Delivery Perfomance
Andrea Paola Sosa A00827359
2022-10-05
Importar base de datos
#file.choose()
bd<-read.csv ("/Users/andreapaolasosa/Desktop/DELIVERYPERORMANCE 2.csv")
bdclientes<-read.csv ("/Users/andreapaolasosa/Desktop/DeliveryPerformancefinal1.csv")Instalar Librerias
library (tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library (janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary (dplyr)
#install.packages ("ggplot2")
library (ggplot2)
library (Hmisc)## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
##
## Attaching package: 'Hmisc'
##
## The following objects are masked from 'package:dplyr':
##
## src, summarize
##
## The following objects are masked from 'package:base':
##
## format.pval, unitsAnalizar base de datos
summary (bd)## fecha cliente dif
## Length:52 Length:52 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00
## Mean :16.07
## 3rd Qu.:29.38
## Max. :71.25str (bd)## 'data.frame': 52 obs. of 3 variables:
## $ fecha : chr "31/07/21" "31/07/21" "31/07/21" "31/07/21" ...
## $ cliente: chr "PRINTEL " "MAHLE" "MAGNA" "VARROC" ...
## $ dif : num 4.9 15.7 0 0 27.7 ...names(bd)<-c('FECHA', 'CLIENTE', 'DIFERENCIA' )Tipos de variables
Variable<-c("FECHA","CLIENTE","DIFERENCIA")
Type<-c("qualitative (ordinal)", "qualitative(nominal)", "quantitative (continuous)")
table<-data.frame(Variable,Type)
knitr::kable(table)| Variable | Type |
|---|---|
| FECHA | qualitative (ordinal) |
| CLIENTE | qualitative(nominal) |
| DIFERENCIA | quantitative (continuous) |
Limpieza de base de datos
Eliminar NA’s y sustituir con 0’s
sum(is.na(bd))## [1] 0bd[is.na(bd)]<-0
bd1<-bd
bd1<-as.data.frame(bd1)
bd1$FECHA<-as.Date(bd1$FECHA,format="%d/%m/%Y")
bd1$CLIENTE<-as.factor(bd1$CLIENTE)
tabyl(bd1, FECHA, CLIENTE)## FECHA MAGNA MAHLE PRINTEL VARROC
## 0021-07-31 1 1 1 1
## 0021-08-31 1 1 1 1
## 0021-09-30 1 1 1 1
## 0021-10-31 1 1 1 1
## 0021-11-30 1 1 1 1
## 0021-12-31 1 1 1 1
## 0022-01-31 1 1 1 1
## 0022-02-28 1 1 1 1
## 0022-03-31 1 1 1 1
## 0022-04-30 1 1 1 1
## 0022-05-31 1 1 1 1
## 0022-06-30 1 1 1 1
## 0022-07-31 1 1 1 1tabyl(bd1, FECHA, DIFERENCIA)## FECHA 0 1.6 10.92 15.7 18.41 27.7 28.77 31.21 33.24 4.9 41.65 46.27 50.65
## 0021-07-31 2 0 0 1 0 0 0 0 0 1 0 0 0
## 0021-08-31 2 0 0 0 0 1 0 0 0 0 0 0 0
## 0021-09-30 2 0 0 0 0 0 0 0 0 0 0 0 0
## 0021-10-31 3 0 0 0 0 0 0 0 0 0 0 0 0
## 0021-11-30 2 0 1 0 0 0 0 0 0 0 0 0 0
## 0021-12-31 2 0 0 0 1 0 0 0 0 0 0 1 0
## 0022-01-31 2 0 0 0 0 0 1 0 0 0 0 0 0
## 0022-02-28 2 0 0 0 0 0 0 1 0 0 0 0 0
## 0022-03-31 3 0 0 0 0 0 0 0 0 0 0 0 0
## 0022-04-30 3 0 0 0 0 0 0 0 0 0 0 0 1
## 0022-05-31 3 0 0 0 0 0 0 0 0 0 0 0 0
## 0022-06-30 3 0 0 0 0 0 0 0 0 0 1 0 0
## 0022-07-31 2 1 0 0 0 0 0 0 1 0 0 0 0
## 56.82 60.1 62.63 63.68 66.44 67.31 67.98 71.25 8.6
## 0 0 0 0 0 0 0 0 0
## 0 0 0 0 0 1 0 0 0
## 1 0 0 0 0 0 0 0 1
## 0 0 0 0 0 0 1 0 0
## 0 1 0 0 0 0 0 0 0
## 0 0 0 0 0 0 0 0 0
## 0 0 0 0 1 0 0 0 0
## 0 0 0 0 0 0 0 1 0
## 0 0 0 1 0 0 0 0 0
## 0 0 0 0 0 0 0 0 0
## 0 0 1 0 0 0 0 0 0
## 0 0 0 0 0 0 0 0 0
## 0 0 0 0 0 0 0 0 0tibble(bd1)## # A tibble: 52 × 3
## FECHA CLIENTE DIFERENCIA
## <date> <fct> <dbl>
## 1 0021-07-31 "PRINTEL " 4.9
## 2 0021-07-31 "MAHLE" 15.7
## 3 0021-07-31 "MAGNA" 0
## 4 0021-07-31 "VARROC" 0
## 5 0021-08-31 "PRINTEL " 27.7
## 6 0021-08-31 "MAHLE" 67.3
## 7 0021-08-31 "MAGNA" 0
## 8 0021-08-31 "VARROC" 0
## 9 0021-09-30 "PRINTEL " 8.6
## 10 0021-09-30 "MAHLE" 56.8
## # … with 42 more rowsLimpieza bdclientes2
bdclientes2<-bdclientes
