Data Cleaning and Preprocessing with R

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.

Lessons and practices

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

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.