“Limpieza Base de Datos Abarrotes”
Karla Georgina Martínez González A00827500
2022-09-06
A la base de datos se le hicieron los siguientes cambios:
1. Se cambió el formato a Formato a fecha corta.
2. Se duplicaron los primeros 5 registros.
3. Se cambió el formato a Hora (Español México).
4. Se cambió el formato a código de Barras (para que salga completo).
5. Se guardó como CSV UTF-8 (Delimitado por comas).
Importar la base de datos
bd <- read.csv("/Users/georginamartinez/Documents/Tec/Séptimo Semestre/Analítica para negocios, de los datos a decisiones/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, union# count(bd, vcClaveTienda, sort = TRUE)
# count(bd, DescGiro, sort = TRUE)
# count(bd, Marca, sort = TRUE)
# count(bd, Fabricante, sort = TRUE)
# count(bd, Producto, sort = TRUE)
# count(bd, NombreFamilia, sort = TRUE)
# count(bd, NombreCategoria, sort = TRUE)
# count(bd, Estado, sort = TRUE)
# count(bd, Mts.2, sort = TRUE)
# count(bd, Tipo.ubicación, sort = TRUE)
# count(bd, Giro, sort = TRUE)
# count(bd, Hora.inicio, sort = TRUE)
# count(bd, Hora.cierre, sort = TRUE)
# install.packages("tidyverse")
library(tidyverse)## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ tidyr 1.2.0 ✔ forcats 0.5.2
## ✔ readr 2.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()# tibble(bd)
# str(bd)
# head(bd)
# head(bd, n=7)
# tail(bd)
# install.packages("janitor")
library(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testtabyl(bd, vcClaveTienda, NombreDepartamento)## vcClaveTienda Abarrotes Bebes e Infantiles Carnes Farmacia Ferretería Mercería
## MX001 95415 515 1 147 245 28
## MX002 6590 21 0 4 10 0
## MX003 4026 15 0 2 8 0
## MX004 82234 932 0 102 114 16
## MX005 10014 0 0 0 0 0
## Papelería Productos a Eliminar Vinos y Licores
## 35 3 80
## 0 0 4
## 0 0 0
## 32 5 20
## 7 0 0Observaciones
1. Casi ningún registro cuenta con PLU.
2. Cambiar formato de fecha.
3. Cambiar formato de hora.
4. Hay precios negativos.
5. Hay unidades menores a 1.
Técnicas para limpieza de datos
Técnica 1. Remover valores irrelevantes
# Eliminar columnas
# Primero crear una segunda copia para no eliminar la original
bd1 <- bd
bd1 <- subset (bd1, select = -c (PLU, Codigo.Barras))
# Eliminar renglones
bd2 <- bd1
bd2 <- bd2[bd$Precio > 0, ]
summary (bd1)## vcClaveTienda DescGiro Fecha Hora
## Length:200625 Length:200625 Length:200625 Length:200625
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200625 Length:200625 Length:200625 Min. :-147.00
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.42
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200625
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33964 Class :character
## Median : 12.31 Median : 1.000 Median :105993 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193990
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383005
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200625 Length:200625 Length:200625 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200625 Length:200625 Length:200625 Length:200625
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## summary (bd2)## vcClaveTienda DescGiro Fecha Hora
## Length:200478 Length:200478 Length:200478 Length:200478
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200478 Length:200478 Length:200478 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200478
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33977 Class :character
## Median : 12.31 Median : 1.000 Median :106034 Mode :character
## Mean : 15.31 Mean : 1.261 Mean :194096
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383062
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200478 Length:200478 Length:200478 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200478 Length:200478 Length:200478 Length:200478
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## # Esto no lo usaremos, pondremos precios negativos como absoluto Técnica 2. Remover valores duplicados
