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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/tidyverse-visualize-data
课程评论：没有评论
课程名称：在 Tidyverse 中可视化数据 课程概述：数据可视化是数据科学项目中的关键环节。在数据被导入并整理好后，可视化可以帮助我们理解数据集的情况。同样，在完成分析并准备呈现结果时，数据可视化是一种高效的沟通方式。本课程将介绍数据可视化的基本概念及常见类型。您将学习 ggplot2 R 包，这是一套强大的工具，能够制作出惊艳的数据图形，并成为行业标准。课程内容涵盖不同类型的图表、效果图的构建，以及成功与失败可视化的要素。 在本专门化课程中，假设您已经熟悉 R 编程语言。如果您尚未掌握 R，建议先学习 R 编程课程后再回来学习本课程。 课程大纲： 1. **绘图生成过程** - 描述：讨论了一些通用指导原则后，在创建图形之前，您应提出一些问题。每次制作数据可视化时，您应该问自己三个主要问题，我们将在此部分讨论。 2. **ggplot2：自定义** - 描述：我们将详细介绍如何定制图表，使其更清晰，以便更好地传达结果。您将学习如何自定义图表的颜色、标签、图例和文本，进而从生成探索性图表过渡到制作解释性图表。课程中将继续使用钻石数据集进行教学。 3. **ggplot2：扩展功能** - 描述：除了 ggplot2 的众多功能，还有一些额外的包在其基础上构建。我们将介绍一些扩展包，以便您能够直接在图上注释点（ggrepel 和 directlabels）、合并多个图表（cowplot + patchwork）以及生成动画图（gganimate）。 4. **项目：在 Tidyverse 中可视化数据** - 描述：在这个项目中，您将练习探索数据并使用 tidyverse 创建数据可视化，数据来自2018年快餐的营养和销售数据。 通过本课程，您将掌握数据可视化的理论与实践，并提升与别人分享数据分析结果的能力。
Part: 1
Title:Plot Generation Process
Description:Having discussed some general guidelines, there are a number of questions you should ask yourself before making a plot. There are three main questions you should ask any time you create a visual display of your data. We will discuss these three questions below.
Part: 2
Title:ggplot2: Customization
Description:So far, we have walked through the steps of generating a number of different graphs (using different geoms) in ggplot2. We discussed the basics of mapping variables to your graph to customize its appearance or aesthetic (using size, shape, and color within aes()). Here, we’ll build on what we’ve previously learned to really get down to how to customize your plots so that they’re as clear as possible for communicating your results to others. The skills learned in this lesson will help take you from generating exploratory plots that help you better understand your data to explanatory plots – plots that help you communicate your results to others. We’ll cover how to customize the colors, labels, legends, and text used on your graph. Since we’re already familiar with it, we’ll continue to use the diamonds dataset that we’ve been using to learn about ggplot2.
Part: 3
Title:ggplot2: Extensions
Description:Beyond the many capabilities of ggplot2, there are a few additional packages that build on top of ggplot2’s capabilities. We’ll introduce a few packages here so that you can (1) directly annotate points on plots (ggrepel and directlabels); (2) combine multiple plots (cowplot + patchwork); and (3) generate animated plots (gganimate). These are referred to as ggplot2 extensions There are dozens of additional ggplot2 extensions available if you’d like to explore other plotting options beyond what is covered here!
Part: 4
Title:Project: Visualizing Data in the Tidyverse
Description:In this project, you will practice exploring data and creating data visualizations with the tidyverse using nutrition and sales data from fast food restaurants in 2018.
Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the data set. Similarly, once you’ve completed your analysis and are ready to present your findings, data visualizations are a highly effective way to communicate your results to others. In this course we will cover what data visualization is and define some of the basic types of data visualizations. In this course you will learn about the ggplot2 R package, a powerful set of tools for making stunning data graphics that has become the industry standard. You will learn about different types of plots, how to construct effect plots, and what makes for a successful or unsuccessful visualization. In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
