ggplot2

On 2 March 2012, ggplot2 version 0.9.0 was released with numerous changes to internal organization, scale construction and layers.{{cite web|author=ggplot2 Development Team|title=Changes and Additions to ggplot2-0.9.0|url=https://cloud.github.com/downloads/hadley/ggplot2/guide-col.pdf|access-date=31 October 2017|archive-date=26 January 2015|archive-url=https://web.archive.org/web/20150126062502/https://cloud.github.com/downloads/hadley/ggplot2/guide-col.pdf|url-status=dead}}

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".{{cite web |last=Wickham|first=Hadley|title=ggplot2 development|url= https://groups.google.com/d/msg/ggplot2/SSxt8B8QLfo/J2dfKR92rsYJ|publisher=ggplot2 Google Group|access-date=26 February 2014}}

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."{{cite web |url=https://blog.rstudio.com/2015/12/21/ggplot2-2-0-0/ |access-date=2021-06-21 |title=ggplot 2.0.0 |date=21 December 2015 |archive-url=https://web.archive.org/web/20210207054047/https://blog.rstudio.com/2015/12/21/ggplot2-2-0-0/ |archive-date=2021-02-07 |url-status=live}}

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.{{cite web|last=Smith|first=David|title=Create beautiful statistical graphics with ggplot2|url=http://blog.revolutionanalytics.com/2009/01/create-beautiful-statistical-graphics-with-ggplot2.html|work=Revolutions|publisher=Revolution Analytics|access-date=11 July 2011}} This abstraction comes at a cost, with ggplot2 being slower than lattice graphics.{{cite web|url=http://learnr.wordpress.com/2009/08/26/ggplot2-version-of-figures-in-lattice-multivariate-data-visualization-with-r-final-part/|title=ggplot2 Version of Figures in "Lattice: Multivariate Data Visualization with R" (Final Part)|date=25 August 2009 }}

Creating separate plots for various subsets of data in base R requires loops and manual management, whereas ggplot2 simplifies that process with a collection of "facet" functions to choose from.{{cite web |last1=Yau |first1=Nathan |title=Comparing ggplot2 and R Base Graphics |url=https://flowingdata.com/2016/03/22/comparing-ggplot2-and-r-base-graphics/ |website=FlowingData |access-date=17 April 2022 |language=en |date=22 March 2016}}

One potential limitation of base R graphics is the "pen-and-paper model" utilized to populate the plotting device.{{cite book|last=Wickham|first=Hadley|title=ggplot2: Elegant Graphics for Data Analysis |year=2009 |publisher=Springer |isbn=978-0-387-98140-6|pages=5}} Graphical output from the interpreter is added directly to the plotting device or window, rather than separately for each distinct element of a plot.{{cite journal |last=Murrell |first=Paul |title=R Graphics|journal=Wiley Interdisciplinary Reviews: Computational Statistics|date=August 2009|volume=1|issue=2|pages=216–220|doi=10.1002/wics.22|s2cid=37743308 }} In this respect it is similar to the lattice package, though Wickham argues ggplot2 inherits a more formal model of graphics from Wilkinson.{{cite book|last=Sarkar|first=Deepayan|title=Lattice: multivariate data visualization with R|year=2008|publisher=Springer|isbn=978-0-387-75968-5|pages=xi}} As such, it allows for a high degree of modularity; the same underlying data can be transformed by many different scales or layers.{{cite book|last=Teetor|first=Paul|title=R Cookbook|year=2011|publisher=O'Reilly|isbn=978-0-596-80915-7|pages=223}}{{cite journal|last=Wickham|first=Hadley|date=March 2010|title=A Layered Grammar of Graphics|url=http://vita.had.co.nz/papers/layered-grammar.pdf|journal=Journal of Computational and Graphical Statistics|volume=19|issue=1|pages=3–28|doi=10.1198/jcgs.2009.07098|s2cid=58971746}}

Plots may be created via the convenience function qplot() where arguments and defaults are meant to be similar to base R's plot() function.{{cite book|title=R: A language and environment for statistical computing|year=2011|publisher=R Foundation for Statistical Computing|location=Vienna, Austria|isbn=978-3-900051-07-5|url=http://www.R-project.org/|author=R Development Core Team}}{{cite journal|last=Ginestet|first=Cedric|title=ggplot2: Elegant Graphics for Data Analysis |journal=Journal of the Royal Statistical Society, Series A |date=January 2011 |volume=174 |issue=1 |pages=245–246 |doi=10.1111/j.1467-985X.2010.00676_9.x}} More complex plotting capacity is available via ggplot() which exposes the user to more explicit elements of the grammar.{{cite book|last1=Muenchen|first1=Robert A.|last2=Hilbe|first2=Joseph M |title=R for Stata Users |publisher=Springer |isbn=978-1-4419-1317-3 |doi=10.1007/978-1-4419-1318-0_16 |chapter=Graphics with ggplot2|series=Statistics and Computing|year=2010|pages=385–452}}

