Abstract
This chapter outlines the main characteristics of the adegraphics package. The structure of graphical objects, classes and associated methods are explained. Several examples show how user-level functions can be used to draw scientific graphs particularly adapted to multivariate data analysis. Automated collection of graphs, spatial representations, and the case of big data sets are detailed.
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References
Auda Y (1983) Rôle des méthodes graphiques en analyse des données: application au dépouillement des enquêtes écologiques. Thèse de 3ème cycle, Université Lyon 1
Bertin J (1967) Les diagrammes, les réseaux, les cartes. Mouton & Gautier-Villars, Paris
Bivand R, Lewin-Koh N (2017) maptools: tools for reading and handling spatial objects. https://CRAN.R-project.org/package=maptools, R package version 0.9-2
Bivand RS, Pebesma E, Gomez-Rubio V (2013) Applied spatial data analysis with R, 2nd edn. Springer, New York
Dray S, Jombart T (2011) Revisiting Guerry’s data: introducing spatial constraints in multivariate analysis. Ann Appl Stat 5:2278–2299
Friendly M, Dray S (2014) Guerry: maps, data and methods related to Guerry (1833) “Moral statistics of France”. http://CRAN.R-project.org/package=Guerry, R package version 1.6-1
Neuwirth E (2014) RColorBrewer: ColorBrewer palettes. https://CRAN.R-project.org/package=RColorBrewer, R package version 1.1-2
Pebesma EJ, Bivand RS (2005) Classes and methods for spatial data in R. R News 5(2):9–13
Sarkar D (2008) Lattice: multivariate data visualization with R. Springer, New York
Siberchicot A, Julien-Laferrière A, Dufour AB, Thioulouse J, Dray S (2017) adegraphics: an S4 lattice-based package for the representation of multivariate data. R J 9(2):198–212
Thioulouse J (1989) Statistical analysis and graphical display of multivariate data on the Macintosh. Comput Appl Biosci 5(4):287–292
Thioulouse J (1990) Macmul and Graphmu: two Macintosh programs for the display and analysis of multivariate data. Comput Geosci 16(8):1235–1240
Thioulouse J (1996) Outils logiciels, méthodes statistiques et implications biologiques: une approche de la biométrie. Mémoire d’habilitation à diriger des recherches, Université Lyon 1
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Thioulouse, J., Dray, S., Dufour, AB., Siberchicot, A., Jombart, T., Pavoine, S. (2018). Multivariate Analysis Graphs. In: Multivariate Analysis of Ecological Data with ade4. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8850-1_4
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DOI: https://doi.org/10.1007/978-1-4939-8850-1_4
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