Getting result of function using common columns in R - r

Question

This is an extension of question on Function and looping in training and testing set using r.
How to get the result of the function (func1) given below in external folder using common columns and then each with its own additional column output? Moreover, how can I get the output of each unique data_by_plot result in external folder. I used write.table(func1, “c:\\Document\\project\\result), but I couldn’t get in the way I want. My code is given below. I tried different ways using cbind and rbind but it doesn’t give me what I want.
My code is :
result<- c()
data$groups <- paste(data$Plot, data$Species, sep = "_")
data_by_plot <- split (data$Count, data$groups)
func1<- do.call(rbind, lapply(data_by_plot, function(df){
Training<-df[1:20,]
Testing<-df[21:30,]
Model1<-lm(count~1, data = Training)
Pred1<-Testing$Count[i]- Model1$coefficients
Model2<-lm(Diff~1, data = Training)
Pred2<-Testing$Count[i]- Model2$coefficients
Model3<-lm(Diff~1+LogCount, data = Training)
Pred3<-Testing$Count[i]- Model3$coefficients
Model4<-lm(Diff~1+Count, data = Training)
Pred4<-Testing$Count[i]- Model4$coefficients
result <- Reduce(merge, list(Pred1, Pred2, Pred3, Pred4))
return(result)
})

Answer 1

Ok with clarification from your comment above. There are two ways you could do this. You could incorporate it into the function itself or pull out the result of the function and pass it to an export function.
I think the easiest way would be the former, so create an export function:
export.function <- function(result){
path <- "//folder/" #whatever your path is to the folder
as.data.frame(result) -> result #turn to data.frame
write.csv(paste0(path, result, ".csv"))
}
This will write the result, as a data frame, as a csv in the path designated. (It will name it "result.csv").
Then add it:
result<- c()
data$groups <- paste(data$Plot, data$Species, sep = "_")
data_by_plot <- split (data$Count, data$groups)
func1<- do.call(rbind, lapply(data_by_plot, function(df){
Training<-df[1:20,]
Testing<-df[21:30,]
Model1<-lm(count~1, data = Training)
Pred1<-Testing$Count[i]- Mean_model$coefficients
Model2<-lm(Diff~1, data = Training)
Pred2<-Testing$Count[i]- Mean_model$coefficients
Model3<-lm(Diff~1+LogCount, data = Training)
Pred3<-Testing$Count[i]- Mean_model$coefficients
Model4<-lm(Diff~1+Count, data = Training)
Pred4<-Testing$Count[i]- Mean_model$coefficients
result <- Reduce(merge, list(Pred1, Pred2, Pred3, Pred4))
export.function(result)
})
The other way would be to just do:
func1(df) -> results
export.function(results)