> library(datasets)
> library(Matrix)
> library(arules)
> data(Groceries)
> Groceries
transactions in sparse format with
 9835 transactions (rows) and
 169 items (columns)
> summary(Groceries)
transactions as itemMatrix in sparse format with
 9835 rows (elements/itemsets/transactions) and
 169 columns (items) and a density of 0.02609146

most frequent items:
      whole milk other vegetables       rolls/buns             soda           yogurt          (Other)
            2513             1903             1809             1715             1372            34055

element (itemset/transaction) length distribution:
sizes
   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   26   27   28   29   32
2159 1643 1299 1005  855  645  545  438  350  246  182  117   78   77   55   46   29   14   14    9   11    4    6    1    1    1    1    3    1

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  1.000   2.000   3.000   4.409   6.000  32.000

includes extended item information - examples:
       labels  level2           level1
1 frankfurter sausage meat and sausage
2     sausage sausage meat and sausage
3  liver loaf sausage meat and sausage
> itemFrequencyPlot(Groceries,topN=20,type="absolute")
> rules <- apriori(Groceries, parameter = list(supp = 0.001, conf = 1))
Apriori

Parameter specification:
 confidence minval smax arem  aval originalSupport maxtime support minlen maxlen target   ext
          1    0.1    1 none FALSE            TRUE       5   0.001      1     10  rules FALSE

Algorithmic control:
 filter tree heap memopt load sort verbose
    0.1 TRUE TRUE  FALSE TRUE    2    TRUE

Absolute minimum support count: 9

set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
sorting and recoding items ... [157 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 6 done [0.01s].
writing ... [28 rule(s)] done [0.00s].
creating S4 object  ... done [0.00s].
> rules<-sort(rules,  by="confidence", decreasing=TRUE)
> rules
set of 28 rules
> subset.matrix <- is.subset(rules, rules)
> subset.matrix[lower.tri(subset.matrix, diag=T)] <- NA
> redundant <- colSums(subset.matrix, na.rm=T) >= 1
> rules.pruned <- rules[!redundant]
> rules<-rules.pruned
> inspect(rules)
     lhs                                                                 rhs                support     confidence lift
[1]  {rice,sugar}                                                     => {whole milk}       0.001220132 1          3.913649
[2]  {canned fish,hygiene articles}                                   => {whole milk}       0.001118454 1          3.913649
[3]  {root vegetables,butter,rice}                                    => {whole milk}       0.001016777 1          3.913649
[4]  {root vegetables,whipped/sour cream,flour}                       => {whole milk}       0.001728521 1          3.913649
[5]  {butter,soft cheese,domestic eggs}                               => {whole milk}       0.001016777 1          3.913649
[6]  {citrus fruit,root vegetables,soft cheese}                       => {other vegetables} 0.001016777 1          5.168156
[7]  {pip fruit,butter,hygiene articles}                              => {whole milk}       0.001016777 1          3.913649
[8]  {root vegetables,whipped/sour cream,hygiene articles}            => {whole milk}       0.001016777 1          3.913649
[9]  {pip fruit,root vegetables,hygiene articles}                     => {whole milk}       0.001016777 1          3.913649
[10] {cream cheese ,domestic eggs,sugar}                              => {whole milk}       0.001118454 1          3.913649
[11] {curd,domestic eggs,sugar}                                       => {whole milk}       0.001016777 1          3.913649
[12] {cream cheese ,domestic eggs,napkins}                            => {whole milk}       0.001118454 1          3.913649
[13] {pip fruit,whipped/sour cream,brown bread}                       => {other vegetables} 0.001118454 1          5.168156
[14] {tropical fruit,grapes,whole milk,yogurt}                        => {other vegetables} 0.001016777 1          5.168156
[15] {ham,tropical fruit,pip fruit,yogurt}                            => {other vegetables} 0.001016777 1          5.168156
[16] {ham,tropical fruit,pip fruit,whole milk}                        => {other vegetables} 0.001118454 1          5.168156
[17] {tropical fruit,root vegetables,yogurt,oil}                      => {whole milk}       0.001118454 1          3.913649
[18] {root vegetables,other vegetables,yogurt,oil}                    => {whole milk}       0.001423488 1          3.913649
[19] {root vegetables,other vegetables,butter,white bread}            => {whole milk}       0.001016777 1          3.913649
[20] {pork,other vegetables,butter,whipped/sour cream}                => {whole milk}       0.001016777 1          3.913649
[21] {other vegetables,butter,whipped/sour cream,domestic eggs}       => {whole milk}       0.001220132 1          3.913649
[22] {tropical fruit,butter,whipped/sour cream,fruit/vegetable juice} => {other vegetables} 0.001016777 1          5.168156
[23] {whole milk,rolls/buns,soda,newspapers}                          => {other vegetables} 0.001016777 1          5.168156
[24] {citrus fruit,whipped/sour cream,rolls/buns,pastry}              => {whole milk}       0.001016777 1          3.913649
[25] {citrus fruit,tropical fruit,root vegetables,whipped/sour cream} => {other vegetables} 0.001220132 1          5.168156
[26] {pip fruit,root vegetables,other vegetables,bottled water}       => {whole milk}       0.001118454 1          3.913649
[27] {sausage,tropical fruit,root vegetables,rolls/buns}              => {whole milk}       0.001016777 1          3.913649
> rules<-apriori(data=Groceries, parameter=list(supp=0.001,conf = 0.15,minlen=2),
+                appearance = list(default="rhs",lhs="whole milk"),
+                control = list(verbose=F))
> rules<-sort(rules, decreasing=TRUE,by="confidence")
> inspect(rules)
    lhs             rhs                support    confidence lift
[1] {whole milk} => {other vegetables} 0.07483477 0.2928770  1.5136341
[2] {whole milk} => {rolls/buns}       0.05663447 0.2216474  1.2050318
[3] {whole milk} => {yogurt}           0.05602440 0.2192598  1.5717351
[4] {whole milk} => {root vegetables}  0.04890696 0.1914047  1.7560310
[5] {whole milk} => {tropical fruit}   0.04229792 0.1655392  1.5775950
[6] {whole milk} => {soda}             0.04006101 0.1567847  0.8991124