Integrador Data Explorer & Clusters Abarrotes
Esteban Lozano Maldonado
2024-08-16
Instalar paquetes y mandar a llamar librerías.
#install.packages("DataExplorer")
library("DataExplorer")
#install.packages("cluster")
library(cluster)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("data.table")
library(data.table)
#install.packages("factoextra")
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaCargar la informacion de Abarrotes en df.
df <- read.csv("C:\\Carpeta de R\\DBs\\abarrotes.csv")
head(df)## vcClaveTienda DescGiro Codigo.Barras PLU Fecha Hora
## 1 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 08:16:21
## 2 MX001 Abarrotes 7.501032e+12 NA 19/06/2020 08:23:33
## 3 MX001 Abarrotes 7.501000e+12 NA 19/06/2020 08:24:33
## 4 MX001 Abarrotes 7.501031e+12 NA 19/06/2020 08:24:33
## 5 MX001 Abarrotes 7.501026e+12 NA 19/06/2020 08:26:28
## 6 MX001 Abarrotes 7.501021e+12 NA 19/06/2020 08:16:21
## 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 08:00 22:00
## 2 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 3 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 4 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 5 Nuevo León 60 Esquina Abarrotes 08:00 22:00
## 6 Nuevo León 60 Esquina Abarrotes 08:00 22:00Crear bases de datos.
clave_tienda <- df$vcClaveTienda
descripcion_giro <- df$DescGiro
codigo_barras <- df$Codigo.Barras
fecha <- df$Fecha
hora <- df$Hora
marca <- df$Marca
fabricante <- df$Fabricante
producto <- df$Producto
precio <- df$Precio
ultimo_costo <- df$Ult.Costo
unidades <- df$Unidades
folio_ticket <- df$F.Ticket
nombre_departamento <- df$NombreDepartamento
nombre_familia <- df$NombreFamilia
nombre_categoria <- df$NombreCategoria
estado <- df$Estado
metros_cuadrados <- df$Mts.2
tipo_ubicacion <- df$Tipo.ubicación
giro <- df$Giro
hora_inicio <- df$Hora.inicio
hora_cierre <- df$Hora.cierreCrear reporte de abarrotes.
create_report(df)##
##
## processing file: report.rmd## | | | 0% | |. | 2% | |.. | 5% [global_options] | |... | 7% | |.... | 10% [introduce] | |.... | 12% | |..... | 14% [plot_intro] | |...... | 17% | |....... | 19% [data_structure] | |........ | 21% | |......... | 24% [missing_profile] | |.......... | 26% | |........... | 29% [univariate_distribution_header] | |........... | 31% | |............ | 33% [plot_histogram] | |............. | 36% | |.............. | 38% [plot_density] | |............... | 40% | |................ | 43% [plot_frequency_bar] | |................. | 45% | |.................. | 48% [plot_response_bar] | |.................. | 50% | |................... | 52% [plot_with_bar] | |.................... | 55% | |..................... | 57% [plot_normal_qq] | |...................... | 60% | |....................... | 62% [plot_response_qq] | |........................ | 64% | |......................... | 67% [plot_by_qq] | |.......................... | 69% | |.......................... | 71% [correlation_analysis] | |........................... | 74% | |............................ | 76% [principal_component_analysis] | |............................. | 79% | |.............................. | 81% [bivariate_distribution_header] | |............................... | 83% | |................................ | 86% [plot_response_boxplot] | |................................. | 88% | |................................. | 90% [plot_by_boxplot] | |.................................. | 93% | |................................... | 95% [plot_response_scatterplot] | |.................................... | 98% | |.....................................| 100% [plot_by_scatterplot] ## output file: C:/Carpeta de R/Ejercicios Markdowns/1 Clase/report.knit.md## "C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/pandoc" +RTS -K512m -RTS "C:\CARPET~1\EJERCI~1\1CLASE~1\REPORT~1.MD" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc31a4cf135ee.html --lua-filter "C:\Users\esteb\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\esteb\AppData\Local\R\win-library\4.4\rmarkdown\rmarkdown\lua\latex-div.lua" --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template "C:\Users\esteb\AppData\Local\R\win-library\4.4\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable "mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" --include-in-header "C:\Users\esteb\AppData\Local\Temp\Rtmp46WQzT\rmarkdown-str31a41dd0cb4.html"##
## Output created: report.htmlCrear una base de datos de “abarrotes 1”, debido a que contiene información distinta.
df_abarrotes1<- read.csv("C:\\Carpeta de R\\DBs\\abarrotes1.csv")
