## ----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