README.md
In katokohaku/inspectDF: Inspect rules objects as data.frame instead of arules::inspect(rules)
title: inspectDF
author: Satoshi Kato (@katokohaku)
output:
html_document:
keep_md: yes
md_document:
variant: markdown_github
Overview
R package for getting inspected rules as data.frame.
How to use
Just inspectDF() instead of arules::inspect() after arules::apriori().
require(dplyr)
require(arules)
require(inspectDF)
data(Groceries)
params <- list(confidence=0.001, support=0.001, maxlen=7, minlen=2)
glo.apriori <- apriori(Groceries, parameter = params, control = list(verbose=FALSE))
print(glo.apriori)
#> set of 40943 rules
glo.inspectDF <- inspectDF(glo.apriori)
glo.inspectDF %>% str(vec.len = 3)
#> 'data.frame': 40943 obs. of 8 variables:
#> $ rule : chr "Rule 1" "Rule 2" "Rule 3" ...
#> $ LHS : chr "whole milk" "other vegetables" "other vegetables" ...
#> $ RHS : chr "sparkling wine" "artif. sweetener" "bathroom cleaner" ...
#> $ n.lhs : num 1 1 1 1 1 1 1 1 ...
#> $ support : num 0.00102 0.00102 0.00102 0.00102 ...
#> $ confidence: num 0.00398 0.00525 0.00525 0.00553 ...
#> $ lift : num 0.712 1.615 1.914 1.647 ...
#> $ count : num 10 10 10 10 10 10 10 10 ...
InspectDF also provides a plot.igraph wrapper utility.
set.seed(0)
glo.inspectDF %>%
arrange(support, confidence) %>%
head(60) %>%
plotRuleGraph()
Detail
Installation
You can install the inspectDF package from GitHub.
install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("katokohaku/inspectDF")
The source code for inspectDF package is available at
Motivation
Usually, we arules::inspect() to enumerate rules after arules::apriori().
Of cource, we could get data.frame object as side effect of cat() on the last line in inspect(). However, it can't be done quietly (Always show all on consol).
!!Don't run following codes with many rules!!
data(Groceries)
params <- list(confidence=0.001, support=0.001, maxlen=7, minlen=2)
glo.apriori <- apriori(Groceries, parameter = params)
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.001 0.1 1 none FALSE TRUE 5 0.001 2
#> maxlen target ext
#> 7 rules FALSE
#>
#> 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 [0.00s].
#> 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 [0.01s].
#> writing ... [40943 rule(s)] done [0.00s].
#> creating S4 object ... done [0.01s].
print(glo.apriori)
#> set of 40943 rules
glo.inspect <- glo.apriori %>% head(10) %>% inspect()
#> lhs rhs support confidence lift
#> [1] {honey} => {whole milk} 0.001118454 0.733333333 2.870009
#> [2] {whole milk} => {honey} 0.001118454 0.004377238 2.870009
#> [3] {soap} => {whole milk} 0.001118454 0.423076923 1.655775
#> [4] {whole milk} => {soap} 0.001118454 0.004377238 1.655775
#> [5] {tidbits} => {soda} 0.001016777 0.434782609 2.493345
#> [6] {soda} => {tidbits} 0.001016777 0.005830904 2.493345
#> [7] {tidbits} => {rolls/buns} 0.001220132 0.521739130 2.836542
#> [8] {rolls/buns} => {tidbits} 0.001220132 0.006633499 2.836542
#> [9] {cocoa drinks} => {whole milk} 0.001321810 0.590909091 2.312611
#> [10] {whole milk} => {cocoa drinks} 0.001321810 0.005173100 2.312611
#> count
#> [1] 11
#> [2] 11
#> [3] 11
#> [4] 11
#> [5] 10
#> [6] 10
#> [7] 12
#> [8] 12
#> [9] 13
#> [10] 13
glo.inspect %>% str
#> 'data.frame': 10 obs. of 7 variables:
#> $ lhs : Factor w/ 7 levels "{cocoa drinks}",..: 2 7 4 7 6 5 6 3 1 7
#> $ : Factor w/ 1 level "=>": 1 1 1 1 1 1 1 1 1 1
#> $ rhs : Factor w/ 7 levels "{cocoa drinks}",..: 7 2 7 4 5 6 3 6 7 1
#> $ support : num 0.00112 0.00112 0.00112 0.00112 0.00102 ...
#> $ confidence: num 0.73333 0.00438 0.42308 0.00438 0.43478 ...
#> $ lift : num 2.87 2.87 1.66 1.66 2.49 ...
#> $ count : num 11 11 11 11 10 10 12 12 13 13
It is noisy when especially inspect() a lot of rules or calling in other function.
Therefore, this must be done invisibly.
Use case
Arules package privides several utilities such as sort(), subset() and etc. But if rules were provided as data.frame, we can explore them as tidy data. InspectDF has a good affinity with tidy schemes, such as dplyr::arrange() or dplyr::filter() because this returns only data.frame.
For example, rules with specific item(s) can be extracted using stringr::str_detect()
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(LHS, pattern = "yogurt|sausage")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(5)
rules.lhs
#> rule LHS RHS n.lhs support
#> 1 Rule 97 whole milk,yogurt UHT-milk 2 0.001016777
#> 2 Rule 98 whole milk,yogurt red/blush wine 2 0.001016777
#> 3 Rule 99 whole milk,yogurt house keeping products 2 0.001016777
#> 4 Rule 100 whole milk,yogurt liver loaf 2 0.001016777
#> 5 Rule 7175 whole milk,yogurt chewing gum 2 0.001118454
#> confidence lift count
#> 1 0.01814882 0.5425339 10
#> 2 0.01814882 0.9444108 10
#> 3 0.01814882 2.1767518 10
#> 4 0.01814882 3.5698730 10
#> 5 0.01996370 0.9485170 11
By default, rule strings are split by separater ",". But, items sometimes contain separater characters e.g. IUPAC of DHA. In such case, user can change rule-separater freely sep = string.
glo.apriori %>%
inspectDF(sep = "###") %>%
filter(n.lhs >3) %>%
select(2:3) %>%
head()
#> LHS RHS
#> 1 root vegetables###other vegetables###whole milk###yogurt waffles
#> 2 root vegetables###other vegetables###whole milk###yogurt sugar
#> 3 root vegetables###other vegetables###whole milk###yogurt onions
#> 4 root vegetables###other vegetables###whole milk###yogurt butter milk
#> 5 tropical fruit###other vegetables###whole milk###yogurt margarine
#> 6 tropical fruit###other vegetables###whole milk###yogurt grapes
Similar to original plot.rules with igraph in arules package, each rule size represents support value. This size can be adjusted by adujust.support.size in plot functions.
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(RHS, pattern = "yogurt")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(15)
s=5
set.seed(s)
rules.lhs %>% plotRuleGraph(label = "default")
set.seed(0)
rules.lhs %>% plotRuleGraph(label = "adjusted rule size",
adujust.support.size = 4000)
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(RHS, pattern = "yogurt")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(15)
rules.lhs
#> rule LHS RHS n.lhs support
#> 1 Rule 7989 canned beer,shopping bags yogurt 2 0.001118454
#> 2 Rule 31170 soda,bottled beer yogurt 2 0.002135231
#> 3 Rule 13529 frankfurter,shopping bags yogurt 2 0.001220132
#> 4 Rule 20442 coffee,shopping bags yogurt 2 0.001423488
#> 5 Rule 26699 rolls/buns,canned beer yogurt 2 0.001728521
#> 6 Rule 31223 soda,canned beer yogurt 2 0.002135231
#> 7 Rule 29426 pork,soda yogurt 2 0.001931876
#> 8 Rule 22978 soda,salty snack yogurt 2 0.001525165
#> 9 Rule 2458 margarine,bottled beer yogurt 2 0.001016777
#> 10 Rule 32635 rolls/buns,bottled beer yogurt 2 0.002338587
#> 11 Rule 38217 soda,shopping bags yogurt 2 0.004270463
#> 12 Rule 13816 whole milk,processed cheese yogurt 2 0.001220132
#> 13 Rule 9106 citrus fruit,coffee yogurt 2 0.001118454
#> 14 Rule 20625 chocolate,shopping bags yogurt 2 0.001423488
#> 15 Rule 2605 waffles,chocolate yogurt 2 0.001016777
#> confidence lift count
#> 1 0.09821429 0.7040361 11
#> 2 0.12574850 0.9014115 21
#> 3 0.14814815 1.0619803 12
#> 4 0.15217391 1.0908385 14
#> 5 0.15315315 1.0978581 17
#> 6 0.15441176 1.1068803 21
#> 7 0.16239316 1.1640938 19
#> 8 0.16304348 1.1687555 15
#> 9 0.16666667 1.1947279 10
#> 10 0.17164179 1.2303914 23
#> 11 0.17355372 1.2440968 42
#> 12 0.17391304 1.2466726 12
#> 13 0.17460317 1.2516197 11
#> 14 0.17500000 1.2544643 14
#> 15 0.17543860 1.2576083 10
edges.lhs <- rules.lhs %>% inspectDF::toEdges()
edges.lhs
#> from to n.lhs support confidence lift
#> 1 Rule 7989 yogurt 2 0.001118454 0.09821429 0.7040361
#> 2 Rule 31170 yogurt 2 0.002135231 0.12574850 0.9014115
#> 3 Rule 13529 yogurt 2 0.001220132 0.14814815 1.0619803
#> 4 Rule 20442 yogurt 2 0.001423488 0.15217391 1.0908385
#> 5 Rule 26699 yogurt 2 0.001728521 0.15315315 1.0978581
#> 6 Rule 31223 yogurt 2 0.002135231 0.15441176 1.1068803
#> 7 Rule 29426 yogurt 2 0.001931876 0.16239316 1.1640938
#> 8 Rule 22978 yogurt 2 0.001525165 0.16304348 1.1687555
#> 9 Rule 2458 yogurt 2 0.001016777 0.16666667 1.1947279
#> 10 Rule 32635 yogurt 2 0.002338587 0.17164179 1.2303914
#> 11 Rule 38217 yogurt 2 0.004270463 0.17355372 1.2440968
#> 12 Rule 13816 yogurt 2 0.001220132 0.17391304 1.2466726
#> 13 Rule 9106 yogurt 2 0.001118454 0.17460317 1.2516197
#> 14 Rule 20625 yogurt 2 0.001423488 0.17500000 1.2544643
#> 15 Rule 2605 yogurt 2 0.001016777 0.17543860 1.2576083
#> 16 canned beer Rule 7989 2 0.001118454 0.09821429 0.7040361
#> 17 shopping bags Rule 7989 2 0.001118454 0.09821429 0.7040361
#> 18 soda Rule 31170 2 0.002135231 0.12574850 0.9014115
#> 19 bottled beer Rule 31170 2 0.002135231 0.12574850 0.9014115
#> 20 frankfurter Rule 13529 2 0.001220132 0.14814815 1.0619803
#> 21 shopping bags Rule 13529 2 0.001220132 0.14814815 1.0619803
#> 22 coffee Rule 20442 2 0.001423488 0.15217391 1.0908385
#> 23 shopping bags Rule 20442 2 0.001423488 0.15217391 1.0908385
#> 24 rolls/buns Rule 26699 2 0.001728521 0.15315315 1.0978581
#> 25 canned beer Rule 26699 2 0.001728521 0.15315315 1.0978581
#> 26 soda Rule 31223 2 0.002135231 0.15441176 1.1068803
#> 27 canned beer Rule 31223 2 0.002135231 0.15441176 1.1068803
#> 28 pork Rule 29426 2 0.001931876 0.16239316 1.1640938
#> 29 soda Rule 29426 2 0.001931876 0.16239316 1.1640938
#> 30 soda Rule 22978 2 0.001525165 0.16304348 1.1687555
#> 31 salty snack Rule 22978 2 0.001525165 0.16304348 1.1687555
#> 32 margarine Rule 2458 2 0.001016777 0.16666667 1.1947279
#> 33 bottled beer Rule 2458 2 0.001016777 0.16666667 1.1947279
#> 34 rolls/buns Rule 32635 2 0.002338587 0.17164179 1.2303914
#> 35 bottled beer Rule 32635 2 0.002338587 0.17164179 1.2303914
#> 36 soda Rule 38217 2 0.004270463 0.17355372 1.2440968
#> 37 shopping bags Rule 38217 2 0.004270463 0.17355372 1.2440968
#> 38 whole milk Rule 13816 2 0.001220132 0.17391304 1.2466726
#> 39 processed cheese Rule 13816 2 0.001220132 0.17391304 1.2466726
#> 40 citrus fruit Rule 9106 2 0.001118454 0.17460317 1.2516197
#> 41 coffee Rule 9106 2 0.001118454 0.17460317 1.2516197
#> 42 chocolate Rule 20625 2 0.001423488 0.17500000 1.2544643
#> 43 shopping bags Rule 20625 2 0.001423488 0.17500000 1.2544643
#> 44 waffles Rule 2605 2 0.001016777 0.17543860 1.2576083
#> 45 chocolate Rule 2605 2 0.001016777 0.17543860 1.2576083
#> count
#> 1 11
#> 2 21
#> 3 12
#> 4 14
#> 5 17
#> 6 21
#> 7 19
#> 8 15
#> 9 10
#> 10 23
#> 11 42
#> 12 12
#> 13 11
#> 14 14
#> 15 10
#> 16 11
#> 17 11
#> 18 21
#> 19 21
#> 20 12
#> 21 12
#> 22 14
#> 23 14
#> 24 17
#> 25 17
#> 26 21
#> 27 21
#> 28 19
#> 29 19
#> 30 15
#> 31 15
#> 32 10
#> 33 10
#> 34 23
#> 35 23
#> 36 42
#> 37 42
#> 38 12
#> 39 12
#> 40 11
#> 41 11
#> 42 14
#> 43 14
#> 44 10
#> 45 10
katokohaku/inspectDF documentation built on May 23, 2019, 8:51 a.m.
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title: inspectDF author: Satoshi Kato (@katokohaku) output: html_document: keep_md: yes md_document: variant: markdown_github
Overview
R package for getting inspected rules as data.frame.
How to use
Just inspectDF() instead of arules::inspect() after arules::apriori().
require(dplyr)
require(arules)
require(inspectDF)
data(Groceries)
params <- list(confidence=0.001, support=0.001, maxlen=7, minlen=2)
glo.apriori <- apriori(Groceries, parameter = params, control = list(verbose=FALSE))
print(glo.apriori)
#> set of 40943 rules
glo.inspectDF <- inspectDF(glo.apriori)
glo.inspectDF %>% str(vec.len = 3)
#> 'data.frame': 40943 obs. of 8 variables:
#> $ rule : chr "Rule 1" "Rule 2" "Rule 3" ...
#> $ LHS : chr "whole milk" "other vegetables" "other vegetables" ...
#> $ RHS : chr "sparkling wine" "artif. sweetener" "bathroom cleaner" ...
#> $ n.lhs : num 1 1 1 1 1 1 1 1 ...
#> $ support : num 0.00102 0.00102 0.00102 0.00102 ...
#> $ confidence: num 0.00398 0.00525 0.00525 0.00553 ...
#> $ lift : num 0.712 1.615 1.914 1.647 ...
#> $ count : num 10 10 10 10 10 10 10 10 ...
InspectDF also provides a plot.igraph wrapper utility.
set.seed(0)
glo.inspectDF %>%
arrange(support, confidence) %>%
head(60) %>%
plotRuleGraph()
Detail
Installation
You can install the inspectDF package from GitHub.
install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("katokohaku/inspectDF")
The source code for inspectDF package is available at
Motivation
Usually, we arules::inspect() to enumerate rules after arules::apriori().
Of cource, we could get data.frame object as side effect of cat() on the last line in inspect(). However, it can't be done quietly (Always show all on consol).
!!Don't run following codes with many rules!!
data(Groceries)
params <- list(confidence=0.001, support=0.001, maxlen=7, minlen=2)
glo.apriori <- apriori(Groceries, parameter = params)
#> Apriori
#>
#> Parameter specification:
#> confidence minval smax arem aval originalSupport maxtime support minlen
#> 0.001 0.1 1 none FALSE TRUE 5 0.001 2
#> maxlen target ext
#> 7 rules FALSE
#>
#> 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 [0.00s].
#> 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 [0.01s].
#> writing ... [40943 rule(s)] done [0.00s].
#> creating S4 object ... done [0.01s].
print(glo.apriori)
#> set of 40943 rules
glo.inspect <- glo.apriori %>% head(10) %>% inspect()
#> lhs rhs support confidence lift
#> [1] {honey} => {whole milk} 0.001118454 0.733333333 2.870009
#> [2] {whole milk} => {honey} 0.001118454 0.004377238 2.870009
#> [3] {soap} => {whole milk} 0.001118454 0.423076923 1.655775
#> [4] {whole milk} => {soap} 0.001118454 0.004377238 1.655775
#> [5] {tidbits} => {soda} 0.001016777 0.434782609 2.493345
#> [6] {soda} => {tidbits} 0.001016777 0.005830904 2.493345
#> [7] {tidbits} => {rolls/buns} 0.001220132 0.521739130 2.836542
#> [8] {rolls/buns} => {tidbits} 0.001220132 0.006633499 2.836542
#> [9] {cocoa drinks} => {whole milk} 0.001321810 0.590909091 2.312611
#> [10] {whole milk} => {cocoa drinks} 0.001321810 0.005173100 2.312611
#> count
#> [1] 11
#> [2] 11
#> [3] 11
#> [4] 11
#> [5] 10
#> [6] 10
#> [7] 12
#> [8] 12
#> [9] 13
#> [10] 13
glo.inspect %>% str
#> 'data.frame': 10 obs. of 7 variables:
#> $ lhs : Factor w/ 7 levels "{cocoa drinks}",..: 2 7 4 7 6 5 6 3 1 7
#> $ : Factor w/ 1 level "=>": 1 1 1 1 1 1 1 1 1 1
#> $ rhs : Factor w/ 7 levels "{cocoa drinks}",..: 7 2 7 4 5 6 3 6 7 1
#> $ support : num 0.00112 0.00112 0.00112 0.00112 0.00102 ...
#> $ confidence: num 0.73333 0.00438 0.42308 0.00438 0.43478 ...
#> $ lift : num 2.87 2.87 1.66 1.66 2.49 ...
#> $ count : num 11 11 11 11 10 10 12 12 13 13
It is noisy when especially inspect() a lot of rules or calling in other function.
Therefore, this must be done invisibly.
Use case
Arules package privides several utilities such as sort(), subset() and etc. But if rules were provided as data.frame, we can explore them as tidy data. InspectDF has a good affinity with tidy schemes, such as dplyr::arrange() or dplyr::filter() because this returns only data.frame.
For example, rules with specific item(s) can be extracted using stringr::str_detect()
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(LHS, pattern = "yogurt|sausage")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(5)
rules.lhs
#> rule LHS RHS n.lhs support
#> 1 Rule 97 whole milk,yogurt UHT-milk 2 0.001016777
#> 2 Rule 98 whole milk,yogurt red/blush wine 2 0.001016777
#> 3 Rule 99 whole milk,yogurt house keeping products 2 0.001016777
#> 4 Rule 100 whole milk,yogurt liver loaf 2 0.001016777
#> 5 Rule 7175 whole milk,yogurt chewing gum 2 0.001118454
#> confidence lift count
#> 1 0.01814882 0.5425339 10
#> 2 0.01814882 0.9444108 10
#> 3 0.01814882 2.1767518 10
#> 4 0.01814882 3.5698730 10
#> 5 0.01996370 0.9485170 11
By default, rule strings are split by separater ",". But, items sometimes contain separater characters e.g. IUPAC of DHA. In such case, user can change rule-separater freely sep = string.
glo.apriori %>%
inspectDF(sep = "###") %>%
filter(n.lhs >3) %>%
select(2:3) %>%
head()
#> LHS RHS
#> 1 root vegetables###other vegetables###whole milk###yogurt waffles
#> 2 root vegetables###other vegetables###whole milk###yogurt sugar
#> 3 root vegetables###other vegetables###whole milk###yogurt onions
#> 4 root vegetables###other vegetables###whole milk###yogurt butter milk
#> 5 tropical fruit###other vegetables###whole milk###yogurt margarine
#> 6 tropical fruit###other vegetables###whole milk###yogurt grapes
Similar to original plot.rules with igraph in arules package, each rule size represents support value. This size can be adjusted by adujust.support.size in plot functions.
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(RHS, pattern = "yogurt")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(15)
s=5
set.seed(s)
rules.lhs %>% plotRuleGraph(label = "default")
set.seed(0)
rules.lhs %>% plotRuleGraph(label = "adjusted rule size",
adujust.support.size = 4000)
require(stringr)
rules.lhs <- glo.inspectDF %>%
filter(str_detect(RHS, pattern = "yogurt")) %>%
arrange(confidence, lift) %>%
filter(n.lhs > 1) %>%
head(15)
rules.lhs
#> rule LHS RHS n.lhs support
#> 1 Rule 7989 canned beer,shopping bags yogurt 2 0.001118454
#> 2 Rule 31170 soda,bottled beer yogurt 2 0.002135231
#> 3 Rule 13529 frankfurter,shopping bags yogurt 2 0.001220132
#> 4 Rule 20442 coffee,shopping bags yogurt 2 0.001423488
#> 5 Rule 26699 rolls/buns,canned beer yogurt 2 0.001728521
#> 6 Rule 31223 soda,canned beer yogurt 2 0.002135231
#> 7 Rule 29426 pork,soda yogurt 2 0.001931876
#> 8 Rule 22978 soda,salty snack yogurt 2 0.001525165
#> 9 Rule 2458 margarine,bottled beer yogurt 2 0.001016777
#> 10 Rule 32635 rolls/buns,bottled beer yogurt 2 0.002338587
#> 11 Rule 38217 soda,shopping bags yogurt 2 0.004270463
#> 12 Rule 13816 whole milk,processed cheese yogurt 2 0.001220132
#> 13 Rule 9106 citrus fruit,coffee yogurt 2 0.001118454
#> 14 Rule 20625 chocolate,shopping bags yogurt 2 0.001423488
#> 15 Rule 2605 waffles,chocolate yogurt 2 0.001016777
#> confidence lift count
#> 1 0.09821429 0.7040361 11
#> 2 0.12574850 0.9014115 21
#> 3 0.14814815 1.0619803 12
#> 4 0.15217391 1.0908385 14
#> 5 0.15315315 1.0978581 17
#> 6 0.15441176 1.1068803 21
#> 7 0.16239316 1.1640938 19
#> 8 0.16304348 1.1687555 15
#> 9 0.16666667 1.1947279 10
#> 10 0.17164179 1.2303914 23
#> 11 0.17355372 1.2440968 42
#> 12 0.17391304 1.2466726 12
#> 13 0.17460317 1.2516197 11
#> 14 0.17500000 1.2544643 14
#> 15 0.17543860 1.2576083 10
edges.lhs <- rules.lhs %>% inspectDF::toEdges()
edges.lhs
#> from to n.lhs support confidence lift
#> 1 Rule 7989 yogurt 2 0.001118454 0.09821429 0.7040361
#> 2 Rule 31170 yogurt 2 0.002135231 0.12574850 0.9014115
#> 3 Rule 13529 yogurt 2 0.001220132 0.14814815 1.0619803
#> 4 Rule 20442 yogurt 2 0.001423488 0.15217391 1.0908385
#> 5 Rule 26699 yogurt 2 0.001728521 0.15315315 1.0978581
#> 6 Rule 31223 yogurt 2 0.002135231 0.15441176 1.1068803
#> 7 Rule 29426 yogurt 2 0.001931876 0.16239316 1.1640938
#> 8 Rule 22978 yogurt 2 0.001525165 0.16304348 1.1687555
#> 9 Rule 2458 yogurt 2 0.001016777 0.16666667 1.1947279
#> 10 Rule 32635 yogurt 2 0.002338587 0.17164179 1.2303914
#> 11 Rule 38217 yogurt 2 0.004270463 0.17355372 1.2440968
