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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-visualization-r
课程评论：没有评论
课程名称：R语言数据可视化 课程概述：本课程将向您介绍图形语法（Grammar of Graphics），这是一个用于描述和构建图形的系统，以及如何使用R的ggplot2数据可视化包来应用这一概念，创建基本的条形图、直方图、饼图、散点图、折线图和箱线图。同时，您还将学习如何通过主题和其他技术进一步自定义图表和可视化。课程还将介绍使用另一个R的可视化包Leaflet创建地图图表，这是一种基于地理位置数据的独特绘图方式。最终，您将学习如何使用R Shiny包创建交互式仪表板，包括创建和自定义Shiny应用，添加HTML和图像组件以更改应用外观，并在网络上部署您的交互式数据应用。您将在每个模块中通过实验室练习所学内容，并在课程结束时完成一个最终项目，从而获得实践经验。 课程要求：本课程需要具备R语言和数据处理的基本知识。如果您没有这些技能，强烈建议您在本课程开始之前先参加IBM的《R编程入门》和《R数据分析》课程。课程先决条件是基本的R编程技能。 课程大纲： 模块1 - 数据可视化入门：学习使用R进行数据可视化的基础，包括所有图表和绘图共享的基本组成部分，以及使用ggplot2包使这些组成部分生动化，创建常见的柱状图、直方图和饼图。 模块2 - 基本图形、地图及自定义：进一步提高数据可视化技能，学习使用ggplot2库创建散点图、折线图和箱线图，并通过注释、自定义轴标题和文本标签对可视化进行自定义。了解分面（faceting）以及使用Leaflet库创建使用地理位置数据的地图。 模块3 - 仪表板：将数据可视化整合到易于交互和探索的仪表板中，学习Shiny包的使用，构建用户界面和服务器组件，并学习如何将仪表板部署和生成报告。 模块4 - 最终作业：实践所学知识。 观看视频、完成实验室练习，提升您的数据科学技能。祝您好运！
Name:Module 1 - Introduction to Data Visualization
Description:Data without a way to convey the story behind it to yourself or others is just numbers on a page. You can observe and tell the story of your data in a more impactful way through visualization. In this module, you will learn the basics of data visualization using R, including the fundamental components that are shared by all charts and plots, and how to bring those components to life using the ggplot2 package for R. You will also learn how to create three common chart types, including bar, histogram, and pie charts, from the qualitative and quantitative data.
Name:Module 2 - Basic Plots, Maps, and Customization
Description:In this module, you will take your data visualization skills to the next level! You will learn how to create three plot types, including scatter plots, line, plots, and box plots, using the ggplot2 library and then customize the visualizations using annotations and customized axis titles and text labels. You will also learn about faceting, a way to visualize each level of a discrete or categorical variable, and different ways to work with themes. Finally, you will learn about a unique chart type called a map that you can create using geolocation data and the Leaflet library.
Name:Module 3 - Dashboards
Description:Your data tells a story. You have built the charts and plots that show important relationships between variables, identify outliers and anomalies, and see the trends that can help you predict what the future might bring. Now you want to put these insightful data visualizations at the fingertips of your stakeholders and make it easy to interact with and explore the data. You need a dashboard! In this module, you will learn why dashboards are important and then build interactive dashboards using the Shiny package for R. You will learn how Shiny dashboards are structured into user interface and server components and then build out these components and develop the logic to make them work together. You will also learn how to deploy your dashboards and provide a way to generate informative reports with R Markdown.
Name:Module 4 - Final Assignment
Description:
In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web. You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Watch the videos, work through the labs, and watch your data science skill grow. Good luck! NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills.
