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

Need a 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

Need a 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

Need a 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

Need a hint?

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