R package ggplot2: An implementation of the Grammar of Graphics , An implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes. See the ggplot2 website for more information, documentation and examples. (Source:

References in zbMATH (referenced in 152 articles , 2 standard articles )

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  1. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  2. Anne Petersen; Claus Ekstrøm: dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R (2019) not zbMATH
  3. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  4. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  5. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  6. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  7. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  8. Górecki, Tomasz; Smaga, Łukasz: fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data (2019)
  9. Hagen Radtke; Florian Börgel; Sandra-Esther Brunnabend; Anja Eggert; Madline Kniebusch; H. E. Markus Meier; Daniel Neumann; Thomas Neumann; Manja Placke: Validator - a Web-Based Interactive Tool for Validation of Ocean Models at Oceanographic Stations (2019) not zbMATH
  10. Hay-Jahans, Christopher: R companion to elementary applied statistics (2019)
  11. Heidi Seibold, Achim Zeileis, Torsten Hothorn: model4you: An R Package for Personalised Treatment Effect Estimation. (2019) not zbMATH
  12. Jonathon Love; Ravi Selker; Maarten Marsman; Tahira Jamil; Damian Dropmann; Josine Verhagen; Alexander Ly; Quentin Gronau; Martin Šmíra; Sacha Epskamp; Dora Matzke; Anneliese Wild; Patrick Knight; Jeffrey Rouder; Richard Morey; Eric-Jan Wagenmakers: JASP: Graphical Statistical Software for Common Statistical Designs (2019) not zbMATH
  13. Jure Demšar, Grega Repovš, Erik Štrumbelj: bayes4psy - an Open Source R Package for Bayesian Statistics in Psychology (2019) arXiv
  14. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  15. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  16. Mateusz Staniak, Przemyslaw Biecek: The Landscape of R Packages for Automated Exploratory Data Analysis (2019) arXiv
  17. Melina Vidoni; Aldo Vecchietti : rsppfp: An R package for the shortest path problem with forbidden paths (2019) not zbMATH
  18. Morimoto, Juliano: Foraging decisions as multi-armed bandit problems: applying reinforcement learning algorithms to foraging data (2019)
  19. Murrell, Paul: R graphics (2019)
  20. N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH

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