pivot_wider (long to wide) in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pivot_wider() function do in R?

pivot_wider() transforms data from a long format to a wide format by spreading key-value pairs across multiple columns.

beginner

Which package in R provides the pivot_wider() function?

The pivot_wider() function is part of the tidyr package, which helps tidy and reshape data.

beginner

In pivot_wider(), what do the names_from and values_from arguments specify?

names_from specifies the column whose values will become new column names.
values_from specifies the column whose values will fill the new columns.

intermediate

Given this long data frame:

df <- data.frame(
  id = c(1,1,2,2),
  key = c('A','B','A','B'),
  value = c(10,20,30,40)
)

What will pivot_wider(df, names_from = key, values_from = value) produce?

  id  A   B
1  1 10  20
2  2 30  40

This spreads the key values A and B into columns, filling with value.

intermediate

How does pivot_wider() handle multiple values for the same id and name combination?

By default, it will give an error if multiple values exist for the same combination.
You can use the values_fn argument to specify how to combine them, like list or sum.

What is the main purpose of pivot_wider() in R?

A Convert data from long to wide format

B Convert data from wide to long format

C Filter rows based on conditions

D Sort data by column values

pivot_wider() changes data from long format to wide format by spreading key-value pairs into columns.

Which argument in pivot_wider() defines the new column names?

A values_from

B id_cols

C cols

D names_from

names_from specifies the column whose values become the new column names.

If you have multiple values for the same id and name in pivot_wider(), what argument helps you handle them?

A values_fill

B values_fn

C names_prefix

D id_cols

values_fn lets you specify a function to combine multiple values, like sum or list.

Which R package must you load to use pivot_wider()?

A tidyr

B ggplot2

C dplyr

D data.table

pivot_wider() is part of the tidyr package.

What will pivot_wider() do if some combinations have missing values?

A Throw an error

B Remove those rows

C Fill with NA by default

D Fill with zero automatically

By default, missing combinations are filled with NA. You can change this with values_fill.

Explain how pivot_wider() transforms a long data frame into a wide one. Include the roles of names_from and values_from.

Think about spreading key-value pairs into columns.

Describe how to handle multiple values for the same id and name combination when using pivot_wider().

Consider what happens if one cell would have many values.

Practice

1. What does the pivot_wider function do in R's tidyverse?

easy

A. It filters rows based on a condition.

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

C. It sorts data in ascending order.

D. It merges two data frames by a common column.

Solution

1. Step 1: Understand the purpose of pivot_wider

   The function is designed to reshape data by turning unique values from one column into multiple columns.

2. Step 2: Compare with other data operations

   Filtering, sorting, and merging are different operations; pivot_wider specifically changes data shape from long to wide.

3. Final Answer:

   It transforms data from long format to wide format by spreading key-value pairs into columns. -> Option B

4. Quick Check:

   pivot_wider = long to wide reshape [OK]

Hint: pivot_wider spreads rows into columns for easier comparison [OK]

Common Mistakes:

• Confusing pivot_wider with filtering or sorting functions
• Thinking pivot_wider merges data frames
• Assuming pivot_wider removes rows instead of reshaping

2. Which of the following is the correct syntax to use pivot_wider to spread the column key into multiple columns with values from value?

easy

A. pivot_wider(data, values_from = key, names_from = value)

B. pivot_wider(data, spread = key, gather = value)

C. pivot_wider(data, key = names_from, value = values_from)

D. pivot_wider(data, names_from = key, values_from = value)

Solution

1. Step 1: Recall pivot_wider argument names

   The correct arguments are names_from for the column to spread and values_from for the values to fill.

2. Step 2: Match arguments to columns

   pivot_wider(data, names_from = key, values_from = value) correctly uses names_from = key and values_from = value, matching the expected syntax.

