filter() for row selection in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select rows where the column 'age' is greater than 30.

R Programming

filtered_data <- filter(data, age [1] 30)

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

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' will select rows with age less than 30.

Using '==' will select rows with age exactly 30.

The filter() function selects rows where the condition is TRUE. To select rows with age greater than 30, use the '>' operator.

2fill in blank

medium

Complete the code to select rows where the column 'score' is equal to 100.

R Programming

perfect_scores <- filter(data, score [1] 100)

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

A>

B!=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' causes an error in filter().

Using '!=' selects rows where score is not 100.

To select rows where 'score' is exactly 100, use the equality operator '=='.

3fill in blank

hard

Fix the error in the code to select rows where 'height' is less than or equal to 170.

R Programming

short_people <- filter(data, height [1] 170)

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

A=>

B<=

C<

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=>' instead of '<=' causes a syntax error.

Using '<' excludes rows where height equals 170.

The correct operator for 'less than or equal to' is '<='. The operator '=>' is invalid in R.

4fill in blank

hard

Fill both blanks to select rows where 'weight' is greater than 60 and 'age' is less than 50.

R Programming

selected <- filter(data, weight [1] 60, age [2] 50)

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

A>

B<

C==

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' or '<' will select only exact matches.

Using '>=' or '<=' changes the condition to include equal values.

To select rows where weight is greater than 60, use '>'. To select rows where age is less than 50, use '<'.

5fill in blank

hard

Fill all three blanks to select rows where 'score' is greater than 80, 'age' is less than 30, and 'height' equals 175.

R Programming

filtered <- filter(data, score [1] 80, age [2] 30, height [3] 175)

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

A<

B>

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up operators like using '<' instead of '>' for score.

Using '=' instead of '==' for equality check.

Use '>' for score greater than 80, '<' for age less than 30, and '==' for height equal to 175.

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