Identifying Frequently-Purchased Groceries
Exploring and preparing the data
load the grocery data into a sparse matrix 

library(arules)
## Loading required package: Matrix
## 
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
## 
##     abbreviate, write
library(arulesViz)
## Loading required package: grid
groceries <- read.transactions("groceries.csv", sep = ",")
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 
##             2513             1903             1809             1715 
##           yogurt          (Other) 
##             1372            34055 
## 
## element (itemset/transaction) length distribution:
## sizes
##    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15 
## 2159 1643 1299 1005  855  645  545  438  350  246  182  117   78   77   55 
##   16   17   18   19   20   21   22   23   24   26   27   28   29   32 
##   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
## 1 abrasive cleaner
## 2 artif. sweetener
## 3   baby cosmetics

look at the first five transactions

inspect(groceries[1:5])
##     items                     
## [1] {citrus fruit,            
##      margarine,               
##      ready soups,             
##      semi-finished bread}     
## [2] {coffee,                  
##      tropical fruit,          
##      yogurt}                  
## [3] {whole milk}              
## [4] {cream cheese,            
##      meat spreads,            
##      pip fruit,               
##      yogurt}                  
## [5] {condensed milk,          
##      long life bakery product,
##      other vegetables,        
##      whole milk}

Examine the frequency of items

itemFrequency(groceries[, 1:3])
## abrasive cleaner artif. sweetener   baby cosmetics 
##     0.0035587189     0.0032536858     0.0006100661

plot the frequency of items

itemFrequencyPlot(groceries, support = 0.1)

itemFrequencyPlot(groceries, topN = 20)

a visualization of the sparse matrix for the first five transactions

image(groceries[1:5])

visualization of a random sample of 100 transactions

image(sample(groceries, 100))

Training a model on the data default settings result in zero rules learned

apriori(groceries)
## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.8    0.1    1 none FALSE            TRUE       5     0.1      1
##  maxlen target   ext
##      10  rules FALSE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 983 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [8 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 done [0.00s].
## writing ... [0 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].
## set of 0 rules

set better support and confidence levels to learn more rules

groceryrules <- apriori(groceries, parameter = list(support =0.006, confidence = 0.25, minlen = 2))
## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##        0.25    0.1    1 none FALSE            TRUE       5   0.006      2
##  maxlen target   ext
##      10  rules FALSE
## 
## Algorithmic control:
##  filter tree heap memopt load sort verbose
##     0.1 TRUE TRUE  FALSE TRUE    2    TRUE
## 
## Absolute minimum support count: 59 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[169 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [109 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [463 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].
groceryrules
## set of 463 rules

Evaluating model performance summary of grocery association rules

summary(groceryrules)
## set of 463 rules
## 
## rule length distribution (lhs + rhs):sizes
##   2   3   4 
## 150 297  16 
## 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   2.000   3.000   2.711   3.000   4.000 
## 
## summary of quality measures:
##     support           confidence          lift       
##  Min.   :0.006101   Min.   :0.2500   Min.   :0.9932  
##  1st Qu.:0.007117   1st Qu.:0.2971   1st Qu.:1.6229  
##  Median :0.008744   Median :0.3554   Median :1.9332  
##  Mean   :0.011539   Mean   :0.3786   Mean   :2.0351  
##  3rd Qu.:0.012303   3rd Qu.:0.4495   3rd Qu.:2.3565  
##  Max.   :0.074835   Max.   :0.6600   Max.   :3.9565  
## 
## mining info:
##       data ntransactions support confidence
##  groceries          9835   0.006       0.25

look at the first three rules

inspect(groceryrules[100:110])
##      lhs               rhs                support    confidence lift     
## [1]  {curd}         => {whole milk}       0.02613116 0.4904580  1.9194805
## [2]  {napkins}      => {other vegetables} 0.01443823 0.2757282  1.4250060
## [3]  {napkins}      => {whole milk}       0.01972547 0.3766990  1.4742678
## [4]  {pork}         => {other vegetables} 0.02165735 0.3756614  1.9414764
## [5]  {pork}         => {whole milk}       0.02216573 0.3844797  1.5047187
## [6]  {frankfurter}  => {rolls/buns}       0.01921708 0.3258621  1.7716161
## [7]  {frankfurter}  => {other vegetables} 0.01647178 0.2793103  1.4435193
## [8]  {frankfurter}  => {whole milk}       0.02053889 0.3482759  1.3630295
## [9]  {bottled beer} => {whole milk}       0.02043721 0.2537879  0.9932367
## [10] {brown bread}  => {other vegetables} 0.01870869 0.2884013  1.4905025
## [11] {brown bread}  => {whole milk}       0.02521607 0.3887147  1.5212930

Improving model performance finding subsets of rules containing any berry items

berryrules <- subset(groceryrules, items %in% "berries")
inspect(berryrules)
##     lhs          rhs                  support     confidence lift    
## [1] {berries} => {whipped/sour cream} 0.009049314 0.2721713  3.796886
## [2] {berries} => {yogurt}             0.010574479 0.3180428  2.279848
## [3] {berries} => {other vegetables}   0.010269446 0.3088685  1.596280
## [4] {berries} => {whole milk}         0.011794611 0.3547401  1.388328

writing the rules to a CSV file

write(groceryrules, file = "groceryrules.csv",sep = ",", quote = TRUE, row.names = FALSE)

converting the rule set to a data frame

groceryrules_df <- as(groceryrules, "data.frame")
str(groceryrules_df)
## 'data.frame':    463 obs. of  4 variables:
##  $ rules     : Factor w/ 463 levels "{baking powder} => {other vegetables}",..: 340 302 207 206 208 341 402 21 139 140 ...
##  $ support   : num  0.00691 0.0061 0.00702 0.00773 0.00773 ...
##  $ confidence: num  0.4 0.405 0.431 0.475 0.475 ...
##  $ lift      : num  1.57 1.59 3.96 2.45 1.86 ...