How to express a variable on a different data frame in a linear regression, lm()

Question

I have a problem in R with the expression of the linear regression.

When I enter the function with "hard values" it works perfectly fine: LM <- lm(`589499102` ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255)

Now I would like to exchange the hard value of 589499102 through a variable that is on another sheet. I tried several different ways like the following but none of them worked LM <- lm(OtherSheet$Col1[1] ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255) or LM <- lm(`OtherSheet$Col1[1]` ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255)

It even does not work, when I create a variable: newvariable <- "589499102" LM <- lm(`newvariable` ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255)

The goal, in the end, would be to integrate this into a for loop. However, this will be a smaller problem when this issue is solved.

This is how the data looks at the moment:

Sheet 1: strategic_dateandcusip. This sheet actually gives the information for the regression itself. Which column needs to be regressed over which row.

strategic_cusip	announcement_date	...	...	...	row_start_regression	row_end_regression
589499102	2010-02-21	...	...	...	3	255
705573103	2010-03-14	...	...	...	18	270
46069S109	2010-03-21	...	...	...	23	275
...	...	...	...	...	...	...

Sheet 2: strategic_return . This sheet contains the "raw data" over which the regression is actually done.

Date	002824100	00430U103	589499102	...	...	Value-Weighted Return
2009-01-05	-0.01717700	0.0022422	-0.038760	...	...	0.016764
2009-01-06	-0.03267477	-0.0044743	0.016129	...	...	0.033647
2009-01-07	-0.00549882	-0.1146067	0.031746	...	...	-0.022271
2009-01-08	0.01105845	0.0329949	0.0076923	...	...	0.011896
2009-01-09	-0.00058594	-0.0491400	0.046512	...	...	-0.018748
2009-01-12	-0.02169240	-0.0051680	-0.022222	...	...	-0.026589
...	...	...	...	...	...	...
...	...	...	...	...	...	...
2020-12-31	0.03292349	-0.0031726	-0.1029381	...	...	0.0213947

The goal is to regress each column of sheet strategic_return (002824100, 00430U103, 589499102) with the column "Value-Weighted Return" over the time period which is noted in the sheet strategic_dateandcusip$row_start_regression to strategic_dateandcusip$row_end_regression.

The field in the first sheet strategic_dateandcusip[1,1] is the same as the header of the fourth column of the second sheet strategic_return = 589499102. And I tried to link these sheets like that.

I have dput() the illustrated tables above. I am sorry for the long codes. I really tried to minimize it to the necessary rows and columns only.

Sheet 1: strategic_dateandcusip:

structure(list(strategic_cusip = structure(2:1, .Label = c("46069S109", 
"589499102"), class = "factor"), strategic_cusip_new = c("589499102", 
"46069S109"), strategic_date = structure(c(14661, 14689), class = "Date"), 
    strategic_dateweekday = structure(c(14662, 14690), class = "Date"), 
    row_of_eventdate = c(286, 306), row_start_regression = c(3, 
    23), row_end_regression = c(255, 275)), row.names = c(1L, 
3L), class = "data.frame")


Sheet 2: strategic_return 

structure(list(Date = structure(c(14246, 14249, 14250, 14251, 
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313L), class = "data.frame")```

Answer 1

This is how I would approach this:

library(data.table)
setDT(strategic_return)

#create column with row numbers
strategic_return[, rowID := .I]

#create a column with x values,
#since data doesn't contain these
strategic_return[, x := rnorm(.N)]

#reshape to long format
DF <- melt(strategic_return, id.vars = c("Date", "rowID", "x"))

#select data
DF <- DF[strategic_dateandcusip, .SD, on = c("variable == strategic_cusip", 
                                       "rowID >= row_start_regression",
                                       "rowID <= row_end_regression")]

library(nlme)
fit <- lmList(value ~ x | variable, data = DF, pool = FALSE)
summary(fit)

library(lmtest)
lapply(fit, coeftest, cov. = vcovHC, type = "HC1")