Nesting and unnesting in R Programming - Mini Project: Build & Apply

Nesting and unnesting in R

📖 Scenario: You work in a small company that keeps track of sales data. The data is grouped by salespeople, and each salesperson has a list of sales with amounts and dates.You want to practice how to group this data into nested lists and then flatten it back to a simple table.

🎯 Goal: You will create a nested data frame with sales grouped by salesperson, then unnest it back to a flat data frame showing all sales.

📋 What You'll Learn

Use a data frame with exact columns: salesperson, amount, date

Create a nested data frame grouped by salesperson

Unnest the nested data frame back to a flat data frame

Print the final unnested data frame

💡 Why This Matters

🌍 Real World

Nesting and unnesting data is useful when you want to group related information together and then expand it back for detailed analysis.

💼 Career

Data analysts and scientists often use nesting to organize complex data and unnesting to prepare data for reports or visualizations.

1

Create the initial sales data frame

Create a data frame called sales_data with these exact rows:
salesperson = c('Alice', 'Alice', 'Bob', 'Bob', 'Charlie'),
amount = c(100, 150, 200, 250, 300),
date = as.Date(c('2024-01-01', '2024-01-05', '2024-01-03', '2024-01-07', '2024-01-10')).

R Programming

# Create the data frame sales_data with the exact columns and values
# Your code here

Hint

Use data.frame() with the exact column names and values.

2

Load the tidyr package

Write a line to load the tidyr package using library(tidyr).

R Programming

sales_data <- data.frame(
  salesperson = c('Alice', 'Alice', 'Bob', 'Bob', 'Charlie'),
  amount = c(100, 150, 200, 250, 300),
  date = as.Date(c('2024-01-01', '2024-01-05', '2024-01-03', '2024-01-07', '2024-01-10'))
)
# Load the tidyr package
# Your code here

Hint

Use library(tidyr) to load the package.

3

Nest the sales data by salesperson

Create a nested data frame called nested_sales by grouping sales_data by salesperson and nesting the other columns using nest().

R Programming

sales_data <- data.frame(
  salesperson = c('Alice', 'Alice', 'Bob', 'Bob', 'Charlie'),
  amount = c(100, 150, 200, 250, 300),
  date = as.Date(c('2024-01-01', '2024-01-05', '2024-01-03', '2024-01-07', '2024-01-10'))
)
library(tidyr)
# Create nested_sales by grouping sales_data by salesperson and nesting
# Your code here

Hint

Use tidyr::nest() with .by = salesperson and nest amount and date.

4

Unnest the nested sales data and print

Unnest the nested_sales data frame back to a flat data frame called unnested_sales using unnest(). Then print unnested_sales.

R Programming

sales_data <- data.frame(
  salesperson = c('Alice', 'Alice', 'Bob', 'Bob', 'Charlie'),
  amount = c(100, 150, 200, 250, 300),
  date = as.Date(c('2024-01-01', '2024-01-05', '2024-01-03', '2024-01-07', '2024-01-10'))
)
library(tidyr)
nested_sales <- tidyr::nest(sales_data, data = c(amount, date), .by = salesperson)
# Unnest nested_sales into unnested_sales and print it
# Your code here

Hint

Use tidyr::unnest() on nested_sales with data, then print the result.

Practice

1. What does nest() do in R when applied to a grouped data frame?

easy

A. It sorts the data frame by a column.

B. It separates a list-column into multiple rows.

C. It removes grouping from the data frame.

D. It groups specified columns into a list-column inside the data frame.

Solution

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

   The nest() function groups specified columns into a list-column, keeping other columns as keys.

2. Step 2: Identify the effect on data frame structure

   This creates a nested data frame where each row contains a key and a list of grouped data.

3. Final Answer:

   It groups specified columns into a list-column inside the data frame. -> Option D

4. Quick Check:

   nest() = groups columns into list-column [OK]

Hint: nest() groups columns into list-columns quickly [OK]

Common Mistakes:

• Confusing nest() with unnest()
• Thinking nest() sorts data
• Assuming nest() removes grouping

2. Which of the following is the correct syntax to unnest a list-column named data in a data frame df using tidyr?

easy

A. unnest(df, cols)

B. df %>% unnest()

C. df %>% unnest(cols = data)

D. unnest(df)

Solution

1. Step 1: Recall unnest() syntax in tidyr

   The correct syntax uses unnest() with the argument cols = specifying the list-column to expand.

