Filtering rows in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

R Programming

filtered_data <- subset(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 where age is exactly 30.

The operator > filters rows where 'age' is greater than 30.

2fill in blank

medium

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

R Programming

filtered_data <- subset(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 assignment, not comparison.

Using '!=' selects rows not equal to 100.

The operator == checks for equality, selecting rows where 'score' is exactly 100.

3fill in blank

hard

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

R Programming

filtered_data <- subset(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 '<' excludes rows where height equals 170.

Using '>=' selects rows with height greater than or equal to 170.

The operator <= correctly filters rows where 'height' is less than or equal to 170.

4fill in blank

hard

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

R Programming

filtered_data <- subset(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 '>=' or '<=' changes the condition to include equal values.

Mixing up the operators reverses the intended filter.

Use > for weight greater than 60 and < for age less than 50.

5fill in blank

hard

Fill all three blanks to filter rows where 'score' is greater than 80, 'age' is less than 30, and 'height' is at least 160.

R Programming

filtered_data <- subset(data, score [1] 80 & age [2] 30 & height [3] 160)

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

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' instead of '>=' for height changes the condition.

Mixing up operators leads to wrong filtering.

Use > for score greater than 80, < for age less than 30, and >= for height at least 160.

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