diff --git a/README.md b/README.md
index 75a90db..6d478d4 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,7 @@ The first workshop supports multiple programming languages. Choose one:
 | **JavaScript** | Node.js 18+ |
 | **Python** | Python 3.10+ |
 | **C#** | .NET 8+ |
+| **R** | R 4.2+ (see [R Data Analysis lab](labs/r-data-analysis.md)) |
 
 ## 📚 Workshop Structure
 
@@ -117,6 +118,15 @@ Encode deep domain expertise:
 - Including resources (scripts, templates, data) alongside skills
 - `/skills` command and visibility controls
 
+### R Track: Data Analysis
+
+### [Lab R - R Data Analysis](labs/r-data-analysis.md)
+A standalone lab for R developers covering the same Copilot features through a data-analysis lens:
+- Inline completion for dplyr pipelines and ggplot2 charts
+- Copilot Chat for statistical summaries and code explanation
+- Agent Mode to scaffold complete analysis scripts
+- Next Edit Suggestions for repetitive transformations
+
 ## 📁 Project Structure
 
 ```
@@ -133,7 +143,8 @@ copilot-katas/
 │   ├── 06-instructions.md
 │   ├── 07-prompts.md
 │   ├── 08-agents.md
-│   └── 09-skills.md
+│   ├── 09-skills.md
+│   └── r-data-analysis.md  ← R track (standalone)
 ├── starter-code/
 │   ├── javascript/          ← Todo App (Labs 00-04)
 │   │   ├── package.json
@@ -145,6 +156,10 @@ copilot-katas/
 │   ├── csharp/              ← Todo App (Labs 00-04)
 │   │   ├── TodoApp.csproj
 │   │   └── src/
+│   ├── r/                   ← R Data Analysis (Lab R)
+│   │   ├── README.md
+│   │   ├── data/
+│   │   └── src/
 │   └── recipe-api/          ← Recipe API (Labs 05-09)
 │       ├── package.json
 │       ├── README.md
diff --git a/labs/r-data-analysis.md b/labs/r-data-analysis.md
new file mode 100644
index 0000000..7974f27
--- /dev/null
+++ b/labs/r-data-analysis.md
@@ -0,0 +1,456 @@
+# Lab R – R Data Analysis with GitHub Copilot
+
+## Learning Goals
+
+By the end of this lab, you will be able to:
+- Use inline completion to write idiomatic R data-wrangling code
+- Use Copilot Chat to explore, clean, and transform a real dataset
+- Generate statistical summaries and visualisations with Copilot's help
+- Apply Agent Mode to build a complete analytical script end-to-end
+- Write better R-specific prompts for more accurate Copilot suggestions
+
+## Introduction
+
+This lab is designed for developers who primarily work in **R**. Instead of a
+generic Todo application, you will analyse a realistic sales dataset using the
+tools and idioms R developers use every day:
+
+- `readr` / base `read.csv` for data loading
+- `dplyr` for data manipulation
+- `ggplot2` for visualisation
+- `lubridate` for date handling
+- Base R statistics (`mean`, `sd`, `cor`, `lm`, …)
+
+All exercises follow the same Copilot workflow as the other labs – but every
+example is grounded in data analysis rather than application development.
+
+> **Note:** You do **not** need to complete the other labs before this one.
+> This lab stands alone and covers inline completion, Chat, and Agent Mode in
+> the context of R.
+
+## Prerequisites
+
+1. R 4.2 or later ([CRAN](https://cran.r-project.org/))
+2. VS Code with the
+   [R extension](https://marketplace.visualstudio.com/items?itemName=REditorSupport.r)
+3. GitHub Copilot Chat extension installed and activated
+4. Required packages – open an R console in your terminal and install them:
+
+```bash
+# Start the R console
+R
+
+# Then inside the R console, run:
+install.packages(c("dplyr", "ggplot2", "lubridate", "readr", "forecast"))
+
+# When done, quit the R console
+q()
+```
+
+> **Tip:** Type `R` in your terminal to start an interactive R session, and
+> `q()` (or press `Ctrl+D`) to exit it.
+
+## Setting Up
+
+```bash
+cd starter-code/r
+```
+
+Open `src/analysis.R` as your primary working file. This is the file
+Copilot will use for most of its context.
+
+---
+
+## Exercise 1: Loading and Exploring Data (Inline Completion)
+
+### Task 1.1: Load the Dataset
+
+Open `src/analysis.R` and let Copilot help you load the CSV file.
