Society of Actuaries (SOA) PA Practice Exam

How do you remove all NA's from a dataset in R?

1. Use the remove_na(data) function

2. Use the na.exclude(data) function

3. Use the na.omit(data) function

4. Use the na.remove(data) function

The correct answer is: Use the na.omit(data) function

Using the na.omit(data) function is the correct approach to remove all NA (not available) values from a dataset in R. This function effectively eliminates all rows that contain any NA values, allowing for a cleaner dataset without missing values. This is particularly useful in situations where subsequent analysis requires complete cases without any gaps, ensuring that computations and visualizations reflect accurate data. The na.omit function preserves the integrity of the dataset by removing entire rows, which is key when conducting statistical analyses that cannot handle missing values. Other options, while they may sound similar, do not function the same way. For instance, remove_na(data) is not a standard function in R, indicating a possible confusion with available packages. Meanwhile, na.exclude(data) serves a slightly different purpose as it also removes NA values but retains the structure of the data, leaving NA entries in the result in some contexts. The na.remove(data) function does not exist in base R, making it an invalid option. Thus, na.omit is the most widely used and recognized function for directly removing NAs from a dataset, ensuring clarity and correctness in the data analysis process.