“Abarrotes”
Jimena Miguel - A01365819
2022-09-07
Observaciones
#A la base de datos se le hicieron los siguientes cambios:
#Se cambió el formato a fecha corta
#Se duplicaron los primeros 5 registros
#Se cambió el formato a Hora (Español México)
#Se cambió el formato a Código de Barras (para que salga completo)
#Se guardó CSV UTF-8 (Delimitado por comas)Importar la base de datos
file.choose()## [1] "C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\abarrotes\\abarrotes.Rmd"bd<-read.csv("C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\abarrotes\\abarrotes_ventas-2_07.csv")Entender la base de datos
summary(bd)## 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
##
##
##
## #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#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.ubicaci?n, sort = TRUE)
#count(bd, Giro, sort = TRUE)
#count(bd, Hora.inicio, sort = TRUE)
#count(bd, Hora.cierre, sort = TRUE)
#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()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 "19/06/2020" "19/06/2020" "19/06/2020" "19/06/2020" ...
## $ 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 19/06/2020 08:16:21 a. m.
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 08:23:33 a. m.
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/2020 08:24:33 a. m.
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/2020 08:24:33 a. m.
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/2020 08:26:28 a. m.
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 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 19/06/2020 08:16:21 a. m.
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 08:23:33 a. m.
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/2020 08:24:33 a. m.
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/2020 08:24:33 a. m.
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/2020 08:26:28 a. m.
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 08:16:21 a. m.
## 7 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 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 12/07/2020 01:08:25 a. m.
## 200621 MX005 Depósito 7.62221e+12 NA 23/10/2020 10:17:37 p. m.
## 200622 MX005 Depósito 7.62221e+12 NA 10/10/2020 08:30:20 p. m.
## 200623 MX005 Depósito 7.62221e+12 NA 10/10/2020 10:40:43 p. m.
## 200624 MX005 Depósito 7.62221e+12 NA 27/06/2020 10:30:19 p. m.
## 200625 MX005 Depósito 7.62221e+12 NA 26/06/2020 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 21:00#install.packages("janitor")
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testtabyl(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 0#### Observaciones
# 1. Casi ningún registro cuenta con PLU
# 2. Cambiar formato de fecha
# 3. Cambiar formato de hora
# 4. Hay precios negativos
# 5. Unidades menores a 1La Herramienta “El Generador de Valor de Datos”
Paso 1. Definir el área del negocio que buscamos impactar o mejorar y su KPI.
Impulsar las ventas del giro de Carnicería.
Paso 2. Seleccionar plantilla(s) para crear valor a partir de los datos de los clientes. Visión | Segmentación | Personalización | Contextualización
Segmentación.
Paso 3. Generar ideas o conceptos específicos.
Promover la venta cruzada de este giro con productos de Abarrotes, debido a que es el giro con mayor rotación en la tienda.
Paso 4. Reunir los datos requeridos.
Hacer un análisis de datos para saber cuáles son los productos con mayores ventas del giro de Abarrotes y ver cómo promover una venta cruzada con algún producto de Carnicería.
Paso 5. Plan de ejecución.
Ofrecer una promoción de “Compra el Jamón Diamante y por $20 pesos más llévate las Medias Noches Bimbo”. De esta forma se promueve un producto con menor venta a través de la compra de un producto con mucho movimiento, en este caso, las Medias Noches Bimbo.
Técnicas para limpieza de datos
Técnica 1. Remover valores irrelevantes
#### Eliminar columnas
bd1<- bd
bd1<- subset (bd1, select = -c (PLU, Codigo.Barras))
#### Eliminar renglones
bd2<- bd1
bd2<- bd2[bd2$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
##
##
## #### Esto no lo usaremos, pondremos precios negativos como absolutoTécnica 2. Remover valores duplicados
#### ¿Cuántos renglones duplicados tenemos?
bd1[duplicated(bd1) ,]## vcClaveTienda DescGiro Fecha Hora Marca
## 6 MX001 Abarrotes 19/06/2020 08:16:21 a. m. NUTRI LECHE
## 7 MX001 Abarrotes 19/06/2020 08:23:33 a. m. DAN UP
## 8 MX001 Abarrotes 19/06/2020 08:24:33 a. m. BIMBO
## 9 MX001 Abarrotes 19/06/2020 08:24:33 a. m. PEPSI
## 10 MX001 Abarrotes 19/06/2020 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] 5#### Eliminar 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.:383009
## 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.:383009
## 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 tipos de datos
#### Convertir de caracter 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… NA 08:16:21… NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… NA 08:23:33… DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… NA 08:24:33… BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… NA 08:24:33… PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… NA 08:26:28… BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… NA 08:26:28… FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… NA 08:26:28… VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… NA 08:26:28… ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… NA 08:26:28… ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… NA 03:24:02… 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, ⁶Unidades#### Convertir de caracter 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… NA 08 NUTRI LE… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… NA 08 DAN UP DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… NA 08 BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… NA 08 PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… NA 08 BLANCA N… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… NA 08 FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… NA 08 VARIOS D… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… NA 08 ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… NA 08 ALWAYS PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… NA 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: NA NA ...
