filter() for row selection in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of filter() with multiple conditions

What is the output of the following R code using filter() from dplyr?

R Programming

library(dplyr)
df <- tibble(x = 1:5, y = c(10, 20, 30, 40, 50))
result <- filter(df, x > 2, y < 50)
print(result)

A

A tibble: 2 × 2
      x     y
  <int> <dbl>
1     3    30
2     4    40

B

A tibble: 3 × 2
      x     y
  <int> <dbl>
1     3    30
2     4    40
3     5    50

C

A tibble: 1 × 2
      x     y
  <int> <dbl>
1     4    40

D

A tibble: 4 × 2
      x     y
  <int> <dbl>
1     2    20
2     3    30
3     4    40
4     5    50

💡 Hint

Remember that filter() keeps rows where all conditions are TRUE.

The filter keeps rows where x > 2 and y < 50. Rows with x=3,y=30 and x=4,y=40 satisfy both conditions.

❓ Predict Output

intermediate

filter() with logical OR condition

What will be the output of this R code using filter()?

R Programming

library(dplyr)
df <- tibble(a = c('red', 'blue', 'green', 'red'), b = 1:4)
result <- filter(df, a == 'red' | b == 3)
print(result)

A

A tibble: 2 × 2
  a         b
  <chr> <int>
1 red       1
2 red       4

B

A tibble: 4 × 2
  a         b
  <chr> <int>
1 red       1
2 blue      2
3 green     3
4 red       4

C

A tibble: 3 × 2
  a         b
  <chr> <int>
1 red       1
2 blue      2
3 green     3

D

A tibble: 3 × 2
  a         b
  <chr> <int>
1 red       1
2 green     3
3 red       4

💡 Hint

The | operator means OR, so rows matching either condition are kept.

Rows where a is 'red' or b is 3 are kept. That includes rows 1 (red,1), 3 (green,3), and 4 (red,4).

🔧 Debug

advanced

Identify the error in filter() usage

What error will this R code produce when run?

R Programming

library(dplyr)
df <- tibble(x = 1:3, y = c(5, 6, 7))
filter(df, x > 1 & y < 7, z == 2)

A Error: unexpected symbol in 'filter(df, x > 1 & y < 7, z == 2)'

B No error, returns rows where x > 1, y < 7, and z == 2

C Error: object 'z' not found

D Warning: z is not a column in df, but filter runs anyway

💡 Hint

Check if all variables used in filter exist in the data frame.

The variable 'z' does not exist in df, so filter throws an error about 'z' not found.

🧠 Conceptual

advanced

Understanding filter() with NA values

Given this data frame, what will filter(df, x > 2) return?

R Programming

library(dplyr)
df <- tibble(x = c(1, 2, NA, 4))
result <- filter(df, x > 2)
print(result)

A A tibble with rows where x is 4 only

B A tibble with rows where x is 4 and NA

C An error because NA cannot be compared

D A tibble with rows where x is 1, 2, and 4

💡 Hint

Remember how comparisons with NA behave in R.

Comparisons with NA return NA, which filter treats as FALSE, so only rows with x > 2 and not NA are kept.

❓ Predict Output

expert

Complex filter() with grouped data

What is the output of this R code using filter() on grouped data?

R Programming

library(dplyr)
df <- tibble(group = c('A', 'A', 'B', 'B'), value = c(10, 20, 30, 40))
result <- df %>% group_by(group) %>% filter(value == max(value))
print(result)

A Error: max() not found in filter

B

A tibble: 2 × 2
Groups:   group [2]
  group value
  <chr> <dbl>
1 A        20
2 B        40

C

A tibble: 1 × 2
Groups:   group [1]
  group value
  <chr> <dbl>
1 B        40

D

A tibble: 4 × 2
Groups:   group [2]
  group value
  <chr> <dbl>
1 A        10
2 A        20
3 B        30
4 B        40

💡 Hint

filter() inside group_by keeps rows where the condition is TRUE per group.

For each group, filter keeps rows where value equals the maximum value in that group.

Practice

1. What does the filter() function in R do when applied to a data frame?

easy

A. Selects rows that meet specific conditions

B. Selects columns based on their names

C. Sorts the data frame by a column

D. Adds new columns to the data frame

Solution

1. Step 1: Understand the purpose of filter()

   The filter() function is used to pick rows from a data frame that satisfy certain conditions.

