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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-analysis-with-tidyverse
课程评论：没有评论
课程名称：使用Tidyverse进行数据分析 课程概述：本课程是对R编程的温和介绍，适合三类学习者： - 想进行数据分析但不懂编程 - 知道编程但对R不太熟悉 - 有一定R编程基础但想深入学习Tidyverse的操作 建议在完成该专业的第一门课程后再学习本课程，或如果您已经熟悉ggplot、RMarkdown和基本的R函数编写。本课程将教您如何使用readr导入数据，使用dplyr分析数据，以及使用stringr和forcats对字符串和因子进行操作。 课程大纲： 第1部分：项目、Tibbles和导入数据 描述：在数据分析时，您通常需要从CSV或文本文件中导入数据。在本模块中，您将学习如何使用基础R和Tidyverse中的readr库导入和解析数据。同时，您将介绍R项目，以帮助存储和组织与分析相关的数据文件。 第2部分：整理数据 描述：数据通常以表格形式存储，并且根据用途的不同而组织方式也不同。在本模块中，您将学习如何重新组织数据，以生成“整理好的”数据集，其中每个变量存储在自己的列中，每个观测值存储在自己的行中，每个值存储在表格单元格中。 第3部分：关系数据 描述：数据分析很少涉及单一的数据表，通常需要将多个相关表格结合起来回答您感兴趣的问题。在本模块中，您将学习和实践从关系数据中变更变量和筛选观察值。 第4部分：字符串操作和正则表达式 描述：本模块将介绍R中的字符串操作。您将学习字符串的基本知识，包括字符串创建、合并和子集提取。接着，您将使用正则表达式描述和查看字符串中的模式。 第5部分：分类变量和因子 描述：在课程的最后一个模块中，您将使用Tidyverse中的forcats包处理分类变量，这些变量具有离散值。forcats包引入了因子——用于按级别对数据进行分类的数据对象。您将练习创建和修改因子。
Part: 1
Title:Projects, Tibbles and Importing Data
Description:When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
Part: 2
Title:Tidying Data
Description:Data are stored in tabular forms and are often organized differently depending on its use. In this module, you will learn how to reorganize data to produce a "tidy" data set, where every variable is stored in its own column, every observation is stored in its own row, and each value is stored in a table cell.
Part: 3
Title:Relational Data
Description:Data analysis rarely involves a single data table and you will be required to combine multiple related tables to answer questions you are interested in. In this module, you will learn and practice mutating variables and filtering observations from relational data.
Part: 4
Title:String Manipulation and Regular Expressions
Description:This module will introduce string manipulation in R. You will learn the basics of strings, including string creation, merging, and subsetting. Then, you will use regular expressions to describe and view patterns in strings.
Part: 5
Title:Categorical Variables and Factors
Description:In the last module of the course, you will use the forcats package in the tidyverse to work with categorical variables, variables that have discrete values. The forcats package introduces factors - data objects used to categorize the data in levels. You will practice creating and modifying factors.
This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if: • you want to do data analysis but don’t know programming • you know programming but aren’t too familiar with R • you know some R programming but want to learn more about the tidyverse verbs It is best taken following the first course in the specialization or if you already are familiar with ggplot, RMarkdown, and basic function writing in R. You will use learn to use readr to read in your data, dplyr to analyze your data, and stringr and forcats to manipulate strings and factors.
