Abarrotes… Market Basket Analysis
ElenaVela_A01283535
2022-09-08
Pasos previos a la limpieza de datos, hecho en Excel:
- Formato a fecha corta
- Se duplicaron los primeros 5 registros
- Se cambió el formato de hora y fecha a español mex
- Cambio de codigo de barras
- Se guardó como CSV UTF8, delimitado por comas
Pasos previos.
Importar base de datos
#file.choose()
bd<-read.csv("/Users/elenavela/Downloads/abarrotes (1).csv")
#bd
resumen<-summary(bd)
resumen## 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
##
##
##
## Instalar paquetes y librerías, y hacer los count
#install.packages("dplyr")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#install.packages("tidyverse")
library(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ tidyr 1.2.0 ✔ forcats 0.5.2
## ✔ readr 2.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#install.packages("janitor")
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test#count(bd,vcClaveTienda,sort=TRUE)
#count(bd,DescGiro,sort=TRUE)
#count(bd,Marca,sort=TRUE)
#count(bd,Fabricante,sort=TRUE)
#count(bd,Producto,sort=TRUE)
#count(bd,NombreDepartamento,sort=TRUE)
#count(bd,NombreFamilia,sort=TRUE)
#count(bd,NombreCategoria,sort=TRUE)
#count(bd,Estado,sort=TRUE)
#count(bd,Mts.2,sort=TRUE)
#count(bd,Tipo.ubicación,sort=TRUE)
#count(bd,Giro,sort=TRUE)
#count(bd,Hora.inicio,sort=TRUE)
#count(bd,Hora.cierre,sort=TRUE)Analizar la base de datos
tibble(bd)## # A tibble: 200,625 × 22
## vcClaveTienda DescGiro Codig…¹ PLU Fecha Hora Marca Fabri…² Produ…³ Precio
## <chr> <chr> <dbl> <int> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 MX001 Abarrot… 7.50e12 NA 19/0… 08:1… NUTR… MEXILAC Nutri … 16
## 2 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… DAN … DANONE… DANUP … 14
## 3 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… BIMBO GRUPO … Rebana… 5
## 4 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… PEPSI PEPSI-… Pepsi … 8
## 5 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… BLAN… FABRIC… Deterg… 19.5
## 6 MX001 Abarrot… 7.50e12 NA 19/0… 08:1… NUTR… MEXILAC Nutri … 16
## 7 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… DAN … DANONE… DANUP … 14
## 8 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… BIMBO GRUPO … Rebana… 5
## 9 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… PEPSI PEPSI-… Pepsi … 8
## 10 MX001 Abarrot… 7.50e12 NA 19/0… 08:2… BLAN… FABRIC… Deterg… 19.5
## # … with 200,615 more rows, 12 more variables: Ult.Costo <dbl>, Unidades <dbl>,
## # F.Ticket <int>, NombreDepartamento <chr>, NombreFamilia <chr>,
## # NombreCategoria <chr>, Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>,
## # Giro <chr>, Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable
## # names ¹Codigo.Barras, ²Fabricante, ³Productostr(bd)## '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 "19/06/20" "19/06/20" "19/06/20" "19/06/20" ...
## $ Hora : chr "08:16:21 a. m." "08:23:33 a. m." "08:24:33 a. m." "08:24:33 a. m." ...
## $ 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 "8:00" "8:00" "8:00" "8:00" ...
## $ Hora.cierre : chr "22:00" "22:00" "22:00" "22:00" ...head(bd)## vcClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 1 MX001 Abarrotes 7.501021e+12 NA 19/06/20 08:16:21 a. m.
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/20 08:23:33 a. m.
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/20 08:24:33 a. m.
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/20 08:24:33 a. m.
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/20 08:26:28 a. m.
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/20 08:16:21 a. m.
## 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 8:00 22:00
## 2 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 3 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 4 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 5 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 6 Nuevo León 60 Esquina Abarrotes 8:00 22:00head(bd,n=7)## vcClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 1 MX001 Abarrotes 7.501021e+12 NA 19/06/20 08:16:21 a. m.
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/20 08:23:33 a. m.
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/20 08:24:33 a. m.
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/20 08:24:33 a. m.
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/20 08:26:28 a. m.
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/20 08:16:21 a. m.
