Loop same actions in R - r

Question

Have an issue here.
I want to loop my operations in R, however, do not know how to make this properly and efficiently.
I have several different sized datasets and performing the same block of code each time is time-consuming.
Here is the code I need to apply to each of the datasets and write the data or the output from a model into the datasets with different names.
##########################################################################################################################
#the combined list of separate data frames where the last letter is changing A, B, C...
z <- list(Data_A, Data_B, Data_C)
#need to loop these operations performed by using data from datasets. Here is an example by using data from Data_A dataset.
# TFP estimation by using ACF method
ACF_A <- prodest::prodestACF(Data_A$turn, fX = Data_A$cogs, sX = Data_A$tfa, pX = Data_A$cogs, idvar = Data_A$ID, timevar = Data_A$Year,
R = 100, cX = NULL, opt = 'DEoptim', theta0 = NULL, cluster = NULL)
omegaACF_A <- prodest::omega(ACF_A)
Data_A$omegaACF_A <- prodest::omega(ACF_A)
#########################################################################################################################
# Growth variables
Data_A <- Data_A %>%
arrange(ID, Year) %>%
group_by(ID) %>%
mutate(domegaACF_A = omegaACF_A - dplyr::lag(omegaACF_A),
debt = LOAN + LTD,
ddebt = debt - dplyr::lag(debt),
dsales = SALE - dplyr::lag(SALE)) %>%
ungroup
# Panel data frame
PData_A <- pdata.frame(Data_A, index = c("ID","Year"))
# Within estimator
within_2way_A <- plm(domegaACF_A ~ dplyr::lag(domegaACF_A, 1) + dplyr::lag(domegaACF_A, 2) + ddebt + lag(ff1, 1) + ddebt:lag(ff1, 1) + log(Age) + ta + dsales,
data = PData_A, effect = "twoways", model ="within", index = c("ID", "Year"))
The main problem is that I do not know how to store the data in separate datasets with according names. For example, in the block of the following code, _A should be changing to _B, _C according to the dataset that is used.
ACF_A <- prodest::prodestACF(Data_A$turn, fX = Data_A$cogs, sX = Data_A$tfa, pX = Data_A$cogs, idvar = Data_A$ID, timevar = Data_A$Year,
R = 100, cX = NULL, opt = 'DEoptim', theta0 = NULL, cluster = NULL)
omegaACF_A <- prodest::omega(ACF_A)
Data_A$omegaACF_A <- prodest::omega(ACF_A)
I know there are lapply and for loops but I do not know how to use them with changing names of storing variables:
z <- list (df1, df2, df3)
for (i in z){
ACF_[1 or 2 or 3] <- prodest::prodestACF(i$turn, fX = i$cogs, sX = i$tfa, pX = i$cogs, idvar = i$ID, timevar = i$Year,
R = 100, cX = NULL, opt = 'DEoptim', theta0 = NULL, cluster = NULL)
omegaACF_[1 or 2 or 3] <- prodest::omega(ACF_[1 or 2 or 3])
Data_[]$omegaACF_[1 or 2 or 3] <- prodest::omega(ACF_[1 or 2 or 3])
{
UPD: Here are several datasets: https://drive.google.com/drive/folders/1gBV2ZkywW6JqDjRICafCwtYhh2DHWaUq?usp=sharing
UPD2:
Data_A
turn cogs tfa SALE
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
Data_B
turn cogs tfa SALE
5 5 5 5
6 6 6 6
7 7 7 7
8 8 8 8
After running the loop I need:
ACF_A, ACF_B, etc. storage variable, where the results of the estimations of prodest function will be stored
omegaACF_A, omegaACF_B, etc. storage where omega variable from prodest will be stored
omegaACF_A, omegaACF_B results of estimations should be added to Data_A, Data_B datasets accordingly as a new variable.
After that, in Data_A, Data_B datasets, growth variables should be created
The plm regression should be stored in within_2way_A, within_2way_B accordingly
So in the end, I need:
Data_A
turn cogs tfa SALE omegaACF_A domegaACF_A debt ddebt dsales
1 1 1 1 0.1 NA 1 NA NA
2 2 2 2 0.3 0.2 2 1 1
3 3 3 3 0.6 0.3 3 1 1
4 4 4 4 0.9 0.3 4 1 1
Data_B
turn cogs tfa SALE omegaACF_B domegaACF_B debt ddebt dsales
5 5 5 5 1.1 NA 5 NA NA
6 6 6 6 1.5 0.4 6 1 1
7 7 7 7 1.7 0.2 7 1 1
8 8 8 8 2.0 0.3 8 1 1

Answer 1

One approach is to separate the ACF estimation and omega calculation from the summary creation with different lapply() commands. Since you did not supply any example data, it's a blind shot, but try the following. Note that I assumed that every dataset has the same column names! In case it doesn't solve your problem I will remove my answer.
data <- list(Data_A, Data_B, Data_C)
Estimates <- lapply(data, function(x){
prodest::prodestACF(x$turn, fX = x$cogs, sX = x$tfa, pX = x$cogs, idvar = x$ID, timevar = x$Year,
R = 100, cX = NULL, opt = 'DEoptim', theta0 = NULL, cluster = NULL)
}
Summaries_estimates <- lapply(Estimates, summary)
Omegas <- lapply(Estimates, function(x) prodest::omega(x))
Summaries_omega <- lapply(Omegas, summary)
Alternative using loops
Since you asked, it is also possible to define a loop that loops everything together though this is usually much slower. For this, we have to define empty lists that carry the results of ACF etc. and loop over the lists of data.frames that we already created.
data <- list(Data_A, Data_B, Data_C)
Estimates <- list()
Summaries_estimates <- list()
Omegas <- list()
Summaries_omegas <- list()
for(i in 1:(length(data))){
Estimates[[i]] <- prodest::prodestACF(data[[i]]$turn, fX = data[[i]]$cogs, sX = data[[i]]$tfa, pX = data[[i]]$cogs, idvar = data[[i]]$ID, timevar = data[[i]]$Year,
R = 100, cX = NULL, opt = 'DEoptim', theta0 = NULL, cluster = NULL)
}
Summaries_estimates[[i]] <- summary(Estimates[[i]])
Omegas[[i]] <- prodest::omega(Estimates[[i]]))
Summaries_omega[[i]] <- summary(Omegas[[i]])
}