“Market_Basket_Analysis-Abarrotes”
Dana Perez - A00227041
9/19/2022
Cambios a la base de datos
- Formato a fecha corta.
- Se duplicaron los primeros 5 registros.
- Se cambio el formato a hora espanol mexico.
- Se cambio el formato a codigo de barras,, para que salga completo.
- Se guardo como CSV UTF/8,, delimitado por comas.
Importar la base de datos
#file.choose()
bd <- read.csv("/Users/dannaperez/Desktop/R/bases de datos/abarrotes.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, unionsummary (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
##
##
##
## count(bd, vcClaveTienda, sort = TRUE)## vcClaveTienda n
## 1 MX001 96469
## 2 MX004 83455
## 3 MX005 10021
## 4 MX002 6629
## 5 MX003 4051count(bd, DescGiro, sort = TRUE)## DescGiro n
## 1 Abarrotes 100520
## 2 Carnicería 83455
## 3 Depósito 16650#count(bd, Marca, sort = TRUE)
#count(bd, Producto, sort = TRUE)
#count(bd, Fabricante, sort = TRUE)
#count(bd, NombreDepartamento, sort = TRUE)
#count(bd, NombreCategoria,sort = TRUE)
#count(bd, Estado, sort = TRUE)
#count(bd, Mts.2, 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()#install.packages("Janitor")
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testtibble(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 cuenta con PLU.
2. Cambiar formato de fecha.
3. Cambiar formato de hora.
4. Hay precios negativos.
5. Hay unidades menores a 1.
Tecnicas para limpieza de datos
Tecnica 1. remover datos 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:0 Length:0 Length:0 Length:0
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## 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
##
##
## Nota: esto no lo usaremos , pondremos precios negativos como absoluto
Tecnica 2. remover valores duplicados Cuantos 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)Tecnica 3. errores tipograficos y errores similares Precios ne 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$Unidades)
summary(bd5)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## Tecnica 4. convertir tipos de datos Convertir de caracter a fecha
bd6 <- bd5
bd6$Fecha <- as.Date(bd6$Fecha, format = "%m/%d/%Y")
tibble(bd6)## # A tibble: 200,620 × 20
## vcCla…¹ DescG…² Fecha Hora Marca Fabri…³ Produ…⁴ Precio Ult.C…⁵ Unida…⁶
## <chr> <chr> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MX001 Abarro… NA 08:16:21… NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… NA 08:23:33… DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… NA 08:24:33… BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… NA 08:24:33… PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… NA 08:26:28… BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… NA 08:26:28… FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… NA 08:26:28… VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… NA 08:26:28… ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… NA 08:26:28… ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… NA 03:24:02… JUMEX JUMEX Jugo D… 12 12 1
## # … with 200,610 more rows, 10 more variables: F.Ticket <int>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>, Giro <chr>,
## # Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable names
## # ¹vcClaveTienda, ²DescGiro, ³Fabricante, ⁴Producto, ⁵Ult.Costo, ⁶UnidadesConvertir de caracter entero
bd7 <- bd6
bd7$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" ...Tecnica 5. valores faltantes Cuantos NA tengo en la base de datos
sum(is.na(bd7))## [1] 113205sum(is.na(bd))## [1] 199188Cuantos na tengo por variable
sapply(bd7, function(x) sum(is.na(x)))## vcClaveTienda DescGiro Fecha Hora
## 0 0 113205 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 0Borrar todos los registros NA de una tabla
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 el promedio
bd10 <- bd
bd10$PLU[is.na(bd10$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
##
##
##
## Tecnica 6. metodo estadistico
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$Fecha)
summary(bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-01-05 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-03-11 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-06-10 Median : 8.000
## Mean :0020-06-20 Mean : 7.299
## 3rd Qu.:0020-09-10 3rd Qu.:10.000
## Max. :0020-12-10 Max. :12.000
## NA's :113205
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
##
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
##
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
##
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## dia_de_la_semana
## Min. :1.00
## 1st Qu.:2.00
## Median :4.00
## Mean :3.96
## 3rd Qu.:6.00
## Max. :7.00
## NA's :113205bd12$subtotal <- bd12$Precio * bd12$Unidades
summary(bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-01-05 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-03-11 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-06-10 Median : 8.000
