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.

Practice

1. What does the drop_na() function do in R's tidyr package?

easy

A. Fills missing values with the mean of the column.

B. Replaces missing values with zeros.

C. Removes rows that contain missing values in specified columns.

D. Sorts the data frame by missing values.

Solution

1. Step 1: Understand the purpose of drop_na()

   The function is designed to remove rows that have missing (NA) values in specified columns or in the entire data frame if no columns are specified.

2. Step 2: Compare with other options

   Options A and C describe filling missing values, which is done by fill() or other functions, not drop_na(). Sorts the data frame by missing values. sorts the data frame by missing values, which is unrelated.

3. Final Answer:

   Removes rows that contain missing values in specified columns. -> Option C

4. Quick Check:

   drop_na() removes rows with NA [OK]

Hint: Remember: drop_na drops rows with NA, fill replaces NA [OK]

Common Mistakes:

• Confusing drop_na() with fill()
• Thinking drop_na() fills missing values
• Assuming drop_na() sorts data

2. Which of the following is the correct syntax to fill missing values in a data frame df using the fill() function from tidyr for the column score?

easy

A. df.fill(score, direction = "down")

B. fill(df, score, direction = "down")

C. fill(score, df, .direction = "down")

D. df %>% fill(score, .direction = "down")

Solution

1. Step 1: Recall the pipe operator usage

   The fill() function is typically used with the pipe operator %>% to pass the data frame as the first argument.

2. Step 2: Check the argument order and syntax

   df %>% fill(score, .direction = "down") correctly uses df %>% fill(score, .direction = "down"). Options B and C have wrong syntax or argument order. df.fill(score, direction = "down") uses incorrect syntax not valid in R.

3. Final Answer:

   df %>% fill(score, .direction = "down") -> Option D

4. Quick Check:

   Use pipe %>% before fill() with column name [OK]

Hint: Use pipe %>% before fill() and specify column [OK]

Common Mistakes:

• Passing data frame as second argument
• Using dot direction without dot prefix
• Using Python-like method syntax

3. Given the data frame df below:

df <- data.frame(id = 1:5, score = c(10, NA, 15, NA, 20))
df_filled <- df %>% fill(score, .direction = "down")
print(df_filled)

What will be the output of print(df_filled)?

medium

A. id score
1 1 10
2 2 10
3 3 15
4 4 15
5 5 20

B. id score
1 1 10
2 2 NA
3 3 15
4 4 NA
5 5 20

C. id score
1 1 10
2 2 15
3 3 15
4 4 20
5 5 20

D. id score
1 1 NA
2 2 NA
3 3 15
4 4 15
5 5 20

Solution

1. Step 1: Understand fill() with .direction = "down"

   The function fills missing values by copying the last known non-NA value downward to replace NA values.

2. Step 2: Apply fill() to the data

   Row 2's NA is replaced by 10 (from row 1), row 4's NA is replaced by 15 (from row 3). Other values remain unchanged.

3. Final Answer:

   id score 1 1 10 2 2 10 3 3 15 4 4 15 5 5 20 -> Option A

4. Quick Check:

   fill() copies last known value down [OK]

Hint: fill() with down copies last known value to NA below [OK]

Common Mistakes:

• Assuming fill() replaces NA with next value
• Thinking fill() leaves NA unchanged
• Confusing fill() direction

4. Consider the following code snippet:

library(tidyr)
df <- data.frame(x = c(1, NA, 3), y = c(NA, 2, 3))
df_clean <- drop_na(df, x)
print(df_clean)

What is the output of print(df_clean)?

medium

A. x y
1 1 NA
3 3 3

B. x y
2 NA 2
3 3 3

C. x y
1 1 NA
2 NA 2
3 3 3

D. x y
3 3 3

Solution

1. Step 1: Understand drop_na(df, x)

   This removes rows where column x has NA values. Row 2 has NA in x, so it is removed.

2. Step 2: Identify remaining rows

   Rows 1 and 3 remain. Row 1 has x=1, y=NA. Row 3 has x=3, y=3.

3. Final Answer:

   x y 1 1 NA 3 3 3 -> Option A

4. Quick Check:

   drop_na(df, x) removes rows with NA in x [OK]

Hint: drop_na(df, col) removes rows with NA in that column [OK]

Common Mistakes:

• Removing rows with NA in all columns instead of specified
• Confusing drop_na() with fill()
• Expecting NA values to be replaced

5. You have a data frame df with columns time and temperature, where some temperature values are missing (NA). You want to fill missing temperatures by carrying the last known temperature forward, but only within each day (grouped by time). Which code correctly achieves this?

hard

A. df %>% fill(temperature, .direction = "up") %>% group_by(time)

B. df %>% group_by(time) %>% fill(temperature, .direction = "down")

C. df %>% fill(temperature, .direction = "down") %>% group_by(time)

D. df %>% group_by(time) %>% drop_na(temperature)

Solution

1. Step 1: Group data by time

   Grouping by time ensures fill() works within each day separately.

2. Step 2: Use fill() with .direction = "down"

   Filling downward copies last known temperature forward within each group.

3. Step 3: Check other options

   df %>% fill(temperature, .direction = "down") %>% group_by(time) fills before grouping, so fill applies to whole data ignoring groups. df %>% fill(temperature, .direction = "up") %>% group_by(time) fills upward, which is opposite of requirement. df %>% group_by(time) %>% drop_na(temperature) drops rows with NA instead of filling.

4. Final Answer:

   df %>% group_by(time) %>% fill(temperature, .direction = "down") -> Option B

5. Quick Check:

   Group then fill down to fill within groups [OK]

Hint: Group first, then fill down to fill missing within groups [OK]

Common Mistakes:

• Filling before grouping
• Using fill direction up instead of down
• Dropping rows instead of filling