have_packages <- require(ggformula) && require(dplyr) && require(ggplot2) && require(mosaicData) && require(maps) && require(palmerpenguins) && requireNamespace("mosaic") have_extra_packages <- require(maps) && require(sf) && require(purrr) knitr::opts_chunk$set( fig.show = "asis", fig.align = "center", fig.width = 6, fig.height = 4, out.width = "60%", eval = have_packages ) theme_set(theme_light())
cat( " ## Warning: Missing packages Because one or more of `ggformula`, `ggplot2`, `dplyr`, `mosaic`, `mosaicData`, `palmerpenguins`, and `maps`, appears to be missing, this vignette is compiling without executing any code. " )
There are several excellent graphics packages provided for R. The
ggformula package currently builds on one of them,
provides a very different user interface for creating plots. The interface
is based on formulas (much like the
lattice interface) and the use of
the chaining operator (
%>%) to build more complex graphics from simpler
ggformula graphics were designed with several user groups in mind:
beginners who want to get started quickly and may find the syntax of
a bit offputting,
those familiar with
lattice graphics, but wanting to be
able to easily create multilayered plots,
those who prefer a formula interface, perhaps because it is
familiar from use with functions like
lm() or from use of
mosaic package for numerical summaries.
The basic template for creating a plot with
gf_plottype(formula, data = mydata)
mydata %>% gf_plottype(formula)
plottype describes the type of plot (layer) desired (points, lines, a histogram,
mydata is a data frame containing the variables used in the plot, and
formula describes how/where those variables are used.
For example, in a bivariate plot,
formula will take the form
y ~ x, where
y is the
name of a variable to be plotted on the y-axis and
x is the name of a variable to
be plotted on the x-axis. (It is also possible to use expressions that can be evaluated
using variables in the data frame as well.)
The first form of the tempate is useful for simple plots or for multi-layered plots where different layers use different data. The second form is useful for multi-layered plots or plots with many arguments.
Here is a simple example:
library(ggformula) gf_point(mpg ~ hp, data = mtcars) mtcars %>% gf_point(mpg ~ hp)
The "kind of graphic" is specified by the name of the graphics function. All of
ggformula data graphics functions have names starting with
gf_, which is intended to remind the user that they are formula-based
f for "formula."
Commonly used functions include
gf_point()for scatter plots
gf_line()for line plots (connecting dots in a scatter plot)
gf_freqpoly()to display distributions of a quantitative variable
gf_violin()for comparing distributions side-by-side
gf_counts()for bar-graph style depictions of counts.
gf_bar()for more general bar-graph style graphics
The function names generally match a corresponding function name from
gf_counts()is a simplified special case of
gf_dens()is an alternative to
gf_density()that displays the density plot slightly differently
gf_dhistogram()produces a density histogram rather than a count histogram.
Each of the
gf_ functions can create the coordinate axes and fill it in one
gf_ functions create a frame and add a
layer, all in one operation.) This is what happens for the first
gf_ function in a chain. For subsequent
gf_ functions, new layers are added,
each one "on top of" the previous layers.
Each of the marks in the plot is a glyph. Every glyph has graphical attributes
(called aesthetics in
ggplot2) that tell where and how to draw the glyph.
In the above plot, the obvious attributes are x- and y-position:
We've told R to put
mpg along the y-axis and
hp along the x-asis, as is clear
from the plot.
But each point also has other attributes, including color, shape, size, stroke, fill,
and alpha (transparency). We didn't specify
those in our example, so
gf_point() uses some default values for those --
in this case smallish black filled-in circles.
gf_ functions, you specify the non-position graphical attributes using
additional arguments to the function.
Attributes can be set to a constant value
(e.g, set the color to "blue"; set the size to 2)
or they can be mapped to a variable in the data or some
expression involving the variables
(e.g., map the color to
sex, so sex determines the color groupings)
Attributes are set or mapped using additional arguments.
attribute = valuesets
attribute = ~ expressionmaps
attribute is one of
value is a constant
0.5, as appropriate), and
may be some more general expression that can be computed using the variables in
(although often is is better to create a new variable in the data and to
use that variable instead of an on-the-fly calculation within the plot).
The following plot, for instance,
cyl to determine the color and
carb to determine the size of each
dot. Color and size are mapped to
A legend is provided to show us how the mapping is being done.
(Later, we can use scales to control precisely how the mapping is done --
which colors and sizes are used to represent which values of
We also set the transparency to 50%. The gives the same value of
all glyphs in this layer.
gf_point(mpg ~ hp, color = ~ cyl, size = ~ carb, alpha = 0.50, data = mtcars)
ggformula allows for on-the-fly calculations of attributes, although the default labeling
of the plot is often better if we create a new variable in our data frame. In the
examples below, since there are only three values for
carb, it is easier to read the
graph if we tell R to treat
cyl as a categorical variable by converting to a factor (or to
a string). Except for the labeling of the legend, these two plots are the same.
In the second example, we see how the ggformula works well with data tranformations
library(dplyr) gf_point(mpg ~ hp, color = ~ factor(cyl), size = ~ carb, alpha = 0.75, data = mtcars) mtcars %>% mutate(cylinders = factor(cyl)) %>% gf_point(mpg ~ hp, color = ~ cylinders, size = ~ carb, alpha = 0.75)
For some plots, we only have to specify the x-position because the y-position is calculated
from the x-values. Histograms, densityplots, and frequency polygons are examples.
To illustrate, we'll use density plots, but the same ideas apply to
gf_freqpolygon() as well.
Note that in the one-variable density graphics, the variable whose density is to be calculated goes to the right of the tilde, in the position reserved for the x-axis variable.
data(penguins, package = "palmerpenguins") gf_density( ~ bill_length_mm, data = penguins) gf_density( ~ bill_length_mm, fill = ~ species, alpha = 0.5, data = penguins) # gf_dens() is similar, but there is no line at bottom/sides and the plot is not fillable gf_dens( ~ bill_length_mm, color = ~ species, alpha = 0.7, data = penguins) # gf_dens2() is like gf_dens() but is fillable gf_dens2( ~ bill_length_mm, fill = ~ species, data = penguins, color = "gray50", alpha = 0.4)
Several of the plotting functions include additional arguments that do not modify
attributes of individual glyphs but control some other aspect of the plot. In this
adjust can be used to increase or decrease the amount of smoothing.
# less smoothing penguins %>% gf_dens( ~ bill_length_mm, color = ~ species, alpha = 0.7, adjust = 0.25) # more smoothing penguins %>% gf_dens( ~ bill_length_mm, color = ~ species, alpha = 0.7, adjust = 4)
To learn more about
ggformula, see the longer version of this vignette available
at https://projectmosaic.github.io/ggformula/. That version include sections on
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