MBA
Adrián Elizondo A00835160
2024-09-10
Instalar paquetes y llamar librerias
# install.packages("tidyverse") # Paquete global para maniplacion y análisis de datos
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# install.packages("dplyr") # Para filtrar bases de datos
library(dplyr)
# install.packages("janitor") # Examinar y limpiar bases de datos sucias
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test# install.packages("Matrix") # Para trabajar con matrices
library(Matrix)##
## Attaching package: 'Matrix'
##
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack# install.packages("arules") # Genera reglas de asociacion
library(arules)##
## Attaching package: 'arules'
##
## The following object is masked from 'package:dplyr':
##
## recode
##
## The following objects are masked from 'package:base':
##
## abbreviate, write# install.packages("arulesViz") # Visualizar reglas de asociacion
library(arulesViz)
# install.packages("datasets")
library(datasets)
# install.packages("plyr")
library(plyr)## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
##
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following object is masked from 'package:purrr':
##
## compact‹span style=“color: red;”›Importar la base de datos
# file.choose()
df <- read.csv("/Users/adrianelizondo/Desktop/abarrotes.csv")‹span style=“color: red;”›Análisis Descriptivo
summary (df)## vcClaveTienda DescGiro Codigo.Barras PLU
## Length:200625 Length:200625 Min. :8.347e+05 Min. : 1.00
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.: 1.00
## Mode :character Mode :character Median :7.501e+12 Median : 1.00
## Mean :5.950e+12 Mean : 2.11
## 3rd Qu.:7.501e+12 3rd Qu.: 1.00
## Max. :1.750e+13 Max. :30.00
## NA's :199188
## Fecha Hora Marca Fabricante
## Length:200625 Length:200625 Length:200625 Length:200625
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Producto Precio Ult.Costo Unidades
## Length:200625 Min. :-147.00 Min. : 0.38 Min. : 0.200
## Class :character 1st Qu.: 11.00 1st Qu.: 8.46 1st Qu.: 1.000
## Mode :character Median : 16.00 Median : 12.31 Median : 1.000
## Mean : 19.42 Mean : 15.31 Mean : 1.262
## 3rd Qu.: 25.00 3rd Qu.: 19.23 3rd Qu.: 1.000
## Max. :1000.00 Max. :769.23 Max. :96.000
##
## F.Ticket NombreDepartamento NombreFamilia NombreCategoria
## Min. : 1 Length:200625 Length:200625 Length:200625
## 1st Qu.: 33964 Class :character Class :character Class :character
## Median :105993 Mode :character Mode :character Mode :character
## Mean :193990
## 3rd Qu.:383005
## Max. :450040
##
## Estado Mts.2 Tipo.ubicación Giro
## Length:200625 Min. :47.0 Length:200625 Length:200625
## Class :character 1st Qu.:53.0 Class :character Class :character
## Mode :character Median :60.0 Mode :character Mode :character
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
##
## Hora.inicio Hora.cierre
## Length:200625 Length:200625
## Class :character Class :character
## Mode :character Mode :character
##
##
##
## str(df)## 'data.frame': 200625 obs. of 22 variables:
## $ vcClaveTienda : chr "MX001" "MX001" "MX001" "MX001" ...
## $ DescGiro : chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Codigo.Barras : num 7.5e+12 7.5e+12 7.5e+12 7.5e+12 7.5e+12 ...
## $ PLU : int NA NA NA NA NA NA NA NA NA NA ...
## $ Fecha : chr "19/06/2020" "19/06/2020" "19/06/2020" "19/06/2020" ...
## $ Hora : chr "08:16:21" "08:23:33" "08:24:33" "08:24:33" ...
## $ Marca : chr "NUTRI LECHE" "DAN UP" "BIMBO" "PEPSI" ...
## $ Fabricante : chr "MEXILAC" "DANONE DE MEXICO" "GRUPO BIMBO" "PEPSI-COLA MEXICANA" ...
## $ Producto : chr "Nutri Leche 1 Litro" "DANUP STRAWBERRY P/BEBER 350GR NAL" "Rebanadas Bimbo 2Pz" "Pepsi N.R. 400Ml" ...
## $ Precio : num 16 14 5 8 19.5 16 14 5 8 19.5 ...
## $ Ult.Costo : num 12.3 14 5 8 15 ...
## $ Unidades : num 1 1 1 1 1 1 1 1 1 1 ...
## $ F.Ticket : int 1 2 3 3 4 1 2 3 3 4 ...
## $ NombreDepartamento: chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ NombreFamilia : chr "Lacteos y Refrigerados" "Lacteos y Refrigerados" "Pan y Tortilla" "Bebidas" ...
## $ NombreCategoria : chr "Leche" "Yogurt" "Pan Dulce Empaquetado" "Refrescos Plástico (N.R.)" ...
## $ Estado : chr "Nuevo León" "Nuevo León" "Nuevo León" "Nuevo León" ...
## $ Mts.2 : int 60 60 60 60 60 60 60 60 60 60 ...
## $ Tipo.ubicación : chr "Esquina" "Esquina" "Esquina" "Esquina" ...
## $ Giro : chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Hora.inicio : chr "08:00" "08:00" "08:00" "08:00" ...
## $ Hora.cierre : chr "22:00" "22:00" "22:00" "22:00" ...# count(df, vcClaveTienda, sort=TRUE)
# count(df, DescGiro, sort=TRUE)
# count(df, Fecha, sort=TRUE)
# count(df, Hora, sort=TRUE)
# count(df, Marca, sort=TRUE)
# count(df, Fabricante, sort=TRUE)
# count(df, Producto, sort=TRUE)
# count(df, NombreDepartamento, sort=TRUE)
# count(df, NombreFamilia, sort=TRUE)
# count(df, NombreCategoria, sort=TRUE)
# count(df, Estado, sort=TRUE)
# count(df, Tipo.ubicación, sort=TRUE)
# count(df, Giro, sort=TRUE)
# count(df, Hora.inicio, sort=TRUE)
# count(df, Hora.cierre, sort=TRUE)
head(df)## vcClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 1 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 08:16:21
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 08:23:33
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/2020 08:24:33
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/2020 08:24:33
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/2020 08:26:28
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 08:16:21
## Marca Fabricante
## 1 NUTRI LECHE MEXILAC
## 2 DAN UP DANONE DE MEXICO
## 3 BIMBO GRUPO BIMBO
## 4 PEPSI PEPSI-COLA MEXICANA
## 5 BLANCA NIEVES (DETERGENTE) FABRICA DE JABON LA CORONA
## 6 NUTRI LECHE MEXILAC
## Producto Precio Ult.Costo Unidades F.Ticket
## 1 Nutri Leche 1 Litro 16.0 12.31 1 1
## 2 DANUP STRAWBERRY P/BEBER 350GR NAL 14.0 14.00 1 2
## 3 Rebanadas Bimbo 2Pz 5.0 5.00 1 3
## 4 Pepsi N.R. 400Ml 8.0 8.00 1 3
## 5 Detergente Blanca Nieves 500G 19.5 15.00 1 4
## 6 Nutri Leche 1 Litro 16.0 12.31 1 1
## NombreDepartamento NombreFamilia NombreCategoria
## 1 Abarrotes Lacteos y Refrigerados Leche
## 2 Abarrotes Lacteos y Refrigerados Yogurt
## 3 Abarrotes Pan y Tortilla Pan Dulce Empaquetado
## 4 Abarrotes Bebidas Refrescos Plástico (N.R.)
## 5 Abarrotes Limpieza del Hogar Lavandería
## 6 Abarrotes Lacteos y Refrigerados Leche
## Estado Mts.2 Tipo.ubicación Giro Hora.inicio Hora.cierre
## 1 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 2 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 3 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 4 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 5 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 6 Nuevo León 60 Esquina Abarrotes 08:00 22:00Tablas
# Tabla de Tienda y Departamento
tabyl(df,vcClaveTienda, NombreDepartamento)## vcClaveTienda Abarrotes Bebes e Infantiles Carnes Farmacia Ferretería Mercería
## MX001 95415 515 1 147 245 28
## MX002 6590 21 0 4 10 0
## MX003 4026 15 0 2 8 0
## MX004 82234 932 0 102 114 16
## MX005 10014 0 0 0 0 0
## Papelería Productos a Eliminar Vinos y Licores
## 35 3 80
## 0 0 4
## 0 0 0
## 32 5 20
## 7 0 0# Tabla de Estado y Hora de Inicio
tabyl(df, Estado, Hora.inicio)## Estado 07:00 08:00 09:00
## Chiapas 4051 0 0
## Jalisco 0 0 6629
## Nuevo León 0 96469 0
## Quintana Roo 0 10021 0
## Sinaloa 83455 0 0Limpieza de Datos
Técnica 1. Eliminar valores irrelevantes
# Eliminar columnas
df <- subset(df, select = -c(PLU))
# Eliminar renglones
df <- df[df$Precio >0, ]Técnica 2. Eliminar valores repetidos
df <- distinct(df)Técnica 3. Corregir errores tipográficos y similares
df$Unidades <- ceiling(df$Unidades)
summary(df$Unidades)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 1.261 1.000 96.000Técnica 4. Convertir tipos de datos
# Convertir de Caracter a Fecha
df$Fecha <- as.Date(df$Fecha, format="%d/%m/%Y")
str(df$Fecha)## Date[1:200473], format: "2020-06-19" "2020-06-19" "2020-06-19" "2020-06-19" "2020-06-19" ...summary(df$Fecha)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "2020-05-01" "2020-06-06" "2020-07-11" "2020-07-18" "2020-08-29" "2020-11-11"Técnica 5. Tratar valores faltantes
# Borrar todos los NAs
# df <- na.omit(df)
# Reemplazar los NAs con CEROS
# df[is.na(df)] <- 0
# Reemplazar los NAs con el PROMEDIO
# df$altura[js.na(df$altura)] <- mean(df$altura, na.rm=TRUE)Técnica 6. Herramientas Estadísticas
boxplot(df$Precio, horizontal = TRUE)
boxplot(df$Unidades, horizontal = TRUE)
Generar basket
# Ordenar de menor a mayor la columna Ticket
df <- df[order(df$F.Ticket), ]
head(df)## vcClaveTienda DescGiro Codigo.Barras Fecha Hora
## 1 MX001 Abarrotes 7.501021e+12 2020-06-19 08:16:21
## 2 MX001 Abarrotes 7.501032e+12 2020-06-19 08:23:33
## 3 MX001 Abarrotes 7.501000e+12 2020-06-19 08:24:33
## 4 MX001 Abarrotes 7.501031e+12 2020-06-19 08:24:33
## 5 MX001 Abarrotes 7.501026e+12 2020-06-19 08:26:28
## 6 MX001 Abarrotes 7.501025e+12 2020-06-19 08:26:28
## Marca Fabricante
## 1 NUTRI LECHE MEXILAC
## 2 DAN UP DANONE DE MEXICO
## 3 BIMBO GRUPO BIMBO
## 4 PEPSI PEPSI-COLA MEXICANA
## 5 BLANCA NIEVES (DETERGENTE) FABRICA DE JABON LA CORONA
## 6 FLASH ALEN
## Producto Precio Ult.Costo Unidades F.Ticket
## 1 Nutri Leche 1 Litro 16.0 12.31 1 1
## 2 DANUP STRAWBERRY P/BEBER 350GR NAL 14.0 14.00 1 2
## 3 Rebanadas Bimbo 2Pz 5.0 5.00 1 3
## 4 Pepsi N.R. 400Ml 8.0 8.00 1 3
## 5 Detergente Blanca Nieves 500G 19.5 15.00 1 4
## 6 Flash Xtra Brisa Marina 500Ml 9.5 7.31 1 4
## NombreDepartamento NombreFamilia NombreCategoria
## 1 Abarrotes Lacteos y Refrigerados Leche
## 2 Abarrotes Lacteos y Refrigerados Yogurt
## 3 Abarrotes Pan y Tortilla Pan Dulce Empaquetado
## 4 Abarrotes Bebidas Refrescos Plástico (N.R.)
## 5 Abarrotes Limpieza del Hogar Lavandería
## 6 Abarrotes Limpieza del Hogar Limpiadores Líquidos
## Estado Mts.2 Tipo.ubicación Giro Hora.inicio Hora.cierre
## 1 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 2 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 3 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 4 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 5 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 6 Nuevo León 60 Esquina Abarrotes 08:00 22:00tail(df)## vcClaveTienda DescGiro Codigo.Barras Fecha Hora
## 107247 MX004 Carnicería 1.024877e+10 2020-10-15 11:51:40
## 167624 MX004 Carnicería 7.501080e+12 2020-10-15 11:51:40
## 149282 MX004 Carnicería 7.501055e+12 2020-10-15 11:54:37
## 168603 MX004 Carnicería 7.501214e+12 2020-10-15 11:56:52
## 161046 MX004 Carnicería 7.501031e+12 2020-10-15 12:01:54
## 112823 MX004 Carnicería 7.500470e+07 2020-10-15 12:02:36
## Marca Fabricante Producto
## 107247 YEMINA HERDEZ PASTA SPAGHETTI YEMINA 200G
## 167624 DEL FUERTE ALIMENTOS DEL FUERTE PURE DE TOMATE DEL FUERTE 345G
## 149282 COCA COLA ZERO COCA COLA COCA COLA ZERO 600ML
## 168603 DIAMANTE EMPACADOS ARROZ DIAMANTE225G
## 161046 PEPSI PEPSI-COLA MEXICANA PEPSI N. R. 500ML
## 112823 COCA COLA COCA COLA COCA COLA RETORNABLE 500ML
## Precio Ult.Costo Unidades F.Ticket NombreDepartamento
## 107247 7 5.38 2 450032 Abarrotes
## 167624 12 9.23 1 450032 Abarrotes
## 149282 15 11.54 2 450034 Abarrotes
## 168603 11 8.46 1 450037 Abarrotes
## 161046 10 7.69 1 450039 Abarrotes
## 112823 10 7.69 8 450040 Abarrotes
## NombreFamilia NombreCategoria Estado Mts.2
## 107247 Sopas y Pastas Fideos, Spaguetti, Tallarines Sinaloa 53
## 167624 Salsas y Sazonadores Salsa para Spaguetti Sinaloa 53
## 149282 Bebidas Refrescos Retornables Sinaloa 53
## 168603 Granos y Semillas Arroz Sinaloa 53
## 161046 Bebidas Refrescos Plástico (N.R.) Sinaloa 53
## 112823 Bebidas Refrescos Retornables Sinaloa 53
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## 107247 Esquina Abarrotes 07:00 23:00
## 167624 Esquina Abarrotes 07:00 23:00
## 149282 Esquina Abarrotes 07:00 23:00
## 168603 Esquina Abarrotes 07:00 23:00
## 161046 Esquina Abarrotes 07:00 23:00
## 112823 Esquina Abarrotes 07:00 23:00# Generar el basket
basket <- ddply(df,c("F.Ticket"), function(df)paste(df$Marca, collapse = ","))
# Eliminar el Numero de Ticket
basket$F.Ticket <- NULL
# Cambiar el titulo de la columna v1 por Marca
colnames(basket) <- c("Marca")
# Exportar basket
write.csv(basket, "basket.csv", quote = FALSE, row.names = FALSE)Market Basket Analysis
# file.choose()
tr <- read.transactions("/Users/adrianelizondo/Desktop/basket.csv", format = "basket", sep=",")
reglas.asociacion <- apriori(tr, parameter = list(supp=0.001, conf=0.2, maxlen=10))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.2 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: 115