## $ 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] 200620sum(is.na(bd))## [1] 199188#### ¿Cuántos NA tengo por variable?
sapply(bd7, function(x) sum(is.na(x)))## vcClaveTienda DescGiro Fecha Hora
## 0 0 200620 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 0sapply(bd, function(x) sum(is.na(x)))## vcClaveTienda DescGiro Codigo.Barras PLU
## 0 0 0 199188
## Fecha Hora Marca Fabricante
## 0 0 0 0
## Producto Precio Ult.Costo Unidades
## 0 0 0 0
## F.Ticket NombreDepartamento NombreFamilia NombreCategoria
## 0 0 0 0
## Estado Mts.2 Tipo.ubicación Giro
## 0 0 0 0
## Hora.inicio Hora.cierre
## 0 0?sapply## starting httpd help server ... done#### Borrar todos los registros NA de una tabla
bd8<-bd7
bd8<-na.omit(bd8)
summary(bd8)## vcClaveTienda DescGiro Fecha Hora
## Length:0 Length:0 Min. :NA Min. : NA
## Class :character Class :character 1st Qu.:NA 1st Qu.: NA
## Mode :character Mode :character Median :NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.:NA 3rd Qu.: NA
## Max. :NA Max. : NA
## Marca Fabricante Producto Precio
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : NA Min. : NA Min. : NA Length:0
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA Class :character
## Median : NA Median : NA Median : NA Mode :character
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NombreFamilia NombreCategoria Estado Mts.2
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:0 Length:0 Length:0 Length:0
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## #### Reemplazar NA con CEROS
bd9<-bd8
bd9[is.na(bd9)]<-0
summary(bd9)## vcClaveTienda DescGiro Fecha Hora
## Length:0 Length:0 Min. :NA Min. : NA
## Class :character Class :character 1st Qu.:NA 1st Qu.: NA
## Mode :character Mode :character Median :NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.:NA 3rd Qu.: NA
## Max. :NA Max. : NA
## Marca Fabricante Producto Precio
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : NA Min. : NA Min. : NA Length:0
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA Class :character
## Median : NA Median : NA Median : NA Mode :character
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NombreFamilia NombreCategoria Estado Mts.2
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:0 Length:0 Length:0 Length:0
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## #### Reemplazar NA con el Promedio
bd10<-bd9
bd10$PLU[is.na(bd10$PLU)]<-mean(bd10$PLU, na.rm= TRUE)## Warning in mean.default(bd10$PLU, na.rm = TRUE): argument is not numeric or
## logical: returning NAsummary(bd10)## vcClaveTienda DescGiro Fecha Hora
## Length:0 Length:0 Min. :NA Min. : NA
## Class :character Class :character 1st Qu.:NA 1st Qu.: NA
## Mode :character Mode :character Median :NA Median : NA
## Mean :NaN Mean :NaN
## 3rd Qu.:NA 3rd Qu.: NA
## Max. :NA Max. : NA
## Marca Fabricante Producto Precio
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : NA Min. : NA Min. : NA Length:0
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA Class :character
## Median : NA Median : NA Median : NA Mode :character
## Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA
## NombreFamilia NombreCategoria Estado Mts.2
## Length:0 Length:0 Length:0 Min. : NA
## Class :character Class :character Class :character 1st Qu.: NA
## Mode :character Mode :character Mode :character Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:0 Length:0 Length:0 Length:0
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## PLU
## Min. : NA
## 1st Qu.: NA
## Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA#### Reemplazar negativos con cero
bd11<-bd10
bd11[bd11<-0]## data frame with 0 columns and 0 rowssummary(bd11)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 0 0 0 0Técnica 6. Método estadístico
bd12<-bd7
boxplot(bd12$Precio, horizontal= TRUE)
boxplot(bd12$Unidades, horizontal= TRUE)
#### 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. :NA Min. : 1.000
## Class :character Class :character 1st Qu.:NA 1st Qu.: 5.000
## Mode :character Mode :character Median :NA Median : 8.000
## Mean :NaN Mean : 7.299
## 3rd Qu.:NA 3rd Qu.:10.000
## Max. :NA Max. :12.000
## NA's :200620
## 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.:383009
## 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. : NA
## 1st Qu.: NA
## Median : NA
## Mean :NaN
## 3rd Qu.: NA
## Max. : NA
## NA's :200620bd12$Subtotal<-bd12$Precio * bd12$Unidades
summary(bd12)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :NA Min. : 1.000
## Class :character Class :character 1st Qu.:NA 1st Qu.: 5.000
## Mode :character Mode :character Median :NA Median : 8.000
## Mean :NaN Mean : 7.299
## 3rd Qu.:NA 3rd Qu.:10.000
## Max. :NA Max. :12.000
## NA's :200620
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
