Title:	Retail Shopping Data
Version:	1.1.0
Description:	Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files https://www.8451.com/area51 which also includes useful metadata on products, coupons, campaigns, and promotions.
License:	CC0
LazyData:	true
Depends:	R (≥ 2.10)
Imports:	curl, dplyr, tibble, progress, stringr, zeallot
Suggests:	lubridate, knitr, rmarkdown, testthat
URL:	https://github.com/bradleyboehmke/completejourney
BugReports:	https://github.com/bradleyboehmke/completejourney/issues
RoxygenNote:	6.1.1
Encoding:	UTF-8
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2019-09-28 18:16:00 UTC; b294776
Author:	Brad Boehmke [aut, cre], Steven M. Mortimer [aut]
Maintainer:	Brad Boehmke <bradleyboehmke@gmail.com>
Repository:	CRAN
Date/Publication:	2019-09-28 18:30:02 UTC

completejourney package

Description

Retail shopping transactions for 2,469 households over one year

Details

Learn more here: GitHub

Author(s)

Maintainer: Brad Boehmke bradleyboehmke@gmail.com (0000-0002-3611-8516)

Authors:

• Steven M. Mortimer reportmort@gmail.com

See Also

Useful links:

• https://github.com/bradleyboehmke/completejourney

• Report bugs at https://github.com/bradleyboehmke/completejourney/issues

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Assign values to names

Description

See %<-% for more details.

Usage

x %<-% value

Arguments

Campaign metadata.

Description

Campaign metadata for all campaigns run for the Customer Journey study. This dataset gives the length of time for which a campaign runs. So, any coupons received as part of a campaign are valid within the dates contained in this dataset.

Usage

campaign_descriptions

Format

A data frame with 27 rows and 4 variables

• campaign_id: Uniquely identifies each campaign; Ranges 1-27

• campaign_type: Type of campaign (Type A, Type B, Type C)

• start_date: Start date of campaign

• end_date: End date of campaign

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
campaign_descriptions

# Join product campaign metadata to campaign_table dataset
require("dplyr")
campaigns %>%
  left_join(campaign_descriptions, "campaign_id")

Campaigns to household data.

Description

Data on the campaigns received by each household in the Complete Journey study. Each household received a different set of marketing campaigns.

Usage

campaigns

Format

A data frame with 6,589 rows and 2 variables

• campaign_id: Uniquely identifies each campaign; Ranges 1-27

• household_id: Uniquely identifies each household

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
campaigns

# Join household demographics metadata to campaigns dataset
require("dplyr")
campaigns %>%
  left_join(demographics, "household_id")

Coupon redemption data.

Description

Coupon data identifying the coupons that each household redeemed in the Complete Journey study.

Usage

coupon_redemptions

Format

A data frame with 2,102 rows and 4 variables

• household_id: Uniquely identifies each household

• coupon_upc: Uniquely identifies each coupon (unique to household and campaign)

• campaign_id: Uniquely identifies each campaign

• redemption_date: Date when the coupon was redeemed

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
coupon_redemptions

# Join coupon metadata to coupon_redempt dataset
require("dplyr")
coupon_redemptions %>%
  left_join(coupons, "coupon_upc")

Coupon metadata.

Description

Coupon metadata for all coupons used in campaigns advertised to households participating in the Customer Journey study.

Usage

coupons

Format

A data frame with 116,204 rows and 3 variables

• coupon_upc: Uniquely identifies each coupon (unique to household and campaign)

• product_id: Uniquely identifies each product

• campaign_id: Uniquely identifies each campaign

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
coupons

# Join product metadata to coupon dataset
require("dplyr")
coupons %>%
  left_join(products, "product_id")

Household demographic metadata.

Description

Household demographic metadata for households participating in the Customer Journey study. Due to nature of the data, the demographic information is not available for all households.

Usage

demographics

Format

A data frame with 801 rows and 8 variables

• household_id: Uniquely identifies each household

• age: Estimated age range

• income: Household income range

• home_ownership: Homeowner status (Homeowner, Renter, Unknown)

• marital_status: Marital status (Married, Single, Unknown)

• household_size: Size of household up to 5+

• household_comp: Household composition description

• kids_count: Number of children present up to 3+

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
demographics

# Transaction line items that don't have household metadata
require("dplyr")
transactions_sample %>%
  anti_join(demographics, "household_id")

Download full promotions and transactions data simultaneously.

Description

The promotions and transactions data sets are too large to be contained within the package. get_data() is a convenience function to download both full promotions and transactions data sets simultaneously from the source GitHub repository. An internet connection is required.

Usage

get_data(which = "both", verbose = TRUE)

Arguments

Value

Downloading a single data set will result in a tibble whereas downloading multiple data sets will return a list containing each tibble. For specific details on a given data set see the data sets respective help file (i.e. ?transactions_sample).

