ggdensity: Improved Bivariate Density Visualization in R

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2023-11-01 DOI:10.32614/rj-2023-048
James Otto, David Kahle
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Abstract

The [ggdensity](https://CRAN.R-project.org/package=ggdensity) R package extends the functionality of [ggplot2](https://CRAN.R-project.org/package=ggplot2) by providing more interpretable visualizations of bivariate density estimates using highest density regions (HDRs). The visualizations are created via drop-in replacements for the standard [ggplot2](https://CRAN.R-project.org/package=ggplot2) functions used for this purpose: geom_hdr() for geom_density_2d_filled() and geom_hdr_lines() for geom_density_2d(). These new geoms improve on those of [ggplot2](https://CRAN.R-project.org/package=ggplot2) by communicating the probabilities associated with the displayed regions. Various statistically rigorous estimators are available, as well as convenience functions geom_hdr_fun() and geom_hdr_fun_lines() for plotting HDRs of user-specified probability density functions. Associated geoms for rug plots and pointdensity scatterplots are also presented.
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在R中改进的二元密度可视化
[ggdensity](https://CRAN.R-project.org/package=ggdensity) R包扩展了[ggplot2](https://CRAN.R-project.org/package=ggplot2)的功能,通过使用最高密度区域(hdr)提供更多可解释的二元密度估计可视化。可视化是通过插入式替换用于此目的的标准[ggplot2](https://CRAN.R-project.org/package=ggplot2)函数创建的:geom_density_2d_fill()的geom_hdr()和geom_density_2d()的geom_hdr_lines()。这些新的几何图形通过传达与显示区域相关的概率来改进[ggplot2](https://CRAN.R-project.org/package=ggplot2)的几何图形。可以使用各种统计上严格的估计器,以及用于绘制用户指定概率密度函数的hdr的方便函数geom_hdr_fun()和geom_hdr_fun_lines()。还给出了地毯图和点密度散点图的相关几何图形。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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