Actividad 2.1 Daniela Garza
Daniela Garza
2023-08-21
Actividad 2.1: Maneja una base de datos - “Abarrotes”
Importar la base de datos
library(readxl)
bd <- read_excel("C:/Users/Asus ZenBook/Downloads/Abarrotes_Ventas-2.xlsx")summary(bd)## vcClaveTienda DescGiro Codigo Barras PLU
## Length:200620 Length:200620 Min. :8.347e+05 Mode:logical
## Class :character Class :character 1st Qu.:7.501e+12 TRUE:1437
## Mode :character Mode :character Median :7.501e+12 NA's:199183
## Mean :5.950e+12
## 3rd Qu.:7.501e+12
## Max. :1.750e+13
## Fecha Hora
## Min. :2020-05-01 00:00:31.08 Min. :1899-12-31 00:00:00.00
## 1st Qu.:2020-06-06 13:24:49.08 1st Qu.:1899-12-31 13:12:42.75
## Median :2020-07-11 14:10:21.46 Median :1899-12-31 17:35:59.00
## Mean :2020-07-19 15:19:40.65 Mean :1899-12-31 16:43:52.05
## 3rd Qu.:2020-08-29 22:07:47.33 3rd Qu.:1899-12-31 20:47:06.00
## Max. :2020-11-11 23:53:47.73 Max. :1899-12-31 23:59:59.00
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 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: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
## Length:200620 Length:200620 Min. :1899-12-31 07:00:00.00
## Class :character Class :character 1st Qu.:1899-12-31 07:00:00.00
## Mode :character Mode :character Median :1899-12-31 08:00:00.00
## Mean :1899-12-31 07:35:49.71
## 3rd Qu.:1899-12-31 08:00:00.00
## Max. :1899-12-31 09:00:00.00
## Hora cierre
## Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 23:00:00.00Observaciones
1. PLU tiene 199188 NA’s
2. La variable Fecha está como caracter.
3. Las variables Hora, Hora.inicio y Hora.cierre están como caracter.
4. La variable Precio tiene negativos.
5. La variable Unidades tiene decimales.
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, unioncount(bd, vcClaveTienda, sort=TRUE)## # A tibble: 5 × 2
## vcClaveTienda n
## <chr> <int>
## 1 MX001 96464
## 2 MX004 83455
## 3 MX005 10021
## 4 MX002 6629
## 5 MX003 4051count(bd, DescGiro, sort=TRUE)## # A tibble: 3 × 2
## DescGiro n
## <chr> <int>
## 1 Abarrotes 100515
## 2 Carnicería 83455
## 3 Depósito 16650count(bd, Marca, sort=TRUE)## # A tibble: 540 × 2
## Marca n
## <chr> <int>
## 1 COCA COLA 18686
## 2 PEPSI 15966
## 3 TECATE 11674
## 4 BIMBO 8316
## 5 LALA 5866
## 6 MARINELA 3696
## 7 DORITOS 3142
## 8 CHEETOS 3130
## 9 NUTRI LECHE 3127
## 10 MARLBORO 2579
## # ℹ 530 more rowscount(bd, Fabricante, sort=TRUE)## # A tibble: 241 × 2
## Fabricante n
## <chr> <int>
## 1 COCA COLA 27519
## 2 PEPSI-COLA MEXICANA 22415
## 3 SABRITAS 14296
## 4 CERVECERIA CUAUHTEMOC MOCTEZUMA 13681
## 5 GRUPO BIMBO 13077
## 6 SIGMA ALIMENTOS 8014
## 7 GRUPO INDUSTRIAL LALA 5868
## 8 GRUPO GAMESA 5527
## 9 NESTLE 3698
## 10 JUGOS DEL VALLE S.A. DE C.V. 3581
## # ℹ 231 more rowscount(bd, Producto, sort=TRUE)## # A tibble: 3,404 × 2
## Producto n
## <chr> <int>
## 1 Pepsi N.R. 1.5L 5108
## 2 Coca Cola Retornable 2.5L 3771
## 3 Caguamon Tecate Light 1.2Lt 3471
## 4 Pepsi N. R. 2.5L 2899
## 5 Cerveza Tecate Light 340Ml 2619
## 6 Cerveza Tecate Light 16Oz 2315
## 7 Coca Cola Retornable 1.5L 2124
## 8 Pepsi N.R. 3L 1832
## 9 Coca Cola Retornable 500Ml 1659
## 10 PEPSI N.R. 1.5L 1631
## # ℹ 3,394 more rowscount(bd, NombreDepartamento, sort=TRUE)## # A tibble: 9 × 2
## NombreDepartamento n
## <chr> <int>
## 1 Abarrotes 198274
## 2 Bebes e Infantiles 1483
## 3 Ferretería 377
## 4 Farmacia 255
## 5 Vinos y Licores 104
## 6 Papelería 74
## 7 Mercería 44
## 8 