How to add up data frames from a loop within a function? - r

Question

I have a function whose purpose is to predict revenue from cost. The twist is, I have as inputs many different dataframes, and many different corresponding models to predict with - the function loops through each dataframe, predicting on it its corresponding model and outputing a prediction output with confidence intervals. Now, I need to find a way to add all of these prediction outputs up.
Here's a simplified example of what I'm doing, feel free to skip over it if you don't need it to answer the question (it might be hard to read), but if it helps read away. Note that each prediction output isn't a dataframe of cost and revenue, but a summary of what revenues you can expect from a variable cost.
predictions <- function(df_list, model_list) {
for(i in 1:length(df_list)) {
sapply(seq(1, 2, .25), function(x) {
df_list[[i]]$cost <- df_list[[i]]$cost * x
predictions <- predict(model_list[[i]], df_list[[i]], interval = "confidence")
temp <- cbind(df_list[[i]]$cost, predictions)
output <- summarise(temp, Cost = sum(cost), Low = sum(lwr), Fit = sum(fit), Upper = sum(upr))
output
}) -> output
output %>% t %>% as.data.frame -> output
}
}
With the output for each index looking like this:
Cost Lower_Rev Fit_Rev Upper_Rev
1 2048884 18114566 20898884 24145077
2 2561105 21684691 25085853 29064495
3 3073326 25092823 29122421 33853693
4 3585547 28369901 33038060 38539706
5 4097768 31537704 36853067 43140547
I need some way to add together each output into one master output, whose cost and revenue values will be the sum of all others. Any ideas?

Answer 1

Your output simply gets replaced every time. You need to assign it to something or just simply use a lapply/sapply. Also added i as an argument as opposed to abusing R's scoping rules and grabbing the argument i from .GlobalEnv
L = lapply(1:length(df_list),
function(i) sapply(seq(1, 2, .25), function(x, i) {
df_list[[i]]$cost <- df_list[[i]]$cost * x
predictions <- predict(model_list[[i]], df_list[[i]], interval = "confidence")
temp <- cbind(df_list[[i]]$cost, predictions)
output <- summarise(temp, Cost = sum(cost), Low = sum(lwr), Fit = sum(fit), Upper = sum(upr))
output %>% t %>% as.data.frame
})
)

Answer 2

Found a solution: Kind of wonky, but it works.
At the beginning of the function, I establish the following vectors:
costs <- c()
lows <- c()
fits <- c()
highs <- c()
Those are in accordance with the four columns of my output. Then, at the end of the loop, after the -> output statement, I run this:
costs[i] <- output[1]
lows[i] <- output[2]
fits[i] <- output[3]
highs[i] <- output[4]
For some reason it wouldn't work to simply assign each full df to a list of dfs; I had to do it vector by vector. Then, once each vector is stored with the full extent of my index, I ran this:
costs <- sapply(costs, unlist) %>% rowSums %>% as.data.frame
lows <- sapply(lows, unlist) %>% rowSums %>% as.data.frame
fits <- sapply(fits, unlist) %>% rowSums %>% as.data.frame
highs <- sapply(highs, unlist) %>% rowSums %>% as.data.frame
output <- cbind(costs, lows, fits, highs)
output
...to end the function. So really weird all in all, but it works.