#> 12 Rule 13816 yogurt 2 0.001220132 0.17391304 1.2466726
#> 13 Rule 9106 yogurt 2 0.001118454 0.17460317 1.2516197
#> 14 Rule 20625 yogurt 2 0.001423488 0.17500000 1.2544643
#> 15 Rule 2605 yogurt 2 0.001016777 0.17543860 1.2576083
#> 16 canned beer Rule 7989 2 0.001118454 0.09821429 0.7040361
#> 17 shopping bags Rule 7989 2 0.001118454 0.09821429 0.7040361
#> 18 soda Rule 31170 2 0.002135231 0.12574850 0.9014115
#> 19 bottled beer Rule 31170 2 0.002135231 0.12574850 0.9014115
#> 20 frankfurter Rule 13529 2 0.001220132 0.14814815 1.0619803
#> 21 shopping bags Rule 13529 2 0.001220132 0.14814815 1.0619803
#> 22 coffee Rule 20442 2 0.001423488 0.15217391 1.0908385
#> 23 shopping bags Rule 20442 2 0.001423488 0.15217391 1.0908385
#> 24 rolls/buns Rule 26699 2 0.001728521 0.15315315 1.0978581
#> 25 canned beer Rule 26699 2 0.001728521 0.15315315 1.0978581
#> 26 soda Rule 31223 2 0.002135231 0.15441176 1.1068803
#> 27 canned beer Rule 31223 2 0.002135231 0.15441176 1.1068803
#> 28 pork Rule 29426 2 0.001931876 0.16239316 1.1640938
#> 29 soda Rule 29426 2 0.001931876 0.16239316 1.1640938
#> 30 soda Rule 22978 2 0.001525165 0.16304348 1.1687555
#> 31 salty snack Rule 22978 2 0.001525165 0.16304348 1.1687555
#> 32 margarine Rule 2458 2 0.001016777 0.16666667 1.1947279
#> 33 bottled beer Rule 2458 2 0.001016777 0.16666667 1.1947279
#> 34 rolls/buns Rule 32635 2 0.002338587 0.17164179 1.2303914
#> 35 bottled beer Rule 32635 2 0.002338587 0.17164179 1.2303914
#> 36 soda Rule 38217 2 0.004270463 0.17355372 1.2440968
#> 37 shopping bags Rule 38217 2 0.004270463 0.17355372 1.2440968
#> 38 whole milk Rule 13816 2 0.001220132 0.17391304 1.2466726
#> 39 processed cheese Rule 13816 2 0.001220132 0.17391304 1.2466726
#> 40 citrus fruit Rule 9106 2 0.001118454 0.17460317 1.2516197
#> 41 coffee Rule 9106 2 0.001118454 0.17460317 1.2516197
#> 42 chocolate Rule 20625 2 0.001423488 0.17500000 1.2544643
#> 43 shopping bags Rule 20625 2 0.001423488 0.17500000 1.2544643
#> 44 waffles Rule 2605 2 0.001016777 0.17543860 1.2576083
#> 45 chocolate Rule 2605 2 0.001016777 0.17543860 1.2576083
#> count
#> 1 11
#> 2 21
#> 3 12
#> 4 14
#> 5 17
#> 6 21
#> 7 19
#> 8 15
#> 9 10
#> 10 23
#> 11 42
#> 12 12
#> 13 11
#> 14 14
#> 15 10
#> 16 11
#> 17 11
#> 18 21
#> 19 21
#> 20 12
#> 21 12
#> 22 14
#> 23 14
#> 24 17
#> 25 17
#> 26 21
#> 27 21
#> 28 19
#> 29 19
#> 30 15
#> 31 15
#> 32 10
#> 33 10
#> 34 23
#> 35 23
#> 36 42
#> 37 42
#> 38 12
#> 39 12
#> 40 11
#> 41 11
#> 42 14
#> 43 14
#> 44 10
#> 45 10
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