Productos a Eliminar 8
## 9 Carnes 1count(bd, NombreFamilia, sort=TRUE)## # A tibble: 51 × 2
## NombreFamilia n
## <chr> <int>
## 1 Bebidas 64917
## 2 Botanas 21583
## 3 Lacteos y Refrigerados 17657
## 4 Cerveza 14017
## 5 Pan y Tortilla 10501
## 6 Limpieza del Hogar 8723
## 7 Galletas 7487
## 8 Cigarros 6817
## 9 Cuidado Personal 5433
## 10 Salsas y Sazonadores 5320
## # ℹ 41 more rowscount(bd, NombreCategoria, sort=TRUE)## # A tibble: 174 × 2
## NombreCategoria n
## <chr> <int>
## 1 Refrescos Plástico (N.R.) 32861
## 2 Refrescos Retornables 13880
## 3 Frituras 11082
## 4 Lata 8150
## 5 Leche 7053
## 6 Cajetilla 6329
## 7 Botella 5867
## 8 Productos sin Categoria 5455
## 9 Papas Fritas 5344
## 10 Jugos y Néctares 5295
## # ℹ 164 more rowscount(bd, Estado, sort=TRUE)## # A tibble: 5 × 2
## Estado n
## <chr> <int>
## 1 Nuevo León 96464
## 2 Sinaloa 83455
## 3 Quintana Roo 10021
## 4 Jalisco 6629
## 5 Chiapas 4051count(bd, Giro, sort=TRUE)## # A tibble: 2 × 2
## Giro n
## <chr> <int>
## 1 Abarrotes 183970
## 2 Mini súper 16650count(bd, "Tipo ubicación", sort=TRUE)## # A tibble: 1 × 2
## `"Tipo ubicación"` n
## <chr> <int>
## 1 Tipo ubicación 200620count(bd, "Hora inicio", sort=TRUE)## # A tibble: 1 × 2
## `"Hora inicio"` n
## <chr> <int>
## 1 Hora inicio 200620count(bd, "Hora cierre", sort=TRUE)## # A tibble: 1 × 2
## `"Hora cierre"` n
## <chr> <int>
## 1 Hora cierre 200620library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ ggplot2 3.4.3 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorstibble(bd)## # A tibble: 200,620 × 22
## vcClaveTienda DescGiro `Codigo Barras` PLU Fecha
## <chr> <chr> <dbl> <lgl> <dttm>
## 1 MX001 Abarrotes 7501020540666 NA 2020-06-19 08:16:20
## 2 MX001 Abarrotes 7501032397906 NA 2020-06-19 08:23:32
## 3 MX001 Abarrotes 7501000112845 NA 2020-06-19 08:24:33
## 4 MX001 Abarrotes 7501031302741 NA 2020-06-19 08:24:33
## 5 MX001 Abarrotes 7501026027543 NA 2020-06-19 08:26:28
## 6 MX001 Abarrotes 7501025433024 NA 2020-06-19 08:26:28
## 7 MX001 Abarrotes 7501032332013 NA 2020-06-19 08:26:28
## 8 MX001 Abarrotes 7501026005688 NA 2020-06-19 08:26:28
## 9 MX001 Abarrotes 7506195178188 NA 2020-06-19 08:26:28
## 10 MX001 Abarrotes 32239052017 NA 2020-06-19 15:24:02
## # ℹ 200,610 more rows
## # ℹ 17 more variables: Hora <dttm>, Marca <chr>, Fabricante <chr>,
## # Producto <chr>, Precio <dbl>, Ult.Costo <dbl>, Unidades <dbl>,
## # F.Ticket <dbl>, NombreDepartamento <chr>, NombreFamilia <chr>,
## # NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>, `Tipo ubicación` <chr>,
## # Giro <chr>, `Hora inicio` <dttm>, `Hora cierre` <dttm>str(bd)## tibble [200,620 × 22] (S3: tbl_df/tbl/data.frame)
## $ vcClaveTienda : chr [1:200620] "MX001" "MX001" "MX001" "MX001" ...
## $ DescGiro : chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Codigo Barras : num [1:200620] 7.5e+12 7.5e+12 7.5e+12 7.5e+12 7.5e+12 ...
## $ PLU : logi [1:200620] NA NA NA NA NA NA ...
## $ Fecha : POSIXct[1:200620], format: "2020-06-19 08:16:20" "2020-06-19 08:23:32" ...
## $ Hora : POSIXct[1:200620], format: "1899-12-31 08:16:21" "1899-12-31 08:23:33" ...
## $ Marca : chr [1:200620] "NUTRI LECHE" "DAN UP" "BIMBO" "PEPSI" ...
## $ Fabricante : chr [1:200620] "MEXILAC" "DANONE DE MEXICO" "GRUPO BIMBO" "PEPSI-COLA MEXICANA" ...
## $ Producto : chr [1:200620] "Nutri Leche 1 Litro" "DANUP STRAWBERRY P/BEBER 350GR NAL" "Rebanadas Bimbo 2Pz" "Pepsi N.R. 400Ml" ...
