{"id":14068654,"url":"https://github.com/bradleyboehmke/completejourney","last_synced_at":"2025-04-13T12:28:54.253Z","repository":{"id":56936696,"uuid":"152166715","full_name":"bradleyboehmke/completejourney","owner":"bradleyboehmke","description":"An R data 📦 of retail shopping transactions for 2469 households over one year","archived":false,"fork":false,"pushed_at":"2020-01-16T14:19:45.000Z","size":228621,"stargazers_count":19,"open_issues_count":2,"forks_count":11,"subscribers_count":8,"default_branch":"master","last_synced_at":"2024-05-01T14:30:05.860Z","etag":null,"topics":["data","dataset","r","r-package"],"latest_commit_sha":null,"homepage":"http://bit.ly/completejourney","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bradleyboehmke.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-10-09T01:03:00.000Z","updated_at":"2024-04-08T10:01:06.000Z","dependencies_parsed_at":"2022-08-21T05:50:45.451Z","dependency_job_id":null,"html_url":"https://github.com/bradleyboehmke/completejourney","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bradleyboehmke%2Fcompletejourney","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bradleyboehmke%2Fcompletejourney/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bradleyboehmke%2Fcompletejourney/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bradleyboehmke%2Fcompletejourney/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bradleyboehmke","download_url":"https://codeload.github.com/bradleyboehmke/completejourney/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248713742,"owners_count":21149761,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data","dataset","r","r-package"],"created_at":"2024-08-13T07:06:19.725Z","updated_at":"2025-04-13T12:28:54.230Z","avatar_url":"https://github.com/bradleyboehmke.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  message = FALSE,\n  warning = FALSE,\n  comment = \"##\",\n  fig.path = \"README-\",\n  eval = FALSE\n)\n```\n\n# completejourney\n\n\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/completejourney)](https://CRAN.R-project.org/package=completejourney)\n[![lifecycle](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://www.tidyverse.org/lifecycle/#stable)\n[![Travis-CI Build Status](https://travis-ci.org/bradleyboehmke/completejourney.svg?branch=master)](https://travis-ci.org/bradleyboehmke/completejourney)\n[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/bradleyboehmke/completejourney?branch=master\u0026svg=true)](https://ci.appveyor.com/project/bradleyboehmke/completejourney)\n\u003c!-- badges: end --\u003e\n\n## Overview\n\nAn R data package that provides access to data in the Complete Journey Study \nprovided by [84.51°](http://www.8451.com). The data represents grocery store \nshopping transactions over one year from a group of 2,469 households who are \nfrequent shoppers at a retailer. It contains all of each household's purchases, \nnot just those from a limited number of categories. For certain households, \ndemographic information as well as direct marketing contact history are included. \n\n* `campaigns`: campaigns received by each household\n* `campaign_descriptions`: campaign metadata (length of time active)\n* `coupons`: coupon metadata (UPC code, campaign, etc.)\n* `coupon_redemptions`: coupon redemptions (household, day, UPC code, campaign)\n* `demographics`: household demographic data (age, income, family size, etc.)\n* `products`: product metadata (brand, description, etc.)\n* `promotions_sample`: a sampling of the product placement in mailers and in stores corresponding to advertising campaigns\n* `transactions_sample`: a sampling of the products purchased by households\n\n## Installation\n\n```{r, eval=FALSE}\ninstall.packages(\"completejourney\")\n```\n\nTo get a bug fix, or use a feature from the development version, you can install `completejourney` from GitHub with:\n\n```{r gh-installation, eval=FALSE}\n# install.packages(\"remotes\")\nremotes::install_github(\"bradleyboehmke/completejourney\")\n```\n\n\n## Downloading full data sets\n\nDue to the size of the transactions and promotions data, the package provides a\nsampling of the data built-in with `transactions_sample` and `promotions_sample`. \nHowever, you can access the full promotions and transactions data sets from the \nsource GitHub repository with the following: \n\n```{r load-transactions, eval=FALSE}\nlibrary(completejourney)\n\n# get the full transactions data set\ntransactions \u003c- get_transactions()\ntransactions\n## # A tibble: 1,469,307 x 11\n##    household_id store_id basket_id product_id quantity sales_value retail_disc\n##    \u003cchr\u003e        \u003cchr\u003e    \u003cchr\u003e     \u003cchr\u003e         \u003cdbl\u003e       \u003cdbl\u003e       \u003cdbl\u003e\n##  1 900          330      31198570… 1095275           1        0.5        0    \n##  2 900          330      31198570… 9878513           1        0.99       0.1  \n##  3 1228         406      31198655… 1041453           1        1.43       0.15 \n##  4 906          319      31198705… 1020156           1        1.5        0.290\n##  5 906          319      31198705… 1053875           2        2.78       0.8  \n##  6 906          319      31198705… 1060312           1        5.49       0.5  \n##  7 906          319      31198705… 1075313           1        1.5        0.290\n##  8 1058         381      31198676… 985893            1        1.88       0.21 \n##  9 1058         381      31198676… 988791            1        1.5        1.29 \n## 10 1058         381      31198676… 9297106           1        2.69       0    \n## # … with 1,469,297 more rows, and 4 more variables: coupon_disc \u003cdbl\u003e,\n## #   coupon_match_disc \u003cdbl\u003e, week \u003cint\u003e, transaction_timestamp \u003cdttm\u003e\n```\n\n```{r load-promotions, eval=FALSE}\n# get the full promotions data set\npromotions \u003c- get_promotions()\npromotions\n## # A tibble: 20,940,529 x 5\n##    product_id store_id display_location mailer_location  week\n##    \u003cchr\u003e      \u003cchr\u003e    \u003cfct\u003e            \u003cfct\u003e           \u003cint\u003e\n##  1 1000050    316      9                0                   1\n##  2 1000050    337      3                0                   1\n##  3 1000050    441      5                0                   1\n##  4 1000092    292      0                A                   1\n##  5 1000092    293      0                A                   1\n##  6 1000092    295      0                A                   1\n##  7 1000092    298      0                A                   1\n##  8 1000092    299      0                A                   1\n##  9 1000092    304      0                A                   1\n## 10 1000092    306      0                A                   1\n## # … with 20,940,519 more rows\n```\n\n```{r load-both, eval=FALSE}\n# a convenience function to get both\nc(promotions, transactions) %\u003c-% get_data(which = 'both', verbose = FALSE)\ndim(promotions)\n## [1] 20940529        5\n\ndim(transactions)\n## [1] 1469307      11\n```\n\n## Learn more\n\nLearn more about the completejourney data, and the type of insights you can look for, at [http://bit.ly/completejourney](https://bradleyboehmke.github.io/completejourney/).\n\n## Source\n\nThe Complete Journey data is available at: http://www.8451.com/area51/.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbradleyboehmke%2Fcompletejourney","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbradleyboehmke%2Fcompletejourney","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbradleyboehmke%2Fcompletejourney/lists"}