利用ggplot2可视化古环境档案

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
D. Dunnington, Nell Libera, J. Kurek, I. Spooner, G. Gagnon
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引用次数: 5

摘要

本文介绍了R的titypaleo包,它可以实现高质量的可重复的时间地层多元数据可视化,这是自然科学的几个学科共同的。tiypaleo软件包并没有引入新的绘图功能,而是定义了ggplot2软件包的几个正交组件,当这些组件组合在一起时,可以创建大多数类型的地层图。为此,我们将多参数数据概念化为具有属性(列)的一系列度量值(行),从而支持使用ggplot2 facet机制来显示多参数数据。正交组件包括(1)表示相对丰度和浓度值的尺度,(2)在其他地方创建的古环境图中常用的几何形状,(3)正确分配表示多个参数的面板的尺度和大小的切面,以及(4)使tiypaleo能够创建优雅图形的主题元素。总的来说,这种方法证明了最小的ggplot2包装器在创建特定于域的图方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
tidypaleo: Visualizing Paleoenvironmental Archives Using ggplot2
This paper presents the tidypaleo package for R, which enables high-quality reproducible visualizations of time-stratigraphic multivariate data that is common to several disciplines of the natural sciences. Rather than introduce new plotting functions, the tidypaleo package defines several orthogonal components of the ggplot2 package that, when combined, enable most types of stratigraphic diagrams to be created. We do so by conceptualizing multi-parameter data as a series of measurements (rows) with attributes (columns), enabling the use of the ggplot2 facet mechanism to display multi-parameter data. The orthogonal components include (1) scales that represent relative abundance and concentration values, (2) geometries that are commonly used in paleoenvironmental diagrams created elsewhere, (3) facets that correctly assign scales and sizes to panels representing multiple parameters, and (4) theme elements that enable tidypaleo to create elegant graphics. Collectively, this approach demonstrates the efficacy of a minimal ggplot2 wrapper to create domain-specific plots.
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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