Basket

Market Basket Abalysis

#install.packages("plyr")
library(plyr)
#install.packages("Matrix")
library(Matrix)
#install.packages("arules")
library(arules)

## 
## Attaching package: 'arules'

## The following objects are masked from 'package:base':
## 
##     abbreviate, write

#install.packages("arulesViz")
library(arulesViz)
#install.packages("datasets")
library(datasets)

Importar datos

#file.choose()
bd_limpia <- read.csv("/Users/fedevaldes/abarrotes_bd_limpia.csv")

Ordenar de menor a mayor los Tickets

bd_limpia <- bd_limpia[order(bd_limpia$F.Ticket),]
head(bd_limpia)

##   vcClaveTienda  DescGiro      Fecha Hora                      Marca
## 1         MX001 Abarrotes 2020-06-19    8                NUTRI LECHE
## 2         MX001 Abarrotes 2020-06-19    8                     DAN UP
## 3         MX001 Abarrotes 2020-06-19    8                      BIMBO
## 4         MX001 Abarrotes 2020-06-19    8                      PEPSI
## 5         MX001 Abarrotes 2020-06-19    8 BLANCA NIEVES (DETERGENTE)
## 6         MX001 Abarrotes 2020-06-19    8                      FLASH
##                   Fabricante                           Producto Precio
## 1                    MEXILAC                Nutri Leche 1 Litro   16.0
## 2           DANONE DE MEXICO DANUP STRAWBERRY P/BEBER 350GR NAL   14.0
## 3                GRUPO BIMBO                Rebanadas Bimbo 2Pz    5.0
## 4        PEPSI-COLA MEXICANA                   Pepsi N.R. 400Ml    8.0
## 5 FABRICA DE JABON LA CORONA      Detergente Blanca Nieves 500G   19.5
## 6                       ALEN      Flash Xtra Brisa Marina 500Ml    9.5
##   Ult.Costo Unidades F.Ticket NombreDepartamento          NombreFamilia
## 1     12.31        1        1          Abarrotes Lacteos y Refrigerados
## 2     14.00        1        2          Abarrotes Lacteos y Refrigerados
## 3      5.00        1        3          Abarrotes         Pan y Tortilla
## 4      8.00        1        3          Abarrotes                Bebidas
## 5     15.00        1        4          Abarrotes     Limpieza del Hogar
## 6      7.31        1        4          Abarrotes     Limpieza del Hogar
##             NombreCategoria     Estado Mts.2 Tipo.ubicación      Giro
## 1                     Leche Nuevo León    60        Esquina Abarrotes
## 2                    Yogurt Nuevo León    60        Esquina Abarrotes
## 3     Pan Dulce Empaquetado Nuevo León    60        Esquina Abarrotes
## 4 Refrescos Plástico (N.R.) Nuevo León    60        Esquina Abarrotes
## 5                Lavandería Nuevo León    60        Esquina Abarrotes
## 6      Limpiadores Líquidos Nuevo León    60        Esquina Abarrotes
##   Hora.inicio Hora.cierre Dia_de_la_Semana Subtotal Utilidad
## 1       08:00       22:00                6     16.0  196.960
## 2       08:00       22:00                6     14.0  196.000
## 3       08:00       22:00                6      5.0   25.000
## 4       08:00       22:00                6      8.0   64.000
## 5       08:00       22:00                6     19.5  292.500
## 6       08:00       22:00                6      9.5   69.445

tail(bd_limpia)

##        vcClaveTienda   DescGiro      Fecha Hora          Marca
## 107394         MX004 Carnicería 2020-10-15   11         YEMINA
## 167771         MX004 Carnicería 2020-10-15   11     DEL FUERTE
## 149429         MX004 Carnicería 2020-10-15   11 COCA COLA ZERO
## 168750         MX004 Carnicería 2020-10-15   11       DIAMANTE
## 161193         MX004 Carnicería 2020-10-15   12          PEPSI
## 112970         MX004 Carnicería 2020-10-15   12      COCA COLA
##                  Fabricante                       Producto Precio Ult.Costo
## 107394               HERDEZ    PASTA SPAGHETTI YEMINA 200G      7      5.38
## 167771 ALIMENTOS DEL FUERTE PURE DE TOMATE DEL FUERTE 345G     12      9.23
## 149429            COCA COLA           COCA COLA ZERO 600ML     15     11.54
## 168750           EMPACADOS              ARROZ DIAMANTE225G     11      8.46
## 161193  PEPSI-COLA MEXICANA              PEPSI N. R. 500ML     10      7.69
## 112970            COCA COLA     COCA COLA RETORNABLE 500ML     10      7.69
##        Unidades F.Ticket NombreDepartamento        NombreFamilia
## 107394        2   450032          Abarrotes       Sopas y Pastas
## 167771        1   450032          Abarrotes Salsas y Sazonadores
## 149429        2   450034          Abarrotes              Bebidas
## 168750        1   450037          Abarrotes    Granos y Semillas
## 161193        1   450039          Abarrotes              Bebidas
## 112970        8   450040          Abarrotes              Bebidas
##                      NombreCategoria  Estado Mts.2 Tipo.ubicación      Giro
## 107394 Fideos, Spaguetti, Tallarines Sinaloa    53        Esquina Abarrotes
## 167771          Salsa para Spaguetti Sinaloa    53        Esquina Abarrotes
## 149429         Refrescos Retornables Sinaloa    53        Esquina Abarrotes
## 168750                         Arroz Sinaloa    53        Esquina Abarrotes
## 161193     Refrescos Plástico (N.R.) Sinaloa    53        Esquina Abarrotes
## 112970         Refrescos Retornables Sinaloa    53        Esquina Abarrotes
##        Hora.inicio Hora.cierre Dia_de_la_Semana Subtotal Utilidad
## 107394       07:00       23:00                5       14    37.66
## 167771       07:00       23:00                5       12   110.76
## 149429       07:00       23:00                5       30   173.10
## 168750       07:00       23:00                5       11    93.06
## 161193       07:00       23:00                5       10    76.90
## 112970       07:00       23:00                5       80    76.90

