FORM Performance 2.3
Karen Neri - A01657974
2022-09-23
Importar base de datos
#Se limpio la base previamente
#file.choose()
performance <- read.csv("/Users/Karen/Documents/FORM.Performance.limpia")Importar librerias
#install.packages("foreign")
library(foreign)
#install.packages("dplyr")
library(dplyr) # data manipulation ##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union#install.packages("forcats")
library(forcats) # to work with categorical variables
#install.packages ("ggplot2")
library(ggplot2) # data visualization
#install.packages ("janitor")
library(janitor) # data exploration and cleaning ##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
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## chisq.test, fisher.test#install.packages("Hmisc")
library(Hmisc) # several useful functions for data analysis ## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, units#install.packages ("psych")
library(psych) # functions for multivariate analysis ##
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## describe## The following objects are masked from 'package:ggplot2':
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## %+%, alpha#install.packages("naniar")
library(naniar) # summaries and visualization of missing values NAs
#install.packages("dlookr")
library(dlookr) # summaries and visualization of missing values NAs##
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## transform#install.packages ("kableExtra")
library(kableExtra)##
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## group_rows#install.packages ("readr")
library(readr)
#install.packages ("corrplot")
library(corrplot)## corrplot 0.92 loaded#install.packages ("jtools")
library(jtools) ##
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## %nin%#install.packages ("lmtest")
library(lmtest) ## Loading required package: zoo##
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## as.Date, as.Date.numeric#install.packages ("car")
library(car) ## Loading required package: carData##
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## logit## The following object is masked from 'package:dplyr':
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## recode#install.packages ("olsrr")
library(olsrr) ##
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## rivers#install.packages ("gmodels")
library(gmodels)Analisis estadistico descriptivo
describe (performance)## # A tibble: 6 × 26
## described_…¹ n na mean sd se_mean IQR skewn…² kurto…³ p00 p01
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Target 1440 0 1 0 0 0 NaN NaN 1 1
## 2 Vueltas 1440 0 1.75 0.829 0.0219 1.25 0.494 -1.37 1 1
## 3 Plan.arrival 1440 0 6.62 7.50 0.198 10.8 0.614 -1.12 0 0
## 4 Real.arrival 1440 0 3.82 6.51 0.171 8 1.51 0.965 0 0
## 5 Real.depart… 1440 0 4.14 6.95 0.183 9 1.44 0.706 0 0
## 6 Diference 1440 0 0.316 0.922 0.0243 0.8 2.73 211. -14.4 0
## # … with 15 more variables: p05 <dbl>, p10 <dbl>, p20 <dbl>, p25 <dbl>,
## # p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, p70 <dbl>, p75 <dbl>,
## # p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, p100 <dbl>, and abbreviated
## # variable names ¹​described_variables, ²​skewness, ³​kurtosisTabla de frecuencia
Se realizó una tabla de frecuencia de la variable plan arrival con el objetivo de validar las frecuencias en el estimado de llegada para cada uno de los clientes.
proportion <- prop.table(table(performance$Cliente,performance$Plan.arrival))
proportion %>%
kbl() %>%
kable_styling()| 0 | 8 | 9 | 16 | 20 | |
|---|---|---|---|---|---|
| MAGNA | 0.125 | 0.000 | 0.000 | 0.000 | 0.000 |
| MAHLE | 0.000 | 0.125 | 0.125 | 0.000 | 0.125 |
| PRINTEL | 0.000 | 0.000 | 0.000 | 0.125 | 0.000 |
| VARROC | 0.375 | 0.000 | 0.000 | 0.000 | 0.000 |
Tabla cruzada
Posterior a la tabla de frecuencia, se realizo una tabla cruzada donde se visualizo como es la proporcion respecto a la variable de las estimaciones de llegada de pedidos por cada cliente.