+
+Type the following comment and press **Enter**:
+
+```r
+# Load the sales data from data/sample_sales.csv into a data frame called sales
+```
+
+**✅ Try This:**
+1. Type the comment and press `Enter`
+2. Wait for Copilot's inline suggestion (ghost text)
+3. Press `Tab` to accept, or `Esc` to dismiss and try a different approach
+4. Observe whether Copilot chooses `read.csv()` or `readr::read_csv()` – try
+   pressing `Alt+]` (Windows/Linux) / `Option+]` (Mac) to see alternative suggestions
+
+### Task 1.2: Explore the Data Structure
+
+After loading, add these comments one by one and let Copilot complete each:
+
+```r
+# Show the first 6 rows of the sales data frame
+```
+
+```r
+# Print a compact summary of the structure of sales (types, sample values)
+```
+
+```r
+# Display descriptive statistics for all numeric columns
+```
+
+```r
+# Count the number of rows and columns in the data frame
+```
+
+**💡 Tip:** Copilot reads the name `sales` as context. If you rename the
+variable, Copilot will automatically use the new name in its suggestions.
+
+### Task 1.3: Unique Values and Distributions
+
+```r
+# Print all unique values in the region column
+```
+
+```r
+# Count how many transactions belong to each product category
+```
+
+```r
+# Find the date range covered by this dataset (earliest and latest date)
+```
+
+**🔬 Experiment:** Try asking for the same thing in different ways:
+- `# List unique regions` vs `# Get distinct values from the region column`
+
+Notice how the phrasing affects the suggested function (`unique()` vs
+`dplyr::distinct()`).
+
+---
+
+## Exercise 2: Data Cleaning and Transformation (Inline Completion)
+
+Open `src/data_processor.R`. You will implement several data-processing
+functions here.
+
+### Task 2.1: Parse Dates
+
+The `date` column is loaded as a character string. Write a comment and let
+Copilot convert it:
+
+```r
+# Convert the date column from character to Date type using as.Date
+```
+
+**Observe:** Copilot should suggest the correct format string (`"%Y-%m-%d"`)
+because it can see the sample data nearby.
+
+### Task 2.2: Add a Revenue Column
+
+Revenue = `units_sold × unit_price × (1 – discount)`.
+
+```r
+# Add a revenue column: units_sold * unit_price * (1 - discount)
+```
+
+Try making the comment more specific and see how the suggestion changes:
+
+```r
+# Add a new column called 'revenue' that equals units_sold multiplied by unit_price multiplied by (1 minus discount), using dplyr mutate
+```
+
+### Task 2.3: Handle Missing Values
+
+```r
+# Remove rows that contain any NA values from the data frame
+```
+
+```r
+# Print the number of rows before and after removing NAs
+```
+
+### Task 2.4: Implement the clean_sales_data Function
+
+Now wrap all the transformations into a reusable function. Type:
+
+```r
+# Function to clean the raw sales data frame:
+# - Convert date column to Date type
+# - Add a revenue column
+# - Remove rows with NA values
+# Returns the cleaned data frame
+clean_sales_data <- function(df) {
+```
+
+Press `Enter` inside the function body and let Copilot suggest the
+implementation. Use **partial accept** (`Ctrl+→` on Windows / `Cmd+→` on Mac)
+to accept one expression at a time.
+
+**💡 Tip – partial accept:**
+
+| Shortcut | Action |
+|---|---|
+| `Cmd+→` / `Ctrl+→` | Accept next word |
+| `Tab` | Accept entire suggestion |
+| `Esc` | Dismiss and keep typing |
+
+---
+
+## Exercise 3: Statistical Summaries (Chat – Ask Mode)
+
+Open Copilot Chat (`Ctrl+Shift+I` / `Cmd+Shift+I`) and make sure **Ask Mode**
+is selected.
+
+### Task 3.1: Ask About Your Data
+
+With `src/analysis.R` open (and mentioned as context), try these prompts:
+
+```
+What dplyr pipeline would calculate total revenue grouped by region, 
+sorted from highest to lowest?
+```
+
+```
+How do I calculate the mean, median, and standard deviation of units_sold 
+for each product category?
