A recommender system on a Grocery dataset

Question

Good morning,

I'm currently working on the dataset Groceries from the library Arules, and unfortunatly, I can't seem to identify the unique items with "plyr".

Here's a snippet of a few entries

> inspect(Groceries[4:8])
items                                                                 
[1] {pip fruit,yogurt,cream cheese ,meat spreads}                         
[2] {other vegetables,whole milk,condensed milk,long life bakery product} 
[3] {whole milk,butter,yogurt,rice,abrasive cleaner}                      
[4] {rolls/buns}                                                          
[5] {other vegetables,UHT-milk,rolls/buns,bottled beer,liquor (appetizer)}

I already generated my association rules. Here's a snippet if that can help :

     > inspect(grules[1:10])
 lhs                rhs                support     confidence coverage   lift     count
 [1]  {baking powder} => {whole milk}       0.009252669 0.5229885  0.01769192 2.046793  91  
 [2]  {grapes}        => {other vegetables} 0.009049314 0.4045455  0.02236909 2.090754  89  
 [3]  {meat}          => {other vegetables} 0.009964413 0.3858268  0.02582613 1.994013  98  
 [4]  {meat}          => {whole milk}       0.009964413 0.3858268  0.02582613 1.509991  98  
 [5]  {frozen meals}  => {whole milk}       0.009862735 0.3476703  0.02836807 1.360659  97

So my question is, how can I identify the unique items (or even the unique users if possible) with the "plyr" package? Because unfortunatly, there's no way to use any specific ID because the dataset doesn't really have any... Maybe by using the "count" parameter maybe?

Thanks again for any insight