Filtering rows in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of filtering rows with dplyr filter()

What is the output of this R code that filters rows where score is greater than 80?

R Programming

library(dplyr)
data <- data.frame(name = c("Anna", "Ben", "Cara", "Dan"), score = c(75, 85, 90, 60))
filtered <- filter(data, score > 80)
print(filtered)

A Error: object 'filter' not found

B

  name score
1  Anna    75
2  Ben    85
3 Cara    90
4  Dan    60

C

  name score
1  Anna    75
2  Dan    60

D

  name score
1  Ben    85
2 Cara    90

💡 Hint

Look for rows where score is more than 80.

The filter() function keeps only rows where the condition score > 80 is TRUE. So only Ben and Cara remain.

❓ Predict Output

intermediate

Filtering rows with base R subset()

What will this R code print after filtering rows where age is less than or equal to 30?

R Programming

data <- data.frame(name = c("Eva", "Frank", "Gina", "Hank"), age = c(25, 35, 30, 40))
result <- subset(data, age <= 30)
print(result)

A

  name age
1  Eva  25
3 Gina  30

B

  name age
2 Frank 35
4 Hank  40

C Error in subset(data, age <= 30): object 'age' not found

D

  name age
1  Eva  25
2 Frank 35
3 Gina  30
4 Hank  40

💡 Hint

subset() keeps rows where the condition is TRUE.

Rows with age 25 and 30 satisfy age <= 30, so Eva and Gina are kept.

❓ Predict Output

advanced

Filtering rows with multiple conditions

What is the output of this R code filtering rows where score is above 70 and grade is 'A'?

R Programming

library(dplyr)
data <- data.frame(name = c("Ivy", "Jack", "Kara", "Liam"), score = c(80, 90, 65, 85), grade = c("B", "A", "A", "A"))
filtered <- filter(data, score > 70 & grade == "A")
print(filtered)

A

  name score grade
1 Jack    90     A
2 Liam    85     A

B

  name score grade
1 Ivy     80     B
2 Jack    90     A
3 Liam    85     A

C

  name score grade
1 Kara    65     A

D

  name score grade
1 Ivy     80     B

💡 Hint

Both conditions score > 70 and grade == 'A' must be true.

Only Jack and Liam have scores above 70 and grade 'A'. Ivy has grade 'B', Kara has score 65.

❓ Predict Output

advanced

Filtering rows with NA values

What will this R code print after filtering rows where value is not NA?

R Programming

data <- data.frame(id = 1:5, value = c(10, NA, 20, NA, 30))
filtered <- subset(data, !is.na(value))
print(filtered)

A

  id value
2  2    NA
4  4    NA

B

  id value
1  1    10
2  2    NA
3  3    20
4  4    NA
5  5    30

C

  id value
1  1    10
3  3    20
5  5    30

D Error in subset(data, !is.na(value)): object 'value' not found

💡 Hint

Use !is.na() to keep rows without missing values.

Rows with NA in value are removed, so only rows 1, 3, and 5 remain.

❓ Predict Output

expert

Filtering rows with complex condition and mutate

What is the output of this R code that filters rows where new_score is above 50 after mutation?

R Programming

library(dplyr)
library(magrittr)
data <- data.frame(name = c("Mia", "Ned", "Oli", "Pam"), score = c(40, 60, 55, 45))
result <- data %>% mutate(new_score = score * 1.2) %>% filter(new_score > 50)
print(result)

A

  name score new_score
1  Mia    40        48
2  Ned    60        72
3  Oli    55        66
4  Pam    45        54

B

  name score new_score
1  Ned    60        72
2  Oli    55        66

C

  name score new_score
1  Mia    40        48
4  Pam    45        54

D Error: could not find function "%>%"

💡 Hint

First multiply score by 1.2, then keep rows where new_score > 50.

Mia's new_score is 48 (40*1.2), Pam's is 54 (45*1.2). Only Ned (72) and Oli (66) have new_score > 50.

