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.