根据R中设置的限制分配值

我有一个商店列表，里面有一些不同的物品，还有一个仓库，里面有这些物品——这是两个独立的数据框

Article <- c('a','b','a','b','c','d')
forecast <- c( 1,5,80,10,100,1000)
StoreID <- c(1,1,2,2,3,4)
StoreData <- data.frame(StoreID, Article, Order)

以及仓库数据：

Stock <- c(10,11,12,100)
WarehouseData <- data.frame(Article, Stock)

WarehouseData
Article Stock
a    10
b    11
c    12
d   100

---

任何人都能告诉我如何正确操作吗？

请参见下面的循环示例：

StoreData$PO <- NA
for (i in 1:nrow(StoreData)) {
  query <- WarehouseData$Article == StoreData[i, "Article"]
  po <- ifelse(StoreData[i, "forecast"] > WarehouseData[query, 2], 
               WarehouseData[query, 2],
               StoreData[i, "forecast"])

  WarehouseData[query, 2] <- WarehouseData[query, 2] - po
  StoreData[i, "PO"] <- po
}

print(StoreData)
# StoreID Article forecast  PO
# 1       1       a        1   1
# 2       1       b        5   5
# 3       2       a       80   9
# 4       2       b       10   6
# 5       3       c      100  12
# 6       4       d     1000 100

---

StoreData$PO下面是另一种使用
dplyr的方法：

library(dplyr)
left_join(storeData, WarehouseData, by = "Article") %>% 
  group_by(Article) %>% 
  mutate(PO = ifelse(cumsum(forecast) <= Stock, forecast, 
                     Stock - cumsum(forecast) + forecast)) %>% ungroup

#Source: local data frame [6 x 5]
#
#  StoreID Article forecast Stock    PO
#    (int)  (fctr)    (int) (dbl) (dbl)
#1       1       a        1    10     1
#2       1       b        5    11     5
#3       2       a       80    10     9
#4       2       b       10    11     6
#5       3       c      100    12    12
#6       4       d     1000   100   100

库（dplyr）
左联合（storeData，WarehouseData，by=“Article”）%>%
按（物品）分组%>%
突变（PO=ifelse（累计（预测）%解组
#来源：本地数据帧[6 x 5]
#
#StoreID商品预测库存采购订单
#（内部）（fctr）（内部）（dbl）（dbl）
#11101
#2 1 b 5 11 5
#3 2 a 80 10 9
#42B1106
#5 3 c 100 12
#6 4 d 1000 100
为什么a=1的第一次观测的
PO
（与预测相同）但是第一次观察到的c和d与
仓库数据
中的
库存
相同？您需要按顺序查看门店ID？例如，如果门店1的商品预测用完了所有可用库存，则门店2的订单将无法完成？@Sotos这正是问题所在-我必须批准此列中的预测umn如果仓库里有足够的数量。如果库存较少，我需要从仓库分配可用库存，而不是从仓库分配库存…好的，这会使sense@NBATrends你是对的，非常感谢。现在，我将尝试了解它是如何工作的：）
StoreData$PO <- NA
for (i in 1:nrow(StoreData)) {
  query <- WarehouseData$Article == StoreData[i, "Article"]
  po <- ifelse(StoreData[i, "forecast"] > WarehouseData[query, 2], 
               WarehouseData[query, 2],
               StoreData[i, "forecast"])

  WarehouseData[query, 2] <- WarehouseData[query, 2] - po
  StoreData[i, "PO"] <- po
}

print(StoreData)
# StoreID Article forecast  PO
# 1       1       a        1   1
# 2       1       b        5   5
# 3       2       a       80   9
# 4       2       b       10   6
# 5       3       c      100  12
# 6       4       d     1000 100

StoreData <- merge(StoreData, WarehouseData)
StoreData$PO <- do.call(c, lapply(split(StoreData, StoreData$Article), function(z) {
  ifelse(cumsum(z$forecast) <= z$Stock, z$forecast, 
         z$Stock - cumsum(z$forecast) + z$forecast) 
}))

StoreData <- read.table(text = "StoreID Article forecast
                        1       a        1
                        1       b        5
                        2       a       80
                        2       b       10
                        3       c      100
                        4       d     1000", header = T)


Article <- c('a','b','c','d')
Stock <- c(10,11,12,100)
WarehouseData <- data.frame(Article, Stock)

library(dplyr)
left_join(storeData, WarehouseData, by = "Article") %>% 
  group_by(Article) %>% 
  mutate(PO = ifelse(cumsum(forecast) <= Stock, forecast, 
                     Stock - cumsum(forecast) + forecast)) %>% ungroup

#Source: local data frame [6 x 5]
#
#  StoreID Article forecast Stock    PO
#    (int)  (fctr)    (int) (dbl) (dbl)
#1       1       a        1    10     1
#2       1       b        5    11     5
#3       2       a       80    10     9
#4       2       b       10    11     6
#5       3       c      100    12    12
#6       4       d     1000   100   100