##
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383009
## Max. :769.23 Max. :96.000 Max. :450040
##
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
##
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Dia_de_la_semana Subtotal
## Min. : NA Min. : 1.00
## 1st Qu.: NA 1st Qu.: 12.00
## Median : NA Median : 18.00
## Mean :NaN Mean : 24.33
## 3rd Qu.: NA 3rd Qu.: 27.00
## Max. : NA Max. :2496.00
## NA's :200620bd12$Utilidad<-bd12$Precio - bd12$Ult.Costo
summary(bd12)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :NA Min. : 1.000
## Class :character Class :character 1st Qu.:NA 1st Qu.: 5.000
## Mode :character Mode :character Median :NA Median : 8.000
## Mean :NaN Mean : 7.299
## 3rd Qu.:NA 3rd Qu.:10.000
## Max. :NA Max. :12.000
## NA's :200620
## 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.:383009
## 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. : NA Min. : 1.00 Min. : 0.000
## 1st Qu.: NA 1st Qu.: 12.00 1st Qu.: 2.310
## Median : NA Median : 18.00 Median : 3.230
## Mean :NaN Mean : 24.33 Mean : 4.142
## 3rd Qu.: NA 3rd Qu.: 27.00 3rd Qu.: 5.420
## Max. : NA Max. :2496.00 Max. :230.770
## NA's :200620#### Exportar base de datos limpia
bd_limpia<-bd12
write.csv(bd_limpia, file="abarrotes_bd_limpia.csv", row.names= FALSE)Market Basket Analysis
#install.packages("plyr")
#install.packages("Matrix")
#install.packages("arules")
#install.packages("arulesViz")
#install.packages("datasets")
library(Matrix)##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpacklibrary(arules)##
## Attaching package: 'arules'## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)
library(datasets)
#### 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 <NA> 8 NUTRI LECHE
## 2 MX001 Abarrotes <NA> 8 DAN UP
## 3 MX001 Abarrotes <NA> 8 BIMBO
## 4 MX001 Abarrotes <NA> 8 PEPSI
## 5 MX001 Abarrotes <NA> 8 BLANCA NIEVES (DETERGENTE)
## 6 MX001 Abarrotes <NA> 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 NA 16.0 3.69
## 2 8:00 22:00 NA 14.0 0.00
## 3 8:00 22:00 NA 5.0 0.00
## 4 8:00 22:00 NA 8.0 0.00
## 5 8:00 22:00 NA 19.5 4.50
## 6 8:00 22:00 NA 9.5 2.19tail(bd_limpia)## vcClaveTienda DescGiro Fecha Hora Marca Fabricante
## 107394 MX004 Carnicería <NA> 11 YEMINA HERDEZ
## 167771 MX004 Carnicería <NA> 11 DEL FUERTE ALIMENTOS DEL FUERTE
## 149429 MX004 Carnicería <NA> 11 COCA COLA ZERO COCA COLA
## 168750 MX004 Carnicería <NA> 11 DIAMANTE EMPACADOS
## 161193 MX004 Carnicería <NA> 12 PEPSI PEPSI-COLA MEXICANA
## 112970 MX004 Carnicería <NA> 12 COCA COLA COCA COLA
## Producto Precio Ult.Costo Unidades F.Ticket
## 107394 PASTA SPAGHETTI YEMINA 200G 7 5.38 2 450032
## 167771 PURE DE TOMATE DEL FUERTE 345G 12 9.23 1 450032
## 149429 COCA COLA ZERO 600ML 15 11.54 2 450034
## 168750 ARROZ DIAMANTE225G 11 8.46 1 450037
## 161193 PEPSI N. R. 500ML 10 7.69 1 450039
## 112970 COCA COLA RETORNABLE 500ML 10 7.69 8 450040
## NombreDepartamento NombreFamilia NombreCategoria
## 107394 Abarrotes Sopas y Pastas Fideos, Spaguetti, Tallarines
## 167771 Abarrotes Salsas y Sazonadores Salsa para Spaguetti
## 149429 Abarrotes Bebidas Refrescos Retornables
## 168750 Abarrotes Granos y Semillas Arroz
## 161193 Abarrotes Bebidas Refrescos Plástico (N.R.)
## 112970 Abarrotes Bebidas Refrescos Retornables
## Estado Mts.2 Tipo.ubicación Giro Hora.inicio Hora.cierre
## 107394 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## 167771 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## 149429 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## 168750 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## 161193 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## 112970 Sinaloa 53 Esquina Abarrotes 7:00 23:00
## Dia_de_la_semana Subtotal Utilidad
## 107394 NA 14 1.62
## 167771 NA 12 2.77
## 149429 NA 30 3.46
## 168750 NA 11 2.54
## 161193 NA 10 2.31
## 112970 NA 80 2.31Market Basket Analysis: Cómo hacerlo
#### 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 = ","))
#### 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()## [1] "C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\abarrotes\\abarrotes.Rmd" tr<- read.transactions("C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\jingle_bells\\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 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 [0.00s]. 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")Conclusiones
Market Basket Analysis es una técnica de data mining utilizada por retailers para incrementar sus ventas a través de la comprensión y análisis de los patrones de compra del consumidor. En el ejercicio anterior se muestra la correlación entre ciertos productos y los productos con mayores ventas de acuerdo a las preferencias del cliente final. En resumen, se muestra que el giro de tienda con mayor rotación es “Abarrotes” y las marcas Nutri Leche y Dan Up son aquellas que más venden. Esto es un claro ejemplo de la implementación de este análisis y cómo le permite al vendedor conocer los patrones de compra de cada individuo dependiendo del segmento, estado o categoría de compra. A lo largo de este análisis se reafirman los productos con mayores ventas, ubicaciones de venta, hora de compra, entre otros.
Entre los factores destacados, se muestra que el Departamento con mayor movimiento es Abarrotes en la ubicación de esquina y sus ventas principales son a la hora de cierre de 22:00 horas.
---
title: <span style="Color:Green"> "Abarrotes"
author: "Jimena Miguel - A01365819"
date: "2022-09-07"
output:
  html_document:
    toc: true
    toc_float: true
    code_download: true
---