## $ Precio : num [1:200620] 16 14 5 8 19.5 9.5 11 9.5 23.5 12 ...
## $ Ult.Costo : num [1:200620] 12.3 14 5 8 15 ...
## $ Unidades : num [1:200620] 1 1 1 1 1 1 1 1 1 1 ...
## $ F.Ticket : num [1:200620] 1 2 3 3 4 4 4 4 4 5 ...
## $ NombreDepartamento: chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ NombreFamilia : chr [1:200620] "Lacteos y Refrigerados" "Lacteos y Refrigerados" "Pan y Tortilla" "Bebidas" ...
## $ NombreCategoria : chr [1:200620] "Leche" "Yogurt" "Pan Dulce Empaquetado" "Refrescos Plástico (N.R.)" ...
## $ Estado : chr [1:200620] "Nuevo León" "Nuevo León" "Nuevo León" "Nuevo León" ...
## $ Mts 2 : num [1:200620] 60 60 60 60 60 60 60 60 60 60 ...
## $ Tipo ubicación : chr [1:200620] "Esquina" "Esquina" "Esquina" "Esquina" ...
## $ Giro : chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Hora inicio : POSIXct[1:200620], format: "1899-12-31 08:00:00" "1899-12-31 08:00:00" ...
## $ Hora cierre : POSIXct[1:200620], format: "1899-12-31 22:00:00" "1899-12-31 22:00:00" ...head(bd, n=7)## # A tibble: 7 × 22
## vcClaveTienda DescGiro `Codigo Barras` PLU Fecha
## <chr> <chr> <dbl> <lgl> <dttm>
## 1 MX001 Abarrotes 7501020540666 NA 2020-06-19 08:16:20
## 2 MX001 Abarrotes 7501032397906 NA 2020-06-19 08:23:32
## 3 MX001 Abarrotes 7501000112845 NA 2020-06-19 08:24:33
## 4 MX001 Abarrotes 7501031302741 NA 2020-06-19 08:24:33
## 5 MX001 Abarrotes 7501026027543 NA 2020-06-19 08:26:28
## 6 MX001 Abarrotes 7501025433024 NA 2020-06-19 08:26:28
## 7 MX001 Abarrotes 7501032332013 NA 2020-06-19 08:26:28
## # ℹ 17 more variables: Hora <dttm>, Marca <chr>, Fabricante <chr>,
## # Producto <chr>, Precio <dbl>, Ult.Costo <dbl>, Unidades <dbl>,
## # F.Ticket <dbl>, NombreDepartamento <chr>, NombreFamilia <chr>,
## # NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>, `Tipo ubicación` <chr>,
## # Giro <chr>, `Hora inicio` <dttm>, `Hora cierre` <dttm>tail(bd)## # A tibble: 6 × 22
## vcClaveTienda DescGiro `Codigo Barras` PLU Fecha
## <chr> <chr> <dbl> <lgl> <dttm>
## 1 MX005 Depósito 7622210464811 NA 2020-07-12 01:08:24
## 2 MX005 Depósito 7622210464811 NA 2020-10-23 22:17:37
## 3 MX005 Depósito 7622210464811 NA 2020-10-10 20:30:20
## 4 MX005 Depósito 7622210464811 NA 2020-10-10 22:40:42
## 5 MX005 Depósito 7622210464811 NA 2020-06-27 22:30:19
## 6 MX005 Depósito 7622210464811 NA 2020-06-26 23:43:33
## # ℹ 17 more variables: Hora <dttm>, Marca <chr>, Fabricante <chr>,
## # Producto <chr>, Precio <dbl>, Ult.Costo <dbl>, Unidades <dbl>,
## # F.Ticket <dbl>, NombreDepartamento <chr>, NombreFamilia <chr>,
## # NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>, `Tipo ubicación` <chr>,
## # Giro <chr>, `Hora inicio` <dttm>, `Hora cierre` <dttm>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 95410 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 0Técnicas para limpieza de datos
Tecnica 1. Remover valores irrelevantes
# Eliminar columnas
bd1 <- bd
bd1 <- subset(bd1, select = -c(PLU))# Eliminar renglones
bd2 <- bd1
bd2 <- bd2[bd2$Precio>0,]
summary(bd1$Precio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -147.00 11.00 16.00 19.42 25.00 1000.00summary(bd2$Precio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.50 11.00 16.00 19.45 25.00 1000.00Técnica 2. Remover valores duplicados
