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 primary focus of the first half of the video?

• Installing R packages

• Visualizing data patterns

• Understanding the distribution of missing data

• Using R programming for data analysis

How does random missing data affect the average earnings in the example dataset?

• It significantly changes the average earnings

• It causes a systematic bias

• It makes the data unusable

• It has no effect on the average earnings

What is a potential issue with non-random missing data?

• It is evenly distributed

• It can introduce systematic bias

• It does not affect data analysis

• It is easy to identify

Which strategy involves replacing missing values with the best guess?

• Substitution

• Randomization

• Imputation

• Deletion

What is the first strategy mentioned for handling missing data?

• Using a different dataset

• Deleting all rows with missing data

• Replacing with zeros

• Imputation

Which R package is recommended for handling missing data in the video?

• ggplot2

• tidyverse

• dplyr

• lubridate

What function is used to view a dataset in R?

• summary()

• print()

• head()

• view()

What does the complete.cases function do in R?

• Filters out rows with missing data

• Calculates the mean of a dataset

• Visualizes data patterns

• Replaces missing values with zeros

How can you change a specific value to NA in R?

• Using the filter function

• Using the replace_na function

• Using the select function

• Using the mutate function

What is the result of applying a function to a vector with missing values without handling them?

• The function skips the missing values

• The function crashes

• The function returns NA

• The function returns a valid result