Market Basket Analysis - Abarrotes
Anuar Gamez A01234986
2026-08-18
Teoria
El Market Basket Analysis es una tecnica en el ambito de analisis y mineria de datosen el campo del comercio. Su objetivo inicial es descubrir patrones de asociacion entre productos que suelen ser comprados juntos por los clientes.
Las 3 metricas principales para evaluar reglas de asociacion son:
- Confidence (Confianza): Probabilidad de comprar B sabiendo
que se compro A. Ej. Pan –> Mantequilla 0.8 de cada 100 clientes que
compraron pan, 80 compraron mantequilla tambien.
- Lift (Elevacion): Cuanto mas probable es comprar B cuando se compra A en comparacion de la probabilidad de comprar B sin saber si se compro A. Ej. Lift > 1 compra A impulsa B. Lift = 1 No tienen relacion de compra. Lift < 1 A reduce la compra de B.
- Support (Soporte): Popularidad del producto dentro de las transacciones. Ej. Pan y Mantequilla 0.05 El 5% de todas las transacciones compraron estos 2 productos juntos.
# Contexto Una cadena
de tiendas de conveniencia tiene 5 tiendas ubicadas en distintas
Ciudades de México. La base de datos “abarrotes” contiene 1 mes de
transacciones, pero presenta errores de calidad que impiden realizar
analisis confiables . El objetivo es limpiar la base de datos de forma
estrategica y posteriormente aplicar MBA para descubrir patrones de
compra y diseñar promociones que aumenten las ventas.
Instalar paquetes y llamar librerias
#install.packages("tidyverse") #Paquete global para manipulacion y analisis de datos.
library(tidyverse)
#install.packages("janitor") # Examinar y limpiar bases de datos sucias
library(janitor)
#install.packages("Matrix") # Para trabajar con matrices
library(Matrix)
#install.packages("arules") # Genera reglas de asociacion
library(arules)
#install.packages("arulesViz") # visualizar reglas de asociacion
library(arulesViz)
#install.packages("plyr")
library(plyr)Importar la base de datos
# file.choose()
df<- read.csv("/Users/anuar/Desktop/abarrotes.csv")Entender la base de datos
summary(df)## ClaveTienda DescGiro Codigo.Barras PLU
## Length :200625 Length :200625 Min. :8.347e+05 Min. : 1.000
## N.unique : 5 N.unique : 3 1st Qu.:7.501e+12 1st Qu.: 1.000
## N.blank : 0 N.blank : 0 Median :7.501e+12 Median : 1.000
## Min.nchar: 5 Min.nchar: 8 Mean :5.950e+12 Mean : 2.112
## Max.nchar: 5 Max.nchar: 10 3rd Qu.:7.501e+12 3rd Qu.: 1.000
## Max. :1.750e+13 Max. :30.000
## NAs :199188
## Fecha Hora Marca Fabricante
## Length :200625 Length :200625 Length :200625 Length :200625
## N.unique : 195 N.unique : 52145 N.unique : 540 N.unique : 241
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 10 Min.nchar: 8 Min.nchar: 3 Min.nchar: 3
## Max.nchar: 10 Max.nchar: 8 Max.nchar: 30 Max.nchar: 40
##
##
## Producto Precio Ult.Costo Unidades
## Length :200625 Min. :-147.00 Min. : 0.38 Min. : 0.200
## N.unique : 3406 1st Qu.: 11.00 1st Qu.: 8.46 1st Qu.: 1.000
## N.blank : 0 Median : 16.00 Median : 12.31 Median : 1.000
## Min.nchar: 4 Mean : 19.42 Mean : 15.31 Mean : 1.262
## Max.nchar: 40 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 N.unique : 9 N.unique : 51 N.unique : 174
## Median :105993 N.blank : 0 N.blank : 0 N.blank : 0
## Mean :193990 Min.nchar: 6 Min.nchar: 3 Min.nchar: 2
## 3rd Qu.:383005 Max.nchar: 20 Max.nchar: 25 Max.nchar: 37
## Max. :450040
##
## Estado Mts.2 Tipo.ubicación Giro
## Length :200625 Min. :47.0 Length :200625 Length :200625
## N.unique : 5 1st Qu.:53.0 N.unique : 3 N.unique : 2
## N.blank : 0 Median :60.0 N.blank : 0 N.blank : 0
## Min.nchar: 7 Mean :56.6 Min.nchar: 7 Min.nchar: 9
## Max.nchar: 12 3rd Qu.:60.0 Max.nchar: 12 Max.nchar: 10
## Max. :62.0
##
## Hora.inicio Hora.cierre
## Length :200625 Length :200625
## N.unique : 3 N.unique : 3
## N.blank : 0 N.blank : 0
## Min.nchar: 5 Min.nchar: 5
## Max.nchar: 5 Max.nchar: 5
##
## str(df)## 'data.frame': 200625 obs. of 22 variables:
## $ ClaveTienda : 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, ClaveTienda, 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,10)## ClaveTienda 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
## 7 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 08:23:33
## 8 MX001 Abarrotes 7.501000e+12 NA 19/06/2020 08:24:33
## 9 MX001 Abarrotes 7.501031e+12 NA 19/06/2020 08:24:33
## 10 MX001 Abarrotes 7.501026e+12 NA 19/06/2020 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 NUTRI LECHE MEXILAC
## 7 DAN UP DANONE DE MEXICO
## 8 BIMBO GRUPO BIMBO
## 9 PEPSI PEPSI-COLA MEXICANA
## 10 BLANCA NIEVES (DETERGENTE) FABRICA DE JABON LA CORONA
## 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
## 7 DANUP STRAWBERRY P/BEBER 350GR NAL 14.0 14.00 1 2
## 8 Rebanadas Bimbo 2Pz 5.0 5.00 1 3
## 9 Pepsi N.R. 400Ml 8.0 8.00 1 3
## 10 Detergente Blanca Nieves 500G 19.5 15.00 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 Lacteos y Refrigerados Leche
