Nothing
inst/doc/completejourney.R
In completejourney: Retail Shopping Data
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "##",
message = FALSE,
warning = FALSE,
eval = FALSE
)
## ----load-pkg------------------------------------------------------------
# library(completejourney)
## ----load-pkg-hidden, echo=FALSE-----------------------------------------
# #devtools::load_all(path = "/Users/b294776/Desktop/Workspace/Packages/completejourney")
## ----load-transactions---------------------------------------------------
# # 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
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 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 <dbl>,
# ## # coupon_match_disc <dbl>, week <int>, transaction_timestamp <dttm>
## ----load-promotions-----------------------------------------------------
# # 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
# ## <chr> <chr> <fct> <fct> <int>
# ## 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
## ----load-both-----------------------------------------------------------
# # 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
## ----data-relationships, echo=FALSE, out.height="95%", out.width="95%", eval=TRUE----
knitr::include_graphics("data_relationships.png")
## ----example-transaction-data, echo=FALSE--------------------------------
# library(dplyr)
# library(lubridate)
#
# l1 <- transactions %>%
# filter(basket_id == "35730137393",
# product_id == "819063")
# l2 <- transactions %>%
# filter(basket_id == "31672240446",
# product_id == "819063")
# l3 <- transactions %>%
# filter(basket_id == "36027750817",
# product_id == "819063")
#
# bind_rows(l1, l2, l3) %>%
# select(product_id, quantity, sales_value, retail_disc, coupon_disc, coupon_match_disc)
# ## # A tibble: 3 x 6
# ## product_id quantity sales_value retail_disc coupon_disc coupon_match_disc
# ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# ## 1 819063 1 1.67 0 0 0
# ## 2 819063 2 3.34 0.36 0 0
# ## 3 819063 2 2.89 0 0.55 0.45
## ------------------------------------------------------------------------
# demographics %>%
# filter(household_id == "208")
# ## # A tibble: 1 x 8
# ## household_id age income home_ownership marital_status household_size household_comp
# ## <chr> <ord> <ord> <ord> <ord> <ord> <ord>
# ## 1 208 45-54 50-74K Homeowner NA 2 2 Adults No K…
# ## # … with 1 more variable: kids_count <ord>
## ------------------------------------------------------------------------
# campaigns %>%
# filter(household_id == "208")
# ## # A tibble: 7 x 2
# ## campaign_id household_id
# ## <chr> <chr>
# ## 1 13 208
# ## 2 17 208
# ## 3 18 208
# ## 4 22 208
# ## 5 26 208
# ## 6 27 208
# ## 7 8 208
## ------------------------------------------------------------------------
# campaigns %>%
# filter(household_id == "208") %>%
# left_join(., campaign_descriptions, by="campaign_id") %>%
# arrange(start_date)
# ## # A tibble: 7 x 5
# ## campaign_id household_id campaign_type start_date end_date
# ## <chr> <chr> <ord> <date> <date>
# ## 1 26 208 Type B 2016-12-28 2017-02-19
# ## 2 27 208 Type A 2017-02-08 2017-03-26
# ## 3 8 208 Type A 2017-05-08 2017-06-25
# ## 4 13 208 Type A 2017-08-08 2017-09-24
# ## 5 17 208 Type B 2017-10-18 2017-11-19
# ## 6 18 208 Type A 2017-10-30 2017-12-24
# ## 7 22 208 Type B 2017-12-06 2018-01-07
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18") %>%
# distinct(coupon_upc)
# ## # A tibble: 209 x 1
# ## coupon_upc
# ## <chr>
# ## 1 10000085475
# ## 2 10000085476
# ## 3 10000085477
# ## 4 10000085478
# ## 5 10000085479
# ## 6 10000085480
# ## 7 10000085484
# ## 8 10000089237
# ## 9 10000089238
# ## 10 10000089239
# ## # … with 199 more rows
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18",
# coupon_upc == "55410000076")
# ## # A tibble: 50 x 3
# ## coupon_upc product_id campaign_id
# ## <chr> <chr> <chr>
# ## 1 55410000076 1004458 18
# ## 2 55410000076 1011841 18
# ## 3 55410000076 1016495 18
# ## 4 55410000076 10182852 18
# ## 5 55410000076 1018696 18
# ## 6 55410000076 1058591 18
# ## 7 55410000076 1065032 18
# ## 8 55410000076 1069973 18
# ## 9 55410000076 107157 18
# ## 10 55410000076 1110721 18
# ## # … with 40 more rows
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18",