Generar basket

basket <- ddply(bd_limpia,c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse = ","))

Eliminar Número de Ticket

basket$F.Ticket <- NULL

Renombramos el nombre de la columna

colnames(basket) <- c("Marca")

Exportar basket

write.csv(basket, "basket.csv", quote = FALSE, row.names = FALSE)

Importar transacciones

#file.choose()
tr <- read.transactions("/Users/fedevaldes/basket.csv", format = "basket", sep=",")

## 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 asMethod(object): removing duplicated items in transactions

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), 115111 transaction(s)] done [0.03s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.03s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [11 rule(s)] done [0.00s].
## creating S4 object  ... done [0.02s].

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.   :0.001016   Min.   :0.2069   Min.   :0.003562   Min.   : 1.325  
##  1st Qu.:0.001103   1st Qu.:0.2356   1st Qu.:0.004504   1st Qu.: 1.787  
##  Median :0.001416   Median :0.2442   Median :0.005803   Median : 3.972  
##  Mean   :0.001519   Mean   :0.2536   Mean   :0.006054   Mean   :17.563  
##  3rd Qu.:0.001651   3rd Qu.:0.2685   3rd Qu.:0.006893   3rd Qu.:21.798  
##  Max.   :0.002745   Max.   :0.3098   Max.   :0.010503   Max.   :65.908  
##      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        115111   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} 0.001051159 0.2439516  0.004308884
## [2]  {SALVO}           => {FABULOSO}  0.001103283 0.3097561  0.003561779
## [3]  {FABULOSO}        => {SALVO}     0.001103283 0.2347505  0.004699811
## [4]  {COCA COLA ZERO}  => {COCA COLA} 0.001416025 0.2969035  0.004769310
## [5]  {SPRITE}          => {COCA COLA} 0.001346526 0.2069426  0.006506763
## [6]  {PINOL}           => {CLORALEX}  0.001016410 0.2363636  0.004300197
## [7]  {BLUE HOUSE}      => {BIMBO}     0.001711392 0.2720994  0.006289581
## [8]  {HELLMANN´S}      => {BIMBO}     0.001537646 0.2649701  0.005803094
## [9]  {REYMA}           => {CONVERMEX} 0.002093631 0.2441743  0.008574333
## [10] {FUD}             => {BIMBO}     0.001589770 0.2183771  0.007279930
## [11] {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730  0.010502906
##      lift      count
## [1]   1.561906 121  
## [2]  65.908196 127  
## [3]  65.908196 127  
## [4]   1.900932 163  
## [5]   1.324955 155  
## [6]  25.030409 117  
## [7]   4.078870 197  
## [8]   3.971997 177  
## [9]  18.564824 241  
## [10]  3.273552 183  
## [11]  1.673447 316

reglas.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.   :0.001016   Min.   :0.2069   Min.   :0.003562   Min.   : 1.325  
##  1st Qu.:0.001103   1st Qu.:0.2356   1st Qu.:0.004504   1st Qu.: 1.787  
##  Median :0.001416   Median :0.2442   Median :0.005803   Median : 3.972  
##  Mean   :0.001519   Mean   :0.2536   Mean   :0.006054   Mean   :17.563  
##  3rd Qu.:0.001651   3rd Qu.:0.2685   3rd Qu.:0.006893   3rd Qu.:21.798  
##  Max.   :0.002745   Max.   :0.3098   Max.   :0.010503   Max.   :65.908  
##      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        115111   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}  0.001103283 0.3097561  0.003561779
## [2]  {COCA COLA ZERO}  => {COCA COLA} 0.001416025 0.2969035  0.004769310
## [3]  {BLUE HOUSE}      => {BIMBO}     0.001711392 0.2720994  0.006289581
## [4]  {HELLMANN´S}      => {BIMBO}     0.001537646 0.2649701  0.005803094
## [5]  {COCA COLA LIGHT} => {COCA COLA} 0.002745176 0.2613730  0.010502906
## [6]  {REYMA}           => {CONVERMEX} 0.002093631 0.2441743  0.008574333
## [7]  {FANTA}           => {COCA COLA} 0.001051159 0.2439516  0.004308884
## [8]  {PINOL}           => {CLORALEX}  0.001016410 0.2363636  0.004300197
## [9]  {FABULOSO}        => {SALVO}     0.001103283 0.2347505  0.004699811
## [10] {FUD}             => {BIMBO}     0.001589770 0.2183771  0.007279930
## [11] {SPRITE}          => {COCA COLA} 0.001346526 0.2069426  0.006506763
##      lift      count
## [1]  65.908196 127  
## [2]   1.900932 163  
## [3]   4.078870 197  
## [4]   3.971997 177  
## [5]   1.673447 316  
## [6]  18.564824 241  
## [7]   1.561906 121  
## [8]  25.030409 117  
## [9]  65.908196 127  
## [10]  3.273552 183  
## [11]  1.324955 155

top10reglas <- head(reglas.asociacion, n = 10, by = "confidence")
plot(top10reglas, method = "graph", engine = "htmlwidget")