After ten years of being developed, ggplot2 has continued to make an impact on the data visualization community: with over 10 million downloads, 400,000 downloads in a given month, and data scientists from the US government to journalists at the New York Times use ggplot2 to analyze and present data.{{Cite web |last=Kopf |first=Dan |date=2017-06-18 |title=All hail ggplot2—The code powering all those excellent charts is 10 years old |url=https://qz.com/1007328/all-hail-ggplot2-the-code-powering-all-those-excellent-charts-is-10-years-old |access-date=2025-05-13 |website=Quartz |language=en}} Wickham posits the success of ggplot2 comes from the increased popularity of the R language and the relative ease of making aesthetically appealing graphics. Along with more serious uses of ggplot2, Wickham also supports the more unusual use cases, like exploring factors for winning in the reality TV show RuPaul's Drag Race.

• ggpy, ggplot for Python,{{cite web |title=yhat/ggpy: ggplot port for python |url=https://github.com/yhat/ggpy |access-date=2024-02-01 |website=GitHub |publisher=yhat}} but has not been updated since 20 November 2016
• plotnine{{cite web |url=https://plotnine.readthedocs.io/en/stable/about-plotnine.html |title=plotnine |access-date=2 August 2023 |archive-date=2 August 2023 |archive-url=https://web.archive.org/web/20230802015937/https://plotnine.readthedocs.io/en/stable/about-plotnine.html |url-status=dead }} started as an effort to improve the scalability of ggplot for Python and is largely compatible with ggplot2 syntax.
• Plotly - Interactive, online ggplot2 graphs{{cite web |title=Plotly graphing library for ggplot2 in ggplot2 |url=https://plot.ly/ggplot2/ |access-date=2024-02-01 |website=Plotly Graphing Libraries |publisher=Plotly}}
• gramm, a plotting class for MATLAB inspired by ggplot2{{cite web |title=ggplot for Matlab |url=https://github.com/piermorel/gramm |access-date=11 December 2015 |website=GitHub |publisher=Pierre Morel (@piermorel)}}
• gadfly, a system for plotting and visualization written in Julia, based largely on ggplot2{{cite web |title=Gadfly.jl |url=http://gadflyjl.org |access-date=11 September 2018 |website=Gadfly.jl}}
• Chart::GGPlot - ggplot2 port in Perl{{cite web |title=Stephan Loyd/Chart-GGPlot-0.0001 |url=https://metacpan.org/release/Chart-GGPlot |access-date=30 March 2019 |website=MetaCPAN}}
• 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.{{cite web |title=JetBrains/lets-plot |url=https://github.com/JetBrains/lets-plot |access-date=3 April 2021 |website=GitHub |publisher=JetBrains}}
• Lets-Plot Kotlin API is an open-source plotting library for statistical data implemented using the Kotlin programming language, and is built on the principles of layered graphics first described in the Leland Wilkinson's work The Grammar of Graphics.{{cite web |title=JetBrains/lets-plot-kotlin |url=https://github.com/JetBrains/lets-plot-kotlin |access-date=4 April 2021 |website=GitHub |publisher=JetBrains}}
• ggplotnim, plotting library using the Nim programming language inspired by ggplot2.{{cite web |title=ggplotnim |url=https://github.com/Vindaar/ggplotnim |access-date=1 August 2023 |website=GitHub |publisher=Vindaar}}
• Vega and Vega-Lite are plotting libraries that use JSON to specify plots.

{{Reflist}}

• {{cite book|last=Wilkinson|first=Leland|author-link=Leland Wilkinson|title=The Grammar of Graphics|year=2005|publisher=Springer|isbn=978-0-387-98774-3}}
• {{cite book|last=Wickham|first=Hadley|title=R for Data Science|url=https://r4ds.had.co.nz/|year=2017|publisher=O'Reilly Media|isbn=978-1491910399}}
• {{cite video |people= Wickham, Hadley|date= 6 June 2011|title=Engineering Data Analysis (with R and ggplot2) |url=https://www.youtube.com/watch?v=TaxJwC_MP9Q |publisher= Google Tech Talks}}

• {{Official website|https://ggplot2.tidyverse.org/}}
• {{GitHub|tidyverse/ggplot2}}

{{R (programming language)}}

{{Statistical software}}

Category:Cross-platform free software

Category:Free data and information visualization software

Category:Free plotting software

Category:Free R (programming language) software