Coercing "factor" to "transactions" R when using Apriori

Question

I want to perform the Apriori method on the Cuisines column on my dataset.

Cusisine column sample:

   [4] Japanese, Sushi                                                                   
   [5] Japanese, Korean                                                                  
   [6] Chinese                                                                           
   [7] Asian, European                                                                   
   [8] Seafood, Filipino, Asian, European                                                
   [9] European, Asian, Indian                                                           
  [10] Filipino                                                                          
  [11] Filipino, Mexican            

My code:

install.packages("arules") 
 library("arules")

 itemsets <- apriori(dataSet$Cuisines, parameter=list(support=0.02, minlen=1, maxlen=1, target="frequent itemsets"))

However i keep getting:

no method or default for coercing “factor” to “transactions”

What goes wrong in here?

Is it illogical to use Apriori method on this column in my dataset?

If yes, what type of columns should i use the apriori method on?

Answer 1

You have to convert your data to transaction type:

dats <- strsplit(as.character(dats$Cuisines),',',fixed=T) # split by comma    
trans <- as(dats, "transactions")
inspect(trans)
    items                               
[1] { Sushi,Japanese}                   
[2] { Korean,Japanese}                  
[3] {Chinese}                           
[4] { European,Asian}                   
[5] { Asian, European, Filipino,Seafood}
[6] { Asian, Indian,European}           
[7] {Filipino}                          
[8] { Mexican,Filipino}

Then you can do apriori:

itemsets <- apriori(trans, parameter=list(support=0.3))
inspect(itemsets)

---

With data:

 dats <- read.table(text ="    Cuisines
       [1] 'Japanese, Sushi'                                                                   
       [2] 'Japanese, Korean'                                                                  
       [3] 'Chinese'                                                                           
       [4] 'Asian, European'                                                                   
       [5] 'Seafood, Filipino, Asian, European'                                                
       [6] 'European, Asian, Indian'                                                           
       [7] 'Filipino'                                                                          
       [8] 'Filipino, Mexican' ", header = T)