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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/tidyverse-importing-data
课程评论：没有评论
课程名称：在 Tidyverse 中导入数据 课程概述：将数据导入统计分析系统是任何数据科学项目中最具挑战性的部分之一。数据必须被导入并以连贯的格式协调，以便获得任何见解。您将学习如何从常用格式将数据导入 R，并协调来自不同来源的不同数据集。如果您在一个组织中，不同部门使用不同的系统和存储格式收集数据，那么本课程将为您提供将这些数据集整合在一起并理解组织中丰富信息的基本工具。 本课程介绍了用于将数据导入 R 的 Tidyverse 工具，以便为分析、可视化和建模做好准备。常见数据格式包括分隔文件、电子表格和关系数据库，同时还演示了从网络获取数据的技术，例如网络抓取和 Web API。 本专业化假定您已熟悉 R 编程语言。如果您尚未熟悉 R，建议您首先完成《R 编程》课程，然后再返回完成此课程。 课程大纲： - 导入（和导出）数据：介绍 tibble 数据类型，存储表格数据，讨论电子表格格式（如 Excel、CSV、TSV 和其他分隔文件）。 - JSON、XML 和数据库：涵盖非表格格式的数据，重点讲解 JSON 和 XML，及用于读取这些格式的包，并介绍 SQLite 关系数据库。 - 网络抓取和 API：学习如何从各种互联网来源读取数据，介绍 rvest 和 httr 包，用于连接网站、Web API 和其他在线数据源。 - 外部格式、图像和谷歌云端硬盘：讨论与他人合作时涉及的读取其他统计软件输出的数据的包，以及处理图像和谷歌云端硬盘中的数据。 - 案例研究：通过案例研究示范如何导入数据，您可以在自己的计算机上使用 RStudio 或使用每个案例研究提供的 Coursera 实验室空间。 - 项目：将数据导入 R 这个项目将提供机会，从多个来源读取数据并进行简单操作。
Name:Importing (and Exporting) Data in R
Description:A basic data type in the tidyverse is the tibble. Tibbles store tabular data and are a modern take on the standard R data frame. They have many user-friendly features that are an improvement over standard data frames when doing interactive data analysis. The remainder of this module covers tabular data in spreadsheet formats like Excel, CSV, TSV, and other delimited files.
Name:JSON, XML, and Databases
Description:Data can come in non-tabular formats, especially unstructured data or data that otherwise would not fit into a table. JSON and XML are common formats for storing arbitrarily structured data and this module covers the packages used to read in those data formats. In addition, relational databases are common for storing very large collections of tables where you do not need to read in the entire dataset at once. There are many relational database formats and we will cover the SQLite format, which is a compact and simple to use format.
Name:Web Scraping and APIs
Description:Reading in data from various Internet sources can be a useful way to build analyses that need to be regularly updated. The rvest and httr packages are useful for connecting to web sites, web APIs and other online sources of data.
Name:Foreign Formats, Images, and googledrive
Description:Working with others in a data science project often involves reading output or data produced using other statistical analysis packages or other software. This module covers packages for reading in these foreign formats, as well as images and data from Google Drive.
Name:Case Studies
Description:Now we will demonstrate how to import data using our case study examples. When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.
Name:Project: Importing Data into R
Description:This project will give you the opportunity to read in data from multiple sources and conduct some simple operations on those data.
Getting data into your statistical analysis system can be one of the most challenging parts of any data science project. Data must be imported and harmonized into a coherent format before any insights can be obtained. You will learn how to get data into R from commonly used formats and harmonizing different kinds of datasets from different sources. If you work in an organization where different departments collect data using different systems and different storage formats, then this course will provide essential tools for bringing those datasets together and making sense of the wealth of information in your organization. This course introduces the Tidyverse tools for importing data into R so that it can be prepared for analysis, visualization, and modeling. Common data formats are introduced, including delimited files, spreadsheets and relational databases, and techniques for obtaining data from the web are demonstrated, such as web scraping and web APIs. 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.