## 7 MX001 Abarrotes 7.501032e+12 NA 19/06/20 08:23:33 a. m.
## 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
## 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
## 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
## Estado Mts.2 Tipo.ubicación Giro Hora.inicio Hora.cierre
## 1 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 2 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 3 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 4 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 5 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 6 Nuevo León 60 Esquina Abarrotes 8:00 22:00
## 7 Nuevo León 60 Esquina Abarrotes 8:00 22:00tail(bd)## vcClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 200620 MX005 Depósito 7.62221e+12 NA 12/07/20 01:08:25 a. m.
## 200621 MX005 Depósito 7.62221e+12 NA 23/10/20 10:17:37 p. m.
## 200622 MX005 Depósito 7.62221e+12 NA 10/10/20 08:30:20 p. m.
## 200623 MX005 Depósito 7.62221e+12 NA 10/10/20 10:40:43 p. m.
## 200624 MX005 Depósito 7.62221e+12 NA 27/06/20 10:30:19 p. m.
## 200625 MX005 Depósito 7.62221e+12 NA 26/06/20 11:43:34 p. m.
## Marca Fabricante Producto Precio
## 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
## 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
## 200620 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## 200621 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## 200622 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## 200623 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## 200624 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## 200625 Gomas de Mazcar Quintana Roo 58 Esquina Mini súper 8:00
## Hora.cierre
## 200620 21:00
## 200621 21:00
## 200622 21:00
## 200623 21:00
## 200624 21:00
## 200625 21:00tabyl(bd,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 0Observaciones 1. Casi ningun registro tiene
PLU
2. Cambiar formato de fecha
3. Cambiar formato de hora
4. Hay precios negativos
5. Unidades menores a 1
Herramienta “El generador de valor de datos” .
Paso 1. Definir el área del negocio que buscamos
impactar o mejorar y su KPI.
Lo que se busca que impacte es en una división, en este caso
mercadotecnia. Puesto que una gran parte del éxito de las ventas de
tiendas es debido a buenas técnicas de mercadotecnia y buenos análisis.
Lo que se busca medir (KPI) son las ventas. Paso 2.
Seleccionar la plantilla (-s) para crear valor a partir de los datos de
los clientes.
Se busca ver lo que un solo cliente podría comprar en una sola
“canasta”, por lo que se busca segmentar.
Visión |
Segmentacion | Personalización |
Contextualizacion
Paso 3. Generar ideas o conceptos
específicos.
Al hacer las segmentaciones de los clientes y lo que comprar en
conjunto, se espera poder generar estrategias de mercadotecnia que
invite aún más a los clientes a comprar diferentes productos de
una sola canaste. Por ejemplo, invitar a los clientes a acompañar su
cerveza con papas (dependiendo de los resultados al final.) Igualmente,
al darle un enfoque de visión es posible poner atención a las
oportunidades de crecimiento y de avanzar en el mundo de los negocios y
de ventas. Paso 4. Reunir los datos
requeridos.
Para poder llevar a cabo esta herramienta, es necesario tener
limpia la base de datos de Abarrotes. Paso 5.
Plan de ejecucion.
1. Analizar los datos obtenidos y definir una tienda de abarrotes.
2. Crear promociones de una sola canasta (ej.cerveza con
sabritas).
3. Llevar una bitácora de ventas para ver el crecimiento (o no).
Primer paso.
Limpiar de datos
Técnica 1, REMOVER DATOS IRRELEVANTES
Eliminar columnas
bd1<-bd
bd1<-subset(bd1,select=-c(PLU,Codigo.Barras))Eliminar renglones
Este no se usará
bd2<-bd1
bd2<-bd2[bd$Precio>0,]
summary(bd1) ## vcClaveTienda DescGiro Fecha Hora
## Length:200625 Length:200625 Length:200625 Length:200625
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200625 Length:200625 Length:200625 Min. :-147.00
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.42
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200625
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33964 Class :character
## Median : 12.31 Median : 1.000 Median :105993 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193990
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383005
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200625 Length:200625 Length:200625 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200625 Length:200625 Length:200625 Length:200625
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## summary(bd2)## vcClaveTienda DescGiro Fecha Hora
## Length:200478 Length:200478 Length:200478 Length:200478
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200478 Length:200478 Length:200478 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200478
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33977 Class :character
## Median : 12.31 Median : 1.000 Median :106034 Mode :character
## Mean : 15.31 Mean : 1.261 Mean :194096
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383062
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200478 Length:200478 Length:200478 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200478 Length:200478 Length:200478 Length:200478