+```
+
+```
+Show me how to find the top 3 sales representatives by total revenue
+```
+
+**✅ Observe:**
+- How Chat structures multi-step dplyr pipelines
+- How it adapts suggestions to your column names
+
+### Task 3.2: Use Slash Commands
+
+**`/explain`** – Select one of the pipelines Chat suggested and type:
+```
+/explain
+```
+
+**`/fix`** – Introduce a deliberate bug:
+```r
+# Bug: wrong column name
+total_by_region <- sales |>
+  group_by(area) |>       # 'area' does not exist
+  summarise(revenue = sum(revenue))
+```
+Select the code and type `/fix`.
+
+**`/doc`** – Select `clean_sales_data` and type:
+```
+/doc
+```
+Observe how Copilot generates roxygen2-style documentation.
+
+**`/tests`** – Select a function and type:
+```
+/tests using testthat
+```
+
+### Task 3.3: Context References
+
+Use `#file:` to reference a specific file as context in Copilot Chat. Start
+typing `#` and select the file from the autocomplete list.
+
+```
+#file:data_processor.R Does this file handle the case where the 
+discount column contains values outside the range 0–1?
+```
+
+```
+@workspace What functions have been defined so far across all R files?
+```
+
+---
+
+## Exercise 4: Data Visualization (Inline Completion)
+
+Open `src/visualizer.R`.
+
+### Task 4.1: Monthly Revenue Trend
+
+```r
+# Plot monthly total revenue as a line chart using ggplot2
+# x-axis: month (extract from date column), y-axis: total revenue
+```
+
+Copilot should suggest something involving `lubridate::floor_date` or
+`format(date, "%Y-%m")` to aggregate by month.
+
+### Task 4.2: Revenue by Region
+
+```r
+# Create a bar chart of total revenue by region, sorted from highest to lowest
+# Use reorder() to sort the bars
+```
+
+### Task 4.3: Top Products
+
+```r
+# Horizontal bar chart of the top products by total revenue
+# Flip coordinates so product names are on the y-axis
+```
+
+> **Note:** The sample dataset contains only 4 unique products, so all
+> products will appear in the chart.
+
+### Task 4.4: NES – Pattern Recognition
+
+1. Create two ggplot charts with similar structure
+2. Edit the title of the first chart
+3. Look for the **Next Edit Suggestion** indicator – Copilot may suggest
+   updating the second chart's title in the same way
+4. Press `Tab` to accept
+
+---
+
+## Exercise 5: Build a Complete Analysis Script (Agent Mode)
+
+Switch Copilot Chat to **Agent Mode** for this exercise.
+
+### Task 5.1: Generate a Full Report Script
+
+In Agent Mode, paste this prompt:
+
+```
+I have a sales CSV at data/sample_sales.csv with columns:
+date, region, product, category, units_sold, unit_price, discount, sales_rep.
+
+Create a complete R script called src/report.R that:
+1. Loads the data using readr::read_csv
+2. Cleans it (parse dates, add revenue column, drop NAs)
+3. Prints a summary table: total revenue and units sold per region
+4. Prints the top products by revenue
+5. Creates the output/ directory if it doesn't exist
+6. Saves a ggplot2 bar chart of revenue by region as output/revenue_by_region.png
+7. Prints a simple linear regression of revenue ~ units_sold and shows the R²
+
+Use dplyr and ggplot2. Add comments explaining each step.
+```
+
+> **Note:** You may notice a very low R² for the `revenue ~ units_sold` model.
+> This is expected – revenue also depends on `unit_price`, which varies widely
+> across products. Try asking Copilot Chat why the R² is low and how to improve
+> the model (e.g., by adding `unit_price` as a predictor).
+
+**Observe Agent Mode:**
+- Copilot creates the file autonomously
+- It may ask clarifying questions before proceeding
+- It runs the file and iterates if there are errors
+
+### Task 5.2: Iterative Refinement
+
+After reviewing the generated script, ask Copilot to improve it:
+
+```
+Add a second chart showing monthly revenue trend as a line plot 
+and save it as output/monthly_trend.png
+```
+
+```
+Wrap the cleaning and aggregation steps in named functions and 
+move them to data_processor.R
+```
+
+---
+
+## Exercise 6: Advanced Techniques
+
+### Task 6.1: Inline Chat for Quick Edits
+
+Press `Ctrl+I` / `Cmd+I` to open inline chat on a selected code block.
