Handling missing values (drop_na, fill) in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output after dropping NA values?

Consider the following R code that creates a data frame and then drops rows with missing values using drop_na(). What will be the resulting data frame?

R Programming

library(tidyr)
df <- data.frame(x = c(1, 2, NA, 4), y = c("a", NA, "c", "d"))
df_clean <- drop_na(df)
df_clean

A

  x y
2 2 NA
3 NA c

B

  x y
1 1 a
2 2 NA
4 4 d

C

  x y
1 1 a
3 NA c
4 4 d

D

  x y
1 1 a
4 4 d

💡 Hint

Remember, drop_na() removes any row that has NA in any column.

The drop_na() function removes rows with any missing values. Rows 2 and 3 have NA in either x or y, so only rows 1 and 4 remain.

❓ Predict Output

intermediate

What is the output after filling missing values?

Given the following data frame with missing values, what will be the result after using fill() to fill missing values in column z?

R Programming

library(tidyr)
df <- data.frame(z = c(NA, 2, NA, 4, NA))
df_filled <- fill(df, z)
df_filled

A

  z
1  2
2  2
3  2
4  4
5  4

B

  z
1 NA
2  2
3  2
4  4
5  4

C

  z
1 NA
2  2
3 NA
4  4
5 NA

D

  z
1 NA
2 NA
3 NA
4  4
5  4

💡 Hint

fill() fills missing values by carrying the last non-missing value forward by default.

The fill() function fills NA values by propagating the last observed non-NA value downward. The first NA remains because there is no previous value to fill from.

🧠 Conceptual

advanced

Which statement about drop_na() behavior is true?

Which of the following statements about drop_na() in R's tidyr package is correct?

A <code>drop_na()</code> removes rows with <code>NA</code> in any column by default.

B <code>drop_na()</code> removes rows only if all columns are <code>NA</code>.

C <code>drop_na()</code> replaces <code>NA</code> values with zeros.

D <code>drop_na()</code> fills <code>NA</code> values with the previous non-<code>NA</code> value.

💡 Hint

Think about what it means to 'drop' rows with missing data.

drop_na() removes any row that contains NA in any column by default. It does not replace or fill values.

❓ Predict Output

advanced

What is the output after filling missing values with direction = 'up'?

Given the data frame below, what will be the output after using fill() with direction = "up" on column a?

R Programming

library(tidyr)
df <- data.frame(a = c(NA, 3, NA, 7, NA))
df_filled <- fill(df, a, .direction = "up")
df_filled

A

   a
1  3
2  3
3  7
4  7
5 NA

B

   a
1  3
2  3
3 NA
4  7
5  7

C

   a
1 NA
2  3
3 NA
4  7
5 NA

D

   a
1 NA
2  3
3  3
4  7
5  7

💡 Hint

fill() with direction = "up" fills missing values by carrying the next non-NA value backward.

Using direction = "up" fills NA values by propagating the next non-NA value upward. The last NA remains because there is no following value to fill from.

🔧 Debug

expert

Why does this fill() code produce an error?

Examine the code below. It tries to fill missing values in column val but produces an error. What is the cause?

R Programming

library(tidyr)
df <- data.frame(val = c(1, NA, 3))
fill(df, val, direction = "down")

A The fill() function requires the column name as a string, not bare name.

B The data frame has no missing values to fill, so fill() fails.

C The argument name for direction is incorrect; it should be .direction, not direction.

D The fill() function cannot be used on numeric columns.

💡 Hint

Check the exact argument names in the fill() function.

The fill() function expects the argument .direction (with a dot) to specify fill direction. Using direction without the dot causes an error.