Course overview
You’ll learn about regular expressions (regex), a powerful tool that allows you to match and manipulate text data with precision. You’ll also learn to work with JSON data, a common format you’ll encounter when pulling data from web APIs. Then you’ll dive into map and anonymous functions, two intermediate-to-advanced concepts in R that can speed up your data cleaning.
You’ll also learn how to resolve missing values in your data, a critical part of almost every data analysis project. Rather than dropping rows or columns, which reduces the amount of data you have to work with, you’ll learn statistical techniques to impute missing data, and you’ll also learn how to insert data from outside sources.
Best of all, you’ll learn by doing – you’ll practice and get feedback directly in the browser.
Key skills
- Cleaning and manipulating text data using regex
- Using map and anonymous functions
- Resolving missing data
- Advanced techniques for data cleaning
Course outline
Advanced Data Cleaning in R [4 lessons]
Regular Expression Basics 2h
Lesson Objectives- Employ regular expressions
- Define Regex components like character classes and quantifiers
- Implement regular expressions with the stringr package
Advanced Regular Expressions 2h
Lesson Objectives- Clean data by substituting regular expression matches
- Employ lookarounds to include and exclude text before and after your regex match
- Extract new columns from text data using capture groups
Map and Anonymous Functions 2h
Lesson Objectives- Read JSON files
- Ceate and transform lists using map functions
- Create anonymous functions
Working with Missing Data 2h
Lesson Objectives- Identify missing data using both code and visualizations
- Resolve missing data using imputation
- Fill missing values using external data
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