R：遍历数据框，提取多个变量的子集，然后存储在聚合数据集中

Question

I have an aggregate data table with about 60 million rows. Simplified, the data looks like this:

ServiceN  Customer  Product  LValue  EDate  CovBDate  CovEDate
1   1   12  3    2016-08-03 2016-07-07 2017-07-06
2   1   12  19   2016-07-07 2016-07-07 2017-07-06
3   2   23  222  2017-09-09 2016-10-01 2017-09-31
4   2   23  100  2017-10-01 2017-10-01 2018-09-31

I need to go through each row and subset the entire dataset by Customer with all entry dates(EDate) between CovBDate and CovEDate. Then, I need to find the sum of the LValue for each product (we're only looking at 10, so it's not terrible).

As an example, the final dataset would look something like this:

ServiceN  Customer  Product  LValue  EDate  CovBDate  CovEDate Prod12 Prod23
1   1   12  3    2016-08-03 2016-07-07 2017-07-06  22  0
2   1   12  19   2016-07-07 2016-07-07 2017-07-06  22  0
3   2   23  222  2017-09-09 2016-10-01 2017-09-31  0   222
4   2   23  100  2017-10-01 2017-10-01 2018-09-31  0   100

I don't know where to begin on this problem, however, I've started with this (which does not work):

for (i in 1:length(nrow)) {
  tempdata<-dataset[Customer==Customer[i] & EDate>=CovBDate[i] & 
  EDate<=CovEDate[i]] #data.table subsetting
  tempdata$Prod12<- with(tempdata, sum(LValue[Product== "12"], na.rm=T))
  #I could make this a function, but I want to get this for loop automated first...
  tempdata$Prod23<- with(tempdata, sum(LValue[Product=="23"], na.rm=T))
}

My questions, therefore, are:
1) How do I make this for loop work with so many variables?
2) How do I make the new variable get added to the original dataset (called dataset)?

Answer 1

Using dplyr you could do something like this:

library(dplyr)

dataset <- data.frame(ServiceN = c("1", "2", "3", "4"),
    Customer = c("1", "1", "2", "2"),
    Product = c("12", "12", "23", "23"),
    LValue = c(3, 19, 222, 100),
    EDate  = c("2016-08-03", "2016-07-07", "2017-09-09", "2017-10-01"),
    CovBDate = c("2016-07-07", "2016-07-07", "2016-10-01", "2017-10-01"),
    CovEDate = c("2017-07-06", "2017-07-06", "2017-09-31", "2018-09-31"),
    stringsAsFactors = FALSE)

## Group by customer and product so summary results are per-customer/product combination
dataset %>% group_by(Customer, Product) %>%
    ## Filter based on dates
    filter(EDate >= CovBDate & EDate <= CovEDate) %>%
    ## Sum the LValue based on the defined groupings
    summarise(Sum = sum(LValue))


## A tibble: 2 x 3
## Groups:   Customer [?]
# Customer Product   Sum
#<chr>    <chr>   <dbl>
#1 1        12         22
#2 2        23        322