+
+**Try these prompts on your summary pipeline:**
+- `"Make this pipeline handle the case where revenue is NA"`
+- `"Rewrite this using base R instead of dplyr"`
+- `"Add a percentage-of-total column to this summary"`
+
+### Task 6.2: Specificity Experiment
+
+Compare how vague vs. specific comments affect suggestions:
+
+**Vague:**
+```r
+# filter the data
+```
+
+**Specific:**
+```r
+# Filter to keep only Electronics category rows where revenue > 1000
+```
+
+**Very specific:**
+```r
+# Filter the sales data frame to rows where category equals "Electronics"
+# and revenue is greater than 1000, returning a new data frame
+```
+
+### Task 6.3: Statistical Modelling
+
+Let Copilot help you build a simple model. Type these comments one at a time:
+
+```r
+# Fit a linear regression model to predict revenue from units_sold
+```
+
+```r
+# Print the model summary including R-squared and coefficient p-values
+```
+
+```r
+# Create a residuals vs fitted plot to check model assumptions
+```
+
+---
+
+## Challenges
+
+### Challenge 1: Sales Rep Performance Dashboard
+
+Write a comment block describing a function that takes the cleaned sales data
+and returns a data frame ranking each sales rep by:
+- Total revenue
+- Average discount given
+- Number of transactions
+- Most sold product category
+
+Let Copilot generate the full implementation.
+
+### Challenge 2: Outlier Detection
+
+Ask Copilot Chat:
+
+```
+How would I detect and flag outlier transactions in this dataset 
+using the IQR method on the revenue column?
+```
+
+Implement the suggestion and test it with the sample data.
+
+### Challenge 3: Time-Series Forecasting
+
+> **Note:** This challenge requires the `forecast` package which was included
+> in the prerequisites install command. If you skipped it, run:
+> `install.packages("forecast")` first.
+
+```
+Using the monthly revenue data I have, show me how to fit a simple 
+exponential smoothing model with the forecast package and plot 
+a 3-month ahead forecast
+```
+
+---
+
+## Key Takeaways
+
+| Concept | R-specific tip |
+|---|---|
+| **Name your variables clearly** | `clean_sales` vs `df2` – Copilot uses variable names as context |
+| **Comment-driven development** | Describe the transformation in plain English before writing code |
+| **Specify packages** | Mention `dplyr`, `ggplot2`, `data.table` in comments to steer suggestions |
+| **Partial accept** | Accept one pipe stage at a time with `Ctrl+→` |
+| **Chat for pipelines** | Long dplyr/ggplot2 chains are ideal for Chat; iterate conversationally |
+| **Agent Mode for scripts** | Let Agent Mode scaffold entire analysis scripts, then refine |
+
+---
+
diff --git a/starter-code/r/README.md b/starter-code/r/README.md
new file mode 100644
index 0000000..0c175b1
--- /dev/null
+++ b/starter-code/r/README.md
@@ -0,0 +1,68 @@
+# GitHub Copilot Katas – R Data Analysis
+
+A self-contained R project for the **R Data Analysis** lab exercise.
+
+## Prerequisites
+
+- R 4.2 or later ([Download](https://cran.r-project.org/))
+- VS Code with the [R extension](https://marketplace.visualstudio.com/items?itemName=REditorSupport.r)
+
+## Installing Packages
+
+Open an R console in your terminal and install the required packages:
+
+```bash
+# Start the R console
+R
+```
+
+```r
+# Inside the R console, run:
+install.packages(c("dplyr", "ggplot2", "lubridate", "readr", "forecast"))
+
+# When done, quit the R console
+q()
+```
+
+> **Tip:** Type `R` in your terminal to start an interactive R session, and
+> `q()` (or press `Ctrl+D`) to exit it.
+
+## Running the Analysis
+
+From the terminal (from this directory):
+
+```bash
+Rscript src/analysis.R
+```
+
+Or open `src/analysis.R` in VS Code and run sections interactively using the
+R extension.
