Using a FOR-loop to calculate AUC of multiple dataframes - r

Question

For a certain problem I want to perform a RandomForest classifier over multiple datasets and compare the AUC's of said datasets. I want to use a 'lazy' approach, so instead of doing the classification n-times over multiple datasets, I wanted to use a for-loop to do this for me instead.
So, a for-loop that loops over multiple datasets, performs randomforest classification, calculate the AUC en store this AUC in a empty matrix/dataframe. The result should be a table/matrix which shows me a column for each dataset and a row showing the AUC of each dataset.
I prepped some code using the Iris dataset to get started, but don't have any experience with using for-loops on this kind of problem. Hopefully somebody can help me out or even to get me thinking in the right direction!?
Example:
require(pROC)
require(randomForest)
#use the Iris dataset as example
data(iris)
#make a simple 2-class outcome over the Iris dataset
iris <- iris[-which(iris$Species=="setosa"),]
iris$Species<-as.factor(as.character(iris$Species))
#create list of dataframes we want to use
df1 <- iris
df2 <- iris
df_list <- list(df1, df2)
#create empty matrix to store results in
results_matrix <- matrix(ncol=2, nrow=1)
#create a for loop to calculate and store AUC of each dataframe
for(df in df_list){
rf_model <- randomForest::randomForest(Species ~., data = df)
rf_model_roc <- roc(iris$Species,rf_model$votes[,2])
df_auc <- auc(rf_model_roc)
#store df_auc of each df in results_matrix
}

Answer 1

You can create a vector to store the values from AUC.
For example,
#create list of dataframes we want to use
df1 <- iris
df2 <- iris
df_list <- list(df1, df2)
results_vec <- numeric(length(df_list))
#create a for loop to calculate and store AUC of each dataframe
for(i in seq_along(df_list)) {
data <- df_list[[i]]
rf_model <- randomForest::randomForest(Species ~., data = data)
rf_model_roc <- roc(data$Species,rf_model$votes[,2])
results_vec[i] <- as.numeric(auc(rf_model_roc))
}
results_vec