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## Técnica 2, REMOVER VALOR DUPLICADOS
¿Cuántos renglones duplicados tenemos?
bd1[duplicated(bd1),]## vcClaveTienda DescGiro Fecha Hora Marca
## 6 MX001 Abarrotes 19/06/20 08:16:21 a. m. NUTRI LECHE
## 7 MX001 Abarrotes 19/06/20 08:23:33 a. m. DAN UP
## 8 MX001 Abarrotes 19/06/20 08:24:33 a. m. BIMBO
## 9 MX001 Abarrotes 19/06/20 08:24:33 a. m. PEPSI
## 10 MX001 Abarrotes 19/06/20 08:26:28 a. m. BLANCA NIEVES (DETERGENTE)
## Fabricante Producto Precio
## 6 MEXILAC Nutri Leche 1 Litro 16.0
## 7 DANONE DE MEXICO DANUP STRAWBERRY P/BEBER 350GR NAL 14.0
## 8 GRUPO BIMBO Rebanadas Bimbo 2Pz 5.0
## 9 PEPSI-COLA MEXICANA Pepsi N.R. 400Ml 8.0
## 10 FABRICA DE JABON LA CORONA Detergente Blanca Nieves 500G 19.5
## Ult.Costo Unidades F.Ticket NombreDepartamento NombreFamilia
## 6 12.31 1 1 Abarrotes Lacteos y Refrigerados
## 7 14.00 1 2 Abarrotes Lacteos y Refrigerados
## 8 5.00 1 3 Abarrotes Pan y Tortilla
## 9 8.00 1 3 Abarrotes Bebidas
## 10 15.00 1 4 Abarrotes Limpieza del Hogar
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro
## 6 Leche Nuevo León 60 Esquina Abarrotes
## 7 Yogurt Nuevo León 60 Esquina Abarrotes
## 8 Pan Dulce Empaquetado Nuevo León 60 Esquina Abarrotes
## 9 Refrescos Plástico (N.R.) Nuevo León 60 Esquina Abarrotes
## 10 Lavandería Nuevo León 60 Esquina Abarrotes
## Hora.inicio Hora.cierre
## 6 8:00 22:00
## 7 8:00 22:00
## 8 8:00 22:00
## 9 8:00 22:00
## 10 8:00 22:00sum(duplicated(bd1)) ## [1] 5Eliminar renglones duplicados
bd3<-bd1
library(dplyr)
bd3<-distinct(bd3)Técnica 3, ERRORES TIPOGRÁFICOS Y ERRORES SIMILARES
Precios en absoluto
bd4<-bd3
bd4$Precio<-abs(bd4$Precio)
summary(bd4)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## Cantidades en enteros
bd5<-bd4
bd5$Unidades<-ceiling(bd5$Unidades)
summary(bd5) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## Técnica 4, CONVERTIR TIPO DE DATOS
Convertir de carácter a fecha
bd6<-bd5
bd6$Fecha<-as.Date(bd6$Fecha,format="%d/%m/%Y")
tibble(bd6)## # A tibble: 200,620 × 20
## vcCla…¹ DescG…² Fecha Hora Marca Fabri…³ Produ…⁴ Precio Ult.C…⁵ Unida…⁶
## <chr> <chr> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MX001 Abarro… 0020-06-19 08:1… NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… 0020-06-19 08:2… DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… 0020-06-19 08:2… BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… 0020-06-19 08:2… PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… 0020-06-19 08:2… BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… 0020-06-19 08:2… FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… 0020-06-19 08:2… VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… 0020-06-19 08:2… ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… 0020-06-19 08:2… ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… 0020-06-19 03:2… JUMEX JUMEX Jugo D… 12 12 1
## # … with 200,610 more rows, 10 more variables: F.Ticket <int>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>, Giro <chr>,
## # Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable names
## # ¹vcClaveTienda, ²DescGiro, ³Fabricante, ⁴Producto, ⁵Ult.Costo, ⁶UnidadesConvertir de carácter a entero
bd7<-bd6
bd7$Hora<-substr(bd7$Hora,start=1,stop=2)
tibble(bd7) ## # A tibble: 200,620 × 20
## vcCla…¹ DescG…² Fecha Hora Marca Fabri…³ Produ…⁴ Precio Ult.C…⁵ Unida…⁶
## <chr> <chr> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MX001 Abarro… 0020-06-19 08 NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… 0020-06-19 08 DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… 0020-06-19 08 BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… 0020-06-19 08 PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… 0020-06-19 08 BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… 0020-06-19 08 FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… 0020-06-19 08 VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… 0020-06-19 08 ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… 0020-06-19 08 ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… 0020-06-19 03 JUMEX JUMEX Jugo D… 12 12 1
## # … with 200,610 more rows, 10 more variables: F.Ticket <int>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>, Giro <chr>,
## # Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable names
## # ¹vcClaveTienda, ²DescGiro, ³Fabricante, ⁴Producto, ⁵Ult.Costo, ⁶Unidadesbd7$Hora<-as.integer(bd7$Hora)
str(bd7) ## 'data.frame': 200620 obs. of 20 variables:
