Filtering rows in R Programming - Mini Project: Build & Apply

Filtering rows

📖 Scenario: You have a small table of fruits with their prices and quantities. You want to find only the fruits that cost more than 2 dollars.

🎯 Goal: Build a program that creates a data frame of fruits, sets a price limit, filters the fruits that cost more than that limit, and prints the filtered data.

📋 What You'll Learn

Create a data frame called fruits with columns name, price, and quantity

Create a variable called price_limit and set it to 2

Filter the fruits data frame to keep only rows where price is greater than price_limit and save it as expensive_fruits

Print the expensive_fruits data frame

💡 Why This Matters

🌍 Real World

Filtering data tables is common when you want to focus on important or relevant information, like finding expensive products in a store.

💼 Career

Data filtering is a key skill in data analysis, reporting, and preparing data for decision making in many jobs.

1

Create the fruits data frame

Create a data frame called fruits with these exact entries: name as c("Apple", "Banana", "Cherry", "Date"), price as c(1.5, 0.5, 3.0, 2.5), and quantity as c(10, 20, 15, 5).

R Programming

# Create the fruits data frame with name, price, and quantity columns
# Your code here

Hint

Use the data.frame() function with named vectors for each column.

2

Set the price limit

Create a variable called price_limit and set it to 2.

R Programming

fruits <- data.frame(
  name = c("Apple", "Banana", "Cherry", "Date"),
  price = c(1.5, 0.5, 3.0, 2.5),
  quantity = c(10, 20, 15, 5)
)
# Create the price_limit variable and set it to 2
# Your code here

Hint

Just assign the number 2 to the variable price_limit.

3

Filter the fruits by price

Create a new data frame called expensive_fruits that contains only the rows from fruits where the price is greater than price_limit.

R Programming

fruits <- data.frame(
  name = c("Apple", "Banana", "Cherry", "Date"),
  price = c(1.5, 0.5, 3.0, 2.5),
  quantity = c(10, 20, 15, 5)
)
price_limit <- 2
# Filter fruits where price is greater than price_limit and save as expensive_fruits
# Your code here

Hint

Use the square bracket notation with a condition on fruits$price.

4

Print the filtered fruits

Write a print() statement to display the expensive_fruits data frame.

R Programming

fruits  price_limit, ]
# Print the expensive_fruits data frame
# Your code here

Hint

Use print(expensive_fruits) to show the filtered table.

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