Demand classification

library(m5)
library(zeallot)
library(ggplot2)

m5_download('data')

c(sales_train,
  sales_test,
  sell_prices,
  calendar,
  weights) %<-% m5_get_raw_evaluation('data')

m5_data  <-
    m5_prepare(sales_train, sales_test, calendar, sell_prices)
head(m5_data)
#>          item_id   dept_id  cat_id store_id state_id d value wm_yr_wk  weekday
#> 1: HOBBIES_1_001 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#> 2: HOBBIES_1_002 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#> 3: HOBBIES_1_003 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#> 4: HOBBIES_1_004 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#> 5: HOBBIES_1_005 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#> 6: HOBBIES_1_006 HOBBIES_1 HOBBIES     CA_1       CA 1     0    11101 Saturday
#>    wday month year event_name_1 event_type_1 event_name_2 event_type_2 snap
#> 1:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#> 2:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#> 3:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#> 4:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#> 5:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#> 6:    1     1 2011         <NA>         <NA>         <NA>         <NA>    0
#>    sell_price
#> 1:         NA
#> 2:         NA
#> 3:         NA
#> 4:         NA
#> 5:         NA
#> 6:         NA

m5_demand <- m5_demand_type(m5_data)

foods_demand <- 
  m5_demand[startsWith(as.character(m5_demand$item_id), "FOODS_1")]

plot <-
  ggplot(foods_demand) +
  geom_point(aes(log(cv2), log(adi),
                 item_id = item_id, col = demand_type)) +
  geom_hline(yintercept = log(1.32)) +
  geom_vline(xintercept = log(0.49)) +
  theme_minimal()

plot