Association Rules For groceries
GRData <- read.csv("D:\\DataScience\\Assignments\\AssociationRules\\groceries.csv")
#install.packages("mvinfluence")
library(car)
## Warning: package 'car' was built under R version 3.5.1
## Loading required package: carData
library(carData)
library(arules)
## Warning: package 'arules' was built under R version 3.5.1
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(arulesViz)
## Warning: package 'arulesViz' was built under R version 3.5.1
## Loading required package: grid
library(mvinfluence)
## Warning: package 'mvinfluence' was built under R version 3.5.1
## Loading required package: heplots
## Warning: package 'heplots' was built under R version 3.5.1
GRData1 <- as(GRData,"transactions")
# Item Frequency plot
itemFrequencyPlot(GRData1,topN=25)
grocires_apriori <- apriori(GRData1, parameter = list(supp=0.005, conf=0.45, minlen=2, maxlen=4))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.45 0.1 1 none FALSE TRUE 5 0.005 2
## maxlen target ext
## 4 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 76
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[655 item(s), 15295 transaction(s)] done [0.03s].
## sorting and recoding items ... [168 item(s)] done [0.00s].
## creating transaction tree ... done [0.02s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [177 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
grocires_apriori
## set of 177 rules
#Ploting Grocires Rules
inspect(head(sort(grocires_apriori),n=20))
## lhs rhs support confidence lift count
## [1] {margarine=} => {ready.soups=} 0.39980386 1.0000000 1.8613849 6115
## [2] {ready.soups=} => {margarine=} 0.39980386 0.7441889 1.8613849 6115
## [3] {semi.finished.bread=} => {margarine=} 0.22785224 1.0000000 2.5012265 3485
## [4] {margarine=} => {semi.finished.bread=} 0.22785224 0.5699101 2.5012265 3485
## [5] {semi.finished.bread=} => {ready.soups=} 0.22785224 1.0000000 1.8613849 3485
## [6] {semi.finished.bread=,
## margarine=} => {ready.soups=} 0.22785224 1.0000000 1.8613849 3485
## [7] {semi.finished.bread=,
## ready.soups=} => {margarine=} 0.22785224 1.0000000 2.5012265 3485
## [8] {margarine=,
## ready.soups=} => {semi.finished.bread=} 0.22785224 0.5699101 2.5012265 3485
## [9] {citrus.fruit=soda} => {ready.soups=} 0.03059823 0.8509091 1.5838694 468
## [10] {citrus.fruit=whole milk} => {ready.soups=} 0.02902909 0.5091743 0.9477694 444
## [11] {citrus.fruit=rolls/buns} => {ready.soups=} 0.02576005 0.7150635 1.3310085 394
## [12] {citrus.fruit=canned beer} => {ready.soups=} 0.02451782 0.9664948 1.7990189 375
## [13] {citrus.fruit=soda} => {margarine=} 0.02451782 0.6818182 1.7053817 375
## [14] {citrus.fruit=soda,
## margarine=} => {ready.soups=} 0.02451782 1.0000000 1.8613849 375
## [15] {citrus.fruit=soda,
## ready.soups=} => {margarine=} 0.02451782 0.8012821 2.0041879 375
## [16] {citrus.fruit=canned beer} => {margarine=} 0.02262177 0.8917526 2.2304752 346
## [17] {citrus.fruit=canned beer,
## margarine=} => {ready.soups=} 0.02262177 1.0000000 1.8613849 346
## [18] {citrus.fruit=canned beer,
## ready.soups=} => {margarine=} 0.02262177 0.9226667 2.3077983 346
## [19] {semi.finished.bread=soda} => {ready.soups=} 0.02000654 0.6270492 1.1671799 306
## [20] {citrus.fruit=rolls/buns} => {margarine=} 0.01922197 0.5335753 1.3345927 294
inspect(tail(sort(grocires_apriori),n=20))