## $ vcClaveTienda : chr "MX001" "MX001" "MX001" "MX001" ...
## $ DescGiro : chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Fecha : Date, format: "0020-06-19" "0020-06-19" ...
## $ Hora : int 8 8 8 8 8 8 8 8 8 3 ...
## $ 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 9.5 11 9.5 23.5 12 ...
## $ 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 4 4 4 4 5 ...
## $ 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 "8:00" "8:00" "8:00" "8:00" ...
## $ Hora.cierre : chr "22:00" "22:00" "22:00" "22:00" ...Técnica 5, VALORES FALTANTES
¿Cuántos NA tengo en la base de datos?
sum(is.na(bd7))## [1] 0sum(is.na(bd))## [1] 199188¿Cuántos NA tengo por variable?
sapply(bd7,function(x) sum(is.na(x)))## vcClaveTienda DescGiro Fecha Hora
## 0 0 0 0
## Marca Fabricante Producto Precio
## 0 0 0 0
## Ult.Costo Unidades F.Ticket NombreDepartamento
## 0 0 0 0
## NombreFamilia NombreCategoria Estado Mts.2
## 0 0 0 0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## 0 0 0 0Borrar todos los registros NA de una table
bd8<-bd
bd8<-na.omit(bd8)
summary(bd8)## vcClaveTienda DescGiro Codigo.Barras PLU
## Length:1437 Length:1437 Min. :6.750e+08 Min. : 1.000
## Class :character Class :character 1st Qu.:6.750e+08 1st Qu.: 1.000
## Mode :character Mode :character Median :6.750e+08 Median : 1.000
## Mean :2.616e+11 Mean : 2.112
## 3rd Qu.:6.750e+08 3rd Qu.: 1.000
## Max. :7.501e+12 Max. :30.000
## Fecha Hora Marca Fabricante
## Length:1437 Length:1437 Length:1437 Length:1437
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Producto Precio Ult.Costo Unidades
## Length:1437 Min. :30.00 Min. : 1.00 Min. :1.000
## Class :character 1st Qu.:90.00 1st Qu.:64.62 1st Qu.:1.000
## Mode :character Median :90.00 Median :64.62 Median :1.000
## Mean :87.94 Mean :56.65 Mean :1.124
## 3rd Qu.:90.00 3rd Qu.:64.62 3rd Qu.:1.000
## Max. :90.00 Max. :64.62 Max. :7.000
## F.Ticket NombreDepartamento NombreFamilia NombreCategoria
## Min. : 772 Length:1437 Length:1437 Length:1437
## 1st Qu.: 99955 Class :character Class :character Class :character
## Median :102493 Mode :character Mode :character Mode :character
## Mean :100595
## 3rd Qu.:106546
## Max. :118356
## Estado Mts.2 Tipo.ubicación Giro
## Length:1437 Min. :58.00 Length:1437 Length:1437
## Class :character 1st Qu.:58.00 Class :character Class :character
## Mode :character Median :58.00 Mode :character Mode :character
## Mean :58.07
## 3rd Qu.:58.00
## Max. :60.00
## Hora.inicio Hora.cierre
## Length:1437 Length:1437
## Class :character Class :character
## Mode :character Mode :character
##
##
## Reemplazar NA con ceros
bd9<-bd
bd9[is.na(bd9)]<-0
summary(bd9) ## vcClaveTienda DescGiro Codigo.Barras PLU
## Length:200625 Length:200625 Min. :8.347e+05 Min. : 0.00000
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.: 0.00000
## Mode :character Mode :character Median :7.501e+12 Median : 0.00000
## Mean :5.950e+12 Mean : 0.01513
## 3rd Qu.:7.501e+12 3rd Qu.: 0.00000
## Max. :1.750e+13 Max. :30.00000
## 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
##
##
## Reemplazar NA con promedio
bd10 <- bd
bd10$PLU [is.na(bd$PLU)]<- mean(bd10$PLU, na.rm=TRUE)
summary (bd10) ## vcClaveTienda DescGiro Codigo.Barras PLU
## Length:200625 Length:200625 Min. :8.347e+05 Min. : 1.000
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.: 2.112
## Mode :character Mode :character Median :7.501e+12 Median : 2.112
## Mean :5.950e+12 Mean : 2.112
## 3rd Qu.:7.501e+12 3rd Qu.: 2.112
## Max. :1.750e+13 Max. :30.000
## 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
##
##
## Reemplazar negativos con cero
bd11 <- bd
bd11[bd11 < 0]<- 0
summary (bd11)## 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. : 0.00 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 : 19.44 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
##
##
##
## Técnica 6, MÉTODO ESTADÍSTICO
bd12<-bd7
boxplot(bd12$Precio, horizontal=TRUE) 
boxplot(bd12$Unidades, horizontal=TRUE) 
Segundo paso.
Agregar columnas
#install.packages("lubridate")
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionbd12$Dia_de_la_semana<-wday(bd12$Fecha)
summary(bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-07-11 Median : 8.000
## Mean :0020-07-18 Mean : 7.299
## 3rd Qu.:0020-08-29 3rd Qu.:10.000
## Max. :0020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana
## Min. :1.000
## 1st Qu.:2.000
## Median :4.000
## Mean :3.912
## 3rd Qu.:6.000
## Max. :7.000bd12$Subtotal <- bd12$Precio * bd12$Unidades
summary (bd12)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-07-11 Median : 8.000
## Mean :0020-07-18 Mean : 7.299
## 3rd Qu.:0020-08-29 3rd Qu.:10.000
## Max. :0020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana Subtotal
## Min. :1.000 Min. : 1.00
## 1st Qu.:2.000 1st Qu.: 12.00
## Median :4.000 Median : 18.00
## Mean :3.912 Mean : 24.33
## 3rd Qu.:6.000 3rd Qu.: 27.00
## Max. :7.000 Max. :2496.00bd12$Utilidad <- bd12$Precio - bd12$Ult.Costo
summary (bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-07-11 Median : 8.000
