Reglas de asociacion

Tipos de variables en R

Vectores

vector_1 <- c(1,2,3,4,5,6,7,8,9)
vector_1

## [1] 1 2 3 4 5 6 7 8 9

Matrices

Mi_matriz <- matrix(data = vector_1, ncol = 3, byrow = FALSE)
Mi_matriz

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

Ponerle nombre a las columnas

colnames(Mi_matriz) <- c("Altura","Peso","Talla")
rownames(Mi_matriz) <- c("Juan","Pedro","Mario")
Mi_matriz

##       Altura Peso Talla
## Juan       1    4     7
## Pedro      2    5     8
## Mario      3    6     9

Importar datos de un archivo csv

library(readr)
datos_groceries <- read_csv("datos_groceries.csv")

## Rows: 43367 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): item
## dbl (1): id_compra
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# datos_groceries <- read_csv("https://ricardorpalma.github.io/R-Dataset/datos_groceries.csv")

Creamos nuestro vector de datos para el analisis de tickets. Estos datos podriamos traerlo de un archivo, ponerlos en una linea de comando de r o traerlos desde la web

datos_crudo <- c(1,1,1,2,2,3,3,"5W40","Filtro Aceite","Filtro Aire","Correa Distr","Bomba de Agua","Filtro Nafta","Limpia Inyectores")
datos_crudo

##  [1] "1"                 "1"                 "1"                
##  [4] "2"                 "2"                 "3"                
##  [7] "3"                 "5W40"              "Filtro Aceite"    
## [10] "Filtro Aire"       "Correa Distr"      "Bomba de Agua"    
## [13] "Filtro Nafta"      "Limpia Inyectores"

Vamos a transformar nuestros datos crudos en una matriz

lista_corta <- matrix(datos_crudo,ncol=2, byrow=FALSE)
lista_corta

##      [,1] [,2]               
## [1,] "1"  "5W40"             
## [2,] "1"  "Filtro Aceite"    
## [3,] "1"  "Filtro Aire"      
## [4,] "2"  "Correa Distr"     
## [5,] "2"  "Bomba de Agua"    
## [6,] "3"  "Filtro Nafta"     
## [7,] "3"  "Limpia Inyectores"

Ahora le colocamos nombre a las columnas

dimnames(lista_corta) <- list(salida = c(1,2,3,4,5,6,7) , producto = c("ticket","producto") )
lista_corta

##       producto
## salida ticket producto           
##      1 "1"    "5W40"             
##      2 "1"    "Filtro Aceite"    
##      3 "1"    "Filtro Aire"      
##      4 "2"    "Correa Distr"     
##      5 "2"    "Bomba de Agua"    
##      6 "3"    "Filtro Nafta"     
##      7 "3"    "Limpia Inyectores"

Datos en forma de lista larga

datos_m <- c(1,1,1,0,0,0,0, 0,0,0,1,1,0,0, 0,0,0,0,0,1,1)
datos_m

##  [1] 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1

Ahora lo transformamos en una matriz

lista_larga <- matrix(datos_m,nrow=3,byrow=TRUE)
lista_larga

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,]    1    1    1    0    0    0    0
## [2,]    0    0    0    1    1    0    0
## [3,]    0    0    0    0    0    1    1

Colocamos nombres a las filas y columnas

dimnames(lista_larga) <- list(ticket = c(1,2,3) , producto = c("5W40","Filtro Aceite","Filtro Aire","Correa Distr","Bomba de Agua","Filtro Nafta","Limpia Inyectores"))
lista_larga

##       producto
## ticket 5W40 Filtro Aceite Filtro Aire Correa Distr Bomba de Agua Filtro Nafta
##      1    1             1           1            0             0            0
##      2    0             0           0            1             1            0
##      3    0             0           0            0             0            1
##       producto
## ticket Limpia Inyectores
##      1                 0
##      2                 0
##      3                 1

Recordemos antes de utilizar cualquier paquete de r que se invoca con el comando library, el paquete debe ser instalado

library(arules)

## Loading required package: Matrix

## 
## Attaching package: 'arules'

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

apriori(lista_larga)

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.8    0.1    1 none FALSE            TRUE       5     0.1      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: 0 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[7 item(s), 3 transaction(s)] done [0.00s].
## sorting and recoding items ... [7 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [13 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

## set of 13 rules

transacciones_1 <- apriori(lista_larga)

## Apriori
## 
## Parameter specification:
##  confidence minval smax arem  aval originalSupport maxtime support minlen
##         0.8    0.1    1 none FALSE            TRUE       5     0.1      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: 0 
## 
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[7 item(s), 3 transaction(s)] done [0.00s].
## sorting and recoding items ... [7 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [13 rule(s)] done [0.00s].
## creating S4 object  ... done [0.00s].

reglas_a <- inspect(transacciones_1)

##      lhs                             rhs                 support   confidence
## [1]  {Correa Distr}               => {Bomba de Agua}     0.3333333 1         
## [2]  {Bomba de Agua}              => {Correa Distr}      0.3333333 1         
## [3]  {Filtro Nafta}               => {Limpia Inyectores} 0.3333333 1         
## [4]  {Limpia Inyectores}          => {Filtro Nafta}      0.3333333 1         
## [5]  {Filtro Aceite}              => {Filtro Aire}       0.3333333 1         
## [6]  {Filtro Aire}                => {Filtro Aceite}     0.3333333 1         
## [7]  {Filtro Aceite}              => {5W40}              0.3333333 1         
## [8]  {5W40}                       => {Filtro Aceite}     0.3333333 1         
## [9]  {Filtro Aire}                => {5W40}              0.3333333 1         
## [10] {5W40}                       => {Filtro Aire}       0.3333333 1         
## [11] {Filtro Aceite, Filtro Aire} => {5W40}              0.3333333 1         
## [12] {5W40, Filtro Aceite}        => {Filtro Aire}       0.3333333 1         
## [13] {5W40, Filtro Aire}          => {Filtro Aceite}     0.3333333 1         
##      coverage  lift count
## [1]  0.3333333 3    1    
## [2]  0.3333333 3    1    
## [3]  0.3333333 3    1    
## [4]  0.3333333 3    1    
## [5]  0.3333333 3    1    
## [6]  0.3333333 3    1    
## [7]  0.3333333 3    1    
## [8]  0.3333333 3    1    
## [9]  0.3333333 3    1    
## [10] 0.3333333 3    1    
## [11] 0.3333333 3    1    
## [12] 0.3333333 3    1    
## [13] 0.3333333 3    1

library(arulesViz)
plot(transacciones_1, method = "graph")

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