3. Final Answer:

   pivot_wider(data, names_from = key, values_from = value) -> Option D

4. Quick Check:

   names_from = key, values_from = value [OK]

Hint: Use names_from for columns, values_from for data [OK]

Common Mistakes:

• Swapping names_from and values_from arguments
• Using incorrect argument names like spread or gather
• Passing arguments in wrong order or with wrong names

3. Given this data frame in R:

library(tidyr)
data <- data.frame(
  id = c(1,1,2,2),
  variable = c('A', 'B', 'A', 'B'),
  value = c(10, 20, 30, 40)
)
result <- pivot_wider(data, names_from = variable, values_from = value)
print(result)

What will be the output?

medium

A. id A B
1 1 10 20
2 2 30 40

B. id variable value
1 1 A 10
2 1 B 20
3 2 A 30
4 2 B 40

C. id A
1 1 10
2 2 30

D. Error: object 'pivot_wider' not found

Solution

1. Step 1: Understand pivot_wider effect on data

   It converts the 'variable' column values ('A', 'B') into separate columns, filling them with corresponding 'value' entries.

2. Step 2: Check the resulting data frame structure

   Each 'id' has columns 'A' and 'B' with values 10, 20 for id=1 and 30, 40 for id=2.

3. Final Answer:

   id A B 1 1 10 20 2 2 30 40 -> Option A

4. Quick Check:

   pivot_wider spreads variable into columns with values [OK]

Hint: pivot_wider creates columns from variable, fills with value [OK]

Common Mistakes:

• Expecting original long format output
• Confusing column names after pivot
• Not loading tidyr library causing error

4. You run this code but get an error:

library(tidyr)
data <- data.frame(
  id = c(1,1,2,2),
  variable = c('A', 'B', 'A', 'B'),
  value = c(10, 20, 30, 40)
)
result <- pivot_wider(data, names_from = variable)
print(result)

What is the likely cause of the error?

medium

A. The library tidyr is not loaded.

B. The data frame has duplicate rows causing conflict.

C. Missing the values_from argument to specify which column fills new columns.

D. The variable column contains invalid characters.

Solution

1. Step 1: Check pivot_wider required arguments

   pivot_wider needs both names_from and values_from to know which columns to spread and fill.

2. Step 2: Identify missing argument

   The code only provides names_from = variable but misses values_from, causing an error.

3. Final Answer:

   Missing the values_from argument to specify which column fills new columns. -> Option C

4. Quick Check:

   values_from argument is required [OK]

Hint: Always include values_from with names_from in pivot_wider [OK]

Common Mistakes:

• Forgetting values_from argument
• Assuming names_from alone is enough
• Not reading error messages carefully

5. You have this long data frame with sales data:

sales <- data.frame(
  store = c('X', 'X', 'Y', 'Y'),
  product = c('apple', 'banana', 'apple', 'banana'),
  sales_amount = c(100, 150, 200, 250)
)

You want to create a wide table where each product is a column showing sales per store. Which code correctly does this and handles missing values by filling with 0?

hard

A. pivot_wider(sales, names_from = product, values_from = sales_amount, values_fill = 0)

B. pivot_wider(sales, names_from = sales_amount, values_from = product, values_fill = 0)

C. pivot_wider(sales, names_from = product, values_from = sales_amount)

D. pivot_wider(sales, names_from = store, values_from = sales_amount, values_fill = NA)

Solution

1. Step 1: Identify columns to spread and fill

   The products ('apple', 'banana') become new columns, so names_from = product. The sales amounts fill these columns, so values_from = sales_amount.

2. Step 2: Handle missing values

   Use values_fill = 0 to replace any missing sales with zero for completeness.

3. Final Answer:

   pivot_wider(sales, names_from = product, values_from = sales_amount, values_fill = 0) -> Option A

4. Quick Check:

   Use values_fill to replace missing with 0 [OK]

Hint: Add values_fill = 0 to fill missing cells with zero [OK]

Common Mistakes:

• Swapping names_from and values_from
• Not using values_fill causing NA in output
• Using wrong columns for names_from or values_from