# ¿Cuántos registros duplicados tenemos?
bd2[duplicated(bd2),]## # A tibble: 0 × 21
## # ℹ 21 variables: vcClaveTienda <chr>, DescGiro <chr>, Codigo Barras <dbl>,
## # Fecha <dttm>, Hora <dttm>, Marca <chr>, Fabricante <chr>, Producto <chr>,
## # Precio <dbl>, Ult.Costo <dbl>, Unidades <dbl>, F.Ticket <dbl>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts 2 <dbl>, Tipo ubicación <chr>, Giro <chr>,
## # Hora inicio <dttm>, Hora cierre <dttm>sum(duplicated(bd1))## [1] 0# Eliminar registros duplicados
bd3 <- bd2
library(dplyr)
bd3 <- distinct(bd3)Técnica 3. Errores tipográficos y errores similares
# Precios en absoluto
bd4 <- bd1
bd4$Precio <- abs(bd4$Precio)
summary(bd4$Precio)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.50 11.00 16.00 19.45 25.00 1000.00# Cantidades en enteros
bd5 <- bd4
bd5$Unidades <- ceiling(bd5$Unidades)
summary(bd5$Unidades)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.000 1.000 1.262 1.000 96.000summary(bd$Unidades)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 1.000 1.000 1.262 1.000 96.000Técnica 4. Convertir tipos de datos
# Convertir de caracter a fecha
bd6 <- bd5
bd6$Fecha <- as.Date(bd6$Fecha, "%d/%m/%Y")## Warning in as.POSIXlt.POSIXct(x, tz = tz): unknown timezone '%d/%m/%Y'tibble(bd6)## # A tibble: 200,620 × 21
## vcClaveTienda DescGiro `Codigo Barras` Fecha Hora Marca
## <chr> <chr> <dbl> <date> <dttm> <chr>
## 1 MX001 Abarrotes 7501020540666 2020-06-19 1899-12-31 08:16:21 NUTRI…
## 2 MX001 Abarrotes 7501032397906 2020-06-19 1899-12-31 08:23:33 DAN UP
## 3 MX001 Abarrotes 7501000112845 2020-06-19 1899-12-31 08:24:33 BIMBO
## 4 MX001 Abarrotes 7501031302741 2020-06-19 1899-12-31 08:24:33 PEPSI
## 5 MX001 Abarrotes 7501026027543 2020-06-19 1899-12-31 08:26:28 BLANC…
## 6 MX001 Abarrotes 7501025433024 2020-06-19 1899-12-31 08:26:28 FLASH
## 7 MX001 Abarrotes 7501032332013 2020-06-19 1899-12-31 08:26:28 VARIO…
## 8 MX001 Abarrotes 7501026005688 2020-06-19 1899-12-31 08:26:28 ZOTE
## 9 MX001 Abarrotes 7506195178188 2020-06-19 1899-12-31 08:26:28 ALWAYS
## 10 MX001 Abarrotes 32239052017 2020-06-19 1899-12-31 15:24:02 JUMEX
## # ℹ 200,610 more rows
## # ℹ 15 more variables: Fabricante <chr>, Producto <chr>, Precio <dbl>,
## # Ult.Costo <dbl>, Unidades <dbl>, F.Ticket <dbl>, NombreDepartamento <chr>,
## # NombreFamilia <chr>, NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>,
## # `Tipo ubicación` <chr>, Giro <chr>, `Hora inicio` <dttm>,
## # `Hora cierre` <dttm># Convertir de caracter a entero
bd7 <- bd6
bd7$Hora <- substr(bd7$Hora, start =1, stop =2)
tibble(bd7)## # A tibble: 200,620 × 21
## vcClaveTienda DescGiro `Codigo Barras` Fecha Hora Marca Fabricante
## <chr> <chr> <dbl> <date> <chr> <chr> <chr>
## 1 MX001 Abarrotes 7501020540666 2020-06-19 18 NUTRI LE… MEXILAC
## 2 MX001 Abarrotes 7501032397906 2020-06-19 18 DAN UP DANONE DE…
## 3 MX001 Abarrotes 7501000112845 2020-06-19 18 BIMBO GRUPO BIM…
## 4 MX001 Abarrotes 7501031302741 2020-06-19 18 PEPSI PEPSI-COL…
## 5 MX001 Abarrotes 7501026027543 2020-06-19 18 BLANCA N… FABRICA D…
## 6 MX001 Abarrotes 7501025433024 2020-06-19 18 FLASH ALEN
## 7 MX001 Abarrotes 7501032332013 2020-06-19 18 VARIOS D… DANONE DE…
## 8 MX001 Abarrotes 7501026005688 2020-06-19 18 ZOTE FABRICA D…
## 9 MX001 Abarrotes 7506195178188 2020-06-19 18 ALWAYS PROCTER &…
## 10 MX001 Abarrotes 32239052017 2020-06-19 18 JUMEX JUMEX
## # ℹ 200,610 more rows
## # ℹ 14 more variables: Producto <chr>, Precio <dbl>, Ult.Costo <dbl>,
## # Unidades <dbl>, F.Ticket <dbl>, NombreDepartamento <chr>,
## # NombreFamilia <chr>, NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>,
## # `Tipo ubicación` <chr>, Giro <chr>, `Hora inicio` <dttm>,
## # `Hora cierre` <dttm>bd7$Hora <- as.integer(bd7$Hora)
str(bd7)## tibble [200,620 × 21] (S3: tbl_df/tbl/data.frame)
## $ vcClaveTienda : chr [1:200620] "MX001" "MX001" "MX001" "MX001" ...
## $ DescGiro : chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Codigo Barras : num [1:200620] 7.5e+12 7.5e+12 7.5e+12 7.5e+12 7.5e+12 ...
## $ Fecha : Date[1:200620], format: "2020-06-19" "2020-06-19" ...
## $ Hora : int [1:200620] 18 18 18 18 18 18 18 18 18 18 ...
## $ Marca : chr [1:200620] "NUTRI LECHE" "DAN UP" "BIMBO" "PEPSI" ...
## $ Fabricante : chr [1:200620] "MEXILAC" "DANONE DE MEXICO" "GRUPO BIMBO" "PEPSI-COLA MEXICANA" ...
## $ Producto : chr [1:200620] "Nutri Leche 1 Litro" "DANUP STRAWBERRY P/BEBER 350GR NAL" "Rebanadas Bimbo 2Pz" "Pepsi N.R. 400Ml" ...
## $ Precio : num [1:200620] 16 14 5 8 19.5 9.5 11 9.5 23.5 12 ...
## $ Ult.Costo : num [1:200620] 12.3 14 5 8 15 ...
## $ Unidades : num [1:200620] 1 1 1 1 1 1 1 1 1 1 ...
## $ F.Ticket : num [1:200620] 1 2 3 3 4 4 4 4 4 5 ...
## $ NombreDepartamento: chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ NombreFamilia : chr [1:200620] "Lacteos y Refrigerados" "Lacteos y Refrigerados" "Pan y Tortilla" "Bebidas" ...
## $ NombreCategoria : chr [1:200620] "Leche" "Yogurt" "Pan Dulce Empaquetado" "Refrescos Plástico (N.R.)" ...
## $ Estado : chr [1:200620] "Nuevo León" "Nuevo León" "Nuevo León" "Nuevo León" ...
## $ Mts 2 : num [1:200620] 60 60 60 60 60 60 60 60 60 60 ...
## $ Tipo ubicación : chr [1:200620] "Esquina" "Esquina" "Esquina" "Esquina" ...
## $ Giro : chr [1:200620] "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Hora inicio : POSIXct[1:200620], format: "1899-12-31 08:00:00" "1899-12-31 08:00:00" ...
## $ Hora cierre : POSIXct[1:200620], format: "1899-12-31 22:00:00" "1899-12-31 22:00:00" ...Técnica 5. Valores faltantes
# ¿Cuántos NA tengo en la base de datos?
sum(is.na(bd7))## [1] 0sum(is.na(bd))## [1] 199183# ¿Cuántos NA tengo por variable?
sapply(bd7,function(x)sum(is.na(x)))## vcClaveTienda DescGiro Codigo Barras Fecha
## 0 0 0 0
## Hora Marca Fabricante Producto
## 0 0 0 0
## Precio Ult.Costo Unidades F.Ticket
## 0 0 0 0
## NombreDepartamento NombreFamilia NombreCategoria Estado
## 0 0 0 0
## Mts 2 Tipo ubicación Giro Hora inicio
## 0 0 0 0
## Hora cierre
## 0# Borrar 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 Mode:logical
## Class :character Class :character 1st Qu.:6.750e+08 TRUE:1437
## Mode :character Mode :character Median :6.750e+08
## Mean :2.616e+11
## 3rd Qu.:6.750e+08
## Max. :7.501e+12
## Fecha Hora
## Min. :2020-06-06 14:36:14.11 Min. :1899-12-31 00:01:22.00
## 1st Qu.:2020-06-20 21:48:46.75 1st Qu.:1899-12-31 15:57:22.00
## Median :2020-07-10 22:17:46.90 Median :1899-12-31 18:49:20.00
## Mean :2020-07-16 11:50:19.00 Mean :1899-12-31 17:46:04.46
## 3rd Qu.:2020-08-08 21:42:17.13 3rd Qu.:1899-12-31 21:09:03.00
## Max. :2020-11-11 20:37:03.69 Max. :1899-12-31 23:58:14.00
## Marca Fabricante Producto Precio
## Length:1437 Length:1437 Length:1437 Min. :30.00
## Class :character Class :character Class :character 1st Qu.:90.00
## Mode :character Mode :character Mode :character Median :90.00
## Mean :87.94
## 3rd Qu.:90.00
## Max. :90.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 1.00 Min. :1.000 Min. : 772 Length:1437
## 1st Qu.:64.62 1st Qu.:1.000 1st Qu.: 99955 Class :character
## Median :64.62 Median :1.000 Median :102493 Mode :character
## Mean :56.65 Mean :1.124 Mean :100595
## 3rd Qu.:64.62 3rd Qu.:1.000 3rd Qu.:106546
## Max. :64.62 Max. :7.000 Max. :118356
## NombreFamilia NombreCategoria Estado Mts 2
## Length:1437 Length:1437 Length:1437 Min. :58.00
## Class :character Class :character Class :character 1st Qu.:58.00
## Mode :character Mode :character Mode :character Median :58.00
## Mean :58.07
## 3rd Qu.:58.00
## Max. :60.00
## Tipo ubicación Giro Hora inicio
## Length:1437 Length:1437 Min. :1899-12-31 08:00:00
## Class :character Class :character 1st Qu.:1899-12-31 08:00:00
## Mode :character Mode :character Median :1899-12-31 08:00:00
## Mean :1899-12-31 08:00:00
## 3rd Qu.:1899-12-31 08:00:00
## Max. :1899-12-31 08:00:00
## Hora cierre
## Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 21:00:00.00
## Median :1899-12-31 21:00:00.00
## Mean :1899-12-31 21:02:06.26
## 3rd Qu.:1899-12-31 21:00:00.00
## Max. :1899-12-31 22:00:00.00# Reemplazar NA con ceros
bd9 <- bd
bd9[is.na(bd9)] <- 0
summary(bd9)## vcClaveTienda DescGiro Codigo Barras PLU