<div>
<p style = 'text-align:center;'>
<img src="https://www.jornada.com.mx/ultimas/2020/08/24/crecen-10-2-ventas-de-abarrotes-en-julio-pasado-5300.html/abarrotes.jpg-8300.html/image_large" alt="JuveYell" width="300px">
</p>
</div>

## Observaciones

```{r}
#A la base de datos se le hicieron los siguientes cambios:
#Se cambió el formato a fecha corta
#Se duplicaron los primeros 5 registros
#Se cambió el formato a Hora (Español México)
#Se cambió el formato a Código de Barras (para que salga completo)
#Se guardó CSV UTF-8 (Delimitado por comas)
```

## Importar la base de datos
```{r}
file.choose()
bd<-read.csv("C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\abarrotes\\abarrotes_ventas-2_07.csv")
```

## Entender la base de datos
```{r}
summary(bd)

#install.packages("dplyr")
library(dplyr)

#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.ubicaci?n, sort = TRUE)
#count(bd, Giro, sort = TRUE)
#count(bd, Hora.inicio, sort = TRUE)
#count(bd, Hora.cierre, sort = TRUE)

#install.packages("tidyverse")
library(tidyverse)

tibble(bd)

str(bd)

head(bd)
head(bd, n=7)

tail(bd)

#install.packages("janitor")
library(janitor)

tabyl(bd, vcClaveTienda, NombreDepartamento)

#### Observaciones
# 1. Casi ningún registro cuenta con PLU
# 2. Cambiar formato de fecha
# 3. Cambiar formato de hora
# 4. Hay precios negativos
# 5. Unidades menores a 1
```

### La Herramienta "El Generador de Valor de Datos"

#### Paso 1. Definir el área del negocio que buscamos impactar o mejorar y su KPI.
Impulsar las ventas del giro de Carnicería.

#### Paso 2. Seleccionar plantilla(s) para crear valor a partir de los datos de los clientes. Visión | Segmentación | Personalización | Contextualización
Segmentación.

#### Paso 3. Generar ideas o conceptos específicos.
Promover la venta cruzada de este giro con productos de Abarrotes, debido a que es el giro con mayor rotación en la tienda.

#### Paso 4. Reunir los datos requeridos.
Hacer un análisis de datos para saber cuáles son los productos con mayores ventas del giro de Abarrotes y ver cómo promover una venta cruzada con algún producto de Carnicería.

#### Paso 5. Plan de ejecución.
Ofrecer una promoción de "Compra el Jamón Diamante y por $20 pesos más llévate las Medias Noches Bimbo". De esta forma se promueve un producto con menor venta a través de la compra de un producto con mucho movimiento, en este caso, las Medias Noches Bimbo.



## Técnicas para limpieza de datos

### Técnica 1. Remover valores irrelevantes
```{r}
#### Eliminar columnas
bd1<- bd
bd1<- subset (bd1, select = -c (PLU, Codigo.Barras))

#### Eliminar renglones
bd2<- bd1
bd2<- bd2[bd2$Precio > 0, ]
summary(bd1)
summary(bd2)
#### Esto no lo usaremos, pondremos precios negativos como absoluto
```

### Técnica 2. Remover valores duplicados
```{r}
#### ¿Cuántos renglones duplicados tenemos?
bd1[duplicated(bd1) ,]
sum(duplicated(bd1))

#### Eliminar renglones duplicados
bd3<- bd1
library(dplyr)
bd3<-distinct(bd3)
```

### Técnica 3. Errores tipográficos y errores similares
```{r}
#### Precios en absoluto
bd4 <- bd3
bd4$Precio <- abs(bd4$Precio)
summary(bd4)

#### Cantidades en enteros
bd5<-bd4
bd5$Unidades <- ceiling(bd5$Unidades)
summary(bd5)
```

### Técnica 4. Convertir tipos de datos
```{r}
#### Convertir de caracter a fecha
bd6<-bd5
bd6$Fecha<-as.Date(bd6$Fecha, format= "%d%m%Y")
tibble(bd6)

#### Convertir de caracter a entero
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
```{r}
#### ¿Cuántos NA tengo en la base de datos?
sum(is.na(bd7))
sum(is.na(bd))

#### ¿Cuántos NA tengo por variable?
sapply(bd7, function(x) sum(is.na(x)))
sapply(bd, function(x) sum(is.na(x)))

?sapply

#### Borrar todos los registros NA de una tabla
bd8<-bd7
bd8<-na.omit(bd8)
summary(bd8)

#### Reemplazar NA con CEROS
bd9<-bd8
bd9[is.na(bd9)]<-0
summary(bd9)

#### Reemplazar NA con el Promedio
bd10<-bd9
bd10$PLU[is.na(bd10$PLU)]<-mean(bd10$PLU, na.rm= TRUE)
summary(bd10)

#### Reemplazar negativos con cero
bd11<-bd10
bd11[bd11<-0]
summary(bd11)
```

### Técnica 6. Método estadístico
```{r}
bd12<-bd7
boxplot(bd12$Precio, horizontal= TRUE)
boxplot(bd12$Unidades, horizontal= TRUE)

#### Agregar columnas

#install.packages("lubridate")
library(lubridate)
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)

#### Exportar base de datos limpia
bd_limpia<-bd12
write.csv(bd_limpia, file="abarrotes_bd_limpia.csv", row.names= FALSE)
```

### Market Basket Analysis
```{r}
#install.packages("plyr")
#install.packages("Matrix")
#install.packages("arules")
#install.packages("arulesViz")
#install.packages("datasets")
library(Matrix)
library(arules)
library(arulesViz)
library(datasets)

#### Ordenar de menor a mayor los tickets
bd_limpia<- bd_limpia[order(bd_limpia$F.Ticket),]
head(bd_limpia)
tail(bd_limpia)
```

### Market Basket Analysis: Cómo hacerlo
```{r}
#### Generar basket
#install.packages("plyr")
library(plyr)
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("C:\\Users\\Migue\\OneDrive\\Documentos\\R\\Primer bloque\\jingle_bells\\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
__Market Basket Analysis__ es una técnica de _data mining_ utilizada por _retailers_ para incrementar sus ventas a través de la comprensión y análisis de los patrones de compra del consumidor. En el ejercicio anterior se muestra la correlación entre ciertos productos y los productos con mayores ventas de acuerdo a las preferencias del cliente final. En resumen, se muestra que el giro de tienda con mayor rotación es "Abarrotes" y las marcas Nutri Leche y Dan Up son aquellas que más venden. Esto es un claro ejemplo de la implementación de este análisis y cómo le permite al vendedor conocer los patrones de compra de cada individuo dependiendo del segmento, estado o categoría de compra. A lo largo de este análisis se reafirman los productos con mayores ventas, ubicaciones de venta, hora de compra, entre otros.

Entre los factores destacados, se muestra que el Departamento con mayor movimiento es Abarrotes en la ubicación de esquina y sus ventas principales son a la hora de cierre de 22:00 horas.