Data Analytics
431 learners
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.
R
Tidyverse
4 lessons
17 practices
2 hours
Data Cleaning and Preprocessing
Lessons and practices
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
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
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
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
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