ggplot2

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: http://cran.r-project.org/web/packages)


References in zbMATH (referenced in 181 articles , 2 standard articles )

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  1. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  2. Irizarry, Rafael A.: Introduction to data science. Data analysis and prediction algorithms with R (2020)
  3. Virta, Joni; Li, Bing; Nordhausen, Klaus; Oja, Hannu: Independent component analysis for multivariate functional data (2020)
  4. von Schroeder, Jonathan; Dickhaus, Thorsten: Efficient calculation of the joint distribution of order statistics (2020)
  5. Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle: An R Package for Spatio-Temporal Change of Support (2019) arXiv
  6. Anne Petersen; Claus Ekstrøm: dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R (2019) not zbMATH
  7. Annette Möller, Jürgen Groß: Probabilistic Temperature Forecasting with a Heteroscedastic Autoregressive Ensemble Postprocessing model (2019) arXiv
  8. Ann-Kristin Kreutzmann; Sören Pannier; Natalia Rojas-Perilla; Timo Schmid; Matthias Templ; Nikos Tzavidis: The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators (2019) not zbMATH
  9. Athanasia M. Mowinckel, Didac Vidal-Piñeiro: Visualisation of Brain Statistics with R-packages ggseg and ggseg3d (2019) arXiv
  10. Balocchi, Cecilia; Jensen, Shane T.: Spatial modeling of trends in crime over time in Philadelphia (2019)
  11. Bien, Jacob: Graph-guided banding of the covariance matrix (2019)
  12. Boonstra, Philip S.; Barbaro, Ryan P.; Sen, Ananda: Default priors for the intercept parameter in logistic regressions (2019)
  13. 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)
  14. Chakraborty, Saptarshi; Khare, Kshitij: Consistent estimation of the spectrum of trace class data augmentation algorithms (2019)
  15. Daniel Kaschek; Wolfgang Mader; Mirjam Fehling-Kaschek; Marcus Rosenblatt; Jens Timmer: Dynamic Modeling, Parameter Estimation, and Uncertainty Analysis in R (2019) not zbMATH
  16. Daniel Sabanés Bové, Wai Yin Yeung, Giuseppe Palermo, Thomas Jaki: Model-Based Dose Escalation Designs in R with crmPack (2019) not zbMATH
  17. Davis, Benjamin J. K.; Curriero, Frank C.: Development and evaluation of geostatistical methods for non-Euclidean-based spatial covariance matrices (2019)
  18. del Rosario, Zachary; Lee, Minyong; Iaccarino, Gianluca: Lurking variable detection via dimensional analysis (2019)
  19. Dena J. Clink, Holger Klinck: GIBBONR: An R package for the detection and classification of acoustic signals using machine learning (2019) arXiv
  20. Górecki, Tomasz; Smaga, Łukasz: fdANOVA: an R software package for analysis of variance for univariate and multivariate functional data (2019)

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