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所在平台: Udemy |
课程主页: https://www.udemy.com/course/multivariate-data-visualization-with-r/
课程评论：没有评论
课程名称：用R进行多变量数据可视化 课程概述：在研究项目中，创建多变量数据的可视化、图表和图形通常是有用且具有启发性的。预分析和后分析的可视化能够帮助研究人员理解数据中的“动态”，这种理解远超仅仅查看模型估计的数值汇总。R语言中的lattice包专门设计用于图形化展示多变量数据集之间的关系。本课程介绍并演示了利用R构建和绘制基于网格的多变量图形的创造性方法。Lattice图形的特点是可以绘制多变量（如3、4、5个或更多变量）图表，并利用条件和面板技术。因此，这种方法成为了视觉呈现多变量统计模型拟合结果的热门选择。 课程内容不仅涵盖了多变量图形的基本构造，还强调了用户对图形的外观和细节的广泛控制能力，这种控制能力远超过R的基础图形方法。该方法基于拱形图（trellis graphics），后者最初在贝尔实验室开发的S语言中流行开来。通过本课程，学习者将掌握如何有效利用R进行复杂数据集的可视化分析。
It is often both useful and revealing to create visualizations, plots and graphs of the multivariate data that is the subject of one's research project. Often, both pre-analysis and post-analysis visualizations can help one understand "what is going on in the data" in a way that looking at numerical summaries of fitted model estimates cannot. The lattice package in R is uniquely designed to graphically depict relationships in multivariate data sets.This course describes and demonstrates this creative approach for constructing and drawing grid-based multivariate graphic plots and figures using R. Lattice graphics are characterized as multi-variable (3, 4, 5 or more variables) plots that use conditioning and paneling. Consequently, it is a popular approach for, and a good fit to visually present the results of multi-variable statistical model fitting. The appearance of most of the plots, graphs and figures are determined by panel functions, rather than by the high-level graphics function calls themselves. Further, the user of lattice graphics has extensive and comprehensive control over many more of the details and features of the visual plots, far greater control that is afforded by the base graphics approach in R. The method is based on trellis graphics which were popularized in the S language developed by Bell Labs.
