Many stat_functions can be invoked directly to generate ggplot layers. The objective of statistical transformation is to supply the inputs necessary to produce a geom for example, stat_bin sets up the data structure necessary for geom_bar and geom_histogram. Statistical transformations ("stats"), which refer to some type of data summarization such as a five-number summary for a box plot (stat_boxplot) or counts of observations by bin (stat_bin).This process entails a conversion from "data units" to "graphical units." The inverse mapping of a scale transformation is rendered in the graphic as a guide, either a positional scale or a legend, in the original data units. Scale transformations, which map aesthetics to unique values of variables, in addition to mathematical transformations to produce positional axes (e.g., logarithms).Geometric objects ("geoms" for short), such as points, lines, polygons, box plots, error bars, etc.Ěesthetics (abbreviated "aes"), which refer to visual attributes that affect how data are displayed in a graphic, e.g., color, point size, or line type.Ěn input data object, usually an R data frame.The package is programmed entirely in the R statistical programming environment3 using the grid graphics system,4 extending Wilkinson's theory to a "layered" grammar of graphics.5 The grammar implemented in ggplot2 provides an infrastructure for composing a graphic from multiple elements: The ggplot2 package, authored by Hadley Wickham,1 is an implementation of the theory described in "The Grammar of Graphics" by Leland Wilkinson.2 In a nutshell, the grammar defines a set of rules by which components of a statistical graphic are organized, coordinated, and rendered. This article summarizes key features of the package with examples from pharmacometrics and pointers to available resources for learning ggplot2.ĬPT: Pharmacometrics & Systems Pharmacology (2013) 2, e79 doi:10.1038/psp.2013.56 published online 16 October 2013 ggplot2 is a contributed visualization package in the R programming language, which creates publication-quality statistical graphics in an efficient, elegant, and systematic manner. Visualization is a powerful mechanism for extracting information from data. Citation: CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e79 doi:10.1038/psp.2013.56 © 2013 ASCPT All rights reserved 2163-8306/12Īpplication of ggplot2 to Pharmacometric Graphics
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