Handling missing values (drop_na, fill) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove rows with missing values from the data frame.

R Programming

clean_data <- [1](data)

Drag options to blanks, or click blank then click option'

Adrop_na

Bfill_na

Cna.omit

Dreplace_na

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill_na which is not a function to drop rows.

Using na.omit which works but is base R, not tidyverse.

The drop_na() function removes rows with missing values from a data frame.

2fill in blank

medium

Complete the code to replace missing values in the 'score' column with 0.

R Programming

data$score <- [1](data$score, 0)

Drag options to blanks, or click blank then click option'

Afill_na

Breplace_na

Cdrop_na

Dna.omit

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_na which removes rows instead of replacing values.

Using fill_na which is not a base or tidyverse function.

The replace_na() function replaces missing values with a specified value.

3fill in blank

hard

Fix the error in the code to fill missing values in the 'age' column with the previous non-missing value.

R Programming

library(tidyr)
data <- [1](data, age, .direction = "down")

Drag options to blanks, or click blank then click option'

Areplace_na

Bdrop_na

Cna.locf

Dfill

Attempts:

3 left

💡 Hint

Common Mistakes

Using replace_na which replaces with a fixed value, not previous values.

Using drop_na which removes rows instead of filling.

The fill() function from tidyr fills missing values using the previous non-missing value when direction is "down".

4fill in blank

hard

Fill both blanks to create a new data frame without missing values and then replace missing values in 'height' with 170.

R Programming

clean_data <- data %>% [1]() %>% mutate(height = [2](height, 170))

Drag options to blanks, or click blank then click option'

Adrop_na

Breplace_na

Cfill

Dna.omit

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill() instead of replace_na() to replace values.

Using na.omit inside the pipe which is less common.

First, drop_na() removes rows with missing values, then replace_na() replaces missing 'height' values with 170.

5fill in blank

hard

Fill all three blanks to create a dictionary of counts for non-missing 'category' values, filtering out categories with zero count.

R Programming

counts <- table(data$category) %>% as.data.frame() %>% filter(Freq [1] 0) %>% mutate(category = as.character([2]), count = [3])

Drag options to blanks, or click blank then click option'

A>

BVar1

CFreq

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in filter condition.

Mixing up column names in mutate.

The code filters categories with frequency greater than 0, converts category to character, and assigns frequency to count.

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