2. Step 2: Compare with other functions

   Functions like select() pick columns, and arrange() sorts rows, so they do not match filter()'s role.

3. Final Answer:

   Selects rows that meet specific conditions -> Option A

4. Quick Check:

   filter() = row selection [OK]

Hint: Remember: filter() picks rows, select() picks columns [OK]

Common Mistakes:

• Confusing filter() with select()
• Thinking filter() sorts data
• Assuming filter() adds columns

2. Which of the following is the correct syntax to filter rows where the column age is greater than 30 using dplyr?

easy

A. filter(data, age => 30)

B. filter(data, age > 30)

C. filter(data, age = 30)

D. filter(data, age < 30)

Solution

1. Step 1: Check the comparison operator

   The correct operator for 'greater than' is >, so age > 30 is valid.

2. Step 2: Validate syntax correctness

   filter(data, age > 30) correctly applies the condition inside filter(). Other options use wrong operators or conditions.

3. Final Answer:

   filter(data, age > 30) -> Option B

4. Quick Check:

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

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

Common Mistakes:

• Using => instead of >
• Using = instead of == or >
• Confusing < with >

3. Given the data frame df below:

df <- data.frame(name = c('Anna', 'Bob', 'Cara'), age = c(25, 35, 30))

What will be the output of filter(df, age >= 30)?

medium

A. All rows

B. Rows with Anna and Bob

C. Rows with Anna and Cara

D. Rows with Bob and Cara

Solution

1. Step 1: Identify rows where age >= 30

   From the data, Bob has age 35 and Cara has age 30, both satisfy age >= 30.

2. Step 2: Confirm which rows are selected

   Anna has age 25, which is less than 30, so she is excluded.

3. Final Answer:

   Rows with Bob and Cara -> Option D

4. Quick Check:

   age >= 30 selects Bob and Cara [OK]

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

Common Mistakes:

• Including rows with age less than 30
• Misreading >= as >
• Confusing row names with values

4. Consider this code snippet:

library(dplyr)
df <- data.frame(score = c(80, 90, 70))
filtered <- filter(df, score => 80)

What is the problem with this code?

medium

A. The operator => is invalid in R

B. The data frame is missing a column

C. filter() needs a pipe operator %>%

D. score should be in quotes

Solution

1. Step 1: Check the operator used in filter()

   The operator => is not valid in R; the correct operator for 'greater or equal' is >=.

2. Step 2: Validate other parts of the code

   The data frame has the correct column, filter() can be used without pipe, and column names do not need quotes inside filter().

3. Final Answer:

   The operator => is invalid in R -> Option A

4. Quick Check:

   Use >=, not =>, for comparisons [OK]

Hint: Use >= for 'greater or equal' in R, not => [OK]

Common Mistakes:

• Using => instead of >=
• Thinking filter() always needs %>%
• Quoting column names inside filter()

5. You have a data frame df with columns city and temperature. You want to select rows where city is either 'Paris' or 'London' and temperature is above 20. Which filter() command correctly does this?

hard

A. filter(df, city == 'Paris' | city == 'London' & temperature > 20)

B. filter(df, city == 'Paris' & city == 'London' & temperature > 20)

C. filter(df, (city == 'Paris' | city == 'London') & temperature > 20)

D. filter(df, city == 'Paris' || city == 'London' && temperature > 20)

Solution

1. Step 1: Understand operator precedence in R

   Logical AND (&) has higher precedence than OR (|), so parentheses are needed to group city conditions.

2. Step 2: Analyze each option's logic

   filter(df, (city == 'Paris' | city == 'London') & temperature > 20) correctly groups city conditions with parentheses and then ANDs with temperature > 20. filter(df, city == 'Paris' | city == 'London' & temperature > 20) lacks parentheses, causing wrong logic. filter(df, city == 'Paris' & city == 'London' & temperature > 20) requires city to be both Paris and London simultaneously, impossible. filter(df, city == 'Paris' || city == 'London' && temperature > 20) uses || and && which are not vectorized and cause errors in filter().

3. Final Answer:

   filter(df, (city == 'Paris' | city == 'London') & temperature > 20) -> Option C

4. Quick Check:

   Use parentheses to group OR conditions in filter() [OK]

Hint: Use parentheses to group OR conditions before AND [OK]

Common Mistakes:

• Missing parentheses causing wrong logic
• Using && and || instead of & and |
• Trying city == 'Paris' & city == 'London' together