Data624HWWeek14
Scott Reed,
12/3/2021
Data 624 HW Week 14
groceriesTran<- read.transactions("GroceryDataSet.csv",format="basket", header=F, sep=",")
summary(groceriesTran)## 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 16
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55 46
## 17 18 19 20 21 22 23 24 26 27 28 29 32
## 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 cosmeticsitemFrequencyPlot(groceriesTran,topN=15, col = brewer.pal(8,'Accent'), type="absolute")
association.rules <- apriori(groceriesTran, parameter = list(supp=0.001, conf=0.8,maxlen=10))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules TRUE
##
## 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 [0.01s].
## sorting and recoding items ... [157 item(s)] done [0.00s].
## creating transaction tree ... done [0.01s].
## checking subsets of size 1 2 3 4 5 6 done [0.04s].
## writing ... [410 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].inspect(head(association.rules, n=10, by="lift"))## lhs rhs support confidence coverage lift count
## [1] {liquor,
## red/blush wine} => {bottled beer} 0.001931876 0.9047619 0.002135231 11.235269 19
## [2] {citrus fruit,
## fruit/vegetable juice,
## other vegetables,
## soda} => {root vegetables} 0.001016777 0.9090909 0.001118454 8.340400 10
## [3] {oil,
## other vegetables,
## tropical fruit,
## whole milk,
## yogurt} => {root vegetables} 0.001016777 0.9090909 0.001118454 8.340400 10
## [4] {citrus fruit,
## fruit/vegetable juice,
## grapes} => {tropical fruit} 0.001118454 0.8461538 0.001321810 8.063879 11
## [5] {other vegetables,
## rice,
## whole milk,
## yogurt} => {root vegetables} 0.001321810 0.8666667 0.001525165 7.951182 13
## [6] {oil,
## other vegetables,
## tropical fruit,
## whole milk} => {root vegetables} 0.001321810 0.8666667 0.001525165 7.951182 13
## [7] {ham,
## other vegetables,
## pip fruit,
## yogurt} => {tropical fruit} 0.001016777 0.8333333 0.001220132 7.941699 10
## [8] {beef,
## citrus fruit,
## other vegetables,
## tropical fruit} => {root vegetables} 0.001016777 0.8333333 0.001220132 7.645367 10
## [9] {butter,
## cream cheese,
## root vegetables} => {yogurt} 0.001016777 0.9090909 0.001118454 6.516698 10
## [10] {butter,
## sliced cheese,
## tropical fruit,
## whole milk} => {yogurt} 0.001016777 0.9090909 0.001118454 6.516698 10summary(association.rules)## set of 410 rules
##
## rule length distribution (lhs + rhs):sizes
## 3 4 5 6
## 29 229 140 12
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 4.000 4.000 4.329 5.000 6.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.8000 Min. :0.001017 Min. : 3.131
## 1st Qu.:0.001017 1st Qu.:0.8333 1st Qu.:0.001220 1st Qu.: 3.312
## Median :0.001220 Median :0.8462 Median :0.001322 Median : 3.588
## Mean :0.001247 Mean :0.8663 Mean :0.001449 Mean : 3.951
## 3rd Qu.:0.001322 3rd Qu.:0.9091 3rd Qu.:0.001627 3rd Qu.: 4.341
## Max. :0.003152 Max. :1.0000 Max. :0.003559 Max. :11.235
## count
## Min. :10.00
## 1st Qu.:10.00
## Median :12.00
## Mean :12.27
## 3rd Qu.:13.00
## Max. :31.00
##
## mining info:
## data ntransactions support confidence
## groceriesTran 9835 0.001 0.8
## call
## apriori(data = groceriesTran, parameter = list(supp = 0.001, conf = 0.8, maxlen = 10))subRules<-association.rules[quality(association.rules)$confidence>0.8]
plot(subRules)## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(head(association.rules, n=10, by="lift"), method="paracoord")
plot(head(association.rules, n=20, by="lift"), method = "graph", engine = "htmlwidget")Dairy products tend to flock together, and root vegetables are situationally popular, though there is a lovely party hearty linkage of liquor, red/blush wine leading to bottled beer.
References
– Market Basket Analysis using R https://www.datacamp.com/community/tutorials/market-basket-analysis-r