Pipe operator (%>% and |>) in R Programming - Mini Project: Build & Apply

Using Pipe Operators %>% and |> in R

📖 Scenario: You are working with a small dataset of fruits and their prices. You want to cleanly and clearly calculate the total cost of all fruits using pipe operators to make your code easy to read.

🎯 Goal: Build a simple R script that uses the pipe operators %>% and |> to calculate the total price of fruits from a named vector.

📋 What You'll Learn

Create a named vector called fruit_prices with exact entries: apple = 2, banana = 1, cherry = 3

Create a variable called price_threshold and set it to 1

Use the %>% pipe operator to filter fruit_prices for prices greater than price_threshold and then sum the filtered prices

Use the |> pipe operator to do the same filtering and summing in a separate variable

Print both results with exact variable names total_price_percent and total_price_base

💡 Why This Matters

🌍 Real World

Pipes help write clear and readable data processing steps, like filtering and summarizing prices in a shopping list.

💼 Career

Data analysts and scientists use pipe operators to write clean, easy-to-understand code for data cleaning and analysis.

1

Create the fruit prices vector

Create a named vector called fruit_prices with these exact entries: apple = 2, banana = 1, cherry = 3

R Programming

# Create the named vector fruit_prices with apple=2, banana=1, cherry=3
# Your code here

Hint

Use the c() function with named elements to create fruit_prices.

2

Set the price threshold

Create a variable called price_threshold and set it to 1

R Programming

fruit_prices <- c(apple = 2, banana = 1, cherry = 3)
# Create price_threshold and set it to 1
# Your code here

Hint

Just assign the number 1 to price_threshold.

3

Calculate total price using %>% pipe

Use the %>% pipe operator to filter fruit_prices for prices greater than price_threshold and then sum the filtered prices. Store the result in total_price_percent. Use the exact code total_price_percent <- fruit_prices %>% .[. > price_threshold] %>% sum()

R Programming

fruit_prices % pipe to filter and sum prices > price_threshold
# Your code here

Hint

Use %>% to pass fruit_prices through filtering and then summing.

4

Calculate total price using |> pipe and print results

Use the |> pipe operator to filter fruit_prices for prices greater than price_threshold and then sum the filtered prices. Store the result in total_price_base. Then print both total_price_percent and total_price_base using two separate print() statements.

R Programming

fruit_prices % .[. > price_threshold] %>% sum()
# Use |> pipe to filter and sum prices > price_threshold
# Store in total_price_base
# Print total_price_percent and total_price_base
# Your code here

Hint

Use |> with an anonymous function to filter, then pipe to sum(). Use print() to show results.

Practice

1. What does the pipe operator %>% do in R?

easy

A. It creates a new variable in the environment.

B. It passes the result of one function as the first argument to the next function.

C. It comments out the following line of code.

D. It repeats a function multiple times automatically.

Solution

1. Step 1: Understand the pipe operator purpose

   The pipe operator %>% takes the output of the left side and sends it as the first argument to the function on the right side.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated actions like creating variables, commenting, or repeating functions, which are not what pipes do.

3. Final Answer:

   It passes the result of one function as the first argument to the next function. -> Option B

4. Quick Check:

   Pipe operator = passes output to next function [OK]

Hint: Pipes send output forward as input to next function [OK]

Common Mistakes:

• Thinking pipe creates variables
• Confusing pipe with comment symbol
• Assuming pipe repeats functions

2. Which of the following is the correct syntax to use the native R pipe operator introduced in R 4.1?

easy

A. data |> filter(value > 10)

B. data %>% filter(value > 10)

C. data ->| filter(value > 10)

D. data |-> filter(value > 10)

Solution

1. Step 1: Identify native pipe syntax

   The native pipe operator in R 4.1+ is |>, used as data |> function().

2. Step 2: Check other options

   data %>% filter(value > 10) uses %>% which is from dplyr, not native. Options A and D are invalid syntax.

3. Final Answer:

   data |> filter(value > 10) -> Option A

4. Quick Check:

   Native pipe = |> [OK]

Hint: Native pipe uses |> without percent signs [OK]

Common Mistakes:

• Using %>% as native pipe
• Typing invalid pipe symbols like ->|
• Confusing pipe with assignment operators

3. What is the output of this R code?

library(dplyr)
data <- tibble(x = 1:3)
data %>% mutate(y = x * 2) %>% pull(y)

medium

A. [1, 2, 3]

B. Error: object 'y' not found

C. [1, 3, 5]

D. [2, 4, 6]

Solution

1. Step 1: Understand the pipe steps

   The data tibble has x = 1, 2, 3. The mutate(y = x * 2) adds y as double x: 2, 4, 6.

2. Step 2: Extract the y column

   pull(y) extracts the y column as a vector: 2, 4, 6.

3. Final Answer:

   [2, 4, 6] -> Option D

4. Quick Check:

   Mutate doubles x, pull extracts y [OK]

Hint: Mutate adds column, pull extracts it [OK]

Common Mistakes:

• Forgetting mutate creates y
• Thinking pull returns whole data frame
• Confusing x and y values

4. Identify the error in this R code using the native pipe operator:

data <- data.frame(a = 1:3)
data |> filter(a > 1)

medium

A. filter() is not found because dplyr is not loaded

B. Native pipe operator |> is invalid syntax here

C. data.frame cannot be piped

D. No error, code runs fine

Solution

1. Step 1: Check package usage

   The function filter() is from dplyr. If dplyr is not loaded, filter() is not available.

2. Step 2: Validate pipe and data

   The native pipe |> works fine with data frames. The error is due to missing dplyr, not pipe or data type.

3. Final Answer:

   filter() is not found because dplyr is not loaded -> Option A

4. Quick Check:

   Missing dplyr causes filter error [OK]

Hint: Load dplyr before using filter() [OK]

Common Mistakes:

• Assuming pipe syntax is wrong
• Thinking data.frame can't be piped
• Ignoring missing library calls

5. Using the native pipe operator, how would you transform a numeric vector v <- c(1, 2, 3, 4) to get the sum of squares of even numbers only?

hard

A. v %>% subset(v %% 2 == 0) %>% sum()^2

B. v |> filter(v %% 2 == 0) |> sum()^2

C. v |> \(x) x[x %% 2 == 0] |> \(x) sum(x^2)

D. v |> map(~ .x^2) |> sum()

Solution

1. Step 1: Filter even numbers using native pipe and anonymous function

   The expression \(x) x[x %% 2 == 0] selects even numbers from vector v.

2. Step 2: Sum squares of filtered numbers

   The next pipe applies \(x) sum(x^2) to calculate sum of squares of those even numbers.

3. Final Answer:

   v |> \(x) x[x %% 2 == 0] |> \(x) sum(x^2) -> Option C

4. Quick Check:

   Filter evens then sum squares [OK]

Hint: Use anonymous functions with |> for complex steps [OK]

Common Mistakes:

• Using filter() without loading dplyr
• Squaring sum instead of summing squares
• Using %>% with native pipe syntax