# ¿Cuántos renglones duplicados tenemos?
bd1[duplicated(bd1),]## vcClaveTienda DescGiro Fecha Hora Marca
## 6 MX001 Abarrotes 19/06/2020 08:16:21 a. m. NUTRI LECHE
## 7 MX001 Abarrotes 19/06/2020 08:23:33 a. m. DAN UP
## 8 MX001 Abarrotes 19/06/2020 08:24:33 a. m. BIMBO
## 9 MX001 Abarrotes 19/06/2020 08:24:33 a. m. PEPSI
## 10 MX001 Abarrotes 19/06/2020 08:26:28 a. m. BLANCA NIEVES (DETERGENTE)
## Fabricante Producto Precio
## 6 MEXILAC Nutri Leche 1 Litro 16.0
## 7 DANONE DE MEXICO DANUP STRAWBERRY P/BEBER 350GR NAL 14.0
## 8 GRUPO BIMBO Rebanadas Bimbo 2Pz 5.0
## 9 PEPSI-COLA MEXICANA Pepsi N.R. 400Ml 8.0
## 10 FABRICA DE JABON LA CORONA Detergente Blanca Nieves 500G 19.5
## Ult.Costo Unidades F.Ticket NombreDepartamento NombreFamilia
## 6 12.31 1 1 Abarrotes Lacteos y Refrigerados
## 7 14.00 1 2 Abarrotes Lacteos y Refrigerados
## 8 5.00 1 3 Abarrotes Pan y Tortilla
## 9 8.00 1 3 Abarrotes Bebidas
## 10 15.00 1 4 Abarrotes Limpieza del Hogar
## NombreCategoria Estado Mts.2 Tipo.ubicación Giro
## 6 Leche Nuevo León 60 Esquina Abarrotes
## 7 Yogurt Nuevo León 60 Esquina Abarrotes
## 8 Pan Dulce Empaquetado Nuevo León 60 Esquina Abarrotes
## 9 Refrescos Plástico (N.R.) Nuevo León 60 Esquina Abarrotes
## 10 Lavandería Nuevo León 60 Esquina Abarrotes
## Hora.inicio Hora.cierre
## 6 8:00 22:00
## 7 8:00 22:00
## 8 8:00 22:00
## 9 8:00 22:00
## 10 8:00 22:00sum(duplicated(bd1))## [1] 5# Eliminar renglones duplicados
bd3 <- bd1
library(dplyr)
bd3 <- distinct(bd3)Técnica 3. Errores tipográficos y errores similares
# Precios en absoluto
bd4 <- bd3
bd4$Precio <- abs(bd4$Precio)
summary(bd4) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 0.200 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## # Cantidades en enteros
bd5 <- bd4
bd5$Unidades <- ceiling(bd5$Unidades)
summary(bd5) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
## Técnica 4. Convertir tipos de datos
# Convertir de caracter a fecha
bd6 <- bd5
bd6$Fecha <- as.Date(bd6$Fecha, format ="%d/%m/%y")
tibble(bd6) ## # A tibble: 200,620 × 20
## vcCla…¹ DescG…² Fecha Hora Marca Fabri…³ Produ…⁴ Precio Ult.C…⁵ Unida…⁶
## <chr> <chr> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MX001 Abarro… 2020-06-19 08:1… NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… 2020-06-19 08:2… DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… 2020-06-19 08:2… BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… 2020-06-19 08:2… PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… 2020-06-19 08:2… BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… 2020-06-19 08:2… FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… 2020-06-19 08:2… VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… 2020-06-19 08:2… ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… 2020-06-19 08:2… ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… 2020-06-19 03:2… JUMEX JUMEX Jugo D… 12 12 1
## # … with 200,610 more rows, 10 more variables: F.Ticket <int>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>, Giro <chr>,
## # Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable names
## # ¹vcClaveTienda, ²DescGiro, ³Fabricante, ⁴Producto, ⁵Ult.Costo, ⁶Unidades# Convertir de caracter a entero
bd7 <- bd6
bd7$Hora <- substr(bd7$Hora, start = 1, stop = 2)
tibble(bd7)## # A tibble: 200,620 × 20
## vcCla…¹ DescG…² Fecha Hora Marca Fabri…³ Produ…⁴ Precio Ult.C…⁵ Unida…⁶
## <chr> <chr> <date> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 MX001 Abarro… 2020-06-19 08 NUTR… MEXILAC Nutri … 16 12.3 1
## 2 MX001 Abarro… 2020-06-19 08 DAN … DANONE… DANUP … 14 14 1
## 3 MX001 Abarro… 2020-06-19 08 BIMBO GRUPO … Rebana… 5 5 1
## 4 MX001 Abarro… 2020-06-19 08 PEPSI PEPSI-… Pepsi … 8 8 1
## 5 MX001 Abarro… 2020-06-19 08 BLAN… FABRIC… Deterg… 19.5 15 1
## 6 MX001 Abarro… 2020-06-19 08 FLASH ALEN Flash … 9.5 7.31 1
## 7 MX001 Abarro… 2020-06-19 08 VARI… DANONE… Danone… 11 11 1
## 8 MX001 Abarro… 2020-06-19 08 ZOTE FABRIC… Jabon … 9.5 7.31 1
## 9 MX001 Abarro… 2020-06-19 08 ALWA… PROCTE… T Feme… 23.5 18.1 1
## 10 MX001 Abarro… 2020-06-19 03 JUMEX JUMEX Jugo D… 12 12 1
## # … with 200,610 more rows, 10 more variables: F.Ticket <int>,
## # NombreDepartamento <chr>, NombreFamilia <chr>, NombreCategoria <chr>,
## # Estado <chr>, Mts.2 <int>, Tipo.ubicación <chr>, Giro <chr>,
## # Hora.inicio <chr>, Hora.cierre <chr>, and abbreviated variable names
## # ¹vcClaveTienda, ²DescGiro, ³Fabricante, ⁴Producto, ⁵Ult.Costo, ⁶Unidadesbd7$Hora <- as.integer(bd7$Hora)
str(bd7) ## 'data.frame': 200620 obs. of 20 variables:
