dataCOICOP

A collection of artificial scanner data on the sale of tomatoes, fruit juices, low fat milk, full fat milk, sugar, chocolate, yoghurt, coffee, eggs and salt in the period from December 2018 to October 2020

datasets

Preparing a scanner data set for price dynamics calculations (data selecting, data classification, data matching, data filtering). Computing bilateral and multilateral indexes. For details on these methods see: Diewert and Fox (2020)
<doi:10.1080/07350015.2020.1816176>, Białek (2019) <doi:10.2478/jos-2019-0014> or Białek (2020) <doi:10.2478/jos-2020-0037>.

Jacek Bia<c5><82>ek

PriceIndices

Calculating Bilateral and Multilateral Price Indexes

dataCOICOP function

A data frame with 8 columns and 96600 rows (some rows are not complete). The used variables are as follows:
<code>time</code> - Dates of transactions (Year-Month-Day)
<code>prices</code> - Prices of sold products [EUR]
<code>quantities</code> - Quantities of sold products [unit defined in the 'unit' column]
<code>prodID</code> - Retailer product codes
<code>retID</code> - Unique codes identifying outlets/retailer sale points (10 retIDs)
<code>description</code> Descriptions of sold products
<code>unit</code> Sales units, e.g.: kg, ml, etc.
<code>coicop</code> Identifiers of COICOP groups (10 groups)

dataCOICOP: An artificial scanner data set for the product classification

Description

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