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

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.

What is the representation of missing values in R?

• NULL

• VOID

• NA

• MISSING

Why is it important for functions and variables to be defined in R?

• To increase execution speed

• To improve code readability

• To ensure R recognizes them

• To avoid syntax errors

What is one of the key advantages of using RStudio?

• It provides easy access to documentation

• It is the only IDE for R

• It is faster than other IDEs

• It supports only R programming

Which type of vector cannot contain elements of different types in R?

• Matrix

• Vector

• Data Frame

• List

What happens when you include different types of elements in a single vector in R?

• R throws an error

• R coerces elements to a common type

• R ignores the different types

• R converts all elements to strings

What is a common challenge when dealing with missing data in datasets?

• Replacing missing data with zeros

• Deciding how to handle missing data

• Finding the missing data

• Ignoring the missing data

Why might replacing missing values with an average introduce bias?

• It increases computational complexity

• It makes the dataset harder to analyze

• It assumes all missing values are similar

• It reduces the dataset size

Which function checks if a vector contains any missing values?

• hasNA

• is.null

• anyNA

• is.na

What does the function is.na return when applied to a vector?

• A single boolean value

• The indices of missing values

• A vector of boolean values

• The number of missing values

How does the function anyNA differ from is.na?

• It only works on numeric vectors

• It returns a single boolean value

• It checks for null values

• It modifies the original vector