Question

这对我来说是一个很大的难题。如果我有足够的声誉来奖励赏金，我会的！

寻求平衡销售代表的帐户区域。我把这个过程分解了，我真的不知道如何在每个地区做到这一点。

在此示例中，有4个地区的1000个帐户，每个地区有2个子集联盟，然后是帐户的各个所有者 - 某些帐户是无主的。每个帐户的随机值介于1,000和100,000之间。

可重复的例子：

帐户清单：

set.seed(1)
Accounts <- paste0("Acc", 1:1000)
Region <- c("NorthEast", "SouthEast", "MidWest", "West")
League <- sample(c("Majors", "Minors"), 1000, replace = TRUE)
AccValue <- sample(1000:100000, 1000, replace = TRUE)
Owner <- sample(c("Chad", NA, "Jimmy", "Adrian", NA, NA, "Steph", "Matt", "Jared", "Eric"), 1000, replace = TRUE)
AccDF <- data.frame(Accounts, Region, League, AccValue, Owner)
AccDF$Accounts <- as.character(AccDF$Accounts)
AccDF$Region <- as.character(AccDF$Region)
AccDF$League <- as.character(AccDF$League)
AccDF$Owner <- as.character(AccDF$Owner)

地区所有权概要：

Summary <- AccDF %>%
  group_by(Region, League, Owner) %>%
  summarise(Count = n(),
            TotalValue = sum(AccValue))

按地区划分的联盟：

Summary2 <- AccDF %>%
  group_by(Region, League) %>%
  summarise(Count = n(),
            TotalValue = sum(AccValue),
            AccountsPerRep = round(Count / 7, 0),
            ValuePerRep = TotalValue / 7)

这就是所有的起始数据，我想对Summary2表的每个分组执行以下过程。

West Minors示例：

西部未成年人账户总数：120

#break out into owned and unowned

WestMinorsOwned <- AccDF %>%
  filter(Region == "West",
         League == "Minors",
         !is.na(Owner))

WestMinorsUnowned <- AccDF %>%
  filter(Region == "West",
         League == "Minors",
         is.na(Owner))

#unassign accounts until threshold is hit

New.WestMinors <- WestMinorsOwned %>% 
  mutate(r = runif(n())) %>% 
  arrange(r) %>% 
  group_by(Owner) %>% 
  mutate(NewOwner = replace(Owner, cumsum(AccValue) > 600000 | row_number() > 14, NA)) %>% 
  ungroup(Owner) %>%
  mutate(Owner = NewOwner) %>%
  select(-r, -NewOwner)

在更新所有者之后，我们将这些部分绑定在一起以拥有WestMinors帐户基础，所有帐户都包含更新的所有者，希望平衡。

AssignableWestMinors <- bind_rows(filter(AccDF, Region == "West" & League == "Minors" & is.na(Owner)), 
                                  filter(New.WestMinors, is.na(Owner))) %>%
  arrange(desc(AccValue))

#check work
OwnerSummary <- New.WestMinors %>%
  filter(!is.na(Owner)) %>%
  group_by(Region, League, Owner) %>%
  summarise(Count = n(), TotalValue = sum(AccValue))

没有人拥有超过14个账户或600,000个账户，因此我们处于一个好地方，可以开始重新分配无主账户以尝试平衡所有人。以下for循环查看OwnerSummary中每个名称分配给他们的最小$$并分配最有价值帐户的名称，然后移动每个帐户，尝试平衡每个所有者的共享。

#Balance Unassigned

for (i in 1:nrow(AssignableWestMinors)){
  idx <- which.min(OwnerSummary$TotalValue)
  OwnerSummary$TotalValue[idx] <- OwnerSummary$TotalValue[idx] + AssignableWestMinors$AccValue[i]
  OwnerSummary$Count[idx] <- OwnerSummary$Count[idx] + 1
  AssignableWestMinors$Owner[i] <- as.character(OwnerSummary$Owner[idx])}

现在我们只将先前拥有的和新分配的绑定在一起，我们拥有完成的西部未成年人平衡区域。

WestMinors.Final <- bind_rows(filter(New.WestMinors, !is.na(Owner)), AssignableWestMinors)

WM.Summary <- WestMinors.Final %>%
  group_by(Region, League, Owner) %>%
  summarise(Count = n(),
            TotalValue = sum(AccValue))

每个人都拥有相似数量的帐户，并且总的$$领域都在合理范围内。

现在我正试图为原来的4个地区，2个联盟的每个分组做到这一点。这样做8次然后将它们拼接在一起。每个子组都有不同的$$值阈值，以及账户数量。如何将原始帐户基础拆分为8个部分，应用所有这些部分，然后将其重新组合在一起？

Answer 1

您应该利用?dplyr::do在Region-League的子集上执行您想要的split-apply-combine操作。首先，对逻辑进行功能化，使其能够在数据帧dta上运行，该数据帧代表主数据帧AccDF的子集化版本。

reAssign <- function(dta) {
  other_acct <- dta %>% 
    filter(!is.na(Owner)) %>% 
    mutate(r = runif(n())) %>% 
    arrange(r) %>% 
    group_by(Owner) %>% 
    mutate(NewOwner = replace(Owner, cumsum(AccValue) > 600000 | row_number() > 14, NA)) %>% 
    ungroup(Owner) %>%
    mutate(Owner = NewOwner) %>%
    select(-r, -NewOwner)

  assignable_acct <- other_acct %>% 
    filter(is.na(Owner)) %>% 
    bind_rows( filter(dta, is.na(Owner)) ) %>% 
    arrange(desc(AccValue))

  acct_summary <- other_acct %>%
    filter(!is.na(Owner)) %>%
    group_by(Owner) %>%
    summarise(Count = n(), TotalValue = sum(AccValue))

  # I have a feeling there's a much better way of doing this, but oh well...  
  for (i in seq(nrow(assignable_acct))) {
    idx <- which.min(acct_summary$TotalValue)
    acct_summary$TotalValue[idx] <- acct_summary$TotalValue[idx] + assignable_acct$AccValue[i]
    acct_summary$Count[idx] <- acct_summary$Count[idx] + 1
    assignable_acct$Owner[i] <- as.character(acct_summary$Owner[idx])
  }
  final <- other_acct %>% 
    filter(!is.na(Owner)) %>% 
    bind_rows(assignable_acct)

  return(final)
}

然后将其应用于已按地区，联盟分组的AccDF。

new_master <- AccDF %>% 
  group_by(Region, League) %>% 
  do( reAssign(.) ) %>% 
  ungroup()

检查以确保完成它的工作......

new_master %>% 
  group_by(Region, League, Owner) %>%
  summarise(Count = n(),
          TotalValue = sum(AccValue)) %>% 
  as.data.frame()

split apply w / function或purr package pmap？

1 个答案: