View(Titanic)
str(Titanic)
## 'table' num [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
## - attr(*, "dimnames")=List of 4
## ..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew"
## ..$ Sex : chr [1:2] "Male" "Female"
## ..$ Age : chr [1:2] "Child" "Adult"
## ..$ Survived: chr [1:2] "No" "Yes"
df <- as.data.frame(Titanic)
head(df)
## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
titanic.new <- NULL
for(i in 1:4) {
titanic.new <- cbind(titanic.new,
rep(as.character(df[,i]),
df$Freq))
}
titanic.new <- as.data.frame(titanic.new)
names(titanic.new) <- names(df[1:4])
dim(titanic.new)
## [1] 2201 4
View(titanic.new)
str(titanic.new)
## 'data.frame': 2201 obs. of 4 variables:
## $ Class : Factor w/ 4 levels "1st","2nd","3rd",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
## $ Age : Factor w/ 2 levels "Adult","Child": 2 2 2 2 2 2 2 2 2 2 ...
## $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
# titanic.new <- as.data.frame(unclass(titanic.new), stringsAsFactors = TRUE)
#install.packages("arules")
library(arules)
## Warning: package 'arules' was built under R version 3.6.3
## Loading required package: Matrix
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
all.rules <- apriori(titanic.new)
## 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 TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 220
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[10 item(s), 2201 transaction(s)] done [0.00s].
## sorting and recoding items ... [9 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [27 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
all.rules
## set of 27 rules
inspect(all.rules)
## lhs rhs support confidence
## [1] {} => {Age=Adult} 0.9504771 0.9504771
## [2] {Class=2nd} => {Age=Adult} 0.1185825 0.9157895
## [3] {Class=1st} => {Age=Adult} 0.1449341 0.9815385
## [4] {Sex=Female} => {Age=Adult} 0.1930940 0.9042553
## [5] {Class=3rd} => {Age=Adult} 0.2848705 0.8881020
## [6] {Survived=Yes} => {Age=Adult} 0.2971377 0.9198312
## [7] {Class=Crew} => {Sex=Male} 0.3916402 0.9740113
## [8] {Class=Crew} => {Age=Adult} 0.4020900 1.0000000
## [9] {Survived=No} => {Sex=Male} 0.6197183 0.9154362
## [10] {Survived=No} => {Age=Adult} 0.6533394 0.9651007
## [11] {Sex=Male} => {Age=Adult} 0.7573830 0.9630272
## [12] {Sex=Female,Survived=Yes} => {Age=Adult} 0.1435711 0.9186047
## [13] {Class=3rd,Sex=Male} => {Survived=No} 0.1917310 0.8274510
## [14] {Class=3rd,Survived=No} => {Age=Adult} 0.2162653 0.9015152
## [15] {Class=3rd,Sex=Male} => {Age=Adult} 0.2099046 0.9058824
## [16] {Sex=Male,Survived=Yes} => {Age=Adult} 0.1535666 0.9209809
## [17] {Class=Crew,Survived=No} => {Sex=Male} 0.3044071 0.9955423
## [18] {Class=Crew,Survived=No} => {Age=Adult} 0.3057701 1.0000000
## [19] {Class=Crew,Sex=Male} => {Age=Adult} 0.3916402 1.0000000
## [20] {Class=Crew,Age=Adult} => {Sex=Male} 0.3916402 0.9740113
## [21] {Sex=Male,Survived=No} => {Age=Adult} 0.6038164 0.9743402
## [22] {Age=Adult,Survived=No} => {Sex=Male} 0.6038164 0.9242003
## [23] {Class=3rd,Sex=Male,Survived=No} => {Age=Adult} 0.1758292 0.9170616
## [24] {Class=3rd,Age=Adult,Survived=No} => {Sex=Male} 0.1758292 0.8130252
## [25] {Class=3rd,Sex=Male,Age=Adult} => {Survived=No} 0.1758292 0.8376623
## [26] {Class=Crew,Sex=Male,Survived=No} => {Age=Adult} 0.3044071 1.0000000
## [27] {Class=Crew,Age=Adult,Survived=No} => {Sex=Male} 0.3044071 0.9955423
## coverage lift count
## [1] 1.0000000 1.0000000 2092
## [2] 0.1294866 0.9635051 261
## [3] 0.1476602 1.0326798 319
## [4] 0.2135393 0.9513700 425
## [5] 0.3207633 0.9343750 627
## [6] 0.3230350 0.9677574 654
## [7] 0.4020900 1.2384742 862
## [8] 0.4020900 1.0521033 885
## [9] 0.6769650 1.1639949 1364
## [10] 0.6769650 1.0153856 1438
## [11] 0.7864607 1.0132040 1667
## [12] 0.1562926 0.9664669 316
## [13] 0.2317129 1.2222950 422
## [14] 0.2398910 0.9484870 476
## [15] 0.2317129 0.9530818 462
## [16] 0.1667424 0.9689670 338
## [17] 0.3057701 1.2658514 670
## [18] 0.3057701 1.0521033 673
## [19] 0.3916402 1.0521033 862
## [20] 0.4020900 1.2384742 862
## [21] 0.6197183 1.0251065 1329
## [22] 0.6533394 1.1751385 1329
## [23] 0.1917310 0.9648435 387
## [24] 0.2162653 1.0337773 387
## [25] 0.2099046 1.2373791 387
## [26] 0.3044071 1.0521033 670
## [27] 0.3057701 1.2658514 670
rules <- apriori(titanic.new,
control = list(verbose=F),
parameter = list(minlen=2, supp=0.005, conf=0.8),
appearance = list(rhs = c("Survived=Yes","Survived=No"),
default="lhs"))
rules
## set of 12 rules
quality(rules) <- round(quality(rules), digits = 3)
rules.ordered <- sort(rules, by = "lift")
inspect(rules.ordered)
## lhs rhs support confidence
## [1] {Class=2nd,Age=Child} => {Survived=Yes} 0.011 1.000
## [2] {Class=2nd,Sex=Female,Age=Child} => {Survived=Yes} 0.006 1.000
## [3] {Class=1st,Sex=Female} => {Survived=Yes} 0.064 0.972
## [4] {Class=1st,Sex=Female,Age=Adult} => {Survived=Yes} 0.064 0.972
## [5] {Class=2nd,Sex=Female} => {Survived=Yes} 0.042 0.877
## [6] {Class=Crew,Sex=Female} => {Survived=Yes} 0.009 0.870
## [7] {Class=Crew,Sex=Female,Age=Adult} => {Survived=Yes} 0.009 0.870
## [8] {Class=2nd,Sex=Female,Age=Adult} => {Survived=Yes} 0.036 0.860
## [9] {Class=2nd,Sex=Male,Age=Adult} => {Survived=No} 0.070 0.917
## [10] {Class=2nd,Sex=Male} => {Survived=No} 0.070 0.860
## [11] {Class=3rd,Sex=Male,Age=Adult} => {Survived=No} 0.176 0.838
## [12] {Class=3rd,Sex=Male} => {Survived=No} 0.192 0.827
## coverage lift count
## [1] 0.011 3.096 24
## [2] 0.006 3.096 13
## [3] 0.066 3.010 141
## [4] 0.065 3.010 140
## [5] 0.048 2.716 93
## [6] 0.010 2.692 20
## [7] 0.010 2.692 20
## [8] 0.042 2.663 80
## [9] 0.076 1.354 154
## [10] 0.081 1.271 154
## [11] 0.210 1.237 387
## [12] 0.232 1.222 422
prune <- is.redundant(rules.ordered)
which(prune)
## [1] 2 4 7 8
pruned.titanic <- rules.ordered[!prune]
inspect(pruned.titanic)
## lhs rhs support confidence
## [1] {Class=2nd,Age=Child} => {Survived=Yes} 0.011 1.000
## [2] {Class=1st,Sex=Female} => {Survived=Yes} 0.064 0.972
## [3] {Class=2nd,Sex=Female} => {Survived=Yes} 0.042 0.877
## [4] {Class=Crew,Sex=Female} => {Survived=Yes} 0.009 0.870
## [5] {Class=2nd,Sex=Male,Age=Adult} => {Survived=No} 0.070 0.917
## [6] {Class=2nd,Sex=Male} => {Survived=No} 0.070 0.860
## [7] {Class=3rd,Sex=Male,Age=Adult} => {Survived=No} 0.176 0.838
## [8] {Class=3rd,Sex=Male} => {Survived=No} 0.192 0.827
## coverage lift count
## [1] 0.011 3.096 24
## [2] 0.066 3.010 141
## [3] 0.048 2.716 93
## [4] 0.010 2.692 20
## [5] 0.076 1.354 154
## [6] 0.081 1.271 154
## [7] 0.210 1.237 387
## [8] 0.232 1.222 422
#install.packages("arulesViz")
library(arulesViz)
## Warning: package 'arulesViz' was built under R version 3.6.3
## Loading required package: grid
plot(rules)
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(pruned.titanic, method="group")
plot(rules, method="graph", control = list(type = "items"))
## Warning: Unknown control parameters: type
## Available control parameters (with default values):
## main = Graph for 12 rules
## nodeColors = c("#66CC6680", "#9999CC80")
## nodeCol = 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")
## edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
## alpha = 0.5
## cex = 1
## itemLabels = TRUE
## labelCol = #000000B3
## measureLabels = FALSE
## precision = 3
## layout = NULL
## layoutParams = list()
## arrowSize = 0.5
## engine = igraph
## plot = TRUE
## plot_options = list()
## max = 100
## verbose = FALSE
library(arules)
data("Groceries")
itemFrequencyPlot(Groceries, topN = 20, type = "absolute")
rules2 <- apriori(Groceries,
parameter = list(supp=0.001,
conf=0.8))
