Data Science and Machine Learning with R - Vectors: Missing Values

Data Science and Machine Learning with R - Vectors: Missing Values

Assessment

Interactive Video

Information Technology (IT), Architecture, Other

University

Hard

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The video tutorial covers the concept of missing values in R, represented by NA. It explains how R handles missing values, the importance of using RStudio documentation, and how to define vectors with missing values. The tutorial also discusses handling different data types, practical scenarios of data import, and functions to test for missing values, such as is.na and anyNA.

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4 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

How does R's treatment of missing values differ from other programming languages?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe how R handles missing values when importing data from external sources.

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

What strategies can be employed to deal with missing data in datasets?

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the function 'is.na()' used for in R?

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