library(data.table)
library(magrittr)
# fake data; 1000 rows
set.seed(1234)
d0 <-
data.table(
item_id = 1L:1000L,
ttl = runif(1000L),
item_category_id = sample(1L:83L, 1000L, replace = T)
)
d1 <-
d0[, .(
sum.of.ttl.by.item_category_id = sum(ttl),
raw.of.item = list(item_id),
raw.of.ttl = list(ttl)
), by = .(item_category_id)]
head(d1)
# item_category_id sum.of.ttl.by.item_category_id raw.of.item
# 1: 75 9.775620 1, 86,113,278,309,377,...
# 2: 64 8.876390 2, 28, 39, 41,172,269,...
# 3: 66 5.105777 3,170,195,211,230,233,...
# 4: 24 7.403771 4, 12, 57,101,183,267,...
# 5: 57 6.665990 5, 37, 73,191,259,360,...
# 6: 31 4.670723 6, 22,452,473,549,603,...
# raw.of.ttl
# 1: 0.1137034,0.8985805,0.9503049,0.5447566,0.5484554,0.7593397,...
# 2: 0.62229940,0.91465817,0.99215042,0.55333359,0.58427152,0.04946115,...
# 3: 0.6092747,0.3252783,0.8100834,0.5663108,0.4944386,0.1066968,...
# 4: 0.62337944,0.54497484,0.49396092,0.03545673,0.60071414,0.74816114,...
# 5: 0.86091538,0.20124804,0.01462726,0.63891131,0.58951654,0.76815200,...
# 6: 0.6403106053,0.3026933707,0.7759688012,0.0006121558,0.9797886356,0.1702244373,...
d1$raw.of.item[1:2]
# [[1]]
# [1] 1 86 113 278 309 377 426 457 493 505 569 627 643 798 840 980 995
#
# [[2]]
# [1] 2 28 39 41 172 269 286 311 431 579 668 702 768 782 828 908 942
d1$raw.of.ttl[1:2]
# [[1]]
# [1] 0.1137034 0.8985805 0.9503049 0.5447566 0.5484554 0.7593397 0.1999333 0.2468865 0.9126530 0.6744341 0.2085592 0.4840459
# [13] 0.5096489 0.8223393 0.6235133 0.5847833 0.6936828
#
# [[2]]
# [1] 0.62229940 0.91465817 0.99215042 0.55333359 0.58427152 0.04946115 0.28077262 0.91342167 0.66349054 0.55109725 0.28465309
# [12] 0.59863873 0.57321496 0.05653095 0.39876005 0.25607750 0.58355793
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item_category_id sum.of.ttl.by.item_category_id raw.of.item
1: 70 5.321331 1,216,257,280,313,318,
2: 41 5.961307 2, 19, 57, 87,277,390,
3: 10 9.898108 3, 71, 92,240,304,335,
4: 30 7.860412 4, 29,106,130,179,255,
5: 64 7.419698 5, 63,129,250,322,400,
6: 33 6.909596 6, 65, 73,315,336,396,
raw.of.ttl
1: 0.1137034,0.9321736,0.2623557,0.3446373,0.4072514,0.4084144,
2: 0.6222994,0.1867228,0.4939609,0.3894998,0.8212286,0.9470380,
3: 0.6092747,0.1214802,0.9004246,0.9897099,0.4440339,0.4726167,
4: 0.62337944,0.83134505,0.32568192,0.31844581,0.11613577,0.06510324,
5: 0.8609154,0.3171822,0.4969662,0.7969238,0.8618053,0.3663996,
6: 0.64031061,0.23902573,0.01462726,0.14608279,0.38605066,0.92318086,
[[1]]
[1] 1 216 257 280 313 318 341 346 462 757
[[2]]
[1] 2 19 57 87 277 390 435 437 494 638 659 741 790
[[1]]
[1] 0.1137034 0.9321736 0.2623557 0.3446373 0.4072514 0.4084144 0.9558402
[8] 0.3104304 0.8947680 0.6917567
[[2]]
[1] 0.6222994 0.1867228 0.4939609 0.3894998 0.8212286 0.9470380 0.2722072
[8] 0.3802893 0.3080208 0.8435601 0.2581553 0.2715826 0.1667424