## Mean :0020-06-20 Mean : 7.299
## 3rd Qu.:0020-09-10 3rd Qu.:10.000
## Max. :0020-12-10 Max. :12.000
## NA's :113205
## 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. :1.00 Min. : 1.00
## 1st Qu.:2.00 1st Qu.: 12.00
## Median :4.00 Median : 18.00
## Mean :3.96 Mean : 24.33
## 3rd Qu.:6.00 3rd Qu.: 27.00
## Max. :7.00 Max. :2496.00
## NA's :113205bd12$Utilidad <- bd12$Precio - bd12$Ult.Costo
summary(bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :0020-01-05 Min. : 1.000
## Class :character Class :character 1st Qu.:0020-03-11 1st Qu.: 5.000
## Mode :character Mode :character Median :0020-06-10 Median : 8.000
## Mean :0020-06-20 Mean : 7.299
## 3rd Qu.:0020-09-10 3rd Qu.:10.000
## Max. :0020-12-10 Max. :12.000
## NA's :113205
## 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. :1.00 Min. : 1.00 Min. : 0.000
## 1st Qu.:2.00 1st Qu.: 12.00 1st Qu.: 2.310
## Median :4.00 Median : 18.00 Median : 3.230
## Mean :3.96 Mean : 24.33 Mean : 4.142
## 3rd Qu.:6.00 3rd Qu.: 27.00 3rd Qu.: 5.420
## Max. :7.00 Max. :2496.00 Max. :230.770
## NA's :113205Exportar base de datos limpia
bd_limpia <- bd12
write.csv(bd_limpia, file = "abarrotes_bd_limpia.csv", row.names = FALSE) Herrmienta “El Generador de Valor de Datos”
Paso 1. Definir el área del negocio que buscamos impactar o mejorar y su KPI
El área de negocio que buscamos impactar son las ventas en general, ya que buscamos que compra de productos desencadena la compra de otros productos. KPI: Ventas
Paso 2. Seleccionar plantilla(-s) para crear valor a partir de los datos de los clientes.
Visión / Segmentación / Personalización / Contextualización
Paso 3. Generar ideas o conceptos específicos.
¿Qué productos se compran en conjunto? ¿Cómo puedo generar más ventas si tengo el conocimiento que un producto desencadena la compra de otro producto?
Paso 4. Reunir los datos requeridos.
Market Basket Analysis
#install.packages("plyr")
library(Matrix) ##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack #install.packages("arules")
#install.packages("arulesViz")
library(arules) ##
## Attaching package: 'arules'## The following object is masked from 'package:dplyr':
##
## recode## The following objects are masked from 'package:base':
##
## abbreviate, write library(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 2.19 tail(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 2.31Generar 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)## ------------------------------------------------------------------------------##
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## compact## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize basket <- ddply(bd_limpia,c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse = ",")) Eliminar número de ticket
basket$F.Ticket <- NULLRenombramos el nombre de la columna
colnames(basket) <- c("Marca")Exportar basket
write.csv(basket,"basket.csv", quote = FALSE, row.names = FALSE)Importar transacciones
#file.choose()
tr <- read.transactions("/Users/dannaperez/Desktop/R/bases de datos/basket (1).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 [0.04s].
## 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 [0.03s]. #summary(reglas.asociacion)
#inspect(reglas.asociacion)
reglas.asociacion <- sort(reglas.asociacion, by = "confidence", decreasing = TRUE)
#summary(reglas.asociacion)
#inspect(reglas.asociacion)
#install.packages("tweenr")
library(tweenr)
#install.packages("arulesViz")
library(arulesViz)
top10reglas <- head(reglas.asociacion, n = 10, by = "confidence")
inspect(top10reglas)## 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
## 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 plot(top10reglas, method = "graph", engine = "htmlwidget")Paso 5. Plan de ejecución.
Después de analizar el Market Basket el plan de ejecución recomendado es poner en promoción paquetes en donde se encuentren los dos productos que desencadenen su compra, o bien, poner su ubicación en diferentes puntos del supermercado para incentivar un recorrido más extenso de los compradores en la tienda.
Conclusion
El comprador tiende a comprar un producto y gracias a la compra de ese producto, tiende a llevarse otro, por ejemplo, en la gráfica se puede observar que las personas que compren Salvo van a comprar Fabuloso. Lo recomendado aquí sería poner en descuento uno de los dos productos o formar paquetes para que la gente compre los dos productos en paquete sin pensarlo. También podemos observar como los productos que son bimbo, FUD, Blue House y Hellmann´s son comprados juntos, en este caso se puede recomendar vender sandwiches ya previamente elaborados. La compra de algun producto de “Coca Cola” desencadena compras de la marca “Coca Cola”.
---
title: <span style="Color:Orange"> "Market_Basket_Analysis-Abarrotes"
author: "Dana Perez - A00227041"
date: "9/19/2022"
output: 
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<img src="/Users/dannaperez/Desktop/imagenes para HTML/776645.png">


