Handling missing values (drop_na, fill) in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the drop_na() function do in R?

It removes rows from a data frame that contain any missing values (NA). This helps clean data by keeping only complete cases.

beginner

How does fill() help with missing values in R?

The fill() function fills missing values (NA) in a column by carrying the last non-missing value forward or backward, useful for filling gaps in data.

beginner

What package do drop_na() and fill() come from?

They are part of the tidyr package, which helps tidy and clean data in R.

intermediate

What happens if you use drop_na() without any arguments?

It removes all rows that have any missing value in any column of the data frame.

intermediate

How can you fill missing values only in specific columns using fill()?

You specify the column names inside fill(), like fill(data, column1, column2), to fill missing values only in those columns.

What does drop_na() do by default?

A Leaves missing values unchanged

B Fills missing values with zeros

C Replaces missing values with the mean

D Removes rows with any missing values

drop_na() removes rows that have any NA values by default.

Which package provides fill() and drop_na() functions?

A dplyr

B tidyr

C ggplot2

D stringr

Both functions are part of the tidyr package.

How does fill() fill missing values by default?

A With the last non-missing value carried forward

B With the next non-missing value

C With zero

D With the mean of the column

fill() carries the last non-missing value forward by default.

If you want to remove rows with missing values only in column age, how do you use drop_na()?

A fill(data, age)

B drop_na(data)

C drop_na(data, age)

D drop_na(data, all)

Specifying the column name removes rows with missing values only in that column.

What is a common use case for fill()?

A Filling missing values in time series data by carrying last observation forward

B Replacing missing values with zeros

C Removing all missing data

D Sorting data alphabetically

fill() is often used to fill gaps in time series by carrying the last known value forward.

Explain how drop_na() and fill() help handle missing values in R.

Think about removing vs filling missing data.

Describe a situation where you would use fill() instead of drop_na().

Consider when data loss is a problem.