Pre-allocation and optimization loop - r

Question

My R script have the form:
for (j in 1:N) {
#construct the DF2 data frame
#operations on the DF2 data frame
}
Where N can be large (like a 1 mln). The columns of DF2 are defined
one after the other with the formula:
DF2$column_i <- function(x,f..) #or constant or ....
DF$column_i can are a constant, a function or a loop "while". I tried to pre allocate defining DF2 before with:
DF2 <- data.frame(matrix(nrow=..,ncol=..))
and computing after the columns DF2$column_i, but I have not had any benefits.
Does anyone have any ideas?

Answer 1

My code is of the type:
par <- data.frame(CA=runif(n = 50, min = 70000, max = 100000),
D=round(runif(n = 50, min = 70, max = 90),0),
P=runif(n = 50, min = 900, max = 20000),
A=round(runif(n = 50, min = 50, max = 70),0))
parpa <- data.frame(matrix(nrow = nrow(par), ncol = 3*V))
comp <- function(CA, D, P, A){
vect <- rep('numeric', 3*V)
b <- 1
k <- 1
while (((b+1) <= (D+1))&(k < V)) {
a <- b+1
b <- min((a+8-1), (D+1))
vect[c(1+4*k, 2+4*k, 3+4*k, 4+4*k)] <- c(mean(DF2$Z[a:b]), sum(DF2$X[a:b]),
mean(DF2$Q[a:b]), sum(DF2$AE[a:b]))
k <- k+1
}
return(vect)
}
#loop
for (j in 1:nrow(par)) {
CA <- par$CA[j]
D <- par$D[j]
R <- 0.01*D
P <- par$P[j]
A <- par$A[j]
COST <- 500
V <- 5
#DF2
DF2 <- data.frame(M=0:D)
OB <- function(x) {
c <- COST*D*DF2$M/R
return(c)
}
DF2$O <- O(D)
DF2$E <- (D*DF2$M+2)/D*(D+4)
DF2$Q <- (CA-DF2$M)*D
DF2$X <- (CA-DF2$O)*(DF2$E+P)
Func <- function(x) {return(round(x/30, 2))}
DF2$Z[(A+2):(D+1)] <- sapply(DF2$E[(A+2):(D+1)], Func)
parpa[j,] <- comp(CA, D, P, A)
}