About This Course
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
What You’ll Learn
➤ Understand common data storage systems
➤ Apply data cleaning basics to make data “tidy”
➤ Apply data cleaning basics to make data “tidy”
➤ Obtain usable data from the web, APIs, and databases
Skills You’ll Gain
➤ Data Manipulation
➤ Regular Expression (REGEX)
➤ R Programming
➤ Data Cleansing
What you will learn from this course
Module 1 – Obtaining data motivation
➤ Raw and Processed Data
➤ Components of Tidy Data
➤ Downloading Files
➤ Reading Local Files
➤ Reading Excel Files
➤ Reading XML
➤ Reading JSON
➤ The data. Table Package
Module 2 – Data storage systems
➤ Reading from MySQL
➤ Reading from HDF5
➤ Reading from The Web
➤ Reading From APIs
➤ Reading From Other Sources
Module 3 – Organizing , merging, and managing Data
➤ Summarizing Data
➤ Creating New Variables
➤ Reshaping Data
➤ Managing Data Frames with dplyr – Introduction
➤Managing Data Frames with dplyr – Basic Tools
➤ Merging Data
Module 4 – Text and Date manipulation in R
➤ Editing text variables
➤ Regular Expressions
➤ Regular Expressions
➤ Working with Dates
➤ Data Resources