Filtering rows in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of filtering rows in R?

Filtering rows means selecting only the rows from a data frame that meet certain conditions. It helps focus on specific data you want to analyze.

beginner

Which function from the dplyr package is commonly used to filter rows?

The filter() function is used to select rows that satisfy given conditions.

beginner

How do you filter rows where the column age is greater than 30 using dplyr?

Use filter(data, age > 30) to keep only rows where the age is more than 30.

intermediate

What happens if you use multiple conditions inside filter() separated by commas?

Multiple conditions separated by commas act like AND. All conditions must be true for a row to be kept.

intermediate

How can you filter rows using base R without dplyr?

Use square brackets with a logical condition, like data[data$age > 30, ] to select rows where age is greater than 30.

Which function is used in dplyr to filter rows?

A arrange()

B select()

C filter()

D mutate()

The filter() function is designed to select rows based on conditions.

What does filter(data, age > 25, gender == 'F') do?

A Selects rows where age > 25 AND gender is 'F'

B Selects rows where age > 25 OR gender is 'F'

C Selects rows where age <= 25 AND gender is not 'F'

D Selects all rows

Multiple conditions separated by commas mean AND, so both must be true.

How do you filter rows in base R where score equals 100?

A data[data$score == 100, ]

B data[score = 100, ]

C filter(data, score == 100)

D data[score == 100]

In base R, use data[data$score == 100, ] to filter rows.

What will filter(data, age > 20, age < 30) return?

A Rows where age is less than 20 or greater than 30

B All rows

C Rows where age is exactly 20 or 30

D Rows where age is between 20 and 30 (exclusive)

Both conditions must be true, so age must be greater than 20 and less than 30.

Which symbol is used for logical AND in dplyr filter conditions?

A &&

B ,

C &

D |

In filter(), multiple conditions separated by commas act as AND.

Explain how to filter rows in R using both dplyr and base R methods.

Think about how you select rows with conditions in both styles.

Describe how multiple conditions work inside the dplyr filter() function.

How do commas affect the filtering?

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