Function reference

All functions and classes exported by contoso.

Functions

Function Description
calendar() Calendar Dimension Data from the Contonso Dataset
create_contoso_duckdb() Creates DuckDB database with Contoso datasets
customer() Customer Data from the Contonso Dataset
fx() Foreign Exchange Data from the Contonso Dataset
launch_ui() Launch the DuckDB UI in your browser
orderrows() Order Rows Data from the Contonso Dataset
orders() Order Data from the Contonso Dataset
product() Product Data from the Contonso Dataset
sales() Sales Data from the Contonso Dataset
store() Store Data from the Contonso Dataset

Function Details

calendar()

Calendar Dimension Data from the Contonso Dataset

Usage

calendar

Description

This dataset contains calendar-related information used for time-based analysis in the Contonso dataset. It includes various representations of date-related attributes, such as year, quarter, month, and day, along with indicators for working days. It is useful for time-series analysis and aggregating data by different time periods.

create_contoso_duckdb()

Creates DuckDB database with Contoso datasets

Usage

create_contoso_duckdb(size = "small")

Arguments

Argument Description
size Dataset size: “small”, “medium”, “large”, or “mega”

Value

A list containing:

sales, product, customer, store, fx, calendar, orders, orderrows: lazy tbl objects con: the DuckDB connection (use DBI::dbDisconnect(db$con, shutdown = TRUE) when done)

Description

Creates a DuckDB connection with Contoso datasets loaded from cloud storage. The datasets are stored as Parquet files on Backblaze B2 and streamed directly into DuckDB.

Details

The create_contoso_duckdb() function creates views for the following Contoso datasets:

sales: Contains sales transaction data. product: Contains details about products, including attributes like product name, manufacturer, and category. customer: Contains customer demographic and geographic information. store: Contains information about store locations and attributes. fx: Contains foreign exchange rate data for currency conversion. calendar: Contains various date-related information, including day, week, month, and year. orders: Contains order header information. orderrows: Contains order line items.

Available sizes (approximate sales rows):

small: ~8,000 rows medium: ~2.3 million rows large: ~47 million rows mega: ~237 million rows

Examples

db <- create_contoso_duckdb(size = "small")
  db$sales |> head()
  DBI::dbDisconnect(db$con, shutdown = TRUE)

customer()

Customer Data from the Contonso Dataset

Usage

customer

Description

This dataset contains information about customers from the Contonso dataset, including demographic details, geographical information, contact information, and other personal attributes. It provides insights into customer profiles, including location, age, occupation, and more.

fx()

Foreign Exchange Data from the Contonso Dataset

Usage

fx

Description

This dataset contains information about foreign exchange (FX) rates between different currencies. It includes details about the exchange rate for a given date, as well as the currencies involved. This dataset is useful for analyzing currency conversions and understanding the exchange rates between different currencies over time.

launch_ui()

Launch the DuckDB UI in your browser

Usage

launch_ui(.con)

Arguments

Argument Description
.con A valid DBIConnection object connected to a DuckDB database. The function will check that the connection is valid before proceeding.

Value

The function is called for its side effects and does not return a value. It launches the DuckDB UI and opens it in your default web browser.

Description

The launch_ui() function installs and launches the DuckDB UI extension for an active DuckDB database connection. This allows users to interact with the database via a web-based graphical interface.

Your connection from create_contoso_duckdb() is returned in the list.

Details

The function performs the following steps:

Checks that the provided DuckDB connection is valid. If the connection is invalid, it aborts with a descriptive error message. Installs the ui extension into the connected DuckDB instance. Calls the start_ui() procedure to launch the DuckDB UI in your browser.

This provides a convenient way to explore and manage DuckDB databases interactively without needing to leave the R environment.

Examples

# Connect to DuckDB
db <- create_contoso_duckdb()

# Launch the DuckDB UI
launch_ui(db$con)

# Clean up
DBI::dbDisconnect(db$con, shutdown = TRUE)

orderrows()

Order Rows Data from the Contonso Dataset

Usage

orderrows

Description

This dataset contains detailed information about the individual items (rows) within each order in the Contonso dataset. It includes details such as the product, quantity, pricing, and cost of each item in an order. This dataset is useful for analyzing the breakdown of order components and individual product sales.

orders()

Order Data from the Contonso Dataset

Usage

orders

Description

This dataset contains information about customer orders, including order dates, delivery dates, and store details.

product()

Product Data from the Contonso Dataset

Usage

product

Description

This dataset contains information about products in the Contonso dataset. It includes product details such as identifiers, descriptions, pricing, weight, and categorization. This dataset is useful for analyzing product characteristics, pricing, and product-related sales insights.

sales()

Sales Data from the Contonso Dataset

Usage

sales

Description

This dataset contains information about sales orders, including order details, pricing, and customer data from the Contonso dataset. It provides insights into the transactions that have occurred, including order dates, delivery dates, customer and store information, as well as product details.

store()

Store Data from the Contonso Dataset

Usage

store

Description

This dataset contains information about stores within the Contonso dataset. It includes details about the store’s geographic location, operational status, and physical characteristics such as size and opening/closing dates. It provides insights into the store network of the company.