pivot_longer (wide to long) in R Programming - Step-by-Step Execution

Concept Flow - pivot_longer (wide to long)

Start with wide data

↓

Select columns to pivot

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Gather columns into key-value pairs

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Create new 'name' column for keys

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Create new 'value' column for values

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Return long format data

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End

pivot_longer takes wide data and turns selected columns into two columns: one for the old column names (keys) and one for their values, making data longer.

Execution Sample

R Programming

library(tidyr)
data <- data.frame(
  id = 1:2,
  A = c(10, 20),
  B = c(30, 40)
)
pivot_longer(data, cols = c(A, B), names_to = "variable", values_to = "value")

This code converts columns A and B into a longer format with 'variable' and 'value' columns.

Execution Table

Step	Action	Input Data	Output Data	Notes
1	Start with wide data	id | A | B 1 |10|30 2 |20|40	Same as input	Initial wide data with columns id, A, B
2	Select columns to pivot	Columns A and B	Columns A and B selected	Choosing which columns to make longer
3	Gather columns into key-value pairs	A=10, B=30 (row 1)	variable='A', value=10 variable='B', value=30	Row 1 split into two rows
4	Gather columns into key-value pairs	A=20, B=40 (row 2)	variable='A', value=20 variable='B', value=40	Row 2 split into two rows
5	Add id column to each new row	id=1 or 2	id=1, variable='A', value=10 id=1, variable='B', value=30 id=2, variable='A', value=20 id=2, variable='B', value=40	id repeats for each new row
6	Return long format data	All rows combined	id | variable | value 1 | A | 10 1 | B | 30 2 | A | 20 2 | B | 40	Final long format data

💡 All selected columns pivoted; data transformed from wide to long format.

Variable Tracker

Variable	Start	After Step 3	After Step 5	Final
data	data.frame with columns id, A, B	id column unchanged; A and B split into key-value pairs	id repeated for each key-value pair	long format data with id, variable, value columns

Key Moments - 3 Insights

Why do the number of rows increase after pivot_longer?

Because each selected column in the wide data becomes its own row in the long data, as shown in execution_table steps 3 and 4 where one row splits into multiple rows.

What do the 'names_to' and 'values_to' arguments do?

'names_to' creates the new column holding the old column names (keys), and 'values_to' creates the new column holding the values, as seen in the output data in execution_table step 6.

Does the original id column get changed or removed?

No, the id column stays the same and repeats for each new row created, as shown in execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. What happens to the row with id=1?

A It stays as one row with combined values.

B It is removed from the data.

C It splits into two rows with variable 'A' and 'B'.

D It duplicates the id column.

💡 Hint

Check the 'Output Data' column in step 3 of execution_table.

At which step does the id column get repeated for each new row?

A Step 4

B Step 5

C Step 2

D Step 6

💡 Hint

Look at the 'Notes' column describing id repetition in execution_table.

If you only pivot column A, how would the final number of rows change compared to pivoting A and B?

A It would be half the number of rows.

B It would be double the number of rows.

C It would be the same number of rows.

D It would be zero rows.

💡 Hint

Refer to variable_tracker and how each selected column creates new rows.

Concept Snapshot

pivot_longer(data, cols, names_to, values_to)
- Converts wide data to long format
- 'cols' selects columns to pivot
- 'names_to' names new key column
- 'values_to' names new value column
- Rows increase as columns become rows

Full Transcript

pivot_longer is a function in R that changes data from wide format to long format. It takes selected columns and turns them into two new columns: one for the old column names and one for their values. This makes the data longer by increasing the number of rows. The original columns not selected stay the same and repeat for each new row. This process helps when you want to analyze or plot data more easily. The execution steps show how each row splits into multiple rows with new key-value pairs, and the id column repeats accordingly.