Source

Downloading from https://github.com/bradleyboehmke/completejourney/tree/master/data. Data originated from 84.51°, Customer Journey study, http://www.8451.com/area51/ and were processes for analysis.

See Also

Use %<-% for unpacking a list with multiple tibbles to their own global environment tibble. You can also download a single data set with get_promotions and get_transactions.

Examples

# download transactions and promotions data sets
# requires internet connection
c(promotions, transactions) %<-% get_data(which = 'both')

Get full Complete Journey promotions data set.

Description

The complete promotions data set for the Complete Journey is too large to be contained within the package. get_promotions() provides an efficient method for downloading the full data set from the source GitHub repository.

Usage

get_promotions(verbose = FALSE)

Arguments

Value

A data frame with 20,940,529 rows and 5 variables

Source

Downloading from https://github.com/bradleyboehmke/completejourney/tree/master/data. Data originated from 84.51°, Customer Journey study, http://www.8451.com/area51/ and were processes for analysis.

See Also

promotions_sample for details regarding the variables.

Examples

# requires internet connection
promotions <- get_promotions()

Get full Complete Journey transactions data set.

Description

The complete transactions data set for the Complete Journey is too large to be contained within the package. get_transactions() provides an efficient method for downloading the full data set from the source GitHub repository.

Usage

get_transactions(verbose = FALSE)

Arguments

Value

A data frame with 1,469,307 rows and 5 variables

Source

Downloading from https://github.com/bradleyboehmke/completejourney/tree/master/data. Data originated from 84.51°, Customer Journey study, http://www.8451.com/area51/ and were processes for analysis.

See Also

transactions_sample for details regarding the variables.

Examples

# requires internet connection
transactions <- get_transactions()

Product metadata.

Description

Product metadata for all products purchased by households participating in the Customer Journey study.

Usage

products

Format

A data frame with 92,331 rows and 7 variables

• product_id: Uniquely identifies each product

• manufacturer_id: Uniquely identifies each manufacturer

• department: Groups similar products together

• brand: Indicates Private or National label brand

• product_category: Groups similar products together at lower level

• product_type: Groups similar products together at lowest level

• package_size: Indicates package size (not available for all products)

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

Examples

# full data set
products

# Transaction line items that don't have product metadata
require("dplyr")
transactions_sample %>%
  anti_join(products, "product_id")

Sampling of the full promotions data set.

Description

A sampling of the promotions data from the Complete Journey study signifying whether a given product was featured in the weekly mailer or was part of an in-store display (other than regular product placement).

Usage

promotions_sample

Format

A data frame with 360,535 rows and 5 variables

• product_id: Uniquely identifies each product

• store_id: Uniquely identifies each store

• display_location: Display location (see details for range of values)

• mailer_location: Mailer location (see details for range of values)

• week: Week of the transaction; Ranges 1-53

Value

Display Location Codes

• 0 - Not on Display

• 1 - Store Front

• 2 - Store Rear

• 3 - Front End Cap

• 4 - Mid-Aisle End Cap

• 5 - Rear End Cap

• 6 - Side-Aisle End Cap

• 7 - In-Aisle

• 9 - Secondary Location Display

• A - In-Shelf

Mailer Location Codes

• 0 - Not on ad

• A - Interior page feature

• C - Interior page line item

• D - Front page feature

• F - Back page feature

• H - Wrap from feature

• J - Wrap interior coupon

• L - Wrap back feature

• P - Interior page coupon

• X - Free on interior page

• Z - Free on front page, back page or wrap

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

See Also

Use get_promotions to download the entire promotions data containing all 20,940,529 rows.

Examples

# sampled promotions data set
promotions_sample

# Join promotions to transactions to analyze
# product promotion/location
require("dplyr")
transactions_sample %>%
  left_join(promotions_sample,
            c("product_id", "store_id", "week"))

Sampling of the full Complete Journey transactions.

Description

A sampling of all products purchased by households within the Complete Journey study. Each line found in this table is essentially the same line that would be found on a store receipt. This is only a subsample of the complete data set to keep package size manageable.

Usage

transactions_sample

Format

A data frame with 75,000 rows and 11 variables

household_id

Uniquely identifies each household

store_id

Uniquely identifies each store

basket_id

Uniquely identifies a purchase occasion

product_id

Uniquely identifies each product

quantity

Number of the products purchased during the trip

sales_value

Amount of dollars retailer receives from sale

retail_disc

Discount applied due to retailer's loyalty card program

coupon_disc

Discount applied due to manufacturer coupon

coupon_match_disc

Discount applied due to retailer's match of manufacturer coupon

week

Week of the transaction; Ranges 1-53

transaction_timestamp

Date and time of when the transaction occurred

Value

Source

84.51°, Customer Journey study, http://www.8451.com/area51/

See Also

Use get_transactions to download the entire transactions data containing all 1,469,307 rows.

Examples

transactions_sample