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Dataset of the 'Contoso' Company
A collection of synthetic datasets simulating sales transactions from a fictional company. The dataset includes various related tables that contain essential business and operational data, useful for analyzing sales performance and other business insights. Key tables included in the package are: - "sales": Contains data on individual sales transactions, including order details, pricing, quantities, and customer information. - "customer": Stores customer-specific details such as demographics, geographic location, occupation, and birthday. - "store": Provides information about stores, including location, size, status, and operational dates. - "orders": Contains details about customer orders, including order and delivery dates, store, and customer data. - "product": Contains data on products, including attributes such as product name, category, price, cost, and weight. - "calendar": A time-based table that includes date-related attributes like year, month, quarter, day, and working day indicators. This dataset is ideal for practicing data analysis, performing time-series analysis, creating reports, or simulating business intelligence scenarios.
Bounds for Accounting Populations
Find an upper bound for the total amount of overstatement
of assets in a set of accounts, or estimate the amount of sales tax
owed on a collection of transactions (Meeden and Sargent, 2007,
Walmart Open API Wrapper
Provides API access to the Walmart Open API < https://developer.walmartlabs.com/>, that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies.
Tidy Pipelines for the 'Sensor Tower' API
Retrieves mobile app intelligence from the 'Sensor Tower' API < https://app.sensortower.com/api/docs/app_analysis>. Composes discovery, metadata, rankings, sales, audience and specialist estimates through ordinary data frames with explicit identifiers, units and error handling.
U.S. Housing Data from 2008 to 2016
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016. Additional data sets containing geographical information and links to Wikipedia are also included.
Make Tidy Bins
Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.
Compute Recency Frequency Monetary Scores for your Customer Data
This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.
Analysis & Measurement of Promotion Effectiveness
Analysis and measurement of promotion effectiveness on a given target variable (e.g. daily sales). After converting promotion schedule into dummy or smoothed predictor variables, the package estimates the effects of these variables controlled for trend/periodicity/structural change using prophet by Taylor and Letham (2017)
'M5 Forecasting' Challenges Data
Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle').
The data itself is set of time series of different product sales in 'Walmart'.
The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'.
For detailed information about the challenges, see:
Makridakis, S. & Spiliotis, E. & Assimakopoulos, V. (2020).
Download Data from the 'Office for National Statistics'
Provides functions to download and tidy statistical data published by the 'Office for National Statistics' < https://www.ons.gov.uk>. Covers GDP, inflation (CPI, CPIH, RPI), unemployment, employment, wages, trade, retail sales, house prices, productivity, population, and public sector finances. Most series are fetched from the 'ONS' website using its CSV time series endpoint. House price data is sourced from 'HM Land Registry' < https://www.gov.uk/government/organisations/land-registry>. Data is cached locally between sessions.