## $ vcClaveTienda : chr "MX001" "MX001" "MX001" "MX001" ...
## $ DescGiro : chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Fecha : Date, format: "2020-06-19" "2020-06-19" ...
## $ Hora : int 8 8 8 8 8 8 8 8 8 3 ...
## $ Marca : chr "NUTRI LECHE" "DAN UP" "BIMBO" "PEPSI" ...
## $ Fabricante : chr "MEXILAC" "DANONE DE MEXICO" "GRUPO BIMBO" "PEPSI-COLA MEXICANA" ...
## $ Producto : chr "Nutri Leche 1 Litro" "DANUP STRAWBERRY P/BEBER 350GR NAL" "Rebanadas Bimbo 2Pz" "Pepsi N.R. 400Ml" ...
## $ Precio : num 16 14 5 8 19.5 9.5 11 9.5 23.5 12 ...
## $ Ult.Costo : num 12.3 14 5 8 15 ...
## $ Unidades : num 1 1 1 1 1 1 1 1 1 1 ...
## $ F.Ticket : int 1 2 3 3 4 4 4 4 4 5 ...
## $ NombreDepartamento: chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ NombreFamilia : chr "Lacteos y Refrigerados" "Lacteos y Refrigerados" "Pan y Tortilla" "Bebidas" ...
## $ NombreCategoria : chr "Leche" "Yogurt" "Pan Dulce Empaquetado" "Refrescos Plástico (N.R.)" ...
## $ Estado : chr "Nuevo León" "Nuevo León" "Nuevo León" "Nuevo León" ...
## $ Mts.2 : int 60 60 60 60 60 60 60 60 60 60 ...
## $ Tipo.ubicación : chr "Esquina" "Esquina" "Esquina" "Esquina" ...
## $ Giro : chr "Abarrotes" "Abarrotes" "Abarrotes" "Abarrotes" ...
## $ Hora.inicio : chr "8:00" "8:00" "8:00" "8:00" ...
## $ Hora.cierre : chr "22:00" "22:00" "22:00" "22:00" ...Técnica 5. Valores faltantes
# ¿Cuántos NA tengo en la base de datos?
sum(is.na(bd7))## [1] 0sum(is.na(bd))## [1] 199188# ¿Cuántos NA tengo por variable?
sapply(bd7, function(x) sum(is.na(x)))## vcClaveTienda DescGiro Fecha Hora
## 0 0 0 0
## Marca Fabricante Producto Precio
## 0 0 0 0
## Ult.Costo Unidades F.Ticket NombreDepartamento
## 0 0 0 0
## NombreFamilia NombreCategoria Estado Mts.2
## 0 0 0 0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## 0 0 0 0sapply(bd, function(x) sum(is.na(x)))## vcClaveTienda DescGiro Codigo.Barras PLU
## 0 0 0 199188
## Fecha Hora Marca Fabricante
## 0 0 0 0
## Producto Precio Ult.Costo Unidades
## 0 0 0 0
## F.Ticket NombreDepartamento NombreFamilia NombreCategoria
## 0 0 0 0
## Estado Mts.2 Tipo.ubicación Giro
## 0 0 0 0
## Hora.inicio Hora.cierre
## 0 0# 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 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
##
##
## # Remplazar 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
##
##
## # Remplazar 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
##
##
##
## Técnica 6. Método estadístico
bd12 <-bd7
boxplot(bd12$Precio, horizontal = TRUE)
boxplot(bd12$Unidades, horizontal = TRUE)
# Agregar columnas
# install.packages("lubridate")
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionbd12$Dia_de_la_semana <- wday (bd12$Fecha)
summary(bd12)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :2020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:2020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :2020-07-11 Median : 8.000
## Mean :2020-07-18 Mean : 7.299
## 3rd Qu.:2020-08-29 3rd Qu.:10.000
## Max. :2020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana
## Min. :1.000
## 1st Qu.:2.000
## Median :4.000
## Mean :3.912
## 3rd Qu.:6.000
## Max. :7.000bd12$Subtotal <- bd12$Precio * bd12$Unidades
summary(bd12) ## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :2020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:2020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :2020-07-11 Median : 8.000
