Combining Data.Tables (R) with a loop or mapply - r

Question

I am new to data tables in R and have managed to get 80% of the way through my analysis. The background is that I want to get the returns of a stock 5 days (before and after), and then 25 and 45 days after they report. I have successfully managed to do it for one set of dates (effectively hardcoding) but when I try and automate the process it falls apart.
I will start with my current formulas and then explain the data.
This formula successfully looks at the data tables and returns the sum that I need. The issue is that datem5 and V1 need to go through a loop (or mapply) to automate the process.
CQR_Date[CQR_DF[CQR_Date, sum(CQR), on = .(unit, date >= date1, date <= datem5),
by = .EACHI], newvar := V1, on = .(unit, date1=date)]
I tried this (along with many other variants). Please note the newvar needs to be addressed as well.
for (i in 1:4) {
CQR_Date[CQR_DF[CQR_Date, sum(CQ), on = .(unit, date >= date1, date <= cols[,..i]),
by = .EACHI], newvar := v, on = .(unit, date1=date)]
but get this error
Error: argument specifying columns specify non existing column(s): cols[3]='cols[, ..i]'
Interestingly, when I try
for (i in 1:2) {
y <- cols[,..i]}
There is no issue.
Now in terms of data;
col just contains the column headings that I need from CQR_Data
cols <- data.table("datem5", "datep5", "datep20" , "datep45")
CQ_Data has the reporting dates for the stock CQ such as the following
CQ_Date <- data.frame("date1" = anydate(c("2016-02-17", "2016-06-12", "2016-08-17")))
CQ_Date$datem5 <- CQ_Date$date1 - 5 # minus five days
CQ_Date$datep5 <- CQ_Date$date1 + 5 # plus five days
CQ_Date$datep20 <- CQ_Date$date1 + 20
CQ_Date$datep45 <- CQ_Date$date1 + 45
CQ_Date$unit <- 1 # I guess I need this for some sort of indexing
Then CQ_DF (it is the log returns for the stock) is formed by:
CQ_DF <- data.frame("unit" = rep(1,300))
CQ_DF$CQ <- rnorm(10)
CQ_DF$date <- seq(as.Date("2015-12-25"), by = "day", length.out = 300)
CQ_DF$unit <- 1
Before setting them as DT
setDT(CQ_DF)
setDT(CQ_Date)
Any help would be greatly appreciated. Note this uses
library(data.table)
library(anytime)
A simplified version is:
CQ_Date <- data.frame("date1" = c(10, 20))
CQ_Date$datep5 <- CQ_Date$date1 + 5 # plus five days
CQ_Date$datep20 <- CQ_Date$date1 + 10
CQ_Date$unit <- 1
CQ_DF <- data.frame("unit" = rep(1,100))
CQ_DF$CQ <- seq(1, by = 1, length.out = 100)
CQ_DF$date <- seq(1, by = 1, length.out = 100)
CQ_DF$unit <- 1
setDT(CQ_DF)
setDT(CQ_Date)
cols <- c("datep5", "datep20" )
tmp <- melt(CQ_Date, measure.vars = cols)
setDT(tmp)
tmp[CQ_DF[tmp, sum(CQ), on = .( unit, date >= date1, date <= value), by =
.EACHI],newvar := V1, on = .(unit, date1=date )]
The issue is now that the sum does not appear to work correctly. It may have something to do with "variable" variable.

Answer 1

Instead of using mapply or for loop, try reshaping the dataset in long format using melt, create sequence between the numbers, perform the join and calculate the sum.
library(data.table)
cols <- c("datep5", "datep20" )
tmp <- melt(CQ_Date, measure.vars = cols)
tmp <- melt(CQ_Date, measure.vars = cols)
tmp <- tmp[, list(date = seq(date1, value)), .(unit, variable, date1, value)]
tmp <- merge(tmp, CQ_DF, by = c('unit', 'date'))
tmp[, .(newvar = sum(CQ)), .(unit, variable, date1)]
# unit variable date1 newvar
#1: 1 datep5 10 75
#2: 1 datep20 10 165
#3: 1 datep5 20 135
#4: 1 datep20 20 275
If you need the data back in wide format you can use dcast.
Equivalent tidyverse option is :
library(tidyverse)
CQ_Date %>%
pivot_longer(cols = cols) %>%
mutate(date = map2(date1, value, seq)) %>%
unnest(date) %>%
left_join(CQ_DF, by = c('unit', 'date')) %>%
group_by(unit, name, date1) %>%
summarise(newvar = sum(CQ))