## Length:200620 Length:200620 Min. :8.347e+05 Mode :logical
## Class :character Class :character 1st Qu.:7.501e+12 FALSE:199183
## Mode :character Mode :character Median :7.501e+12 TRUE :1437
## Mean :5.950e+12
## 3rd Qu.:7.501e+12
## Max. :1.750e+13
## Fecha Hora
## Min. :2020-05-01 00:00:31.08 Min. :1899-12-31 00:00:00.00
## 1st Qu.:2020-06-06 13:24:49.08 1st Qu.:1899-12-31 13:12:42.75
## Median :2020-07-11 14:10:21.46 Median :1899-12-31 17:35:59.00
## Mean :2020-07-19 15:19:40.65 Mean :1899-12-31 16:43:52.05
## 3rd Qu.:2020-08-29 22:07:47.33 3rd Qu.:1899-12-31 20:47:06.00
## Max. :2020-11-11 23:53:47.73 Max. :1899-12-31 23:59:59.00
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 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: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
## Length:200620 Length:200620 Min. :1899-12-31 07:00:00.00
## Class :character Class :character 1st Qu.:1899-12-31 07:00:00.00
## Mode :character Mode :character Median :1899-12-31 08:00:00.00
## Mean :1899-12-31 07:35:49.71
## 3rd Qu.:1899-12-31 08:00:00.00
## Max. :1899-12-31 09:00:00.00
## Hora cierre
## Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 23:00:00.00# Reemplazar los 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:200620 Length:200620 Min. :8.347e+05 Min. :1
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.:1
## Mode :character Mode :character Median :7.501e+12 Median :1
## Mean :5.950e+12 Mean :1
## 3rd Qu.:7.501e+12 3rd Qu.:1
## Max. :1.750e+13 Max. :1
## Fecha Hora
## Min. :2020-05-01 00:00:31.08 Min. :1899-12-31 00:00:00.00
## 1st Qu.:2020-06-06 13:24:49.08 1st Qu.:1899-12-31 13:12:42.75
## Median :2020-07-11 14:10:21.46 Median :1899-12-31 17:35:59.00
## Mean :2020-07-19 15:19:40.65 Mean :1899-12-31 16:43:52.05
## 3rd Qu.:2020-08-29 22:07:47.33 3rd Qu.:1899-12-31 20:47:06.00
## Max. :2020-11-11 23:53:47.73 Max. :1899-12-31 23:59:59.00
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 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: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
## Length:200620 Length:200620 Min. :1899-12-31 07:00:00.00
## Class :character Class :character 1st Qu.:1899-12-31 07:00:00.00
## Mode :character Mode :character Median :1899-12-31 08:00:00.00
## Mean :1899-12-31 07:35:49.71
## 3rd Qu.:1899-12-31 08:00:00.00
## Max. :1899-12-31 09:00:00.00
## Hora cierre
## Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 23:00:00.00Técnica 6. Método estadístico
# Gráfica de caja y bigotes
bd12 <- bd7
boxplot(bd12$Precio, horizontal = TRUE)
boxplot(bd12$Unidades, horizontal = TRUE)
# Agregar columnas
library(lubridate)
bd12$Dia_de_la_semana <- wday(bd12$Fecha)
summary(bd12)## vcClaveTienda DescGiro Codigo Barras Fecha
## Length:200620 Length:200620 Min. :8.347e+05 Min. :2020-05-01
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.:2020-06-06
## Mode :character Mode :character Median :7.501e+12 Median :2020-07-11
## Mean :5.950e+12 Mean :2020-07-18
## 3rd Qu.:7.501e+12 3rd Qu.:2020-08-29
## Max. :1.750e+13 Max. :2020-11-11
## Hora Marca Fabricante Producto
## Min. :18 Length:200620 Length:200620 Length:200620
## 1st Qu.:18 Class :character Class :character Class :character
## Median :18 Mode :character Mode :character Mode :character
## Mean :18
## 3rd Qu.:18
## Max. :18
## Precio Ult.Costo Unidades F.Ticket
## Min. : 0.50 Min. : 0.38 Min. : 1.000 Min. : 1
## 1st Qu.: 11.00 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967
## Median : 16.00 Median : 12.31 Median : 1.000 Median :105996
## Mean : 19.45 Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 25.00 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383009
## Max. :1000.00 Max. :769.23 Max. :96.000 Max. :450040
## NombreDepartamento NombreFamilia NombreCategoria Estado
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Mts 2 Tipo ubicación Giro
## Min. :47.0 Length:200620 Length:200620
## 1st Qu.:53.0 Class :character Class :character
## Median :60.0 Mode :character Mode :character
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Hora inicio Hora cierre
