pivot_longer (wide to long) in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this pivot_longer operation?

Given the data frame df below, what will be the output after applying the pivot_longer function as shown?

R Programming

library(tidyr)
df <- data.frame(
  id = 1:2,
  math = c(90, 80),
  science = c(85, 95)
)
result <- pivot_longer(df, cols = c(math, science), names_to = "subject", values_to = "score")
print(result)

A

  id  subject score
1  1     math    90
2  1  science    85
3  2     math    80
4  2  science    95

B

  id  subject score
1  1  science    85
2  1     math    90
3  2  science    95
4  2     math    80

C

  id  subject score
1  1     math    90
2  2     math    80
3  1  science    85
4  2  science    95

D

  id  subject score
1  1     math    90
2  1  science    85
3  2  science    95
4  2     math    80

💡 Hint

Remember that pivot_longer stacks the selected columns in order, keeping the original row order.

pivot_longer takes the columns math and science and stacks them under a new column 'subject'. The values go into 'score'. The order preserves the original row order and the order of columns selected.

🧠 Conceptual

intermediate

Which statement best describes the purpose of pivot_longer?

What does the pivot_longer function do in data manipulation?

A It converts data from a wide format to a long format by gathering multiple columns into key-value pairs.

B It converts data from a long format to a wide format by spreading key-value pairs into multiple columns.

C It filters rows based on a condition in one or more columns.

D It sorts the data frame by one or more columns.

💡 Hint

Think about how columns are transformed into rows.

pivot_longer gathers multiple columns into two columns: one for the column names (keys) and one for the values, changing wide data into long data.

🔧 Debug

advanced

What error does this pivot_longer code produce?

Consider the code below. What error will it raise when run?

R Programming

library(tidyr)
df <- data.frame(
  id = 1:2,
  math = c(90, 80),
  science = c(85, 95)
)
result <- pivot_longer(df, cols = math science, names_to = "subject", values_to = "score")

A Error: unused argument (names_to = "subject")

B Error: unexpected symbol in "pivot_longer(df, cols = math science"

C Error: object 'math science' not found

D No error, runs successfully

💡 Hint

Check the syntax for specifying multiple columns in the cols argument.

The cols argument requires columns to be separated by commas or use c(). Writing 'math science' without a comma or c() causes a syntax error.

❓ Predict Output

advanced

What is the output of pivot_longer with regex selection?

Given the data frame df below, what will be the output of the pivot_longer call that selects columns starting with 'score_'?

R Programming

library(tidyr)
df <- data.frame(
  id = 1:2,
  score_math = c(90, 80),
  score_science = c(85, 95),
  age = c(15, 16)
)
result <- pivot_longer(df, cols = starts_with("score_"), names_to = "subject", values_to = "score")
print(result)

A

  id       subject score
1  1   score_math    90
2  1 score_science    85
3  2 score_science    95
4  2   score_math    80

B

  id       subject score
1  1   score_math    90
2  2   score_math    80
3  1 score_science    85
4  2 score_science    95

C

  id       subject score
1  1   score_math    90
2  1 score_science    85
3  2   score_math    80
4  2 score_science    95

D

  id       subject score
1  1 score_science    85
2  1   score_math    90
3  2 score_science    95
4  2   score_math    80

💡 Hint

The order of rows follows the original row order and the order of selected columns.

pivot_longer stacks columns selected by starts_with("score_") in the order they appear, preserving row order.

🚀 Application

expert

How many rows are in the result after pivot_longer?

You have a data frame with 3 rows and 4 columns: one ID column and three measurement columns. After applying pivot_longer to gather the three measurement columns, how many rows will the resulting data frame have?

A 12

B 4

C 3

D 9

💡 Hint

Each original row will produce one row per gathered column.

pivot_longer stacks the selected columns into rows. With 3 original rows and 3 columns gathered, the result has 3 * 3 = 9 rows.

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