autoimage: Multiple Heat Maps for Projected Coordinates.

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2017-01-01 Epub Date: 2017-05-10
Joshua P French
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Abstract

Heat maps are commonly used to display the spatial distribution of a response observed on a two-dimensional grid. The autoimage package provides convenient functions for constructing multiple heat maps in unified, seamless way, particularly when working with projected coordinates. The autoimage package natively supports: 1. automatic inclusion of a color scale with the plotted image, 2. construction of heat maps for responses observed on regular or irregular grids, as well as non-gridded data, 3. construction of a matrix of heat maps with a common color scale, 4. construction of a matrix of heat maps with individual color scales, 5. projecting coordinates before plotting, 6. easily adding geographic borders, points, and other features to the heat maps. After comparing the autoimage package's capabilities for constructing heat maps to those of existing tools, a carefully selected set of examples is used to highlight the capabilities of the autoimage package.

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autoimage:投影坐标的多个热图。
热图通常用于显示在二维网格上观察到的响应的空间分布。autoimage包为以统一、无缝的方式构建多个热图提供了方便的功能,特别是在使用投影坐标时。autoimage包本身支持:2.自动包含绘制图像的色阶。2 .在规则或不规则网格以及非网格数据上观测响应的热图构建;构造一个具有共同色标度的热图矩阵,3。具有单独颜色尺度的热图矩阵的构造,5。绘图前的投影坐标,6。轻松添加地理边界、点和其他特征到热图。在将autoimage包用于构造热图的功能与现有工具的功能进行比较之后,使用一组精心挑选的示例来突出显示autoimage包的功能。
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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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