# coupon_upc == "55410000076") %>%
# left_join(., products, by="product_id") %>%
# select(product_id, manufacturer_id, department, brand,
# product_category, product_type, package_size)
# ## # A tibble: 50 x 7
# ## product_id manufacturer_id department brand product_category product_type
# ## <chr> <chr> <chr> <fct> <chr> <chr>
# ## 1 1004458 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 2 1011841 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 3 1016495 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLD VEG …
# ## 4 10182852 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 5 1018696 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 6 1058591 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 7 1065032 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 8 1069973 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 9 107157 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 10 1110721 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## # … with 40 more rows, and 1 more variable: package_size <chr>
## ------------------------------------------------------------------------
# coupon_redemptions %>%
# filter(household_id == "208")
# ## # A tibble: 5 x 4
# ## household_id coupon_upc campaign_id redemption_date
# ## <chr> <chr> <chr> <date>
# ## 1 208 55100090033 8 2017-05-23
# ## 2 208 51800015050 18 2017-11-09
# ## 3 208 51920021576 18 2017-11-09
# ## 4 208 55410000076 18 2017-11-13
# ## 5 208 10000085475 18 2017-11-18
## ------------------------------------------------------------------------
# transactions %>%
# filter(household_id == "208")
# ## # A tibble: 756 x 11
# ## household_id store_id basket_id product_id quantity sales_value retail_disc
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 1 208 327 31268866… 845379 1 7.64 0
# ## 2 208 327 31268866… 854133 1 4.69 0.5
# ## 3 208 327 31268866… 862349 1 1 0.99
# ## 4 208 327 31268866… 879504 1 2 1.19
# ## 5 208 327 31268866… 990519 1 1.69 0
# ## 6 208 327 31268866… 1068830 1 1.09 0
# ## 7 208 327 31268866… 1097635 1 2.96 0
# ## 8 208 324 31344175… 883932 1 2 0.59
# ## 9 208 324 31344175… 885290 1 1.99 0
# ## 10 208 324 31344175… 915502 2 4 2.78
# ## # … with 746 more rows, and 4 more variables: coupon_disc <dbl>,
# ## # coupon_match_disc <dbl>, week <int>, transaction_timestamp <dttm>
## ------------------------------------------------------------------------
# transactions %>%
# filter(household_id == "208",
# product_id == "896292",
# as_date(transaction_timestamp) == "2017-11-13")
# ## # A tibble: 1 x 11
# ## household_id store_id basket_id product_id quantity sales_value retail_disc
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 1 208 327 40715247… 896292 2 4 2.58
# ## # … with 4 more variables: coupon_disc <dbl>, coupon_match_disc <dbl>, week <int>,
# ## # transaction_timestamp <dttm>
## ------------------------------------------------------------------------
# promotions %>%
# filter(product_id == "896292",
# store_id == "327")
# ## # A tibble: 2 x 5
# ## product_id store_id display_location mailer_location week
# ## <chr> <chr> <fct> <fct> <int>
# ## 1 896292 327 A 0 47
# ## 2 896292 327 A 0 49
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## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "##",
message = FALSE,
warning = FALSE,
eval = FALSE
)
## ----load-pkg------------------------------------------------------------
# library(completejourney)
## ----load-pkg-hidden, echo=FALSE-----------------------------------------
# #devtools::load_all(path = "/Users/b294776/Desktop/Workspace/Packages/completejourney")
## ----load-transactions---------------------------------------------------
# # 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
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 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 <dbl>,
# ## # coupon_match_disc <dbl>, week <int>, transaction_timestamp <dttm>
## ----load-promotions-----------------------------------------------------
# # 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
# ## <chr> <chr> <fct> <fct> <int>
# ## 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
## ----load-both-----------------------------------------------------------
# # 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
## ----data-relationships, echo=FALSE, out.height="95%", out.width="95%", eval=TRUE----
knitr::include_graphics("data_relationships.png")
## ----example-transaction-data, echo=FALSE--------------------------------
# library(dplyr)
# library(lubridate)
#
# l1 <- transactions %>%
# filter(basket_id == "35730137393",
# product_id == "819063")
# l2 <- transactions %>%
# filter(basket_id == "31672240446",
# product_id == "819063")
# l3 <- transactions %>%
# filter(basket_id == "36027750817",
# product_id == "819063")
#
# bind_rows(l1, l2, l3) %>%
# select(product_id, quantity, sales_value, retail_disc, coupon_disc, coupon_match_disc)
# ## # A tibble: 3 x 6
# ## product_id quantity sales_value retail_disc coupon_disc coupon_match_disc
# ## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
# ## 1 819063 1 1.67 0 0 0
# ## 2 819063 2 3.34 0.36 0 0
# ## 3 819063 2 2.89 0 0.55 0.45
## ------------------------------------------------------------------------
# demographics %>%
# filter(household_id == "208")
# ## # A tibble: 1 x 8
# ## household_id age income home_ownership marital_status household_size household_comp
# ## <chr> <ord> <ord> <ord> <ord> <ord> <ord>
# ## 1 208 45-54 50-74K Homeowner NA 2 2 Adults No K…
# ## # … with 1 more variable: kids_count <ord>
## ------------------------------------------------------------------------
# campaigns %>%
# filter(household_id == "208")
# ## # A tibble: 7 x 2