## Mean :0020-07-18 Mean : 7.299
## 3rd Qu.:0020-08-29 3rd Qu.:10.000
## Max. :0020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana Subtotal Utilidad
## Min. :1.000 Min. : 1.00 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 12.00 1st Qu.: 2.310
## Median :4.000 Median : 18.00 Median : 3.230
## Mean :3.912 Mean : 24.33 Mean : 4.142
## 3rd Qu.:6.000 3rd Qu.: 27.00 3rd Qu.: 5.420
## Max. :7.000 Max. :2496.00 Max. :230.770Tercer paso.
Exportar nueva base de datos (limpia)
bd_limpia <-bd12
write.csv(bd_limpia, file ="nueva_abarrotes.csv", row.names = FALSE)Cuarto paso.
MARKET BASKET ANALYSIS
a. Instalar paquetes y librerías
#install.packages("plyr")
library(Matrix)##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack#install.packages("arules")
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")
library(arulesViz)
library(datasets) b. 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 0020-06-19 8 NUTRI LECHE
## 2 MX001 Abarrotes 0020-06-19 8 DAN UP
## 3 MX001 Abarrotes 0020-06-19 8 BIMBO
## 4 MX001 Abarrotes 0020-06-19 8 PEPSI
## 5 MX001 Abarrotes 0020-06-19 8 BLANCA NIEVES (DETERGENTE)
## 6 MX001 Abarrotes 0020-06-19 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 8:00 22:00 6 16.0 3.69
## 2 8:00 22:00 6 14.0 0.00
## 3 8:00 22:00 6 5.0 0.00
## 4 8:00 22:00 6 8.0 0.00
## 5 8:00 22:00 6 19.5 4.50
## 6 8:00 22:00 6 9.5 2.19tail(bd_limpia) ## vcClaveTienda DescGiro Fecha Hora Marca
## 107394 MX004 Carnicería 0020-10-15 11 YEMINA
## 167771 MX004 Carnicería 0020-10-15 11 DEL FUERTE
## 149429 MX004 Carnicería 0020-10-15 11 COCA COLA ZERO
## 168750 MX004 Carnicería 0020-10-15 11 DIAMANTE
## 161193 MX004 Carnicería 0020-10-15 12 PEPSI
## 112970 MX004 Carnicería 0020-10-15 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 7:00 23:00 5 14 1.62
## 167771 7:00 23:00 5 12 2.77
## 149429 7:00 23:00 5 30 3.46
## 168750 7:00 23:00 5 11 2.54
## 161193 7:00 23:00 5 10 2.31
## 112970 7:00 23:00 5 80 2.31c. Generar basket
#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 object is masked from 'package:purrr':
##
## compact## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarizebasket<-ddply(bd_limpia,c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse=","))d. Eliminar número de ticket
basket$F.Ticket<-NULLe. Renombrar columna
colnames(basket)<-c("Marca")f. Exportar base de datos
write.csv(basket,"basket.csv", quote = FALSE, row.names = FALSE)g. Importar transacciones
#file.choose()
tr<-read.transactions("/Users/elenavela/Downloads/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
## within quoted string## Warning in asMethod(object): removing duplicated items in transactionsreglas.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 [0.05s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.04s].
## 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. :0.001016 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 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. :0.001016 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 155top10reglas<-head(reglas.asociacion,n=10, by="confidence")
plot(top10reglas, method = "graph", engine = "htmlwidget")Conclusiones.
Se podría reflexionar que de los códigos realizados este ha sido de los más extensos y de los más complicados. Por lo mismo, ha sido una práctica que ha aportado mucho conocimiento y me he podido dar cuenta, de nuevo, de la gran ventaja que tiene la herramienta de R Studio para el análisis de datos.
En la herramienta de generador de valor de datos, pudimos determinar que lo que interesaba era ayudar al equipo de mercadotecnia a desarrollar promociones y estrategias para invitar de manera indirecta a la gente a comprar más productos juntos.
Una de las reglas observadas en el análisis son los productos que frecuentemente se venden con Bimbo (pan de barra), los cuáles son: Hellmann’s (mayonesa), Fud (jamón), Blue House (queso americano). Así como estas relaciones, podemos ver muchas otras más que pueden dar un gran valor para el departamento de mercadotecnia, y mediante estrategias pertinentes impactar positivamente a las ventas de las tiendas de abarrotes (KPI).
Este tipo de análisis resulta interesante para diferentes negocios, principalmente para los comercios que pueden ofrecer a sus clientes diferentes productos relacionados o que saben que compraran en conjuntos, ya sea de manera digital o de manera presencial. Es posible sacar una gran ventaja al poder analizar e interpretar de manera adecuada.
---
title: <span style="color:red"> **Abarrotes...** *Market Basket Analysis*
author: "ElenaVela_A01283535"
date: "2022-09-08"
output: 
  html_document:
    toc: true
    toc_float: true
    theme: united
    highlight: tango
    code_download: true
---