## Mean :2020-07-18 Mean : 7.299
## 3rd Qu.:2020-08-29 3rd Qu.:10.000
## Max. :2020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana Subtotal
## Min. :1.000 Min. : 1.00
## 1st Qu.:2.000 1st Qu.: 12.00
## Median :4.000 Median : 18.00
## Mean :3.912 Mean : 24.33
## 3rd Qu.:6.000 3rd Qu.: 27.00
## Max. :7.000 Max. :2496.00bd12$Utilidad <- bd12$Precio - bd12$Ult.Costo
summary(bd12)## vcClaveTienda DescGiro Fecha Hora
## Length:200620 Length:200620 Min. :2020-05-01 Min. : 1.000
## Class :character Class :character 1st Qu.:2020-06-06 1st Qu.: 5.000
## Mode :character Mode :character Median :2020-07-11 Median : 8.000
## Mean :2020-07-18 Mean : 7.299
## 3rd Qu.:2020-08-29 3rd Qu.:10.000
## Max. :2020-11-11 Max. :12.000
## Marca Fabricante Producto Precio
## Length:200620 Length:200620 Length:200620 Min. : 0.50
## Class :character Class :character Class :character 1st Qu.: 11.00
## Mode :character Mode :character Mode :character Median : 16.00
## Mean : 19.45
## 3rd Qu.: 25.00
## Max. :1000.00
## Ult.Costo Unidades F.Ticket NombreDepartamento
## Min. : 0.38 Min. : 1.000 Min. : 1 Length:200620
## 1st Qu.: 8.46 1st Qu.: 1.000 1st Qu.: 33967 Class :character
## Median : 12.31 Median : 1.000 Median :105996 Mode :character
## Mean : 15.31 Mean : 1.262 Mean :193994
## 3rd Qu.: 19.23 3rd Qu.: 1.000 3rd Qu.:383008
## Max. :769.23 Max. :96.000 Max. :450040
## NombreFamilia NombreCategoria Estado Mts.2
## Length:200620 Length:200620 Length:200620 Min. :47.0
## Class :character Class :character Class :character 1st Qu.:53.0
## Mode :character Mode :character Mode :character Median :60.0
## Mean :56.6
## 3rd Qu.:60.0
## Max. :62.0
## Tipo.ubicación Giro Hora.inicio Hora.cierre
## Length:200620 Length:200620 Length:200620 Length:200620
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Dia_de_la_semana Subtotal Utilidad
## Min. :1.000 Min. : 1.00 Min. : 0.000
## 1st Qu.:2.000 1st Qu.: 12.00 1st Qu.: 2.310
## Median :4.000 Median : 18.00 Median : 3.230
## Mean :3.912 Mean : 24.33 Mean : 4.142
## 3rd Qu.:6.000 3rd Qu.: 27.00 3rd Qu.: 5.420
## Max. :7.000 Max. :2496.00 Max. :230.770Exportar base de datos limpia
bd_limpia <- bd12
write.csv(bd_limpia, file="abarrotes_bd_limpia.csv", row.names = FALSE)Conclusiones
Dentro de las bases de datos es muy común que se encuentren “sucias”, “incompletas” y es por eso que acudimos a limpieza de datos para que al momento de realizar análisis no entorpezca los resultados o que no tengan validez.
Existen distintas técnicas que nos ayudan a tener nuestra base de datos limpia y en este caso utilizamos:
Técnica 1. Remover valores irrelevantes
Técnica 2. Remover valores duplicados
Técnica 3. Errores tipográficos y errores similares
Técnica 4. Convertir tipos de datos
Técnica 5. Valores faltantes
Técnica 6. Método estadístico
En este caso nos dimos cuenta que necesitabamos eliminar columnas que no tuvieran relevancia, convertir los precios negativos a absolutos, eliminar valores faltantes, etc., para terminar creando una nueva base de datos limpia y descargarla ya modificada.
En conclusión, R es un gran programa que nos facilito este proceso para no hacerlo de manera manual si no con funciones que te permiten hacerlo de mejor manera y más rápida.
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