Using zoo rollsum in a data table. For derivatives transactions

Question

Partly related to this question and this one , I am having problems calculating the moving amount. Unlike these questions, I would like to try using zoo:rollsumas a similar rollapplyanswer here . (But if there is a way data.tableto do this, by all means.)

Let's start with some data:

set.seed(123)
some_dates <- function(){as.Date('1980-01-01') + sort(sample.int(1e4,100))}
d <- data.table(cust_id = c(rep(123,100),rep(456,100)),
                purch_dt = c(some_dates(), some_dates()),
                purch_amt = round(runif(200, 1, 100),2) )

head(d)
#    cust_id   purch_dt purch_amt
# 1:     123 1980-01-08     24.63
# 2:     123 1980-09-03     96.27
# 3:     123 1981-02-24     60.54

I would like to make a rolling 365-day purchase amount for each customer, calculated for each day of the transaction.

The answer here suggests the following approach:

First, create dummy strings for all pairs of client data using cross-connect, for example:

setkey(d, cust_id, purch_dt)
dummy <- d[ CJ(unique(cust_id), seq(min(purch_dt), max(purch_dt), by='day') ) ]
#    cust_id   purch_dt purch_amt
# 1:     123 1980-01-08     24.63
# 2:     123 1980-01-09        NA
# 3:     123 1980-01-10        NA

, ( , min/max customer_dt).

, rollsumr 365- .

:

dummy[, purch_365 := rollsumr(x=purch_amt, k=365, na.rm=TRUE) , by=cust_id]

purch_365 NA :

Warning messages:
1: In `[.data.table`(dummy, , `:=`(purch_365, rollsumr(x = purch_amt,  :
  Supplied 9550 items to be assigned to group 1 of size 9914 in column 'purch_365' (recycled leaving remainder of 364 items).

, 364 = k-1 2 2 cust_id s. , .

# Desired output:
#    cust_id   purch_dt purch_amt purch_365
# 1:     123 1980-01-08     24.63     24.63
# 2:     123 1980-09-03     96.27    120.90
# 3:     123 1981-02-24     60.54    156.81

!

Answer 1

. -, , , :

d[, old.date := purch_dt - 365]
d[, idx := .I]

( 1.9.5+) (.. .EACHI):

res = d[d, .(idx = i.idx, seq = idx:i.idx), by = .EACHI, roll = -Inf,
        on = c(cust_id = 'cust_id', purch_dt = 'old.date')]

, data.table :

d[, purch_365 := d[res$seq, sum(purch_amt), by = res$idx]$V1][]
#     cust_id   purch_dt purch_amt idx   old.date purch_365
#  1:     123 1980-01-08     24.63   1 1979-01-08     24.63
#  2:     123 1980-09-03     96.27   2 1979-09-04    120.90
#  3:     123 1981-02-24     60.54   3 1980-02-25    156.81
#  4:     123 1981-04-01     51.99   4 1980-04-01    208.80
#  5:     123 1981-04-02     40.85   5 1980-04-02    249.65
# ---                                                      
#196:     456 2006-01-29     24.72 196 2005-01-29    187.81
#197:     456 2006-02-15     27.78 197 2005-02-15    215.59
#198:     456 2006-09-22     11.00 198 2005-09-22     74.94
#199:     456 2006-09-27     12.67 199 2005-09-27     87.61
#200:     456 2006-11-18     99.13 200 2005-11-18    186.74

Answer 2

- , , , , NA buyer_amt 0 ? , R NA, , NA.

NA + 1 = NA. NA .