## lhs rhs support confidence lift count
## [1] {citrus.fruit=domestic eggs} => {ready.soups=} 0.005295848 0.4525140 0.8423027 81
## [2] {citrus.fruit=hygiene articles} => {margarine=} 0.005230467 0.8888889 2.2233124 80
## [3] {semi.finished.bread=specialty chocolate} => {ready.soups=} 0.005230467 0.8421053 1.5674820 80
## [4] {citrus.fruit=napkins} => {semi.finished.bread=} 0.005230467 0.5925926 2.6007758 80
## [5] {citrus.fruit=hygiene articles,
## margarine=} => {ready.soups=} 0.005230467 1.0000000 1.8613849 80
## [6] {citrus.fruit=hygiene articles,
## ready.soups=} => {margarine=} 0.005230467 0.8888889 2.2233124 80
## [7] {citrus.fruit=napkins,
## semi.finished.bread=} => {margarine=} 0.005230467 1.0000000 2.5012265 80
## [8] {citrus.fruit=napkins,
## margarine=} => {semi.finished.bread=} 0.005230467 0.6400000 2.8088379 80
## [9] {citrus.fruit=napkins,
## semi.finished.bread=} => {ready.soups=} 0.005230467 1.0000000 1.8613849 80
## [10] {citrus.fruit=napkins,
## ready.soups=} => {semi.finished.bread=} 0.005230467 0.6015038 2.6398852 80
## [11] {citrus.fruit=napkins,
## semi.finished.bread=,
## margarine=} => {ready.soups=} 0.005230467 1.0000000 1.8613849 80
## [12] {citrus.fruit=napkins,
## semi.finished.bread=,
## ready.soups=} => {margarine=} 0.005230467 1.0000000 2.5012265 80
## [13] {citrus.fruit=napkins,
## margarine=,
## ready.soups=} => {semi.finished.bread=} 0.005230467 0.6400000 2.8088379 80
## [14] {semi.finished.bread=coffee} => {ready.soups=} 0.005165087 0.5524476 1.0283176 79
## [15] {margarine=pip fruit} => {semi.finished.bread=tropical fruit} 0.005165087 0.5337838 22.7415682 79
## [16] {semi.finished.bread=specialty bar} => {ready.soups=} 0.005099706 0.8764045 1.6313261 78
## [17] {semi.finished.bread=napkins} => {margarine=} 0.005099706 0.6610169 1.6533531 78
## [18] {margarine=napkins} => {ready.soups=} 0.005099706 0.6000000 1.1168310 78
## [19] {semi.finished.bread=napkins,
## margarine=} => {ready.soups=} 0.005099706 1.0000000 1.8613849 78
## [20] {semi.finished.bread=napkins,
## ready.soups=} => {margarine=} 0.005099706 0.6902655 1.7265103 78
plot(head(sort(grocires_apriori),n=20), method="graph", control=list(cex=0.70))
plot(grocires_apriori)
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(head(sort(grocires_apriori),n=10), method="grouped", control=list(cex=0.2))
## Warning: Unknown control parameters: cex
## Available control parameters (with default values):
## main = Grouped Matrix for 10 Rules
## k = 20
## rhs_max = 10
## lhs_items = 2
## aggr.fun = function (x, ...) UseMethod("mean")
## col = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
## reverse = TRUE
## xlab = NULL
## ylab = NULL
## legend = Size: support Color: lift
## spacing = -1
## panel.function = function (row, size, shading, spacing) { size[size == 0] <- NA shading[is.na(shading)] <- 1 grid.circle(x = c(1:length(size)), y = row, r = size/2 * (1 - spacing), default.units = "native", gp = gpar(fill = shading, col = shading, alpha = 0.9)) }
## gp_main = list(cex = 1.2, fontface = "bold", font = c(bold = 2))
## gp_labels = list(cex = 0.8)
## gp_labs = list(cex = 1.2, fontface = "bold", font = c(bold = 2))
## gp_lines = list(col = "gray", lty = 3)
## newpage = TRUE
## max.shading = NA
## engine = default
## verbose = FALSE