head(df_abarrotes1)## Cliente Ticket_Promedio Visitas
## 1 12346 1.04000 1
## 2 12347 68.74429 7
## 3 12349 605.10000 1
## 4 12350 65.30000 1
## 5 12352 169.26375 8
## 6 12353 24.30000 1Definir los grupos o centroides generados.
grupos <- 3 df_abarrotes1_numerico <- df_abarrotes1[, c("Ticket_Promedio", "Visitas")]segmentos <- kmeans(df_abarrotes1_numerico,3)
segmentos## K-means clustering with 3 clusters of sizes 1, 358, 3939
##
## Cluster means:
## Ticket_Promedio Visitas
## 1 184.43826 21664.000000
## 2 234.54534 3.229050
## 3 53.37922 4.317593
##
## Clustering vector:
## [1] 3 3 2 3 2 3 2 3 3 2 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3
## [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 2 3 2 3 3 2 3 2 3
## [75] 3 3 3 2 3 3 3 2 3 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 2
## [112] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 2 3 3 3 3 3 3 3
## [149] 2 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 2 3 3 3 3 3
## [186] 2 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [223] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [260] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [297] 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3
## [334] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
## [371] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 2 3 3 3
## [408] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
## [445] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [482] 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [519] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
## [556] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [593] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [630] 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3
## [667] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [704] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 2 3 2 3 3 3 3 2 3 3 3 3 2
## [741] 2 2 3 2 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [778] 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3
## [815] 3 2 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3
## [852] 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 2 3
## [889] 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 2 3 3 3
## [926] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3
## [963] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1000] 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3
## [1037] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1074] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2
## [1111] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1148] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1185] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [1222] 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [1259] 3 2 3 3 3 2 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3
## [1296] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 2 3 3 2 3 3 3 2 3 3 2 3 3 3 3
## [1333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1370] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3
## [1407] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1444] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [1481] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [1518] 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1555] 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 3 3 3 3 3
## [1592] 3 2 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1629] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2
## [1666] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1703] 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 2 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1740] 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [1777] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3
## [1814] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [1851] 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3
## [1888] 3 3 2 3 3 3 3 3 3 3 3 2 3 2 3 2 3 2 3 2 3 3 3 3 2 3 3 2 3 2 3 3 3 3 3 3 3
## [1925] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 2 3 3 3 3 3 2
## [1962] 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2
## [1999] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [2036] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3
## [2110] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2147] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2184] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 2 3 3 3 3
## [2221] 3 3 2 2 3 3 3 3 3 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3
## [2258] 2 2 3 3 3 2 3 3 3 3 3 3 3 3 2 2 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [2295] 3 3 2 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2332] 3 3 2 3 2 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2369] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2406] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 2
## [2443] 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
## [2480] 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3
## [2517] 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2554] 3 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3
## [2591] 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2628] 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
## [2665] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3
## [2702] 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3
## [2739] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2776] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3
## [2813] 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 2 2 3 2
## [2850] 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2887] 3 2 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 2 3 3 2 3 3 3 3
## [2924] 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [2961] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [2998] 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3035] 2 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3072] 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3109] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [3146] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3 3 3 3 2
## [3183] 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3 3 3 3 3
## [3220] 3 2 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3257] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [3294] 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3331] 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3
## [3368] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
## [3405] 3 3 3 3 3 3 2 3 2 3 3 3 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3
## [3442] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3
## [3479] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [3516] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
## [3553] 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3
## [3590] 3 3 3 2 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3627] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3
## [3664] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3701] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3738] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [3775] 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 2 2 3 3 3 3 3 3 2 3 3 3 3
## [3812] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3849] 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3886] 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [3923] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3
## [3960] 3 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3
## [3997] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 2 3 3 3 3 3
## [4034] 3 3 3 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3
## [4071] 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3
## [4108] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3
## [4145] 3 3 2 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3
## [4182] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4219] 3 2 3 3 3 3 3 3 3 2 3 3 2 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4256] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4293] 3 3 3 3 3 1
##
## Within cluster sum of squares by cluster:
## [1] 0 19483409 4914085
## (between_SS / total_SS = 95.2 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"asignacion <- cbind(df_abarrotes1, cluster = segmentos$cluster)
asignacion## Cliente Ticket_Promedio Visitas cluster
## 1 12346 1.0400000 1 3
## 2 12347 68.7442857 7 3
## 3 12349 605.1000000 1 2
## 4 12350 65.3000000 1 3
## 5 12352 169.2637500 8 2
## 6 12353 24.3000000 1 3
## 7 12354 261.2200000 1 2
## 8 12355 54.6500000 1 3
## 9 12356 62.9566667 3 3
## 10 12357 438.6700000 1 2
## 11 12358 78.6050000 2 3
## 12 12360 152.6366667 3 2
## 13 12361 33.3500000 1 3
## 14 12362 104.8290000 10 3
## 15 12363 26.5850000 2 3
## 16 12364 40.5925000 4 3
## 17 12370 64.7000000 1 3
## 18 12371 122.0400000 2 3
## 19 12373 64.1500000 1 3
## 20 12374 139.2500000 1 3
## 21 12377 104.6750000 2 3
## 22 12378 656.4400000 1 2
## 23 12379 57.3250000 2 3
## 24 12380 87.8450000 4 3
## 25 12381 81.4880000 5 3
## 26 12383 61.1220000 5 3
## 27 12384 106.3700000 2 3
## 28 12386 11.9550000 2 3
## 29 12388 46.2950000 6 3
## 30 12390 127.9000000 1 3
## 31 12393 36.4750000 4 3
## 32 12394 63.3200000 1 3
## 33 12395 51.5950000 12 3
## 34 12397 218.5000000 2 2
## 35 12398 169.3800000 1 2
## 36 12399 31.1900000 4 3
## 37 12401 48.4000000 1 3
## 38 12402 36.2000000 1 3
## 39 12407 50.3180000 5 3
## 40 12408 63.4800000 5 3
## 41 12409 129.8733333 3 3
## 42 12410 82.3600000 2 3
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## 4298 Total general 184.4382602 21664 1Generación de las gráficas de clústers.
fviz_cluster(segmentos, data=df_abarrotes1_numerico)
set.seed(123)
optimizacion <- clusGap(df_abarrotes1_numerico, FUN=kmeans, nstart=1, K.max =7)
plot(optimizacion, xlab="Número de clusters k")
Conclusión
En esta actividad, se llevó a cabo el análisis y segmentación de datos de una base de datos de abarrotes. Se aplicaron técnicas de clustering para identificar grupos homogéneos dentro de los datos, lo cual permite a la empresa tomar decisiones más informadas y dirigir sus esfuerzos de marketing de manera más efectiva. La generación de gráficos y el análisis de la cantidad óptima de clústers ayudaron a visualizar los resultados y optimizar la segmentación.