# Cambios a la base de datos
1. Formato a fecha corta.  
2. Se duplicaron los primeros 5 registros.  
3. Se cambio el formato a hora espanol mexico.  
4. Se cambio el formato a codigo de barras,, para que salga completo.  
5. Se guardo como CSV UTF/8,, delimitado por comas.  

 Importar la base de datos
```{r}
#file.choose()
bd <- read.csv("/Users/dannaperez/Desktop/R/bases de datos/abarrotes.csv")
```

 Entender la base de datos
```{r}
summary (bd)
```

Install.packages ("dplyr")
```{r}
library(dplyr)
summary (bd)
count(bd, vcClaveTienda, sort = TRUE)
count(bd, DescGiro, sort = TRUE)
#count(bd, Marca, sort = TRUE)
#count(bd, Producto, sort = TRUE)
#count(bd, Fabricante, sort = TRUE)
#count(bd, NombreDepartamento, sort = TRUE)
#count(bd, NombreCategoria,sort = TRUE)
#count(bd, Estado, sort = TRUE)
#count(bd, Mts.2, sort = TRUE)
#count(bd, Giro, sort = TRUE)
#count(bd, Hora.inicio, sort = TRUE)
#count(bd, Hora.cierre, sort = TRUE)

#install.packages("tidyverse")
library(tidyverse)
#install.packages("Janitor")
library(janitor)

tibble(bd)

str(bd)

head(bd)
head(bd, n=7)

tail(bd)

tabyl(bd, vcClaveTienda, NombreDepartamento)
```
Observaciones
1. Casi ningun registro cuenta con PLU.  
2. Cambiar formato de fecha.  
3. Cambiar formato de hora.  
4. Hay precios negativos.  
5. Hay unidades menores a 1.   

Tecnicas para limpieza de datos

Tecnica 1. remover datos irrelevantes 
Eliminar columnas
```{r}
bd1 <- bd
bd1 <- subset(bd1,select = -c (PLU, Codigo.Barras))
```
Eliminar renglones
```{r}
bd2 <- bd1
bd2 <- bd2[bd2$precio > 0,]
summary (bd1)
summary (bd2)  
```
Nota: esto no lo usaremos , pondremos precios negativos como absoluto
  
Tecnica 2. remover valores duplicados
Cuantos renglones duplicados tenemos
```{r}
bd1[duplicated(bd1),]
sum(duplicated(bd1))
```
Eliminar renglones duplicados
```{r}
bd3 <- bd1
library(dplyr)
bd3 <- distinct(bd3)
```

Tecnica 3. errores tipograficos y errores similares 
Precios ne absoluto
```{r}
bd4 <- bd3
bd4$Precio <- abs(bd4$Precio)
summary(bd4)
```
Cantidades en enteros
```{r}
bd5 <- bd4
bd5$Unidades <- ceiling(bd5$Unidades)
summary(bd5)
```