<img src="/Users/elenavela/Downloads/abarrotes.png">

### **Pasos previos a la limpieza de datos, hecho en Excel:**
1. Formato a fecha corta   
2. Se duplicaron los primeros 5 registros  
3. Se cambió el formato de hora y fecha a español mex  
4. Cambio de codigo de barras  
5. Se guardó como CSV UTF8, delimitado por comas  

### Pasos previos.  
**Importar base de datos**

```{r}
#file.choose()

bd<-read.csv("/Users/elenavela/Downloads/abarrotes (1).csv")
#bd

resumen<-summary(bd)
resumen
```

**Instalar paquetes y librerías, y hacer los *count***

```{r}
#install.packages("dplyr")
library(dplyr)
#install.packages("tidyverse")
library(tidyverse)
#install.packages("janitor")
library(janitor)

#count(bd,vcClaveTienda,sort=TRUE)
#count(bd,DescGiro,sort=TRUE)
#count(bd,Marca,sort=TRUE)
#count(bd,Fabricante,sort=TRUE)
#count(bd,Producto,sort=TRUE)
#count(bd,NombreDepartamento,sort=TRUE)
#count(bd,NombreFamilia,sort=TRUE)
#count(bd,NombreCategoria,sort=TRUE)
#count(bd,Estado,sort=TRUE)
#count(bd,Mts.2,sort=TRUE)
#count(bd,Tipo.ubicación,sort=TRUE)
#count(bd,Giro,sort=TRUE)
#count(bd,Hora.inicio,sort=TRUE)
#count(bd,Hora.cierre,sort=TRUE)
```

