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plot2: Simplified and Enhanced Data Visualisation in R

plot2 is a simple yet powerful extension of ggplot2, designed to streamline the process of creating high-quality data visualisations in R by taking away most of the work. Built with the philosophy of Less Typing, More Plotting, plot2 automates many of the routine tasks that typically require manual intervention in ggplot2. For plotting on-the-fly, it even renders pre-processing steps in, e.g., dplyr, tidyr and forcats largely superfluous. This package allows you to focus on the insights and stories your data can tell, rather than on the intricate details of plot construction.

For a comprehensive guide to using plot2, including advanced features and customisation options, please see the full vignette here.

Philosophy

ggplot2 is a versatile tool that has become a cornerstone of data visualisation in R, giving users unparalleled control over their plots. However, with this flexibility often comes the need for repetitive and verbose code, especially for routine tasks such as setting axis labels, choosing plot types, or transforming data.

plot2 is designed to complement ggplot2 by offering a more streamlined and intuitive interface. It simplifies the process of creating plots by automatically handling many of the details, without sacrificing the flexibility and power that ggplot2 provides. Whether you’re quickly exploring data or preparing a polished visualisation for publication, plot2 helps you get there faster with less code.

Key Features

  • Plotting With As Few Lines As Possible: no need to type ggplot(), geom_col(), aes(), scale_colour_manual(), facet_wrap() or theme() anymore, just one plot2() call will suffice.
  • Automatic Plot Selection: plot2 automatically chooses the best plot type based on your data, saving you time and effort.
  • Inline Data Transformations: Eliminate the need for manual pre-processing steps e.g. in dplyr and tidyr, by performing data transformations directly within the plotting function, even for axis and plot titles.
  • Enhanced Sorting and Faceting: Easily sort and facet your data with simple arguments, streamlining the creation of complex multi-panel plots.
  • New Clean Theme: Includes theme_minimal2(), a new minimalist theme based on theme_minimal() that is further optimised for clear and professional outputs, therefore perfect for PDF publications, scientific manuscripts, and presentations.
  • Seamless Integration with ggplot2: Retain all the power and flexibility of ggplot2 while benefiting from plot2’s streamlined interface.

Installation

You can install the latest version of plot2 here:

# Directly from R Universe
install.packages("plot2",
                 repos = c(options()$repos, "https://msberends.r-universe.dev"))

# from GitHub using the 'remotes' package
remotes::install_github("msberends/plot2")

Basic Usage

Here’s how easy it is to get started with plot2:

library(plot2)

# Use inline transformations
mtcars |> 
  plot2(x = log(mpg), y = sqrt(hp))

# Select using tidyselect language
iris |>
  plot2(x = Species, y = where(is.double))

# Count with n() or n_distinct(), sort by frequency
mtcars |> 
  plot2(carb, y = n(), x.sort = "freq-desc")
  
# Easily add plot types using the pipe
iris |>
  plot2(x = Sepal.Length,
        y = Sepal.Width,
        category = Species) |>
  add_line(y = mean(Sepal.Width)) # adds 3 lines, for each of `Species`

# Lose the need for seperate grouping and summarising
admitted_patients |> 
  plot2(x = hospital,
        y = n_distinct(patient_id),
        category = ifelse(date < "2010-01-01", "Prior to 2010", "Since 2010"),
        facet = ward)
      
# Like plot(), just pass x, y in the way you prefer
plot2(mtcars, mpg, hp)
mtcars %>% plot2(mpg, hp)
plot2(hp ~ mpg, data = mtcars)
mtcars |> plot2(mpg, hp) # preferred method since R 4.1

Getting Involved

We welcome contributions to plot2, whether it’s through reporting issues, suggesting features, or submitting pull requests. If you’re familiar with ggplot2 and the tidyverse, your insights will be especially valuable as we continue to develop and refine the package.

Previous Iteration

Though only released here in August 2024, this package has had years of development with hundreds of Git commits in an earlier iteration here.

License

This project is licensed under the GNU GPL v2.0 License - see the LICENSE file for details.