Tecnica 4. convertir tipos de datos
Convertir de caracter a fecha
```{r}
bd6 <- bd5
bd6$Fecha <- as.Date(bd6$Fecha, format = "%m/%d/%Y")
tibble(bd6)
```
Convertir de caracter entero
```{r}
bd7 <- bd6
bd7$Hora <- substr(bd7$Hora, start = 1, stop = 2)
tibble(bd7)
bd7$Hora <- as.integer(bd7$Hora)
str(bd7)
```

Tecnica 5. valores faltantes
Cuantos NA tengo en la base de datos
```{r}
sum(is.na(bd7))
sum(is.na(bd))
```
Cuantos na tengo por variable
```{r}
sapply(bd7, function(x) sum(is.na(x)))
sapply(bd, function(x) sum(is.na(x)))
```
Borrar todos los registros NA de una tabla
```{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 el promedio
```{r}
bd10 <- bd
bd10$PLU[is.na(bd10$PLU)]<-mean(bd10$PLU, na.rm = TRUE)
summary(bd10)
```
Reemplazar negativos con cero
```{r}
bd11 <- bd
bd11[bd11 < 0] <- 0
summary(bd11)
```

Tecnica 6. metodo estadistico
```{r}
    bd12 <- bd7
    boxplot(bd12$Precio, horizontal = TRUE)
    boxplot(bd12$Unidades, horizontal = TRUE)
```
Agregar columnas
```{r}
#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
```{r}
bd_limpia <- bd12
write.csv(bd_limpia, file = "abarrotes_bd_limpia.csv", row.names = FALSE)   
```

# Herrmienta "El Generador de Valor de Datos"
*Paso 1. Definir el área del negocio que buscamos impactar o mejorar y su KPI*  
El área de negocio que buscamos impactar son las ventas en general, ya que buscamos que compra de productos desencadena la compra de otros productos. KPI: Ventas

*Paso 2. Seleccionar plantilla(-s) para crear valor a partir de los datos de los clientes.*    
**Visión** / Segmentación / Personalización / Contextualización  

*Paso 3. Generar ideas o conceptos específicos.*   
¿Qué productos se compran en conjunto?
¿Cómo puedo generar más ventas si tengo el conocimiento que un producto desencadena la compra de otro producto?

*Paso 4. Reunir los datos requeridos.*    

# Market Basket Analysis
  
```{r}
  #install.packages("plyr")
    library(Matrix)    
    #install.packages("arules")    
    #install.packages("arulesViz")
    library(arules)    
    library(arulesViz)    
    library(datasets) 
```

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

# Generar basket
```{r}
    #install.packages("plyr")
    library(plyr)    
    basket <- ddply(bd_limpia,c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse = ","))    
```

# Eliminar número de ticket 
```{r}
    basket$F.Ticket <- NULL
```

# Renombramos el nombre de la columna
```{r}
    colnames(basket) <- c("Marca")
```

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

# Importar transacciones
```{r}
    #file.choose()
    tr <- read.transactions("/Users/dannaperez/Desktop/R/bases de datos/basket (1).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)
 
    #install.packages("tweenr")
    library(tweenr)
   #install.packages("arulesViz")
    library(arulesViz)
    top10reglas <- head(reglas.asociacion, n = 10, by = "confidence")
    inspect(top10reglas)
    plot(top10reglas, method = "graph", engine = "htmlwidget")
```

*Paso 5. Plan de ejecución.*  
Después de analizar el Market Basket el plan de ejecución recomendado es poner en promoción paquetes en donde se encuentren los dos productos que desencadenen su compra, o bien, poner su ubicación en diferentes puntos del supermercado para incentivar un recorrido más extenso de los compradores en la tienda. 

# Conclusion
El comprador tiende a comprar un producto y gracias a la compra de ese producto, tiende a llevarse otro, por ejemplo, en la gráfica se puede observar que las personas que compren Salvo van a comprar Fabuloso. Lo recomendado aquí sería poner en descuento uno de los dos productos o formar paquetes para que la gente compre los dos productos en paquete sin pensarlo. También podemos observar como los productos que son bimbo, FUD, Blue House y Hellmann´s son comprados juntos, en este caso se puede recomendar vender sandwiches ya previamente elaborados. La compra de algun producto de *"Coca Cola"* desencadena compras de la marca *"Coca Cola"*.  