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[604 item(s), 115031 transaction(s)] done [0.01s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.02s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object ... done [0.01s].summary(reglas.asociacion)## set of 11 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 11
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.2069 Min. :0.003564 Min. : 1.326
## 1st Qu.:0.001104 1st Qu.:0.2358 1st Qu.:0.004507 1st Qu.: 1.789
## Median :0.001417 Median :0.2442 Median :0.005807 Median : 3.972
## Mean :0.001521 Mean :0.2537 Mean :0.006056 Mean :17.558
## 3rd Qu.:0.001652 3rd Qu.:0.2685 3rd Qu.:0.006894 3rd Qu.:21.808
## Max. :0.002747 Max. :0.3098 Max. :0.010502 Max. :65.862
## count
## Min. :117.0
## 1st Qu.:127.0
## Median :163.0
## Mean :174.9
## 3rd Qu.:190.0
## Max. :316.0
##
## mining info:
## data ntransactions support confidence
## tr 115031 0.001 0.2
## call
## apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))inspect(reglas.asociacion)## lhs rhs support confidence coverage
## [1] {FANTA} => {COCA COLA} 0.001051890 0.2439516 0.004311881
## [2] {SALVO} => {FABULOSO} 0.001104050 0.3097561 0.003564257
## [3] {FABULOSO} => {SALVO} 0.001104050 0.2347505 0.004703080
## [4] {COCA COLA ZERO} => {COCA COLA} 0.001417009 0.2969035 0.004772627
## [5] {SPRITE} => {COCA COLA} 0.001347463 0.2069426 0.006511288
## [6] {PINOL} => {CLORALEX} 0.001017117 0.2368421 0.004294495
## [7] {BLUE HOUSE} => {BIMBO} 0.001712582 0.2720994 0.006293956
## [8] {HELLMANN´S} => {BIMBO} 0.001538716 0.2649701 0.005807130
## [9] {REYMA} => {CONVERMEX} 0.002095087 0.2441743 0.008580296
## [10] {FUD} => {BIMBO} 0.001590876 0.2186380 0.007276299
## [11] {COCA COLA LIGHT} => {COCA COLA} 0.002747086 0.2615894 0.010501517
## lift count
## [1] 1.562646 121
## [2] 65.862391 127
## [3] 65.862391 127
## [4] 1.901832 163
## [5] 1.325583 155
## [6] 25.063647 117
## [7] 4.078691 197
## [8] 3.971823 177
## [9] 18.551922 241
## [10] 3.277319 183
## [11] 1.675626 316reglas.asociacion <- sort(reglas.asociacion, by= "confidence", decreasing = TRUE)
summary(reglas.asociacion)## set of 11 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 11
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.001017 Min. :0.2069 Min. :0.003564 Min. : 1.326
## 1st Qu.:0.001104 1st Qu.:0.2358 1st Qu.:0.004507 1st Qu.: 1.789
## Median :0.001417 Median :0.2442 Median :0.005807 Median : 3.972
## Mean :0.001521 Mean :0.2537 Mean :0.006056 Mean :17.558
## 3rd Qu.:0.001652 3rd Qu.:0.2685 3rd Qu.:0.006894 3rd Qu.:21.808
## Max. :0.002747 Max. :0.3098 Max. :0.010502 Max. :65.862
## count
## Min. :117.0
## 1st Qu.:127.0
## Median :163.0
## Mean :174.9
## 3rd Qu.:190.0
## Max. :316.0
##
## mining info:
## data ntransactions support confidence
## tr 115031 0.001 0.2
## call
## apriori(data = tr, parameter = list(supp = 0.001, conf = 0.2, maxlen = 10))inspect(reglas.asociacion)## lhs rhs support confidence coverage
## [1] {SALVO} => {FABULOSO} 0.001104050 0.3097561 0.003564257
## [2] {COCA COLA ZERO} => {COCA COLA} 0.001417009 0.2969035 0.004772627
## [3] {BLUE HOUSE} => {BIMBO} 0.001712582 0.2720994 0.006293956
## [4] {HELLMANN´S} => {BIMBO} 0.001538716 0.2649701 0.005807130
## [5] {COCA COLA LIGHT} => {COCA COLA} 0.002747086 0.2615894 0.010501517
## [6] {REYMA} => {CONVERMEX} 0.002095087 0.2441743 0.008580296
## [7] {FANTA} => {COCA COLA} 0.001051890 0.2439516 0.004311881
## [8] {PINOL} => {CLORALEX} 0.001017117 0.2368421 0.004294495
## [9] {FABULOSO} => {SALVO} 0.001104050 0.2347505 0.004703080
## [10] {FUD} => {BIMBO} 0.001590876 0.2186380 0.007276299
## [11] {SPRITE} => {COCA COLA} 0.001347463 0.2069426 0.006511288
## lift count
## [1] 65.862391 127
## [2] 1.901832 163
## [3] 4.078691 197
## [4] 3.971823 177
## [5] 1.675626 316
## [6] 18.551922 241
## [7] 1.562646 121
## [8] 25.063647 117
## [9] 65.862391 127
## [10] 3.277319 183
## [11] 1.325583 155top10reglas <- head(reglas.asociacion, n=10, by="confidence")
plot(top10reglas, method= "graph", engine = "htmlwidget")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