pivot_longer (wide to long) in R Programming - Mini Project: Build & Apply

Convert Wide Data to Long Format using pivot_longer

📖 Scenario: You have a small dataset showing sales of different fruits in two months. The data is in a wide format, where each month is a separate column.We want to change this data into a long format, where each row shows the fruit, the month, and the sales amount. This format is easier to analyze and plot.

🎯 Goal: Transform the wide data frame fruit_sales into a long format using pivot_longer from the tidyr package. The result should have columns Fruit, Month, and Sales.

📋 What You'll Learn

Create a data frame called fruit_sales with columns Fruit, Jan, and Feb and the exact data provided.

Create a vector called months containing the column names Jan and Feb.

Use pivot_longer on fruit_sales with cols = months, naming the new columns Month and Sales.

Print the resulting long format data frame.

💡 Why This Matters

🌍 Real World

Data often comes in wide format from spreadsheets or reports. Converting it to long format helps in analysis and visualization.

💼 Career

Data analysts and scientists frequently reshape data using pivot_longer to prepare datasets for plotting and statistical modeling.

1

Create the wide data frame

Create a data frame called fruit_sales with columns Fruit, Jan, and Feb containing these exact rows: 'Apple', 30, 45, 'Banana', 25, 40, 'Cherry', 15, 20.

R Programming

# Create the data frame fruit_sales with the exact data
# Your code here

Hint

Use data.frame() with vectors for each column named exactly Fruit, Jan, and Feb.

2

Create a vector of month columns

Create a character vector called months containing the strings 'Jan' and 'Feb'.

R Programming

fruit_sales <- data.frame(
  Fruit = c('Apple', 'Banana', 'Cherry'),
  Jan = c(30, 25, 15),
  Feb = c(45, 40, 20)
)

# Create the vector months with 'Jan' and 'Feb'
# Your code here

Hint

Use c() to create a vector with the two month names as strings.

3

Use pivot_longer to reshape the data

Use pivot_longer from the tidyr package on fruit_sales with cols = months, names_to = 'Month', and values_to = 'Sales'. Save the result in a variable called long_sales. Remember to load tidyr first.

R Programming

fruit_sales <- data.frame(
  Fruit = c('Apple', 'Banana', 'Cherry'),
  Jan = c(30, 25, 15),
  Feb = c(45, 40, 20)
)

months <- c('Jan', 'Feb')

# Load tidyr and use pivot_longer to create long_sales
# Your code here

Hint

Use library(tidyr) to load the package. Then use pivot_longer() with the exact arguments.

4

Print the long format data frame

Write a line to print the variable long_sales.

R Programming

library(tidyr)

fruit_sales <- data.frame(
  Fruit = c('Apple', 'Banana', 'Cherry'),
  Jan = c(30, 25, 15),
  Feb = c(45, 40, 20)
)

months <- c('Jan', 'Feb')

long_sales <- pivot_longer(fruit_sales, cols = months, names_to = 'Month', values_to = 'Sales')

# Print the long_sales data frame
# Your code here

Hint

Use print(long_sales) to display the transformed data.

Practice

1. What does the pivot_longer() function do in R's tidyverse?

easy

A. It filters rows based on a condition.

B. It creates new columns by splitting existing ones.

C. It transforms wide data into long data by stacking columns into rows.

D. It sorts data by a specific column.

Solution

1. Step 1: Understand the purpose of pivot_longer()

   The function is designed to reshape data from wide format (many columns) to long format (fewer columns, more rows).

2. Step 2: Identify the correct description

   Among the options, only It transforms wide data into long data by stacking columns into rows. correctly describes stacking columns into rows, which is what pivot_longer does.

3. Final Answer:

   It transforms wide data into long data by stacking columns into rows. -> Option C

4. Quick Check:

   pivot_longer = wide to long [OK]

Hint: pivot_longer stacks columns into rows to tidy data [OK]

Common Mistakes:

• Confusing pivot_longer with filtering or sorting functions
• Thinking it splits columns instead of stacking
• Assuming it creates new columns instead of reshaping

2. Which of the following is the correct syntax to pivot columns Jan and Feb into longer format with new columns named Month and Sales?

easy

A. pivot_longer(data, cols = c(Jan, Feb), names_to = "Month", values_to = "Sales")

B. pivot_longer(data, columns = c(Jan, Feb), names = "Month", values = "Sales")

C. pivot_longer(data, cols = c("Jan", "Feb"), names_to = Month, values_to = Sales)

D. pivot_longer(data, cols = Jan, Feb, names_to = "Month", values_to = "Sales")

Solution

1. Step 1: Check argument names and syntax

   The correct argument names are cols, names_to, and values_to. Columns should be passed as symbols or strings inside c().

