计算最后一个订单的退回商品百分比

时间:2014-11-26 09:58:10

标签: r

数据:

DB <- data.frame(orderID  = c(1,2,3,4,5,6,7,8,9,10),     
orderDate = c("1.1.14","1.1.14","1.1.14","1.1.14","2.1.14", "2.1.14","2.1.14","2.1.14","2.1.14","2.1.14"),  
itemID = c(2,3,2,5,12,4,2,3,1,5),  
price = c(29.90, 39.90, 29.90, 19.90, 49.90, 9.90, 29.90, 39.90, 14.90, 19.90),
customerID = c(1, 2, 3, 1, 1, 3, 2, 2, 1, 1),
ItemReturned = c(0, 0, 0, 1, 1, 0, 1, 0, 0, 0))

预期结果:

percentageOfReturnedItemsLastOrder = c(0.33, 0.5, 0, 0.33, 0.33, 0,  0.5, 0.5, 0.33, 0.33)
你好,伙计们, 不幸的是,我有另一个问题,我无法单独解决 - 所以如果你偷看再帮助我,我会很高兴:)在数据集中,每个订单都有自己的ID,每个注册用户都有他唯一的customerID。每个客户都可以订购具有特定价格的物品(带有ItemID)。 “ItemReturned”列显示商品是否返回商店(“1”)或者客户是否将商品保留在家(“0”)我想计算从最后一个订单返回商店的商品百分比。我也是想要将结果添加为现有数据集中的新列...

已经用数据表尝试过,但我一定是犯了错误 - 因为它不起作用;)

library(data.table)
setDT(DB)[, orderDate := as.Date(orderDate, format = "%d.%m.%y")]
DB[, `:=` (percentageOfReturnedItemsLastOrder  = sum(ItemRetuned> 0[orderDate == max(orderDate)])), by = customerID]

希望你能告诉我什么是错的,或者告诉我另一个解决问题的可能性......

干杯和THX!

1 个答案:

答案 0 :(得分:1)

尝试

DB[, percentageOfReturnedItemsLastOrder := 
     sum(ItemReturned[orderDate == max(orderDate)])/length(ItemReturned[orderDate == max(orderDate)]), 
   by = customerID]

#     orderID  orderDate itemID price customerID ItemReturned percentageOfReturnedItemsLastOrder
#  1:       1 2014-01-01      2  29.9          1            0                          0.3333333
#  2:       2 2014-01-01      3  39.9          2            0                          0.5000000
#  3:       3 2014-01-01      2  29.9          3            0                          0.0000000
#  4:       4 2014-01-01      5  19.9          1            1                          0.3333333
#  5:       5 2014-01-02     12  49.9          1            1                          0.3333333
#  6:       6 2014-01-02      4   9.9          3            0                          0.0000000
#  7:       7 2014-01-02      2  29.9          2            1                          0.5000000
#  8:       8 2014-01-02      3  39.9          2            0                          0.5000000
#  9:       9 2014-01-02      1  14.9          1            0                          0.3333333
# 10:      10 2014-01-02      5  19.9          1            0                          0.3333333
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