## Min. :1899-12-31 07:00:00.00 Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 07:00:00.00 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 08:00:00.00 Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 07:35:49.71 Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 08:00:00.00 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 09:00:00.00 Max. :1899-12-31 23:00:00.00
## Dia_de_la_semana
## Min. :1.000
## 1st Qu.:2.000
## Median :4.000
## Mean :3.912
## 3rd Qu.:6.000
## Max. :7.000bd12$Subtotal <- bd12$Precio * bd12$Unidades
summary(bd12)## vcClaveTienda DescGiro Codigo Barras Fecha
## Length:200620 Length:200620 Min. :8.347e+05 Min. :2020-05-01
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.:2020-06-06
## Mode :character Mode :character Median :7.501e+12 Median :2020-07-11
## Mean :5.950e+12 Mean :2020-07-18
## 3rd Qu.:7.501e+12 3rd Qu.:2020-08-29
## Max. :1.750e+13 Max. :2020-11-11
## Hora Marca Fabricante Producto
## Min. :18 Length:200620 Length:200620 Length:200620
## 1st Qu.:18 Class :character Class :character Class :character
## Median :18 Mode :character Mode :character Mode :character
## Mean :18
## 3rd Qu.:18
## Max. :18
## Precio Ult.Costo Unidades F.Ticket
## Min. : 0.50 Min. : 0.38 Min. : 1.000 Min. : 1
## 1st Qu.: 11.00 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967
## Median : 16.00 Median : 12.31 Median : 1.000 Median :105996
## Mean : 19.45 Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 25.00 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383009
## Max. :1000.00 Max. :769.23 Max. :96.000 Max. :450040
## NombreDepartamento NombreFamilia NombreCategoria Estado
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Mts 2 Tipo ubicación Giro
## Min. :47.0 Length:200620 Length:200620
## 1st Qu.:53.0 Class :character Class :character
## Median :60.0 Mode :character Mode :character
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Hora inicio Hora cierre
## Min. :1899-12-31 07:00:00.00 Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 07:00:00.00 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 08:00:00.00 Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 07:35:49.71 Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 08:00:00.00 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 09:00:00.00 Max. :1899-12-31 23:00:00.00
## Dia_de_la_semana Subtotal
## Min. :1.000 Min. : 1.00
## 1st Qu.:2.000 1st Qu.: 12.00
## Median :4.000 Median : 18.00
## Mean :3.912 Mean : 24.33
## 3rd Qu.:6.000 3rd Qu.: 27.00
## Max. :7.000 Max. :2496.00bd12$Utilidad <- bd12$Precio - bd12$Ult.Costo
summary(bd12)## vcClaveTienda DescGiro Codigo Barras Fecha
## Length:200620 Length:200620 Min. :8.347e+05 Min. :2020-05-01
## Class :character Class :character 1st Qu.:7.501e+12 1st Qu.:2020-06-06
## Mode :character Mode :character Median :7.501e+12 Median :2020-07-11
## Mean :5.950e+12 Mean :2020-07-18
## 3rd Qu.:7.501e+12 3rd Qu.:2020-08-29
## Max. :1.750e+13 Max. :2020-11-11
## Hora Marca Fabricante Producto
## Min. :18 Length:200620 Length:200620 Length:200620
## 1st Qu.:18 Class :character Class :character Class :character
## Median :18 Mode :character Mode :character Mode :character
## Mean :18
## 3rd Qu.:18
## Max. :18
## Precio Ult.Costo Unidades F.Ticket
## Min. : 0.50 Min. : 0.38 Min. : 1.000 Min. : 1
## 1st Qu.: 11.00 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967
## Median : 16.00 Median : 12.31 Median : 1.000 Median :105996
## Mean : 19.45 Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 25.00 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383009
## Max. :1000.00 Max. :769.23 Max. :96.000 Max. :450040
## NombreDepartamento NombreFamilia NombreCategoria Estado
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Mts 2 Tipo ubicación Giro
## Min. :47.0 Length:200620 Length:200620
## 1st Qu.:53.0 Class :character Class :character
## Median :60.0 Mode :character Mode :character
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Hora inicio Hora cierre