2. Step 2: Identify the pipe-friendly syntax

   Using the pipe operator, the syntax is df %>% unnest(cols = data), which is the modern recommended style.

3. Final Answer:

   df %>% unnest(cols = data) -> Option C

4. Quick Check:

   unnest() needs cols= and pipe for data frame [OK]

Hint: Use pipe and cols= to unnest list-columns [OK]

Common Mistakes:

• Omitting cols= argument
• Passing data frame as first argument without pipe
• Using unnest(df, data) without cols=

3. Given the code:

library(tidyr)
library(dplyr)
df <- tibble(group = c('A', 'A', 'B', 'B'), value = 1:4)
nested_df <- df %>% group_by(group) %>% nest()
unnested_df <- nested_df %>% unnest(cols = data)

What will be the output of unnested_df?

medium

A. A data frame identical to the original df with columns group and value.

B. A data frame with columns group and a list-column data.

C. An error because unnest() cannot be applied here.

D. A data frame with only the group column.

Solution

1. Step 1: Understand the nesting step

   The nest() groups rows by group and stores the remaining columns in a list-column named data.

2. Step 2: Understand the unnesting step

   The unnest() expands the list-column data back into regular rows, restoring the original structure.

3. Final Answer:

   A data frame identical to the original df with columns group and value. -> Option A

4. Quick Check:

   nest() then unnest() returns original data [OK]

Hint: nest() then unnest() restores original data frame [OK]

Common Mistakes:

• Thinking unnest() returns list-column
• Expecting error from unnest()
• Assuming columns are dropped

4. Consider this code snippet:

library(tidyr)
df <- tibble(id = 1:2, data = list(tibble(x = 1:2), tibble(x = 3:4)))
unnested <- unnest(df)

It throws an error. What is the likely cause?

medium

A. The list-column data contains data frames, which cannot be unnested.

B. The unnest() function requires the cols argument to specify the list-column.

C. The tibble package is not loaded.

D. The unnest() function cannot unnest list-columns with numeric data.

Solution

1. Step 1: Check unnest() argument requirements

   Modern unnest() requires specifying which columns to unnest using the cols = argument.

2. Step 2: Identify missing argument causing error

   Omitting cols = data causes the function to fail because it doesn't know which list-column to expand.

3. Final Answer:

   The unnest() function requires the cols argument to specify the list-column. -> Option B

4. Quick Check:

   unnest() needs cols= argument [OK]

Hint: Always specify cols= when using unnest() [OK]

Common Mistakes:

• Forgetting cols= argument
• Assuming unnest() works without specifying columns
• Confusing list-column content type

5. You have a data frame df with columns category, subcat, and value. You want to nest subcat and value by category, then filter out groups where the sum of value is less than 10, and finally unnest the filtered data. Which sequence of commands correctly achieves this?

hard

A. df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(map_dbl(data, ~sum(.x$value)) >= 10) %>% unnest(cols = data)

B. df %>% nest(data = c(subcat, value)) %>% filter(sum(value) >= 10) %>% unnest(data)

C. df %>% group_by(category) %>% nest(cols = c(subcat, value)) %>% filter(sum(value) >= 10) %>% unnest(cols = data)

D. df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(sum(data$value) >= 10) %>% unnest(cols = data)

Solution

1. Step 1: Nest subcat and value by category

   Use group_by(category) then nest(data = c(subcat, value)) to create a list-column named data with those columns.

2. Step 2: Filter groups by sum of value

   Use filter(map_dbl(data, ~sum(.x$value)) >= 10) to keep only groups where sum of value in nested data is at least 10.

3. Step 3: Unnest the filtered data

   Finally, use unnest(cols = data) to expand the filtered nested data back into rows.

4. Final Answer:

   df %>% group_by(category) %>% nest(data = c(subcat, value)) %>% filter(map_dbl(data, ~sum(.x$value)) >= 10) %>% unnest(cols = data) -> Option A

5. Quick Check:

   nest(), filter with map_dbl(), then unnest() [OK]

Hint: Use map_dbl() on nested data to filter before unnesting [OK]

Common Mistakes:

• Filtering before nesting
• Using sum() directly on list-column
• Omitting cols= in unnest()