ggplot2

On 2 March 2012, ggplot2 version 0.9.0 was released with numerous changes to internal organization, scale construction and layers.[5]

On 25 February 2014, Hadley Wickham formally announced that "ggplot2 is shifting to maintenance mode. This means that we are no longer adding new features, but we will continue to fix major bugs, and consider new features submitted as pull requests. In recognition [of] this significant milestone, the next version of ggplot2 will be 1.0.0".[6]

On 21 December 2015, ggplot 2.0.0 was released. In the announcement, it was stated that "ggplot2 now has an official extension mechanism. This means that others can now easily create their [own] stats, geoms and positions, and provide them in other packages."[7]

In contrast to base R graphics, ggplot2 allows the user to add, remove or alter components in a plot at a high level of abstraction.[8] This abstraction comes at a cost, with ggplot2 being slower than lattice graphics.[9]

Creating a different plot for various subsets of the data requires for loops and manual management in base R graphics, whereas ggplot2 simplifies that process with a collection of "facet" functions to choose from.[10]

One potential limitation of base R graphics is the "pen-and-paper model" utilized to populate the plotting device.[11] Graphical output from the interpreter is added directly to the plotting device or window rather than separately for each distinct element of a plot.[12] In this respect it is similar to the lattice package, though Wickham argues ggplot2 inherits a more formal model of graphics from Wilkinson.[13] As such, it allows for a high degree of modularity; the same underlying data can be transformed by many different scales or layers.[14][15]

Plots may be created via the convenience function qplot() where arguments and defaults are meant to be similar to base R's plot() function.[16][17] More complex plotting capacity is available via ggplot() which exposes the user to more explicit elements of the grammar.[18]

• ggplot for Python[19]
• plotnine[20] started as an effort to improve the scalability of ggplot for Python and is largely compatible with ggplot2 syntax.
• Plotly - Interactive, online ggplot2 graphs[21]
• gramm, a plotting class for MATLAB inspired by ggplot2[22]
• gadfly, a system for plotting and visualization written in Julia, based largely on ggplot2[23]
• Chart::GGPlot - ggplot2 port in Perl[24]
• The Lets-Plot for Python library includes a native backend and a Python API, which was mostly based on the ggplot2 package well-known to data scientists who use R.[25]
• Lets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language and is built on the principles of layered graphics first described in the Leland Wilkinson work The Grammar of Graphics.[26]
• ggplotnim, plotting library using the Nim programming language inspired by ggplot2.[27]

• Wilkinson, Leland (2005). The Grammar of Graphics. Springer. ISBN 978-0-387-98774-3.
• Wickham, Hadley (2017). R for Data Science. O'Reilly Media. ISBN 978-1491910399.
• Wickham, Hadley (6 June 2011). Engineering Data Analysis (with R and ggplot2). Google Tech Talks.

• Official website
• ggplot2 on GitHub