Perform the following steps to analyze the association rules:

1. First, you need to load the Groceries dataset:

        > data(Groceries)  

2. You can then examine the summary of the Groceries dataset:

        > summary(Groceries)  

3. Next, you can use itemFrequencyPlot to examine the relative item frequency of itemsets:

        > itemFrequencyPlot(Groceries, support = 0.1, cex.names=0.8,
        topN=5) 

4. Use apriori to discover rules with support over 0.001 and confidence over 0.5:

        > rules = apriori(Groceries, parameter = list(supp = 0.001, conf =
        0.5, target= "rules"))
        > summary(rules)
        Output
        set of 5668 rules
    
        rule length distribution (lhs + rhs):sizes
           2    3    4    5    6 
          11 1461 3211  939   46 
    
           Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
           2.00    3.00    4.00    3.92    4.00    6.00 
    
        summary of quality measures:
            support           confidence          lift       
         Min.   :0.001017   Min.   :0.5000   Min.   : 1.957  
         1st Qu.:0.001118   1st Qu.:0.5455   1st Qu.: 2.464  
         Median :0.001322   Median :0.6000   Median : 2.899  
         Mean   :0.001668   Mean   :0.6250   Mean   : 3.262  
         3rd Qu.:0.001729   3rd Qu.:0.6842   3rd Qu.: 3.691  
         Max.   :0.022267   Max.   :1.0000   Max.   :18.996  
    
        mining info:
              data ntransactions support confidence
         Groceries          9835   0.001        0.5  

5. We can then inspect the first few rules:

        > inspect(head(rules))
        Output
        lhsrhs              support confidence     lift
        1 {honey}             => {whole milk} 0.001118454  0.7333333
        2.870009
        2 {tidbits}           => {rolls/buns} 0.001220132  0.5217391
        2.836542
        3 {cocoa drinks}      => {whole milk} 0.001321810  0.5909091
        2.312611
        4 {pudding powder}    => {whole milk} 0.001321810  0.5652174
        2.212062
        5 {cooking chocolate} => {whole milk} 0.001321810  0.5200000
        2.035097
        6 {cereals}           => {whole milk} 0.003660397  0.6428571
        2.515917 

6. You can sort rules by confidence and inspect the first few rules:

        > rules=sort(rules, by="confidence", decreasing=TRUE)
        > inspect(head(rules))
        Output
        lhsrhs 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