Handling Missing Values in R

Instruction: Explain how you would handle missing data in a dataset using R functions.

Context: This question tests the candidate's ability to manage and manipulate datasets with missing values. Understanding different strategies for handling missing data is crucial for accurate statistical analysis and model building.

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First and foremost, it's essential to understand the nature and pattern of the missing data. R provides several functions to identify missing values, such as is.na() which returns a logical vector indicating the presence of NA values in a dataset. Using sum(is.na(data)) can give us a count of missing values across the dataset. This initial exploration helps in deciding the subsequent steps for handling these missing values. Once we've identified the missing values, one common strategy is to use the na.omit() function, which removes any rows containing NA values...

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