Practice

1. What does the subset() function do in R when working with data frames?

easy

A. It sorts the data frame by a column.

B. It changes the column names.

C. It adds new columns to the data frame.

D. It selects rows based on a condition.

Solution

1. Step 1: Understand the purpose of subset() and compare with other options

   The subset() function is used to pick rows from a data frame that meet a specific condition. Changing column names, adding columns, or sorting are done by other functions, not subset().

2. Final Answer:

   It selects rows based on a condition. -> Option D

3. Quick Check:

   subset() filters rows [OK]

Hint: Remember: subset() picks rows that match conditions [OK]

Common Mistakes:

• Thinking subset() changes columns
• Confusing subset() with sorting functions
• Using subset() to add data

2. Which of the following is the correct syntax to filter rows where column age is greater than 30 in a data frame df?

easy

A. subset(df, age > 30)

B. subset(df, age => 30)

C. subset(df, age = 30)

D. subset(df, age < 30)

Solution

1. Step 1: Check the condition syntax and validate the subset syntax

   The correct comparison operator for 'greater than' is >, so age > 30 is correct. subset(df, age > 30) correctly filters rows where age is greater than 30.

2. Final Answer:

   subset(df, age > 30) -> Option A

3. Quick Check:

   Use > for greater than in subset() [OK]

Hint: Use >, <, == for conditions inside subset() [OK]

Common Mistakes:

• Using => instead of >
• Using = instead of == or >
• Filtering with wrong comparison signs

3. Given the data frame df <- data.frame(name=c('Anna','Bob','Cara'), age=c(25, 35, 30)), what is the output of subset(df, age >= 30)?

medium

A. Rows with Bob and Cara

B. All rows

C. Rows with Anna and Bob

D. Rows with Anna and Cara

Solution

1. Step 1: Identify rows where age >= 30 and check which are selected

   Bob has age 35 and Cara has age 30, both satisfy age >= 30. Anna has age 25, which does not meet the condition, so only Bob and Cara rows remain.

2. Final Answer:

   Rows with Bob and Cara -> Option A

3. Quick Check:

   Filter keeps rows with age 30 or more [OK]

Hint: Check each row's value against condition carefully [OK]

Common Mistakes:

• Including rows with age less than 30
• Confusing >= with >
• Assuming all rows are returned

4. What is wrong with this code to filter rows where score is less than 50 in data frame df?

subset(df, score < 50 &)

medium

A. The subset function cannot filter numeric columns.

B. The condition ends with an incomplete operator '&'.

C. The comparison operator '<' is invalid in subset.

D. The data frame name is missing.

Solution

1. Step 1: Analyze the condition syntax and check other options

   The condition ends with '&' which expects another condition after it, but none is provided. Subset can filter numeric columns, '<' is valid, and data frame name is present as 'df'.

2. Final Answer:

   The condition ends with an incomplete operator '&'. -> Option B

3. Quick Check:

   Conditions must be complete without dangling operators [OK]

Hint: Avoid ending conditions with & or | without second part [OK]

Common Mistakes:

• Leaving dangling & or | operators
• Thinking subset can't filter numbers
• Misusing comparison operators

5. You have a data frame df with columns age and status. How do you filter rows where age is over 25 and status is 'active'?

hard

A. subset(df, age > 25, status == 'active')

B. subset(df, age > 25 | status == 'active')

C. subset(df, age > 25 & status == 'active')

D. subset(df, age > 25 && status = 'active')

Solution

1. Step 1: Use correct logical operator for AND and check comparison operators and syntax

   The operator for AND in R is a single ampersand '&', so use age > 25 & status == 'active'. Use '==' to compare values, not '=', and combine conditions inside one subset call.

2. Final Answer:

   subset(df, age > 25 & status == 'active') -> Option C

3. Quick Check:

   Use & and == for combined conditions [OK]

Hint: Use & for AND, | for OR, and == for equality in subset() [OK]

Common Mistakes:

• Using | instead of & for AND
• Using = instead of == for comparison
• Separating conditions with commas