R通过关联规则挖掘（规则）与不断变化的支持和项目外观进行循环

Question

fairly new to R here. I am looking to mine association rules in R for specific items, but I want to vary the minimum support target for these rules by each item (ie 10% of the item's total frequency in the transaction list). Each item has a different amount of transactions so I believe there is value in varying the support.

I've calculated the support targets for each item in a separate spreadsheet using Excel.

I can do this manually writing the arules code and manually inputting support minimum and item appearances, but the process is slow, especially with many different items.

ex.

arules <- apriori(trans, parameter = list(sup = 0.001, conf = 0.25,target="rules"), 
    appearance=list(rhs= c("Apples")))
arules2 <- apriori(trans, parameter = list(sup = 0.002, conf = 0.25,target="rules"), 
    appearance=list(rhs= c("Oranges")))
combined <- c(arules,arules2)

How can I do this using a for loop in R that will calculate the rules for each specified item at a specific support minimum, and also save those generated rules to a new variable each time the loop runs? I intend to later group these rules depending on their type.

Tried something like this which looped through too many times. I also couldn't figure out a way to save each loop to a new variable (ie arules1, arules2, arules3....)

min_supp <- c(0.001,0.002,0.003)
names <- c("Apples","Oranges","Grape")

for (inames in names) {
    for (supports in min_supp) {
        apriori(trans, parameter = list(sup = supports, conf = 0.25,target="rules"),
        appearance=list(rhs= inames))
        }}

Thanks in advance!

Answer 1

Consider Map (the simplified wrapper to mapply ) that can iterate elementwise through same length vectors for a m ultiple apply method. Additionally, Map will output a list of the returned items which can be named with setNames . Lists are always preferred as you avoid separate, similarly structured objects flooding global environment.

min_supp <- c(0.001,0.002,0.003)
names <- c("Apples","Oranges","Grape")

arules_fun <- function(n, s) apriori(trans, parameter = list(sup = s, conf = 0.25, target="rules"), 
                                     appearance=list(rhs= n))

# PROCESS FUNCTION ELEMENTWISE 
arules_list <- Map(arules_fun, names, min_supp)    
# NAME LIST ITEMS
arules_list <- setNames(arules_list, paste0("arules", 1:length(arules_list)))

arules_list$arules1
arules_list$arules2
arules_list$arules3
...