Understanding missing data and missing values. 5 ways to deal with missing data using R programming

The video tutorial is divided into two main sections. The first part discusses the concept of missing data, its distribution, and five strategies to handle it. The second part focuses on using R programming to apply these strategies, with practical examples using the Star Wars dataset. The tutorial emphasizes understanding the distribution of missing data and introduces tools like the tidyverse and mice package for effective data management.

What is the significance of understanding the distribution of missing data?

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Explain the impact of random missingness on the average earnings of men and women.

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Describe the systematic bias that can occur when data is collected only from urban areas.

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What are the five strategies mentioned for dealing with missing data?

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What are the potential drawbacks of deleting all rows with missing data?

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In what scenarios might you want to replace missing values with a specific value?

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What is imputation and how is it used in the context of missing data?

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How can you visualize the distribution of missing data in a dataset?

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What is the role of the 'mutate' function in handling missing data?

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How can you identify and change values in a variable to missing data?

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