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https://github.com/bradleyboehmke/completejourney

An R data 📦 of retail shopping transactions for 2469 households over one year
https://github.com/bradleyboehmke/completejourney

data dataset r r-package

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An R data 📦 of retail shopping transactions for 2469 households over one year

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  message = FALSE,

  warning = FALSE,

  comment = "##",

  fig.path = "README-",

  eval = FALSE

)

```



# completejourney



[![CRAN status](https://www.r-pkg.org/badges/version/completejourney)](https://CRAN.R-project.org/package=completejourney)

[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)

[![Travis-CI Build Status](https://travis-ci.org/bradleyboehmke/completejourney.svg?branch=master)](https://travis-ci.org/bradleyboehmke/completejourney)

[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/bradleyboehmke/completejourney?branch=master&svg=true)](https://ci.appveyor.com/project/bradleyboehmke/completejourney)



## Overview



An R data package that provides access to data in the Complete Journey Study 

provided by [84.51°](http://www.8451.com). The data represents grocery store 

shopping transactions over one year from a group of 2,469 households who are 

frequent shoppers at a retailer. It contains all of each household's purchases, 

not just those from a limited number of categories. For certain households, 

demographic information as well as direct marketing contact history are included. 



* `campaigns`: campaigns received by each household

* `campaign_descriptions`: campaign metadata (length of time active)

* `coupons`: coupon metadata (UPC code, campaign, etc.)

* `coupon_redemptions`: coupon redemptions (household, day, UPC code, campaign)

* `demographics`: household demographic data (age, income, family size, etc.)

* `products`: product metadata (brand, description, etc.)

* `promotions_sample`: a sampling of the product placement in mailers and in stores corresponding to advertising campaigns

* `transactions_sample`: a sampling of the products purchased by households



## Installation



```{r, eval=FALSE}

install.packages("completejourney")

```



To get a bug fix, or use a feature from the development version, you can install `completejourney` from GitHub with:



```{r gh-installation, eval=FALSE}

# install.packages("remotes")

remotes::install_github("bradleyboehmke/completejourney")

```



## Downloading full data sets



Due to the size of the transactions and promotions data, the package provides a

sampling of the data built-in with `transactions_sample` and `promotions_sample`. 

However, you can access the full promotions and transactions data sets from the 

source GitHub repository with the following: 



```{r load-transactions, eval=FALSE}

library(completejourney)



# get the full transactions data set

transactions <- get_transactions()

transactions

## # A tibble: 1,469,307 x 11

##    household_id store_id basket_id product_id quantity sales_value retail_disc

##                                            

##  1 900          330      31198570… 1095275           1        0.5        0    

##  2 900          330      31198570… 9878513           1        0.99       0.1  

##  3 1228         406      31198655… 1041453           1        1.43       0.15 

##  4 906          319      31198705… 1020156           1        1.5        0.290

##  5 906          319      31198705… 1053875           2        2.78       0.8  

##  6 906          319      31198705… 1060312           1        5.49       0.5  

##  7 906          319      31198705… 1075313           1        1.5        0.290

##  8 1058         381      31198676… 985893            1        1.88       0.21 

##  9 1058         381      31198676… 988791            1        1.5        1.29 

## 10 1058         381      31198676… 9297106           1        2.69       0    

## # … with 1,469,297 more rows, and 4 more variables: coupon_disc ,

## #   coupon_match_disc , week , transaction_timestamp 

```



```{r load-promotions, eval=FALSE}

# get the full promotions data set

promotions <- get_promotions()

promotions

## # A tibble: 20,940,529 x 5

##    product_id store_id display_location mailer_location  week

##                                     

##  1 1000050    316      9                0                   1

##  2 1000050    337      3                0                   1

##  3 1000050    441      5                0                   1

##  4 1000092    292      0                A                   1

##  5 1000092    293      0                A                   1

##  6 1000092    295      0                A                   1

##  7 1000092    298      0                A                   1

##  8 1000092    299      0                A                   1

##  9 1000092    304      0                A                   1

## 10 1000092    306      0                A                   1

## # … with 20,940,519 more rows

```



```{r load-both, eval=FALSE}

# a convenience function to get both

c(promotions, transactions) %<-% get_data(which = 'both', verbose = FALSE)

dim(promotions)

## [1] 20940529        5



dim(transactions)

## [1] 1469307      11

```



## Learn more



Learn more about the completejourney data, and the type of insights you can look for, at [http://bit.ly/completejourney](https://bradleyboehmke.github.io/completejourney/).



## Source



The Complete Journey data is available at: http://www.8451.com/area51/.