Market Basket Analysis - Abarrotes
Danara Esparza A00840602
2026-08-18
Teoría
El Market Basket Analysis es una técnica en el ámbito de análisis y minería de datos en el campo del comercio. Su objetivo principal es descrubir patrones de asociación entre productos que suelen ser comprados juntos por los clientes.
La 3 métricas principales para evaluar reglas de asociación son:
- Confidence (confianza): Probabilidad de comprar B sabiendo
que se compró A. Ej. Pan –> Mantequilla 0.8 De cada 100 clientes que
compraron pan, 80 compraron mantequilla también.
- Lift (elevación): Cuánto más probable es comprar B cuando
se compra A en comparación de la probabilidad de comprar B sin saber si
se compró A. Ej. Lift > 1 Comprar A impulsa B. Lift = 1 No tiene
relación 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 dos 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 análisis confiables. El objetivo es limpiar la base de datos de forma estratégica y posteriormente aplicar MBA para descubrir patrones de compra y diseñar promociones que aumenten las ventas.
Instalar paquetes y llamar librerías
#install.packages("tidyverse") # manipulación de datos
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── 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("janitor") #examinar y limpiar bases de datos
library(janitor) ##
## Attaching package: 'janitor'
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## chisq.test, fisher.test#install.packages("Matrix") #generar matirces
library(Matrix)##
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## expand, pack, unpack#install.packages("arules") #genera reglas de asociación
library(arules)##
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## recode
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## abbreviate, write#install.packages("arulesViz") #visualizar reglas de asociación
library(arulesViz)
#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)
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## arrange, count, desc, mutate, rename, summarise, summarize
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## compactImportar base de datos
#file.choose()
df <- read.csv("/Users/danaraesparzamacias/Desktop/abarrotes.csv") Entender 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 departamento
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 base de datos
Técnica 1. Eliminar valores irrelevantes
# Eliminar columnas
df <- subset(df, select=-c(PLU)) #el -c es para dejar todas las columnas menos la que indicas
# 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$Unidades) #redondear las unidadesTécnica 4. Convertir los tipos de datos
df$Fecha <- as.Date(df$Fecha, format = "%d/%m/%Y")
#si el año son 2 digitos es "y", si son 4 digitos es "Y"Técnica 5. Tratar valores faltantes
#Borrar todos los NA's
# df <- na.omit(df) se borrarían todos los NA
#Reemmplazar las NA's con CEROS
# df[is.na(df)] <- 0
#Reemplazar los NA's con PROMEDIO
# df$altura[is.na(df$altura)] <- mean(df$altura, na.rn= TRUE) Técnica 6. Herramientas estadísticas
boxplot(df$Precio, horizontal = TRUE)
boxplot(df$Unidades, horizontal = TRUE) 
Generar Masket
#Ordenar de menor a mayor la columna ticket
df <- df[order(df$F.Ticket), ]
#Genera basket
basket <- ddply (df, c("F.Ticket"), function(df)paste(df$Marca, collapse = ","))
#Eliminar numero de ticket
basket$F.Ticket <- NULL
#Cambiar el titulo de lla columna V1 por marca
colnames(basket) <- c("Marca")
#Explorar market
write.csv(basket, "basket.csv", row.names=FALSE, quote=FALSE) Market basket Analysis
#file.choose()
tr <- read.transactions("/Users/danaraesparzamacias/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
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## 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 [0.02s].
## 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.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")