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

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this pivot_wider code?

Given the following R code using pivot_wider, what will be the resulting data frame?

R Programming

library(tidyr)
library(dplyr)

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

wide_df <- pivot_wider(long_df, names_from = key, values_from = value)
print(wide_df)

A An error because pivot_wider needs a grouping variable

B A tibble with columns: id, key, value; rows doubled

C A tibble with columns: id, A, B; rows: (1,10,20), (2,30,40)

D A tibble with columns: key, value; rows unchanged

💡 Hint

Think about how pivot_wider spreads key-value pairs into columns.

pivot_wider takes the unique values in 'key' and makes them column names, filling with corresponding 'value' entries for each 'id'.

❓ Predict Output

intermediate

What does this pivot_wider code produce?

Consider this R code snippet using pivot_wider. What is the output?

R Programming

library(tidyr)
long_df <- data.frame(
  person = c("Alice", "Alice", "Bob", "Bob"),
  measurement = c("height", "weight", "height", "weight"),
  value = c(165, 60, 180, 75)
)
wide_df <- pivot_wider(long_df, names_from = measurement, values_from = value)
print(wide_df)

A An error because 'value' column is numeric

B A tibble with columns: measurement, value; rows unchanged

C A tibble with columns: person, measurement, value; rows doubled

D A tibble with columns: person, height, weight; rows: (Alice,165,60), (Bob,180,75)

💡 Hint

pivot_wider reshapes long data by spreading measurement names into columns.

pivot_wider uses 'measurement' values as new column names and fills them with 'value' for each 'person'.

🔧 Debug

advanced

What error does this pivot_wider code raise?

What error will this R code produce when run?

R Programming

library(tidyr)
long_df <- data.frame(
  id = c(1, 1, 2),
  key = c("X", "X", "X"),
  value = c(5, 10, 15)
)

pivot_wider(long_df, names_from = key, values_from = value)

A Error: Values in 'value' are not uniquely identified; multiple rows per id-key pair

B No error; returns a wide data frame with columns id, X, Y

C Error: 'names_from' column 'key' not found

D Error: 'values_from' column 'value' is missing

💡 Hint

Check if each id-key pair has only one value.

pivot_wider requires unique combinations of id and key to fill values; duplicates cause an error.

❓ Predict Output

advanced

What is the output of pivot_wider with multiple value columns?

What will this R code output when run?

R Programming

library(tidyr)
long_df <- data.frame(
  id = c(1, 1, 2, 2),
  key = c("A", "B", "A", "B"),
  val1 = c(10, 20, 30, 40),
  val2 = c(100, 200, 300, 400)
)
wide_df <- pivot_wider(long_df, names_from = key, values_from = c(val1, val2))
print(wide_df)

A A tibble with columns: id, val1_A, val1_B, val2_A, val2_B; rows: (1,10,20,100,200), (2,30,40,300,400)

B A tibble with columns: id, A, B; rows with nested lists of val1 and val2

C Error: values_from must be a single column name

D A tibble with columns: id, key, val1, val2; rows unchanged

💡 Hint

pivot_wider can handle multiple value columns by creating multiple new columns per key.

pivot_wider creates new columns for each combination of value column and key, naming them like val1_A, val1_B, etc.

🧠 Conceptual

expert

How many rows are in the output after pivot_wider?

Given this long data frame with 6 rows and 3 unique ids, what will be the number of rows after applying pivot_wider with names_from = variable and values_from = value?

R Programming

library(tidyr)
long_df <- data.frame(
  id = c(1,1,2,2,3,3),
  variable = c("X", "Y", "X", "Y", "X", "Y"),
  value = c(5, 10, 15, 20, 25, 30)
)
wide_df <- pivot_wider(long_df, names_from = variable, values_from = value)

A 6

B 3

C 2

D 1

💡 Hint

pivot_wider groups by the columns not used in names_from or values_from.

Since there are 3 unique ids, pivot_wider will produce 3 rows, one per id.

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