes the following parts:
<DATA>is a data frame or tibble that contains the data that is to be plotted.
<GEOM_fun>is a function that maps data to a geometric object (“geom”) according to an aesthetic mapping that is specified in
aes(<MAPPING>). (A mapping specifies a relation between two entities. Here, the mapping specifies the correspondence of variables to graphical elements, i.e., what goes where.)
A geom’s visual appearance (e.g., colors, shapes, sizes, …) can be customized
- in the aesthetic mapping (when varying visual features according to data properties), or
- by setting its arguments to specific values in
<arg_1 = val_1, ..., arg_n = val_n>(when remaining constant).
<FACET_fun>uses one or more variable(s) to split a complex plot into multiple subplots.
A sequence of optional
<LOOK_GOOD_fun>adjust the visual features of plots (e.g., by adding titles and text labels, color scales, plot themes, or setting coordinate systems).
Actually, a lot of the generic template is not necessary for using
ggplot() for generating a graph.
A minimal template of a
ggplot() command can be reduced to the following structure:
# Minimal ggplot template: ggplot(<DATA>) + # 1. specify data set to use <GEOM_fun>(aes(<MAPPING>) # 2. specify geom + mappings
A comparison of the generic and the minimal templates shows that large parts of a typical
ggplot() command are optional.
In fact, the bare essentials only include some
<DATA>, at least one
<GEOM_fun>, and its required mappings in
This creates the basic visualization specified by the geom and its variable mappings.
All other arguments (e.g., adding aesthetic elements, facetting, titles and labels) provide additional functionality and fluff.
Thus, when creating a new visualization, it always makes sense to start with a minimal working recipe of a
ggplot() command and then add more elements and fluff. And as looks can have a major impact — for human beings and other animals — we must not underestimate the effects of visual fluff on communication.