Handling missing values (na.rm, na.omit) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the vector, ignoring missing values.

R Programming

mean(c(1, 2, NA, 4), [1] = TRUE)

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

Ana.rm

Bna.omit

Cremove.na

DskipNA

Attempts:

3 left

💡 Hint

Common Mistakes

Using na.omit inside mean() instead of na.rm

Misspelling the argument name

Setting na.rm to FALSE

The na.rm argument tells mean() to remove missing values before calculation.

2fill in blank

medium

Complete the code to remove missing values from the vector using a function.

R Programming

clean_vector <- [1](c(3, NA, 5, NA, 7))

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

Ana.rm

Bna.omit

CremoveNA

DdropNA

Attempts:

3 left

💡 Hint

Common Mistakes

Using na.rm as a function instead of an argument

Using incorrect function names like removeNA

Trying to subset manually without na.omit

The na.omit() function removes all missing values from a vector or data frame.

3fill in blank

hard

Fix the error in the code to calculate the sum ignoring missing values.

R Programming

sum_values <- sum(c(10, NA, 20), [1] = TRUE)

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

Ana.rm

Bna.omit

CremoveNA

DskipNA

Attempts:

3 left

💡 Hint

Common Mistakes

Using na.omit as an argument instead of na.rm

Misspelling the argument name

Not setting the argument to TRUE

The correct argument to ignore missing values in sum() is na.rm.

4fill in blank

hard

Fill both blanks to create a named vector with lengths of words longer than 3 characters.

R Programming

lengths <- {word: [1] for word in words if nchar(word) [2] 3}

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

Anchar(word)

B>

C<

Dlength(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using length() instead of nchar() for strings

Using < instead of > for filtering

Not filtering words at all

Use nchar(word) to get the length of each word and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a named vector of word lengths for words longer than 4 characters.

R Programming

result <- list([1]: [2] for [3] in words if nchar([3]) > 4)

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

Aword

Bnchar(word)

Dlength(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently

Using length() instead of nchar() for strings

Not filtering words longer than 4 characters

Use word as the key and nchar(word) as the value for each word longer than 4 characters.

Practice

1. What does the argument na.rm = TRUE do when used inside functions like sum() or mean() in R?

easy

A. It tells the function to ignore missing values (NA) during calculation.

B. It replaces missing values with zero before calculation.

C. It stops the function and returns an error if there are missing values.

D. It counts the number of missing values in the data.

Solution

1. Step 1: Understand the role of na.rm argument

   The na.rm argument stands for "NA remove" and controls whether missing values should be ignored.

2. Step 2: Effect of setting na.rm = TRUE

   When set to TRUE, the function skips over any NA values and calculates the result using only the available data.

3. Final Answer:

   It tells the function to ignore missing values (NA) during calculation. -> Option A

4. Quick Check:

   na.rm = TRUE means ignore NA values [OK]

Hint: Remember: na.rm = TRUE means "remove NA" during calculation [OK]

Common Mistakes:

• Thinking na.rm replaces NA with zero
• Assuming na.rm causes an error on NA
• Confusing na.rm with counting NAs

2. Which of the following is the correct syntax to remove missing values from a vector x using na.omit() in R?

easy

A. clean_x <- na.remove(x)

B. clean_x <- omit.na(x)

C. clean_x <- na.omit(x)

D. clean_x <- remove.na(x)

Solution

1. Step 1: Recall the function name for removing NAs

   The correct function in R to remove missing values is na.omit().

2. Step 2: Check the syntax usage

   It is used as na.omit(x) where x is the vector or data frame.

3. Final Answer:

   clean_x <- na.omit(x) -> Option C

4. Quick Check:

   Use na.omit() to remove NAs [OK]

Hint: Use na.omit() exactly as written to remove missing values [OK]

Common Mistakes:

• Swapping function name parts like omit.na
• Using non-existent functions like na.remove
• Confusing na.omit with na.rm argument

3. What is the output of the following R code?

x <- c(2, NA, 5, NA, 8)
sum(x)
sum(x, na.rm = TRUE)

medium

A. [1] 15 and [1] 15

B. [1] NA and [1] 15

C. [1] 15 and [1] NA

D. [1] NA and [1] NA

Solution

1. Step 1: Calculate sum without removing NAs

   Calling sum(x) with NA values returns NA because missing values cause the sum to be undefined.

2. Step 2: Calculate sum with na.rm = TRUE

   Calling sum(x, na.rm = TRUE) ignores NAs and sums only 2 + 5 + 8 = 15.

3. Final Answer:

   [1] NA and [1] 15 -> Option B

4. Quick Check:

   sum() returns NA if NA present unless na.rm=TRUE [OK]

Hint: sum() returns NA if NA present; use na.rm=TRUE to ignore [OK]

Common Mistakes:

• Assuming sum ignores NA by default
• Mixing output order of the two sums
• Thinking sum with na.rm=TRUE returns NA

4. You run this code in R:

data <- c(4, NA, 7, NA, 10)
mean(data)

But it returns NA. How can you fix it to get the mean ignoring missing values?

medium

A. Both A and C are correct

B. Use mean(data, na.rm = TRUE)

C. Use mean(na.omit(data))

D. Replace NA with zero manually before mean

Solution

1. Step 1: Understand why mean returns NA

   By default, mean() returns NA if any missing values exist in the data.

2. Step 2: Ways to ignore missing values

   You can either set na.rm = TRUE inside mean() or remove NAs first using na.omit() and then calculate mean.

3. Step 3: Check options

   Both mean(data, na.rm = TRUE) and mean(na.omit(data)) will give the correct mean ignoring NAs.

4. Final Answer:

   Both A and C are correct -> Option A

5. Quick Check:

   Use na.rm=TRUE or na.omit() to ignore NAs in mean [OK]

Hint: Use na.rm=TRUE or na.omit() to ignore missing values [OK]

Common Mistakes:

• Forgetting to add na.rm=TRUE
• Thinking mean automatically ignores NA
• Replacing NA with zero without reason

5. Given a data frame df with numeric column score containing some NAs, which code correctly calculates the average score excluding missing values and also removes rows with NA in score before calculating the mean?

hard

A. mean(df$score, na.rm = FALSE); df_clean <- na.omit(df); mean(df_clean$score)

B. mean(na.omit(df)); df_clean <- na.omit(df); mean(df_clean$score)

C. mean(df$score); df_clean <- na.omit(df$score); mean(df_clean)

D. mean(df$score, na.rm = TRUE); df_clean <- na.omit(df); mean(df_clean$score)

Solution

1. Step 1: Calculate mean ignoring NAs in the column

   Using mean(df$score, na.rm = TRUE) calculates average excluding NAs directly.

2. Step 2: Remove rows with NA in entire data frame

   na.omit(df) removes all rows with any NA values, including in score.

3. Step 3: Calculate mean on cleaned data

   Then mean(df_clean$score) calculates mean on data without missing values.

4. Final Answer:

   mean(df$score, na.rm = TRUE); df_clean <- na.omit(df); mean(df_clean$score) -> Option D

5. Quick Check:

   Use na.rm=TRUE for mean and na.omit() to clean data frame [OK]

Hint: Use na.rm=TRUE for mean and na.omit() to clean data frame [OK]

Common Mistakes:

• Using na.omit on vector but not on data frame
• Forgetting na.rm=TRUE in mean calculation
• Using na.omit incorrectly on a vector instead of data frame