# ## campaign_id household_id
# ## <chr> <chr>
# ## 1 13 208
# ## 2 17 208
# ## 3 18 208
# ## 4 22 208
# ## 5 26 208
# ## 6 27 208
# ## 7 8 208
## ------------------------------------------------------------------------
# campaigns %>%
# filter(household_id == "208") %>%
# left_join(., campaign_descriptions, by="campaign_id") %>%
# arrange(start_date)
# ## # A tibble: 7 x 5
# ## campaign_id household_id campaign_type start_date end_date
# ## <chr> <chr> <ord> <date> <date>
# ## 1 26 208 Type B 2016-12-28 2017-02-19
# ## 2 27 208 Type A 2017-02-08 2017-03-26
# ## 3 8 208 Type A 2017-05-08 2017-06-25
# ## 4 13 208 Type A 2017-08-08 2017-09-24
# ## 5 17 208 Type B 2017-10-18 2017-11-19
# ## 6 18 208 Type A 2017-10-30 2017-12-24
# ## 7 22 208 Type B 2017-12-06 2018-01-07
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18") %>%
# distinct(coupon_upc)
# ## # A tibble: 209 x 1
# ## coupon_upc
# ## <chr>
# ## 1 10000085475
# ## 2 10000085476
# ## 3 10000085477
# ## 4 10000085478
# ## 5 10000085479
# ## 6 10000085480
# ## 7 10000085484
# ## 8 10000089237
# ## 9 10000089238
# ## 10 10000089239
# ## # … with 199 more rows
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18",
# coupon_upc == "55410000076")
# ## # A tibble: 50 x 3
# ## coupon_upc product_id campaign_id
# ## <chr> <chr> <chr>
# ## 1 55410000076 1004458 18
# ## 2 55410000076 1011841 18
# ## 3 55410000076 1016495 18
# ## 4 55410000076 10182852 18
# ## 5 55410000076 1018696 18
# ## 6 55410000076 1058591 18
# ## 7 55410000076 1065032 18
# ## 8 55410000076 1069973 18
# ## 9 55410000076 107157 18
# ## 10 55410000076 1110721 18
# ## # … with 40 more rows
## ------------------------------------------------------------------------
# coupons %>%
# filter(campaign_id == "18",
# coupon_upc == "55410000076") %>%
# left_join(., products, by="product_id") %>%
# select(product_id, manufacturer_id, department, brand,
# product_category, product_type, package_size)
# ## # A tibble: 50 x 7
# ## product_id manufacturer_id department brand product_category product_type
# ## <chr> <chr> <chr> <fct> <chr> <chr>
# ## 1 1004458 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 2 1011841 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 3 1016495 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLD VEG …
# ## 4 10182852 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 5 1018696 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 6 1058591 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 7 1065032 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 8 1069973 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 9 107157 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## 10 1110721 1318 GROCERY Nati… PICKLE/RELISH/P… PICKLES
# ## # … with 40 more rows, and 1 more variable: package_size <chr>
## ------------------------------------------------------------------------
# coupon_redemptions %>%
# filter(household_id == "208")
# ## # A tibble: 5 x 4
# ## household_id coupon_upc campaign_id redemption_date
# ## <chr> <chr> <chr> <date>
# ## 1 208 55100090033 8 2017-05-23
# ## 2 208 51800015050 18 2017-11-09
# ## 3 208 51920021576 18 2017-11-09
# ## 4 208 55410000076 18 2017-11-13
# ## 5 208 10000085475 18 2017-11-18
## ------------------------------------------------------------------------
# transactions %>%
# filter(household_id == "208")
# ## # A tibble: 756 x 11
# ## household_id store_id basket_id product_id quantity sales_value retail_disc
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 1 208 327 31268866… 845379 1 7.64 0
# ## 2 208 327 31268866… 854133 1 4.69 0.5
# ## 3 208 327 31268866… 862349 1 1 0.99
# ## 4 208 327 31268866… 879504 1 2 1.19
# ## 5 208 327 31268866… 990519 1 1.69 0
# ## 6 208 327 31268866… 1068830 1 1.09 0
# ## 7 208 327 31268866… 1097635 1 2.96 0
# ## 8 208 324 31344175… 883932 1 2 0.59
# ## 9 208 324 31344175… 885290 1 1.99 0
# ## 10 208 324 31344175… 915502 2 4 2.78
# ## # … with 746 more rows, and 4 more variables: coupon_disc <dbl>,
# ## # coupon_match_disc <dbl>, week <int>, transaction_timestamp <dttm>
## ------------------------------------------------------------------------
# transactions %>%
# filter(household_id == "208",
# product_id == "896292",
# as_date(transaction_timestamp) == "2017-11-13")
# ## # A tibble: 1 x 11
# ## household_id store_id basket_id product_id quantity sales_value retail_disc
# ## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
# ## 1 208 327 40715247… 896292 2 4 2.58
# ## # … with 4 more variables: coupon_disc <dbl>, coupon_match_disc <dbl>, week <int>,
# ## # transaction_timestamp <dttm>
## ------------------------------------------------------------------------
# promotions %>%
# filter(product_id == "896292",
# store_id == "327")
# ## # A tibble: 2 x 5
# ## product_id store_id display_location mailer_location week
# ## <chr> <chr> <fct> <fct> <int>
# ## 1 896292 327 A 0 47
# ## 2 896292 327 A 0 49
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