## 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 [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.02s].
## writing ... [410 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
rules2
## set of 410 rules
options(digits = 2)
inspect(rules2[20:30])
## lhs rhs support confidence coverage lift count
## [1] {herbs,
## rolls/buns} => {whole milk} 0.0024 0.80 0.0031 3.1 24
## [2] {pickled vegetables,
## chocolate} => {whole milk} 0.0012 0.86 0.0014 3.4 12
## [3] {grapes,
## onions} => {other vegetables} 0.0011 0.92 0.0012 4.7 11
## [4] {meat,
## margarine} => {other vegetables} 0.0017 0.85 0.0020 4.4 17
## [5] {hard cheese,
## oil} => {other vegetables} 0.0011 0.92 0.0012 4.7 11
## [6] {onions,
## butter milk} => {other vegetables} 0.0013 0.81 0.0016 4.2 13
## [7] {pork,
## butter milk} => {other vegetables} 0.0018 0.86 0.0021 4.4 18
## [8] {onions,
## waffles} => {other vegetables} 0.0012 0.80 0.0015 4.1 12
## [9] {hamburger meat,
## curd} => {whole milk} 0.0025 0.81 0.0032 3.2 25
## [10] {hamburger meat,
## bottled beer} => {whole milk} 0.0017 0.81 0.0021 3.2 17
## [11] {other vegetables,
## yogurt,
## specialty cheese} => {whole milk} 0.0013 0.81 0.0016 3.2 13
prune2 <- is.redundant(rules2)
pruned.market <- rules2[!prune2]
rules3 <- pruned.market
inspect(rules3[1:10])
## lhs rhs support confidence coverage lift count
## [1] {liquor,
## red/blush wine} => {bottled beer} 0.0019 0.90 0.0021 11.2 19
## [2] {curd,
## cereals} => {whole milk} 0.0010 0.91 0.0011 3.6 10
## [3] {yogurt,
## cereals} => {whole milk} 0.0017 0.81 0.0021 3.2 17
## [4] {butter,
## jam} => {whole milk} 0.0010 0.83 0.0012 3.3 10
## [5] {soups,
## bottled beer} => {whole milk} 0.0011 0.92 0.0012 3.6 11
## [6] {napkins,
## house keeping products} => {whole milk} 0.0013 0.81 0.0016 3.2 13
## [7] {whipped/sour cream,
## house keeping products} => {whole milk} 0.0012 0.92 0.0013 3.6 12
## [8] {pastry,
## sweet spreads} => {whole milk} 0.0010 0.91 0.0011 3.6 10
## [9] {turkey,
## curd} => {other vegetables} 0.0012 0.80 0.0015 4.1 12
## [10] {rice,
## sugar} => {whole milk} 0.0012 1.00 0.0012 3.9 12
rules3
## set of 392 rules
rules4 <- apriori(Groceries,
control = list(verbose=F),
parameter = list(supp=0.001, conf=0.8),
appearance = list(rhs="whole milk",
default = "lhs"))
rules4
## set of 252 rules
inspect(rules4[1:10])
## lhs rhs support confidence coverage lift count
## [1] {curd,
## cereals} => {whole milk} 0.0010 0.91 0.0011 3.6 10
## [2] {yogurt,
## cereals} => {whole milk} 0.0017 0.81 0.0021 3.2 17
## [3] {butter,
## jam} => {whole milk} 0.0010 0.83 0.0012 3.3 10
## [4] {soups,
## bottled beer} => {whole milk} 0.0011 0.92 0.0012 3.6 11
## [5] {napkins,
## house keeping products} => {whole milk} 0.0013 0.81 0.0016 3.2 13
## [6] {whipped/sour cream,
## house keeping products} => {whole milk} 0.0012 0.92 0.0013 3.6 12
## [7] {pastry,
## sweet spreads} => {whole milk} 0.0010 0.91 0.0011 3.6 10
## [8] {rice,
## sugar} => {whole milk} 0.0012 1.00 0.0012 3.9 12
## [9] {butter,
## rice} => {whole milk} 0.0015 0.83 0.0018 3.3 15
## [10] {domestic eggs,
## rice} => {whole milk} 0.0011 0.85 0.0013 3.3 11
plot(rules4, method = "graph",
control = list(type="items",
nodeCol = "red", edgeCol = "blue"))
## Warning: Unknown control parameters: type
## Available control parameters (with default values):
## main = Graph for 100 rules
## nodeColors = c("#66CC6680", "#9999CC80")
## nodeCol = 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")
## edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
## alpha = 0.5
## cex = 1
## itemLabels = TRUE
## labelCol = #000000B3
## measureLabels = FALSE
## precision = 3
## layout = NULL
## layoutParams = list()
## arrowSize = 0.5
## engine = igraph
## plot = TRUE
## plot_options = list()
## max = 100
## verbose = FALSE
## Warning: plot: Too many rules supplied. Only plotting the best 100 rules using
## 'support' (change control parameter max if needed)
