Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->ggframe
{ggframe} is a {ggplot2} wrapper. A “ggframe” object is a data.frame
with a “layers” and an “aes” attributes. It has a printing method which
plots by default, but print with plot = FALSE and you’ll print the
data, aesthetics and layers.
You cannot do all you can do with {ggplot2}, it’s just an exploration of some ideas. Unlikely to go much further.
Some nice things that you can do more easily than in native {ggplot2} :
- data wrangling on plot data (assuming you use functions that don’t drop attributes, it seems {dplyr} functions don’t, base R functions often do)
- keep track of the steps that built the plot.
Installation
Install with:
remotes::install_github("moodymudskipper/ggframe")
Functions
map_aes() sets an aes attribute.
geom(), facet(), set_labs, set_coord(), set_theme(),
set_scale() and set_guide() are quick and dirty wrappers that add
the relevant plot building call to the layer attribute, I suppose a
more serious version of the package would provide a wrapper for all
relevant {ggplot2} functions, with explicit arguments rather than just
.... One advantage here is that it works with other packages extending
{ggplot2}.
as_ggplot() converts a ggframe object to a standard ggplot object.
Examples
library(dplyr, warn.conflicts = FALSE)
library(ggplot2) # needs to be attached, geoms etc can come from other packages too
library(ggframe)
# fast to type for a quick plot
iris %>%
geom("point", aes(Petal.Length, Petal.Width, color = Species))
# defining default aes
iris %>%
map_aes(Petal.Length, Petal.Width, color = Species) %>%
geom("point")
# wrangle the data after the layer definitions
iris %>%
map_aes(Petal.Length, Petal.Width, color = Species) %>%
geom("point") %>%
filter(Species != "versicolor")
# print data, aesthetics and layers
iris %>%
map_aes(Petal.Length, Petal.Width, color = Species) %>%
geom("point") %>%
slice_sample(n = 15) %>%
print(plot = FALSE)
#> # A ggframe
#> # aes: x=Petal.Length, y=Petal.Width, color=Species
#> # layers:
#> # geom_point()
#> # A tibble: 15 x 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 7.4 2.8 6.1 1.9 virginica
#> 2 5.7 2.8 4.1 1.3 versicolor
#> 3 5.8 2.8 5.1 2.4 virginica
#> 4 5.7 4.4 1.5 0.4 setosa
#> 5 7.9 3.8 6.4 2 virginica
#> 6 5.2 4.1 1.5 0.1 setosa
#> 7 5.4 3.7 1.5 0.2 setosa
#> 8 5.5 2.5 4 1.3 versicolor
#> 9 6.9 3.1 5.1 2.3 virginica
#> 10 4.8 3 1.4 0.3 setosa
#> 11 5.9 3.2 4.8 1.8 versicolor
#> 12 5.8 2.6 4 1.2 versicolor
#> 13 5.6 2.8 4.9 2 virginica
#> 14 6.3 3.3 4.7 1.6 versicolor
#> 15 6.4 2.7 5.3 1.9 virginica
# use set_* functions to leverage other features
iris %>%
map_aes(Petal.Length, Petal.Width, color = Species) %>%
geom("point") %>%
set_coord("fixed") %>%
set_theme("classic") %>%
set_scale("color_viridis_d") %>%
set_labs(title = "hello")
In {ggplot2} the ggplot() functions takes as optional arguments the
data and aesthetics to be inherited by further layers. It doesn’t link
explicitly aesthetics to a datasets (you can define aesthetics to be
inherited, but use a different dataset for every layer) but it’s quite
rare to start without a default data layer nor default aesthetics.
Moreover when providing custom data for a layer, in my experience we
generally don’t provide completely new data, but a transformation of the
main data. Indeed the data argument of geoms can be a function or
formula to be applied on the main data.
It works quite nicely here:
iris %>%
map_aes(Petal.Length, Petal.Width) %>%
geom("point", data = head, color = "blue") %>%
geom("point", data = tail, color = "red")
Thanks to the fact that ggplot2::ggplot_build() is generic and called
by layer_data(), layer_scales() and layer_grob(), we could make
those work directly on ggframe objects :
# leverage layer_data easily
iris %>%
map_aes(Petal.Length, Petal.Width) %>%
geom("point", data = head, color = "blue") %>%
geom("point", data = tail, color = "red") %>%
layer_data(2)
#> x y PANEL group shape colour size fill alpha stroke
#> 1 5.7 2.5 1 -1 19 red 1.5 NA NA 0.5
#> 2 5.2 2.3 1 -1 19 red 1.5 NA NA 0.5
#> 3 5.0 1.9 1 -1 19 red 1.5 NA NA 0.5
#> 4 5.2 2.0 1 -1 19 red 1.5 NA NA 0.5
#> 5 5.4 2.3 1 -1 19 red 1.5 NA NA 0.5
#> 6 5.1 1.8 1 -1 19 red 1.5 NA NA 0.5