2. Step 2: Validate each option

   pivot_longer(data, cols = c(Jan, Feb), names_to = "Month", values_to = "Sales") uses correct argument names and syntax with symbols inside c(). pivot_longer(data, columns = c(Jan, Feb), names = "Month", values = "Sales") uses wrong argument names. pivot_longer(data, cols = c("Jan", "Feb"), names_to = Month, values_to = Sales) misses quotes around new column names. pivot_longer(data, cols = Jan, Feb, names_to = "Month", values_to = "Sales") incorrectly separates columns without c().

3. Final Answer:

   pivot_longer(data, cols = c(Jan, Feb), names_to = "Month", values_to = "Sales") -> Option A

4. Quick Check:

   Correct args: cols, names_to, values_to [OK]

Hint: Use cols = c(), names_to and values_to with quotes [OK]

Common Mistakes:

• Using wrong argument names like columns or names
• Forgetting quotes around new column names
• Not wrapping columns in c()

3. Given this data frame in R:

df <- data.frame(ID = 1:2, Jan = c(10, 20), Feb = c(15, 25))

What is the output of:

pivot_longer(df, cols = c(Jan, Feb), names_to = "Month", values_to = "Value")

medium

A. A data frame with columns ID, Month, and Value, where Jan and Feb values are stacked under Value.

B. A data frame with columns ID, Jan, Feb unchanged.

C. An error because cols argument is missing quotes.

D. A data frame with columns ID, Month, Value but values are all NA.

Solution

1. Step 1: Understand pivot_longer behavior on df

   The function stacks the Jan and Feb columns into two rows per ID, creating Month and Value columns.

2. Step 2: Predict the output structure

   The output will have 4 rows (2 IDs x 2 months) and columns: ID, Month (Jan or Feb), and Value (corresponding numbers).

3. Final Answer:

   A data frame with columns ID, Month, and Value, where Jan and Feb values are stacked under Value. -> Option A

4. Quick Check:

   pivot_longer stacks columns into rows [OK]

Hint: pivot_longer stacks selected columns into new key-value columns [OK]

Common Mistakes:

• Expecting original wide format unchanged
• Thinking it causes errors without quotes on cols
• Assuming values become NA

4. What is wrong with this code?

pivot_longer(df, cols = Jan, Feb, names_to = "Month", values_to = "Value")

medium

A. The function pivot_longer does not exist.

B. The cols argument should be a vector using c(), not separate arguments.

C. The values_to argument is missing.

D. The names_to argument should not be in quotes.

Solution

1. Step 1: Check the cols argument format

   The cols argument must be a vector of columns, written as cols = c(Jan, Feb). Passing them separately is invalid syntax.

2. Step 2: Verify other arguments

   names_to and values_to are correctly used with quotes. The function exists in the tidyverse package.

3. Final Answer:

   The cols argument should be a vector using c(), not separate arguments. -> Option B

4. Quick Check:

   cols = c(...) required [OK]

Hint: Wrap multiple columns in c() inside cols argument [OK]

Common Mistakes:

• Passing multiple columns without c()
• Removing quotes from names_to or values_to
• Assuming pivot_longer is not available

5. You have a data frame:

df <- data.frame(ID = 1:2, Q1_2019 = c(5, 6), Q2_2019 = c(7, 8), Q1_2020 = c(9, 10))

You want to pivot it longer so that the quarter and year are separate columns named Quarter and Year, and the values go into Score. Which code achieves this?

hard

A. pivot_longer(df, cols = c(Q1_2019, Q2_2019, Q1_2020), names_to = c("Quarter", "Year"), values_to = "Score")

B. pivot_longer(df, cols = c(Q1_2019, Q2_2019, Q1_2020), names_to = "Quarter_Year", values_to = "Score")

C. pivot_longer(df, cols = c(Q1_2019, Q2_2019, Q1_2020), names_to = c("Year", "Quarter"), names_sep = "_", values_to = "Score")

D. pivot_longer(df, cols = starts_with("Q"), names_to = c("Quarter", "Year"), names_sep = "_", values_to = "Score")

Solution

1. Step 1: Identify columns to pivot

   All columns starting with "Q" represent quarters and years, so starts_with("Q") selects them.

2. Step 2: Separate names into Quarter and Year

   Using names_to = c("Quarter", "Year") with names_sep = "_" splits the column names at underscore into two new columns.

3. Step 3: Assign values to Score

   The values_to = "Score" argument names the new value column.

4. Final Answer:

   pivot_longer(df, cols = starts_with("Q"), names_to = c("Quarter", "Year"), names_sep = "_", values_to = "Score") -> Option D

5. Quick Check:

   Use names_sep to split names_to vector [OK]

Hint: Use names_to vector and names_sep to split column names [OK]

Common Mistakes:

• Not using names_sep to split names into multiple columns
• Swapping order of Quarter and Year in names_to
• Not selecting all relevant columns with starts_with