## Min. :1899-12-31 07:00:00.00 Min. :1899-12-31 21:00:00.00
## 1st Qu.:1899-12-31 07:00:00.00 1st Qu.:1899-12-31 22:00:00.00
## Median :1899-12-31 08:00:00.00 Median :1899-12-31 22:00:00.00
## Mean :1899-12-31 07:35:49.71 Mean :1899-12-31 22:23:11.42
## 3rd Qu.:1899-12-31 08:00:00.00 3rd Qu.:1899-12-31 23:00:00.00
## Max. :1899-12-31 09:00:00.00 Max. :1899-12-31 23:00:00.00
## Dia_de_la_semana Subtotal Utilidad
## Min. :1.000 Min. : 1.00 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 12.00 1st Qu.: 2.310
## Median :4.000 Median : 18.00 Median : 3.230
## Mean :3.912 Mean : 24.33 Mean : 4.142
## 3rd Qu.:6.000 3rd Qu.: 27.00 3rd Qu.: 5.420
## Max. :7.000 Max. :2496.00 Max. :230.770# Exportar la base de datos limpia
bd_limpia <- bd12
write.csv(bd_limpia, file="abarrotes_bd_limpia.csv", row.names = FALSE)# Market basket analysis
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,
## summarizelibrary(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)## # A tibble: 6 × 24
## vcClaveTienda DescGiro `Codigo Barras` Fecha Hora Marca Fabricante
## <chr> <chr> <dbl> <date> <int> <chr> <chr>
## 1 MX001 Abarrotes 7501020540666 2020-06-19 18 NUTRI LEC… MEXILAC
## 2 MX001 Abarrotes 7501032397906 2020-06-19 18 DAN UP DANONE DE…
## 3 MX001 Abarrotes 7501000112845 2020-06-19 18 BIMBO GRUPO BIM…
## 4 MX001 Abarrotes 7501031302741 2020-06-19 18 PEPSI PEPSI-COL…
## 5 MX001 Abarrotes 7501026027543 2020-06-19 18 BLANCA NI… FABRICA D…
## 6 MX001 Abarrotes 7501025433024 2020-06-19 18 FLASH ALEN
## # ℹ 17 more variables: Producto <chr>, Precio <dbl>, Ult.Costo <dbl>,
## # Unidades <dbl>, F.Ticket <dbl>, NombreDepartamento <chr>,
## # NombreFamilia <chr>, NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>,
## # `Tipo ubicación` <chr>, Giro <chr>, `Hora inicio` <dttm>,
## # `Hora cierre` <dttm>, Dia_de_la_semana <dbl>, Subtotal <dbl>,
## # Utilidad <dbl>tail(bd_limpia)## # A tibble: 6 × 24
## vcClaveTienda DescGiro `Codigo Barras` Fecha Hora Marca Fabricante
## <chr> <chr> <dbl> <date> <int> <chr> <chr>
## 1 MX004 Carnicería 10248765241 2020-10-15 18 YEMINA HERDEZ
## 2 MX004 Carnicería 7501079702855 2020-10-15 18 DEL FUER… ALIMENTOS…
## 3 MX004 Carnicería 7501055320639 2020-10-15 18 COCA COL… COCA COLA
## 4 MX004 Carnicería 7501214100256 2020-10-15 18 DIAMANTE EMPACADOS
## 5 MX004 Carnicería 7501031311620 2020-10-15 18 PEPSI PEPSI-COL…
## 6 MX004 Carnicería 75004699 2020-10-15 18 COCA COLA COCA COLA
## # ℹ 17 more variables: Producto <chr>, Precio <dbl>, Ult.Costo <dbl>,
## # Unidades <dbl>, F.Ticket <dbl>, NombreDepartamento <chr>,
## # NombreFamilia <chr>, NombreCategoria <chr>, Estado <chr>, `Mts 2` <dbl>,
## # `Tipo ubicación` <chr>, Giro <chr>, `Hora inicio` <dttm>,
## # `Hora cierre` <dttm>, Dia_de_la_semana <dbl>, Subtotal <dbl>,
## # Utilidad <dbl># Generar basket
basket <- ddply(bd_limpia, c("F.Ticket"), function(bd_limpia)paste(bd_limpia$Marca, collapse = ","))# Eliminar número de ticket
basket$F.Ticket <- NULL# Renombrar el nombre de la columna
colnames(basket) <- c("Marca")# Exportar basket
write.csv(basket, "basket.csv", quote = FALSE, row.names = FALSE)library(arules)
library(arulesViz)# Importar transacciones
tr <- read.transactions( "C:\\Users\\Asus ZenBook\\Documents\\basket.csv", format = "basket", sep = ",")## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string
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## Warning in scan(text = l, what = "character", sep = sep, quote = quote, : EOF
## within quoted string## Warning in asMethod(object): removing duplicated items in transactions# Generar reglas de asociación
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 [0.02s].
## sorting and recoding items ... [207 item(s)] done [0.00s].
## creating transaction tree ... done [0.02s].
## 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# Ordenar reglas de asociación
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# Visualizar reglas de asociación
top10reglas <- head(reglas.asociacion, n=10, by= "confidence")
plot(top10reglas, method = "graph", engine = "htmlwidget")Conclusión
Esta actividad es esencial para el reto, ya que trabajeremos con bases de datos extensas que pueden contener errores, valores nulos o datos que no utilizaremos, por lo tanto es importante aprender a limpiar y organizar los datos para facilitar el proceso de análisis y tener información relevante para el caso.
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