How to group sales data using R? - r

Question

I am trying to find a way to match new products with the products those I have historical data. Then I will use historical data from the preview years' products to make some prediction for the new products.
Please consider the following subset of the data:
# A tibble: 13 x 11
prdct_id prdct_grp_1 prdct_grp_2 prdct_grp_3 prdct_grp_4 Start_season January February March April sales_total
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.00 WUW SW BH B21 2017 2.00 10.0 5.00 4.00 21.0
2 2.00 WUW SW BK R21 2017 7.00 9.00 4.00 5.00 25.0
3 3.00 MUW NW UW P1 2018 6.00 8.00 10.0 6.00 32.0
4 4.00 LNG KW LW L1 2016 8.00 9.00 12.0 7.00 36.0
5 5.00 QKQ MZ KA AQ 2013 10.0 8.67 16.7 8.00 43.3
6 6.00 MUW NW UW P1 2019 0 0 0 0 0
7 7.00 WUW SW BK R21 2019 0 0 0 0 0
8 8.00 LNG NW UW P2 2014 15.1 8.67 28.7 11.0 63.4
9 9.00 QKQ KW LW L2 2016 16.8 8.67 32.7 12.0 70.1
10 10.0 WUW MZ KA AQ 2017 18.5 8.67 36.7 13.0 76.8
11 11.0 QKQ MZ KA AQ 2019 0 0 0 0 0
12 12.0 WUW MZ KA AQ 2019 0 0 0 0 0
13 13.0 MUW NW UW P1 2019 0 0 0 0 0
prdct_grp stands for a product group (for example prdct_grp_1=WUW means the product is in "women underwear" and prdct_grp_2=SW will specify that it is in the "swimwear" group and so on). If a product in the same prdct_grp from(1-4) then I will assume that they will have very similar sales figures.
I would like to have the following outcome
# A tibble: 3 x 11
new_prdct_id prdct_grp_1 prdct_grp_2 prdct_grp_3 prdct_grp_4 Start_s January February March April sales_total
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 6~3 MUW NW UW P1 2019 6.00 8.00 10.0 6.00 32.0
2 7~2 WUW SW BK R21 2019 7.00 9.00 4.00 5.00 25.0
3 11~5 QKQ MZ KA AQ 2019 10.0 9.00 17.0 8.00 43.0
I used tidyverse to have the outcome I wanted to have but the result was not very good.
If a product matches more than one product or match to another product which has start season 2019 is another problem. how could I handle this?
Thank you for your help.
Best
A

Answer 1

Below is a possible dplyr solution along with detailed comments. Please always make sure that your problem is reproducible by providing dput() output or at least a code snippet for creating your dataset.
# import required package
library(dplyr)
# reproduce your data frame (or at least something similar to it)
# please give more details next time
prdct_df <- data_frame(
prdct_id = 1:13,
prdct_grp_1 = c("WUW", "WUW", "MUW", "LNG", "QKQ", "MUW", "WUW", "LNG", "QKQ", "WUW", "QKQ", "WUW", "MUW"),
prdct_grp_2 = c("SW", "SW", "NW", "KW", "MZ", "NW", "SW", "NW", "KW", "MZ", "MZ", "MZ", "NW"),
prdct_grp_3 = c("BH", "BK", "UW", "LW", "KA", "UW", "BK", "UW", "LW", "KA", "KA", "KA", "UW"),
prdct_grp_4 = c("B21", "R21", "P1", "L1", "AQ", "P1", "R21", "P2", "L2", "AQ", "AQ", "AQ", "P1"),
Start_season = c(2017, 2017, 2018, 2016, 2013, 2019, 2019, 2014, 2016, 2017, 2019, 2019, 2019),
January = c(2, 7, 6 , 8, 10, 0, 0, 15.1, 16.8, 18.5, 0, 0, 0),
February = c(10, 9, 8, 9, 8.67, 0, 0, 8.86, 8.67, 8.67, 0, 0, 0),
March = c(4, 5, 10, 12, 16.7, 0, 0, 28.7, 32.7, 36.7, 0, 0, 0),
April = c(4, 5, 6, 7, 8, 0, 0, 11, 12, 13, 0, 0, 0),
sales_total = c(21, 25, 32, 36, 43.3, 0, 0, 63.4, 70.1, 76.8, 0, 0, 0)
)
# define new season in case you have additional seasons in the furture
new_prdct_seasons <- 2019 # with new seasons: c(2019, 2020, 2012) and so on
# keep the historical and new data separate (optional but clean)
# filter your data to separate new products
new_prdct_df <- prdct_df %>%
filter(Start_season %in% new_prdct_seasons)
# filter your data to separate old products
old_prdct_df <- prdct_df %>%
filter(!(Start_season %in% new_prdct_seasons))
# match the new and old products to get the data frame you want
final_df <- old_prdct_df %>%
inner_join(
# only the first 6 columns are needed from new product data frame
new_prdct_df[1:6],
# inner join by product group features
by = c("prdct_grp_1", "prdct_grp_2", "prdct_grp_3", "prdct_grp_4")
) %>%
# reorder the columns and change their names when necessary
select(
new_prdct_id = 12,
old_prdct_id = 1,
2:5,
Start_season = 13,
7:11
)
# we obtained the data frame you asked for
# note that we avoided matches among new products by keeping new and old products in distinct data frames
final_df
# # A tibble: 5 x 12
# new_prdct_id old_prdct_id prdct_grp_1 prdct_grp_2 prdct_grp_3 prdct_grp_4 Start_season January
# <int> <int> <chr> <chr> <chr> <chr> <dbl> <dbl>
# 1 7 2 WUW SW BK R21 2019 7
# 2 6 3 MUW NW UW P1 2019 6
# 3 13 3 MUW NW UW P1 2019 6
# 4 11 5 QKQ MZ KA AQ 2019 10
# 5 12 10 WUW MZ KA AQ 2019 18.5
# # ... with 4 more variables: February <dbl>, March <dbl>, April <dbl>, sales_total <dbl>
# you can also exclude matches with more than one old product if needed
final_df[-3, ] # this removes the match 13-3 as there is already 6-3