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R Academy · Lesson

Data Cleaning Basics

Learn to deal with missing values and correct data types to ensure data quality.

1

Introduction to Data Cleaning in R

Cleaning data is an essential step before analysis. In this lesson, you'll learn how to handle missing values, correct data types, and remove duplicates.

Data Cleaning Basics — illustration 1

2

Handling Missing Values

Missing values in R are represented by NA. You can check for missing values using is.na().

sum(is.na(data))

All lessons in this course

  1. Importing Data
  2. Data Cleaning Basics
  3. Combining and Reshaping Data
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