## 7 Abarrotes Lacteos y Refrigerados Yogurt
## 8 Abarrotes Pan y Tortilla Pan Dulce Empaquetado
## 9 Abarrotes Bebidas Refrescos Plástico (N.R.)
## 10 Abarrotes Limpieza del Hogar Lavandería
## 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 22:00
## 7 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 8 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 9 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 10 Nuevo León 60 Esquina Abarrotes 08:00 22:00tail(df,10)## ClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 200616 MX005 Depósito 7.62221e+12 NA 07/08/2020 19:30:13
## 200617 MX005 Depósito 7.62221e+12 NA 25/07/2020 18:42:24
## 200618 MX005 Depósito 7.62221e+12 NA 18/07/2020 22:45:58
## 200619 MX005 Depósito 7.62221e+12 NA 12/07/2020 00:36:34
## 200620 MX005 Depósito 7.62221e+12 NA 12/07/2020 01:08:25
## 200621 MX005 Depósito 7.62221e+12 NA 23/10/2020 22:17:37
## 200622 MX005 Depósito 7.62221e+12 NA 10/10/2020 20:30:20
## 200623 MX005 Depósito 7.62221e+12 NA 10/10/2020 22:40:43
## 200624 MX005 Depósito 7.62221e+12 NA 27/06/2020 22:30:19
## 200625 MX005 Depósito 7.62221e+12 NA 26/06/2020 23:43:34
## Marca Fabricante Producto Precio
## 200616 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200617 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200618 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200619 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200620 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200621 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200622 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200623 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200624 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## 200625 TRIDENT XTRA CARE CADBURY ADAMS Trident Xtracare Freshmint 16.32G 9
## Ult.Costo Unidades F.Ticket NombreDepartamento NombreFamilia
## 200616 6.92 1 106411 Abarrotes Dulcería
## 200617 6.92 1 104693 Abarrotes Dulcería
## 200618 6.92 1 103856 Abarrotes Dulcería
## 200619 6.92 1 103087 Abarrotes Dulcería
## 200620 6.92 1 103100 Abarrotes Dulcería
## 200621 6.92 1 116598 Abarrotes Dulcería
## 200622 6.92 1 114886 Abarrotes Dulcería
## 200623 6.92 1 114955 Abarrotes Dulcería
## 200624 6.92 1 101121 Abarrotes Dulcería
## 200625 6.92 1 100879 Abarrotes Dulcería
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro Hora.inicio
## 200616 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200617 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200618 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200619 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200620 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200621 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200622 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200623 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200624 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## 200625 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 08:00
## Hora.cierre
## 200616 21:00
## 200617 21:00
## 200618 21:00
## 200619 21:00
## 200620 21:00
## 200621 21:00
## 200622 21:00
## 200623 21:00
## 200624 21:00
## 200625 21:00#Tabla de Tienda y Departamentio
tabyl(df, ClaveTienda, NombreDepartamento)## ClaveTienda 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 0Limpiar la base de datos
Tecnica 1. Eliminar valores irrelevantes
# Eliminar columnas
df<- subset(df, select= -c(PLU))
#Eliminar Renglones
df <- df[df$Precio>0, ]Tecnica 2. Eliminar valores repetidos
df<- distinct(df)Tecnica 3. Corregir errores tipograficos y similares
df$Unidades <- ceiling(df$Unidades)Tecnica 4. Convertir tipos de datos
df$Fecha <- as.Date(df$Fecha, format="%d/%m/%Y")Tecnica 5. Tratar valores faltantes
# Borrar todos los NA's
# df <- na.omit(df)
#Reemplazar los NA's con CEROS
# df[is.na(df)] <- 0
#Reemplazar los NA's con el PROMEDIO
# df$altura[is.na(df$altura)] <- mean(df$altura, na.rn=TRUE)Tecnica 6. Herramientas estadisticas
boxplot(df$Precio, horizontal= TRUE)
boxplot(df$Unidades, horizontal= TRUE)
Tecnica 6. Herramientas estadisticas
#Ordenar de menor a mayor la columna ticket
df <- df[order(df$F.Ticket), ]
#Generar basket
basket <- ddply(df, c("F.Ticket"), function(df)paste(df$Marca, collapse =","))
#eliminar 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", row.names=FALSE, quote=FALSE)Market basket analysis
#file.choose()
tr <- read.transactions("/Users/anuar/Desktop/basket.csv",format = "basket",sep = ",")## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string## Warning in asMethod(object): removing duplicated items in transactionsreglas.asosiacion <- 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 [0.01s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].# summary(reglas.asosiacion)
# inspect(reglas.asosiacion)
reglas.asosiacion <- sort(reglas.asosiacion,by = "confidence",decreasing = TRUE)
# summary(reglas.asosiacion)
# inspect((reglas.asosiacion))
top10reglas <- head(reglas.asosiacion, n = 10, by = "confidence")
plot(top10reglas,method="graph",engine = "htmlwidget")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