**Analizar la base de datos**

```{r}
tibble(bd)
str(bd)
head(bd)
head(bd,n=7)
tail(bd)
tabyl(bd,vcClaveTienda,NombreDepartamento)
```

***Observaciones***
1. Casi ningun registro tiene PLU  
2. Cambiar formato de fecha  
3. Cambiar formato de hora  
4. Hay precios negativos  
5. Unidades menores a 1  


### <span style = "color:green" > Herramienta "El generador de valor de datos" </span>.

***Paso 1.* Definir el área del negocio que buscamos impactar o mejorar y su KPI.**   
Lo que se busca que impacte es en una división, en este caso mercadotecnia. Puesto que una gran parte del éxito de las ventas de tiendas es debido a buenas técnicas de mercadotecnia y buenos análisis. Lo que se busca medir (KPI) son las ventas. 
***Paso 2.* Seleccionar la plantilla (-s) para crear valor a partir de los datos de los clientes.**  
Se busca ver lo que un solo cliente podría comprar en una sola "canasta", por lo que se busca segmentar.  
***Visión*** | ***Segmentacion*** | Personalización | Contextualizacion  
***Paso 3.* Generar ideas o conceptos específicos.**  
Al hacer las segmentaciones de los clientes y lo que comprar en conjunto, se espera poder generar estrategias de mercadotecnia que invite *aún más* a los clientes a comprar diferentes productos de una sola canaste. Por ejemplo, invitar a los clientes a acompañar su cerveza con papas (dependiendo de los resultados al final.)  Igualmente, al darle un enfoque de visión  es posible poner atención a las oportunidades de crecimiento y de avanzar en el mundo de los negocios y de ventas. 
***Paso 4.* Reunir los datos requeridos.**   
Para poder llevar a cabo esta herramienta, es necesario tener *limpia* la base de datos de Abarrotes. 
***Paso 5.* Plan de ejecucion.**  
1. Analizar los datos obtenidos y definir una tienda de abarrotes.  
2. Crear promociones de una sola canasta (ej.cerveza con sabritas).  
3. Llevar una bitácora de ventas para ver el crecimiento (o no).  

### Primer paso. 
**Limpiar de datos**

#### **Técnica 1, *REMOVER DATOS IRRELEVANTES***

#### Eliminar columnas
```{r}
  bd1<-bd
  bd1<-subset(bd1,select=-c(PLU,Codigo.Barras))
```

#### Eliminar renglones
Este no se usará
```{r}
  bd2<-bd1
  bd2<-bd2[bd$Precio>0,]
  summary(bd1)  
  summary(bd2)
```

#### **Técnica 2, *REMOVER VALOR DUPLICADOS***

#### ¿Cuántos renglones duplicados tenemos?
```{r}
bd1[duplicated(bd1),]
sum(duplicated(bd1))  
```

#### Eliminar renglones duplicados
```{r}
bd3<-bd1
library(dplyr)    
bd3<-distinct(bd3)
```


#### **Técnica 3, *ERRORES TIPOGRÁFICOS Y ERRORES SIMILARES***

#### Precios  en absoluto
```{r}
bd4<-bd3
bd4$Precio<-abs(bd4$Precio)
summary(bd4)
```

#### Cantidades en enteros
```{r}
bd5<-bd4
bd5$Unidades<-ceiling(bd5$Unidades)
summary(bd5) 
```

#### **Técnica 4, *CONVERTIR TIPO DE DATOS***   
     
#### Convertir de carácter a fecha
```{r}
bd6<-bd5
bd6$Fecha<-as.Date(bd6$Fecha,format="%d/%m/%Y")  
tibble(bd6)
```

#### Convertir de carácter a entero
```{r}
bd7<-bd6
bd7$Hora<-substr(bd7$Hora,start=1,stop=2)      
tibble(bd7)      
bd7$Hora<-as.integer(bd7$Hora)
str(bd7) 
```

