filter() for row selection in R Programming - Mini Project: Build & Apply

Filter Rows in a Data Frame Using filter()

📖 Scenario: You work in a small bookstore and have a list of books with their prices and stock. You want to find which books cost less than $20 to plan a sale.

🎯 Goal: Build a small R program that creates a data frame of books, sets a price limit, filters the books cheaper than that limit using filter(), and prints the filtered list.

📋 What You'll Learn

Create a data frame called books with columns title, price, and stock with exact values

Create a variable called max_price set to 20

Use dplyr::filter() with books and price < max_price to get cheaper books

Print the filtered data frame

💡 Why This Matters

🌍 Real World

Filtering data frames is a common task in data analysis to focus on relevant information, like finding affordable products or available stock.

💼 Career

Data analysts and scientists often use filtering to clean and prepare data before making reports or decisions.

1

Create the books data frame

Create a data frame called books with these exact entries:
title: "Book A", "Book B", "Book C", "Book D"
price: 15, 25, 10, 30
stock: 5, 2, 8, 1

R Programming

# Create the data frame books with columns title, price, and stock
# Your code here

Hint

Use data.frame() with vectors for each column named title, price, and stock.

2

Set the maximum price limit

Create a variable called max_price and set it to 20

R Programming

books <- data.frame(
  title = c("Book A", "Book B", "Book C", "Book D"),
  price = c(15, 25, 10, 30),
  stock = c(5, 2, 8, 1)
)
# Create max_price variable and set to 20
# Your code here

Hint

Just assign the number 20 to a variable named max_price.

3

Filter books cheaper than max_price

Use dplyr::filter() with books and the condition price < max_price to create a new data frame called cheap_books

R Programming

books <- data.frame(
  title = c("Book A", "Book B", "Book C", "Book D"),
  price = c(15, 25, 10, 30),
  stock = c(5, 2, 8, 1)
)
max_price <- 20
# Load dplyr and filter books with price less than max_price
# Your code here

Hint

Remember to load dplyr with library(dplyr) before using filter().

4

Print the filtered books

Write a print() statement to display the cheap_books data frame

R Programming

library(dplyr)

books <- data.frame(
  title = c("Book A", "Book B", "Book C", "Book D"),
  price = c(15, 25, 10, 30),
  stock = c(5, 2, 8, 1)
)
max_price <- 20

cheap_books <- filter(books, price < max_price)
# Print the cheap_books data frame
# Your code here

Hint

Use print(cheap_books) to show the filtered books.

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