使用R data.table聚合选定行

这就是单个客户的客户订单数据：

order_no customer_id  product amount  order_total
      23           1        A    100          100 
      24           1        A    100          300
      24           1        B    100          300
      24           1        C    100          300
      25           1        B    100          100
      26           1        A    100          200
      26           1        B    100          200

我想在一个新列中计算每个客户的平均订单量，因此对于这个客户，它将是175=（100+300+100+200）/4：

我尝试过使用这个版本，但没有成功：

customer_stats <- data.table(customer_stats)[, avg_order_size := mean(order_total), by=list(order_no, customer_id)]

---

customer\u stats这样做可以避免创建
order\u total
：

customer_stats[ , avg_order_size := sum(amount, na.rm=TRUE) / length(unique(order_no)), by=customer_id]

但是，我对速度有保留。
这一个怎么样，它似乎转换了您的方法，不需要在这里计算订单总数

dat[, sum(amount), by = list(customer_id, order_no)][ ,avg_order := mean(V1), by = customer_id]

我认为关键在于按客户和订单为原始表设置键，按客户和订单求和订单总数，按客户求平均订单总数，然后将其连接回原始表

# Your data (next time, consider putting R-formatted data in the question...):
dt <- data.table(customer_id=1,
                 order_no=c(23,24,24,24,25,26,26),
                 product=c("A","A","B","C","B","A","B"),
                 product_amount=100,
                 key=c("customer_id","order_no")) # 1: key by customer and order

dt
#   customer_id order_no product product_amount
#1:           1       23       A            100
#2:           1       24       A            100
#3:           1       24       B            100
#4:           1       24       C            100
#5:           1       25       B            100
#6:           1       26       A            100
#7:           1       26       B            100

dt[ # 4: join summary back to original
  dt[,list(order_total=sum(product_amount)),by=list(customer_id,order_no)] [ # 2: order total by customer and order
    ,avg_order_size:=mean(order_total),by=list(customer_id)] # 3: add the average of order total by customer
  ]
#   customer_id order_no product product_amount order_total avg_order_size
#1:           1       23       A            100         100            175
#2:           1       24       A            100         300            175
#3:           1       24       B            100         300            175
#4:           1       24       C            100         300            175
#5:           1       25       B            100         100            175
#6:           1       26       A            100         200            175
#7:           1       26       B            100         200            175

<代码>你的数据（下一次，考虑在问题中放置R格式数据）…
dt您是否尝试使用
：=
执行分配，因此无需额外分配
数据。表
@dickoa，如果您同时按
订单号
和
客户id
分组，则取
100、300的平均值，分别为100200
（最终值相同）。@Arun Yep，你是right@Bryan，我想你的问题已经回答了。如果能一直回答这个问题就好了。很抱歉，你为什么在这里求和？他总结了
100+300+100+200
（按
total
，而不是
amount
）？框架效果！他通过
order\u no
创建了
order\u total
总和
amount
。请参见编辑他的问题，他询问这是否可以在不计算订单总数的情况下完成。
# Your data (next time, consider putting R-formatted data in the question...):
dt <- data.table(customer_id=1,
                 order_no=c(23,24,24,24,25,26,26),
                 product=c("A","A","B","C","B","A","B"),
                 product_amount=100,
                 key=c("customer_id","order_no")) # 1: key by customer and order

dt
#   customer_id order_no product product_amount
#1:           1       23       A            100
#2:           1       24       A            100
#3:           1       24       B            100
#4:           1       24       C            100
#5:           1       25       B            100
#6:           1       26       A            100
#7:           1       26       B            100

dt[ # 4: join summary back to original
  dt[,list(order_total=sum(product_amount)),by=list(customer_id,order_no)] [ # 2: order total by customer and order
    ,avg_order_size:=mean(order_total),by=list(customer_id)] # 3: add the average of order total by customer
  ]
#   customer_id order_no product product_amount order_total avg_order_size
#1:           1       23       A            100         100            175
#2:           1       24       A            100         300            175
#3:           1       24       B            100         300            175
#4:           1       24       C            100         300            175
#5:           1       25       B            100         100            175
#6:           1       26       A            100         200            175
#7:           1       26       B            100         200            175