+
+## Project Structure
+
+```
+r/
+├── README.md
+├── data/
+│   └── sample_sales.csv    ← provided sample dataset
+└── src/
+    ├── analysis.R           ← main script (start here)
+    ├── data_processor.R     ← data cleaning & transformation functions
+    └── visualizer.R         ← ggplot2 visualisation functions
+```
+
+## Dataset
+
+`data/sample_sales.csv` contains fictional quarterly sales records with the
+following columns:
+
+| Column       | Description                        |
+|--------------|------------------------------------|
+| date         | Transaction date (YYYY-MM-DD)      |
+| region       | Sales region (North/South/East/West)|
+| product      | Product name                       |
+| category     | Product category                   |
+| units_sold   | Number of units sold               |
+| unit_price   | Price per unit (USD)               |
+| discount     | Discount applied (0.0 – 1.0)       |
+| sales_rep    | Name of the sales representative   |
diff --git a/starter-code/r/data/sample_sales.csv b/starter-code/r/data/sample_sales.csv
new file mode 100644
index 0000000..2fb2308
--- /dev/null
+++ b/starter-code/r/data/sample_sales.csv
@@ -0,0 +1,41 @@
+date,region,product,category,units_sold,unit_price,discount,sales_rep
+2024-01-03,North,Widget A,Electronics,12,149.99,0.0,Alice
+2024-01-05,South,Gadget B,Electronics,8,89.99,0.05,Bob
+2024-01-07,East,Gizmo C,Hardware,25,34.99,0.0,Carol
+2024-01-10,West,Widget A,Electronics,5,149.99,0.10,Dave
+2024-01-12,North,Gadget B,Electronics,15,89.99,0.0,Alice
+2024-01-14,South,Gizmo C,Hardware,30,34.99,0.0,Bob
+2024-01-17,East,Widget A,Electronics,7,149.99,0.05,Carol
+2024-01-19,West,Doohickey D,Tools,20,19.99,0.0,Dave
+2024-01-21,North,Gizmo C,Hardware,18,34.99,0.0,Alice
+2024-01-24,South,Widget A,Electronics,10,149.99,0.0,Bob
+2024-02-01,North,Gadget B,Electronics,22,89.99,0.10,Alice
+2024-02-04,South,Doohickey D,Tools,35,19.99,0.0,Bob
+2024-02-06,East,Widget A,Electronics,9,149.99,0.0,Carol
+2024-02-08,West,Gizmo C,Hardware,14,34.99,0.05,Dave
+2024-02-11,North,Doohickey D,Tools,40,19.99,0.0,Alice
+2024-02-13,South,Widget A,Electronics,6,149.99,0.0,Bob
+2024-02-15,East,Gadget B,Electronics,11,89.99,0.0,Carol
+2024-02-18,West,Widget A,Electronics,4,149.99,0.10,Dave
+2024-02-20,North,Gizmo C,Hardware,28,34.99,0.0,Alice
+2024-02-22,South,Doohickey D,Tools,50,19.99,0.05,Bob
+2024-03-01,East,Widget A,Electronics,13,149.99,0.0,Carol
+2024-03-03,West,Gadget B,Electronics,17,89.99,0.0,Dave
+2024-03-05,North,Gizmo C,Hardware,22,34.99,0.0,Alice
+2024-03-07,South,Widget A,Electronics,8,149.99,0.05,Bob
+2024-03-10,East,Doohickey D,Tools,45,19.99,0.0,Carol
+2024-03-12,West,Gadget B,Electronics,19,89.99,0.0,Dave
+2024-03-14,North,Widget A,Electronics,11,149.99,0.0,Alice
+2024-03-17,South,Gizmo C,Hardware,32,34.99,0.10,Bob
+2024-03-19,East,Doohickey D,Tools,38,19.99,0.0,Carol
+2024-03-21,West,Widget A,Electronics,7,149.99,0.0,Dave
+2024-04-02,North,Gadget B,Electronics,24,89.99,0.0,Alice
+2024-04-04,South,Widget A,Electronics,14,149.99,0.05,Bob
+2024-04-06,East,Gizmo C,Hardware,29,34.99,0.0,Carol
+2024-04-09,West,Doohickey D,Tools,55,19.99,0.0,Dave
+2024-04-11,North,Widget A,Electronics,16,149.99,0.0,Alice
+2024-04-13,South,Gadget B,Electronics,20,89.99,0.10,Bob
+2024-04-16,East,Doohickey D,Tools,42,19.99,0.0,Carol
+2024-04-18,West,Gizmo C,Hardware,17,34.99,0.0,Dave
+2024-04-20,North,Widget A,Electronics,9,149.99,0.05,Alice
+2024-04-23,South,Gizmo C,Hardware,26,34.99,0.0,Bob
diff --git a/starter-code/r/src/analysis.R b/starter-code/r/src/analysis.R
new file mode 100644
index 0000000..02221ee
--- /dev/null
+++ b/starter-code/r/src/analysis.R
@@ -0,0 +1,57 @@
+# ============================================================
+# Sales Data Analysis
+#
+# This is the main entry point for the R data analysis exercises.
+#
+# GitHub Copilot Katas - R Version
+#
+# To run:
+#   Rscript src/analysis.R
+#
+# Exercises:
+#   1. Load and explore sales data
+#   2. Clean and transform the data
+#   3. Compute sales statistics by region and product
+#   4. Visualize trends (if ggplot2 is available)
+# ============================================================
+
+# Load required packages
+# Hint: use library() - let Copilot suggest which packages to load
+
+# ============================================================
+# Exercise 1: Load and Explore the Data
+# ============================================================
+
+# Load the CSV file from data/sample_sales.csv into a data frame called `sales`
+# Then let Copilot help you explore it (head, str, summary, nrow, ncol)
+
+# ============================================================
+# Exercise 2: Data Cleaning and Transformation
+# ============================================================
+
+# Source the data processing functions
+source("src/data_processor.R")
+
+# Call the cleaning function and store the result
+# Then verify that the date column is of type Date and
+# that the revenue column has been added
+
+# ============================================================
+# Exercise 3: Aggregation and Summary Statistics
+# ============================================================
+
+# Use dplyr (or base R) to:
+#   - Calculate total revenue by region
+#   - Calculate total revenue by product category
+#   - Identify the top-performing sales representative
+#   - Find the best-selling product by units sold
+
+# ============================================================
+# Exercise 4: Visualization (optional – requires ggplot2)
+# ============================================================
+
+# Source the visualisation functions
+source("src/visualizer.R")
+
+# Plot monthly revenue trend
+# Plot revenue breakdown by region
diff --git a/starter-code/r/src/data_processor.R b/starter-code/r/src/data_processor.R
new file mode 100644
index 0000000..7a5ac00
--- /dev/null
+++ b/starter-code/r/src/data_processor.R
@@ -0,0 +1,32 @@
+# ============================================================
+# Data Processor
+#
+# This file will contain functions for cleaning and
+# transforming the sales data.
+#
+# GitHub Copilot Katas - R Version
+#
+# Functions to implement (let Copilot help you!):
+#   - clean_sales_data(df)      : parse dates, add revenue column, remove NAs
+#   - add_revenue_column(df)    : calculate revenue = units_sold * unit_price * (1 - discount)
+#   - filter_by_region(df, region) : return rows matching the given region
+#   - summarise_by_category(df) : total revenue and units per category
+# ============================================================
+
+# ============================================================
+# Exercise 2: Implement the data processing functions below.
+# Start by writing a comment describing each function,
+# then let Copilot suggest the implementation.
+# ============================================================
+
+# Function to clean the raw sales data frame:
+#   - Convert the 'date' column to Date type
+#   - Add a 'revenue' column: units_sold * unit_price * (1 - discount)
+#   - Remove any rows that contain NA values
+
+# Function to add a revenue column to a sales data frame
+# Revenue = units_sold * unit_price * (1 - discount)
+
+# Function to filter sales data by a specific region (case-insensitive)
+
+# Function to summarise total revenue and total units sold, grouped by category
diff --git a/starter-code/r/src/visualizer.R b/starter-code/r/src/visualizer.R
new file mode 100644
index 0000000..2c5cfac
--- /dev/null
+++ b/starter-code/r/src/visualizer.R
@@ -0,0 +1,28 @@
+# ============================================================
+# Visualizer
+#
+# This file will contain functions for creating charts and
+# plots from the sales data using ggplot2.
+#
+# GitHub Copilot Katas - R Version
+#
+# Functions to implement (let Copilot help you!):
+#   - plot_monthly_revenue(df)   : line chart of total revenue per month
+#   - plot_revenue_by_region(df) : bar chart of revenue per region
+#   - plot_top_products(df, n)   : bar chart of the top n products by revenue
+# ============================================================
+
+# ============================================================
+# Exercise 4: Implement the visualisation functions below.
+# Write a comment describing the chart, then let Copilot
+# suggest the ggplot2 implementation.
+# ============================================================
+
+# Function to plot monthly revenue as a line chart
+# Hint: you may need to extract the month from the date column
+
+# Function to create a bar chart of total revenue by region,
+# with bars sorted from highest to lowest
+
+# Function to create a horizontal bar chart of the top n products
+# (default n = 5) ranked by total revenue