#### **Técnica 5, *VALORES FALTANTES***  

#### ¿Cuántos NA  tengo en la base de datos?
```{r}
sum(is.na(bd7))
sum(is.na(bd))
```
      
#### ¿Cuántos NA tengo por variable?
```{r}
sapply(bd7,function(x) sum(is.na(x)))
```

####  Borrar todos los registros NA de una table
```{r}
bd8<-bd
bd8<-na.omit(bd8)      
summary(bd8)
```

#### Reemplazar NA con ceros
```{r}
bd9<-bd
bd9[is.na(bd9)]<-0      
summary(bd9) 
```

#### Reemplazar NA con  promedio
```{r}
bd10 <- bd
bd10$PLU [is.na(bd$PLU)]<- mean(bd10$PLU, na.rm=TRUE)
summary (bd10)   
```

#### Reemplazar negativos con cero
```{r}
bd11 <- bd
bd11[bd11 < 0]<- 0 
summary (bd11)
```

#### **Técnica 6, *MÉTODO ESTADÍSTICO***  
```{r}
bd12<-bd7
boxplot(bd12$Precio, horizontal=TRUE)      
boxplot(bd12$Unidades, horizontal=TRUE)  
```

### Segundo paso.
**Agregar columnas**

```{r}
#install.packages("lubridate")
library(lubridate)
```

```{r}
bd12$Dia_de_la_semana<-wday(bd12$Fecha)      
summary(bd12)      

bd12$Subtotal <- bd12$Precio * bd12$Unidades
summary (bd12)
      
bd12$Utilidad <- bd12$Precio - bd12$Ult.Costo      
summary (bd12)  
```

### Tercer paso.
**Exportar nueva base de datos (limpia)**

```{r}
bd_limpia <-bd12
write.csv(bd_limpia, file ="nueva_abarrotes.csv", row.names = FALSE)
```



### Cuarto paso.
**MARKET BASKET ANALYSIS**

#### a. Instalar paquetes y librerías
```{r}
#install.packages("plyr")
library(Matrix)
#install.packages("arules")
library(arules)
#install.packages("arulesViz") 
library(arulesViz)
library(datasets) 
```

     
#### b. Ordenar de menor a mayor los tickets
```{r}
bd_limpia<-bd_limpia[order(bd_limpia$F.Ticket),]
head(bd_limpia)    
tail(bd_limpia)  
```

#### c. Generar basket
```{r}
#install.packages("plyr")
library(plyr) 
```

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

#### d. Eliminar número de ticket
```{r}
basket$F.Ticket<-NULL
```
      
#### e. Renombrar columna
```{r}
colnames(basket)<-c("Marca")
```

#### f. Exportar base de datos
```{r}
write.csv(basket,"basket.csv", quote = FALSE, row.names = FALSE)
```

      
#### g. Importar transacciones
```{r}
#file.choose()
tr<-read.transactions("/Users/elenavela/Downloads/basket.csv", format="basket",sep=",")

reglas.asociacion<-apriori(tr, parameter = list(supp = 0.001, conf =  0.2, maxlen = 10))
summary(reglas.asociacion)
inspect(reglas.asociacion)
      
reglas.asociacion<-sort(reglas.asociacion,by="confidence", decreasing=TRUE)
summary(reglas.asociacion)      
inspect(reglas.asociacion)      
      
top10reglas<-head(reglas.asociacion,n=10, by="confidence")
plot(top10reglas, method = "graph", engine = "htmlwidget")
      

```

### Conclusiones.

Se podría reflexionar que de los códigos realizados este ha sido de los más extensos y de los más *complicados*. Por lo mismo, ha sido una práctica que ha aportado mucho conocimiento y me he podido dar cuenta, de nuevo, de la gran ventaja que tiene la herramienta de R Studio para el análisis de datos.  

En la herramienta de generador de valor de datos, pudimos determinar que lo que interesaba era ayudar al equipo de mercadotecnia a desarrollar promociones y estrategias para *invitar* de manera indirecta a la gente a comprar más productos juntos.  

Una de las reglas observadas en el análisis son los productos que frecuentemente se venden con Bimbo (pan de barra), los cuáles son: Hellmann's (mayonesa), Fud (jamón), Blue House (queso americano). Así como estas relaciones, podemos ver muchas otras más que pueden dar un gran valor para el departamento de mercadotecnia, y mediante estrategias pertinentes impactar positivamente a las ventas de las tiendas de abarrotes (KPI).  

Este tipo de análisis resulta interesante para diferentes negocios, principalmente para los comercios que pueden ofrecer a sus clientes diferentes productos relacionados o que saben que compraran en conjuntos, ya sea de manera digital o de manera presencial. Es posible sacar una gran ventaja al poder analizar e interpretar de manera adecuada. 












      