filter() for row selection in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the filter() function do in R?

It selects rows from a data frame that meet a specific condition, like picking only the apples from a basket of fruits.

beginner

How do you use filter() to select rows where a column age is greater than 30?

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

intermediate

Can filter() handle multiple conditions? How?

Yes! Use commas or logical operators like & or |. For example, filter(data, age > 30, gender == 'F') selects rows where age is over 30 AND gender is female.

beginner

What package do you need to use filter() for data frames?

The dplyr package. It makes data manipulation easy and clear.

beginner

What happens if no rows meet the condition in filter()?

You get an empty data frame with the same columns but zero rows, like an empty basket after picking fruits.

Which function is used to select rows based on conditions in R's dplyr package?

A mutate()

B select()

C arrange()

D filter()

filter() selects rows by condition. select() chooses columns, arrange() sorts rows, and mutate() adds or changes columns.

What will filter(data, age > 25, gender == 'M') do?

A Select rows where age is over 25 and gender is male

B Select rows where age is over 25 or gender is male

C Select rows where age is under 25 and gender is male

D Select all rows

Using commas means AND condition, so both must be true.

If no rows match the filter condition, what does filter() return?

A An empty data frame with zero rows

B The original data frame

C An error

D NULL

filter() returns an empty data frame with the same columns but no rows.

Which package must be loaded to use filter() for data frames?

A ggplot2

B dplyr

C tidyr

D base

dplyr provides filter() for easy row selection.

How do you combine multiple conditions inside filter()?

A Using parentheses only

B Using plus signs (+)

C Using commas or logical operators like && and |

D Using semicolons (;)

Commas act like AND, and logical operators combine conditions logically.

Explain how to use filter() to select rows from a data frame based on one or more conditions.

Think about how you pick only certain fruits from a basket based on color or size.

What happens when filter() finds no rows matching the condition? How can this be useful?

Imagine looking for red apples but finding none.

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