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@@ -15,7 +15,40 @@ agp(table_name, j, by, index)
```
The data can be aggregated for, say, recent 3, 6, 12 months and year to date by suitably defining the values of the `date_period` list
(check the code for this variable definition).
(check the code for this variable definition). For example, for the following glimpse of data:
```
> DT
dates grp_1 grp_2 sales
1: 2019-01-01 India Buttons 5.857764
2: 2019-01-08 Brazil Salt 94.159590
3: 2019-01-15 China Buttons 95.128017
4: 2019-01-22 Russia Salt 17.057105
5: 2019-01-29 South Africa Buttons 68.489827
---
99999996: 2022-09-27 India Salt 43.997368
99999997: 2022-10-04 Brazil Buttons 46.981903
99999998: 2022-10-11 China Salt 7.700367
99999999: 2022-10-18 Russia Buttons 84.551482
100000000: 2022-10-25 South Africa Salt 75.611129
```
the summarized dataset will look something like:
```
> agp_DT(DT, "sales", c("grp_1", "grp_2"), "dates")
grp_1 grp_2 sales_r3m sales_p3m sales_r6m sales_p6m sales_ytd
1: Brazil Buttons 24986904 50009826 74996730 50008718 262554380
2: Brazil Salt 24996211 24986346 49982557 75021112 245026785
3: China Buttons 25022494 24988808 50011302 74985508 245044854
4: China Salt 49967206 25022877 74990082 50008190 262566617
5: India Buttons 25030072 24992465 50022537 75059273 245038138
6: India Salt 24998593 49995222 74993814 50016927 262498605
7: Russia Buttons 50015343 24993829 75009172 75012270 262531196
8: Russia Salt 24980912 25010533 49991445 74935847 262490950
9: South Africa Buttons 24999193 50004908 75004101 50006625 262463146
10: South Africa Salt 50010186 24989640 74999826 75005350 262494492
```
## decile
Often, in sales analytics, one is required to group certain categorical variables into buckets that contribute to 10% of overall sales,