bdclientes2<-as.data.frame(bdclientes2)
bdclientes2$FECHA<-as.Date(bdclientes2$FECHA,format="%m/%d/%Y")
bdclientes2$PRINTEL<-as.factor(bdclientes2$PRINTEL)
tabyl(bdclientes2, FECHA, PRINTEL)## FECHA 0 1.6 4.9 8.6 10.92 18.41 27.7 28.77 31.21
## 2021-01-07 0 0 1 0 0 0 0 0 0
## 2021-01-08 0 0 0 0 0 0 1 0 0
## 2021-01-09 0 0 0 1 0 0 0 0 0
## 2021-01-10 1 0 0 0 0 0 0 0 0
## 2021-01-11 0 0 0 0 1 0 0 0 0
## 2021-01-12 0 0 0 0 0 1 0 0 0
## 2022-01-01 0 0 0 0 0 0 0 1 0
## 2022-01-02 0 0 0 0 0 0 0 0 1
## 2022-01-03 1 0 0 0 0 0 0 0 0
## 2022-01-04 1 0 0 0 0 0 0 0 0
## 2022-01-05 1 0 0 0 0 0 0 0 0
## 2022-01-06 1 0 0 0 0 0 0 0 0
## 2022-01-07 0 1 0 0 0 0 0 0 0tabyl(bdclientes2, FECHA, MAHLE)## FECHA 15.7 33.24 41.65 46.27 50.65 56.82 60.1 62.63 63.68 66.44 67.31
## 2021-01-07 1 0 0 0 0 0 0 0 0 0 0
## 2021-01-08 0 0 0 0 0 0 0 0 0 0 1
## 2021-01-09 0 0 0 0 0 1 0 0 0 0 0
## 2021-01-10 0 0 0 0 0 0 0 0 0 0 0
## 2021-01-11 0 0 0 0 0 0 1 0 0 0 0
## 2021-01-12 0 0 0 1 0 0 0 0 0 0 0
## 2022-01-01 0 0 0 0 0 0 0 0 0 1 0
## 2022-01-02 0 0 0 0 0 0 0 0 0 0 0
## 2022-01-03 0 0 0 0 0 0 0 0 1 0 0
## 2022-01-04 0 0 0 0 1 0 0 0 0 0 0
## 2022-01-05 0 0 0 0 0 0 0 1 0 0 0
## 2022-01-06 0 0 1 0 0 0 0 0 0 0 0
## 2022-01-07 0 1 0 0 0 0 0 0 0 0 0
## 67.98 71.25
## 0 0
## 0 0
## 0 0
## 1 0
## 0 0
## 0 0
## 0 0
## 0 1
## 0 0
## 0 0
## 0 0
## 0 0
## 0 0tibble(bdclientes2)## # A tibble: 13 × 5
## FECHA PRINTEL MAHLE MAGNA VARROC
## <date> <fct> <dbl> <int> <int>
## 1 2021-01-07 4.9 15.7 0 0
## 2 2021-01-08 27.7 67.3 0 0
## 3 2021-01-09 8.6 56.8 0 0
## 4 2021-01-10 0 68.0 0 0
## 5 2021-01-11 10.92 60.1 0 0
## 6 2021-01-12 18.41 46.3 0 0
## 7 2022-01-01 28.77 66.4 0 0
## 8 2022-01-02 31.21 71.2 0 0
## 9 2022-01-03 0 63.7 0 0
## 10 2022-01-04 0 50.6 0 0
## 11 2022-01-05 0 62.6 0 0
## 12 2022-01-06 0 41.6 0 0
## 13 2022-01-07 1.6 33.2 0 0Analisis Profundo de la Base de datos
media_bd <- mean(bd$DIFERENCIA)
media_bd## [1] 16.07365median_bd <- median(bd$DIFERENCIA)
median_bd## [1] 0mode_bd <- mode(bd$DIFERENCIA)
mode_bd## [1] "numeric"hist(bd1$DIFERENCIA) 
Analisis Profundo de la Base de datos BDCLIENTES
media_bdclientes <- mean(bdclientes$PRINTEL)
media_bdclientes## [1] 10.16231median_bdclientes <- median(bdclientes$PRINTEL)
median_bdclientes## [1] 4.9mode_bdclientes <- mode(bdclientes$PRINTEL)
mode_bdclientes## [1] "numeric"media_bdclientes <- mean(bdclientes$MAHLE)
media_bdclientes## [1] 54.13231median_bdclientes <- median(bdclientes$MAHLE)
median_bdclientes## [1] 60.1mode_bdclientes <- mode(bdclientes$MAHLE)
mode_bdclientes## [1] "numeric"bdclientes3 <-bdclientes
bdclientes3 <- subset (bdclientes3, select = -c (MAGNA,VARROC))
hist(bdclientes3$PRINTEL)
hist(bdclientes3$MAHLE)
Graficas
Clientes con los Retrasos mas Altos
ggplot(bd,aes(x=FECHA, y=DIFERENCIA,fill=CLIENTE))+
geom_bar(stat="identity")+
geom_hline(yintercept=33,linetype="dashed",color="black")+
labs(x="Fecha",y="Retraso en Minutos", color="Legend")+
ggtitle("Retraso en Desempeño de los Clientes")
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