Overview
Transparency and reproducibility are fundamental principles in data analysis across various fields, from academic research to industry applications. The ggcall package enhances the functionality of ggplot2 by enabling users to retrieve the complete code used to generate a ggplot object inside a function. This package is beneficial for understanding and replicating complex ggplot2 plots returned by a function. From technical point of view, ggcall extends ggplot2 + operator and ggplot function to track the history of plot construction.
ggcall makes a developer’s life easier and limits the need to use base r metaprogramming or rlang.
patchwork ggplot2 related operators like +, -, *, |, & and / are optionally supported. patchwork is a package that expands the API to allow for arbitrarily complex composition of plots by, among others, providing mathematical operators for combining multiple plots.
An excellent implementation example is to create a bunch of ggplot templates, and we want them to be functions. Then, each template will generate the expected plot, and the ggplot2 code behind is easy to get. Please access such templates here.
Implementation in Your Own Package
The ggcall can be implemented as a standalone solution.
A “standalone” file implements a minimum set of functionality in such a way that it can be copied into another package. usethis::use_standalone() makes it easy to get such a file into your own repo/package and later update it if needed.
Example of standalone file in another package, rlang
The usethis >= 2.2.0 is required.
install.packages("usethis")STANDALONE means copy paste the files and add dependencies to your own package.
Please create an R package if not having such yet.
usethis::create_package()WITH patchwork support
# Add ggplot2, patchwork as your package dependencies
# copy paste the ggcall.R file to your own package R directory
# copy paste the patchwork.R file to your own package R directory
usethis::use_standalone("polkas/ggcall", "patchwork.R", ref = "v0.3.4")
# you may need to update the files time to time with usethis::use_standaloneWITHOUT patchwork support
# Add ggplot2as your package dependencies
# copy paste the ggcall.R file to your own package R directory
usethis::use_standalone("polkas/ggcall", "ggcall.R", ref = "v0.3.4")
# you may need to update the files time to time with usethis::use_standaloneGENERAL COMMENTS:
# Apply only if needed
# In your own code remove all ggplot2:: prefix-ing before ggplot function calls
# ggplot2::ggplot(...) -> ggplot(...)# DO NOT import ggplot function from ggplot2
#' @rawNamespace import(ggplot2, except = c(ggplot))# Combine ggcall +.gg operator with your own one if you already overwrited it in your package
# e.g. GGally package requires such step# Consider moving the ggplot2 from DESCRIPTION Imports to Depends
# if your users will benefit from extending results with further layersExample
The ggcall_example repository contains a simple implementation of ggcall. forest_plot and barbell_plot functions are a part of ggcall.example package. Typically, it will be a function returning ggplot2 object from your own R package where you implemented ggcall.
remotes::install_github("https://github.com/Polkas/ggcall_example")
library(ggcall.example)
# Print the body of the function
forest_plot
df <- data.frame(
Treatment = c("Treatment A", "Treatment B", "Treatment C"),
Estimate = c(0.2, 0.5, -0.1),
CI_lower = c(0.1, 0.3, -0.3),
CI_upper = c(0.3, 0.7, 0.1)
)
# Call the function, gg_plot is a ggplot object
gg_forest <- forest_plot(df, "Estimate", "CI_lower", "CI_upper", "Treatment")
gg_forest
# Retrieve the plot construction code
call_forest <- ggcall(gg_forest)
# Optionally: Style the code with styler
# install.packages("styler")
styler::style_text(paste(deparse(call_forest), collapse = "\n"))
# Optionally: add assignments to call
styler::style_text(paste(deparse(ggcall_add_assignments(call_forest)), collapse = "\n"))
# Optionally: reevaulate the call
eval_ggcall(call_forest)