Master the art of cleaning data using tidyr as part of the tidyverse ecosystem. Focus on techniques for dealing with missing values, reshaping data, and preparing datasets for analysis.
Overview
Syllabus
- Lesson 1: Identifying and Handling Missing Values in R Data Cleaning Process
- Handling Missing Client Data in R
- Filling Missing Values with Median and Handling Missing Addresses
- Filling Missing Values in Client Data
- Managing Missing Values in Museum Artifact Data
- Lesson 2: Data Cleaning Techniques: Managing Duplicates and Outliers in R
- Cleaning School Data: Handling Duplicates and Outliers in R
- Handling Duplicates and Outliers in R Data Frames
- Replace Outlier Grades with Mean Value
- Cleaning School Data: Removing Duplicates and Handling Age Outliers in R
- Lesson 3: Data Normalization Techniques in R
- Normalizing Planetary Temperatures with Min-Max Technique in R
- Normalize Space Explorer Weights Without Centering
- Normalizing Moon Mass Data: A Bug Fix Challenge in R
- Normalization of Planetary Distances in R
- Normalize Space Rover Weights with Min-Max Scaling in R
- Lesson 4: Categorical Data Encoding in R
- Encoding Categorical Data in R
- Label Encoding in R for Clothing Inventory Data
- Encode Clothing Items into Numerical Values
- One-Hot Encoding in R for Clothing Colors Dataset
