Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Getting started with theme()
This tutorial provides an introduction to the theme() function in ggplot2 and explains how to modify plot themes and elements using this function. It covers basic plot building, using built-in ggplot2 themes, modifying the legend position, and introduces the element_*() functions.
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Getting started with theme()
This blog post offers a beginner-friendly introduction to the theme() function in {ggplot2} for R, emphasizing its utility in customizing plot aesthetics like text and overall style. The author, Jack Kennedy, breaks down the overwhelming number of arguments into manageable, impactful components, providing a step-by-step guide to modifying plots using theme() and related element_*() functions. The content is approachable, combining practical examples with a touch of personal style advice, aiming to empower readers to tailor their {ggplot2} plots confidently.
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Getting started with theme()
This content is a tutorial on the theme() function in the ggplot2 package for R. It provides a practical guide to customizing the appearance of plots using theme(), starting from basic modifications to more advanced tweaks. The tutorial includes examples of modifying plot themes with pre-built ggplot2 themes and the use of the theme() function. It discusses altering legend positions, grid lines, and more nuanced theme elements for personalizing plots. The content is designed to help readers become comfortable and confident in adjusting plot aesthetics to match their preferred style or organizational standard.
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ggfoundry
ggfoundry is an R package that extends ggplot2 by adding custom fillable shapes. Users can request new shapes through GitHub issues. The package is available on CRAN, with a development version on GitHub. It includes a vignette and articles on the package website for guidance. Examples showcase the integration of ggfoundry with ggplot2, presenting how to create plots with unique shapes like violins and dendrograms. This package is especially useful for creating distinctive and visually appealing data visualizations in R.
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ggmagnify
ggmagnify, an R package, enhances ggplot visualizations by adding a 'magnifying glass' effect. It allows for the creation of magnified insets to highlight important areas of plots, offering flexible customization options like borders, shadows, and shapes (rectangular, elliptical, convex hull, arbitrary). The package, installable from GitHub or r-universe, provides functions to specify both the target region and the inset location using coordinates. The comprehensive examples cover various use cases, including faceting and maps, illustrating ggmagnify's capability to accentuate data points or regions for increased clarity in ggplot graphics.
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ggplot2
ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics.
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ggplot2 4.0.0
ggplot2 4.0.0 marks a significant update of the popular data visualization package for R, introducing a shift in the underlying object-oriented system from S3 to S7 and other quality-of-life improvements. Celebrating its 18th anniversary, this release enhances the package's flexibility while enforcing stricter type validation for argument inputs. New theme functionalities differentiate between ink (foreground) and paper (background) aesthetics, adapting the way defaults are set and contributing to a more refined control over plot styling. This version is available for installation from CRAN and heralds future phased transitions favoring the S7 architecture.
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ggplot2 styling
This content introduces the styling capabilities of the ggplot2 package, explaining how to apply various themes to enhance the visual appearance of plots. It covers the theme system in ggplot2, including new updates, and guides the reader through using pre-existing themes or creating custom theme functions. Different components like panels, axes, titles, and legends are addressed, and a practical example plot is provided. The article also mentions additional themes available through packages like cowplot, ggthemes, and tvthemes, which offer styles inspired by external sources or even TV shows.
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ggplot2 Theme Elements Demonstration | Henry Wang
This is a demonstration by Henry Wang about customizing theme elements in ggplot2, a powerful package in R for data visualizations. The demonstration provides step-by-step instructions on how to identify and modify theme elements in ggplot2.
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ggplot2 v3.5: Ignored Position Aesthetics
Evangeline 'Gina' Reynolds discusses the 'ignored position aesthetics' introduced in ggplot2 v3.5 on LinkedIn. This new feature allows x and y axis annotations within plots using I(), making it easier to mark high-level comments. Annotations are not bound to specific observations and can be placed anywhere in the plot space from 0 to 1, horizontally and vertically. The update simplifies adding comments without worrying about x and y axis limits. This enhancement is highly praised by the community for its usefulness in data visualization.
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