proportion %>%
kbl() %>%
kable_material (c("striped","hover"))| 0 | 8 | 9 | 16 | 20 | |
|---|---|---|---|---|---|
| MAGNA | 0.125 | 0.000 | 0.000 | 0.000 | 0.000 |
| MAHLE | 0.000 | 0.125 | 0.125 | 0.000 | 0.125 |
| PRINTEL | 0.000 | 0.000 | 0.000 | 0.125 | 0.000 |
| VARROC | 0.375 | 0.000 | 0.000 | 0.000 | 0.000 |
location <- CrossTable(performance$Cliente, performance$Plan.arrival, prop.t=TRUE, prop.r=TRUE, prop.c=TRUE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 1440
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##
## | performance$Plan.arrival
## performance$Cliente | 0 | 8 | 9 | 16 | 20 | Row Total |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
## MAGNA | 180 | 0 | 0 | 0 | 0 | 180 |
## | 90.000 | 22.500 | 22.500 | 22.500 | 22.500 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.125 |
## | 0.250 | 0.000 | 0.000 | 0.000 | 0.000 | |
## | 0.125 | 0.000 | 0.000 | 0.000 | 0.000 | |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
## MAHLE | 0 | 180 | 180 | 0 | 180 | 540 |
## | 270.000 | 187.500 | 187.500 | 67.500 | 187.500 | |
## | 0.000 | 0.333 | 0.333 | 0.000 | 0.333 | 0.375 |
## | 0.000 | 1.000 | 1.000 | 0.000 | 1.000 | |
## | 0.000 | 0.125 | 0.125 | 0.000 | 0.125 | |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
## PRINTEL | 0 | 0 | 0 | 180 | 0 | 180 |
## | 90.000 | 22.500 | 22.500 | 1102.500 | 22.500 | |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.125 |
## | 0.000 | 0.000 | 0.000 | 1.000 | 0.000 | |
## | 0.000 | 0.000 | 0.000 | 0.125 | 0.000 | |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
## VARROC | 540 | 0 | 0 | 0 | 0 | 540 |
## | 270.000 | 67.500 | 67.500 | 67.500 | 67.500 | |
## | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.375 |
## | 0.750 | 0.000 | 0.000 | 0.000 | 0.000 | |
## | 0.375 | 0.000 | 0.000 | 0.000 | 0.000 | |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
## Column Total | 720 | 180 | 180 | 180 | 180 | 1440 |
## | 0.500 | 0.125 | 0.125 | 0.125 | 0.125 | |
## --------------------|-----------|-----------|-----------|-----------|-----------|-----------|
##
## kbl(location) %>%
kable_classic()
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Graficos de datos cualitativos y cuantitativos
#Boxplot
#En dicha grafica de boxplot se presenta como se encuentran posicionados los datos de la base, tanto el estimado (Plan arrival), como la llegado real (real arrival) y la salida real (real departure) de los pedidos realizados por todos los clientes
Delivery <- data.frame(performance$Cliente, performance$Plan.arrival, performance$Real.arrival, performance$Real.departure, performance$Date)
colnames(Delivery)<-c('Cliente', 'Plan.arrival', 'Real.arrival', 'Real.departure', 'Date')
Realarrival_boxplot = subset(Delivery, select =-c (Cliente, Date))
boxplot(Realarrival_boxplot,data=data,main="Deliverys",
col=rainbow(ncol(trees)))
#Bar plots 1
#En la siguiente grafica se presenta la llegada real de los pedidos a lo largo del primer semestre del 2022
Realarrival <- data.frame(performance$Date,performance$Real.arrival)
colnames(Realarrival)<-c('Date','Real.arrival')
ggplot(data = Realarrival, aes (x=Date, y=Real.arrival)) +
geom_bar(stat = "identity", fill="#FFAEB9") + scale_fill_grey() + # Add bars to the plot
labs(title = "Real arrival", # Add a title
x = "2022")
#Bar plots 2
#A modo de comparacion, en la siguiente grafica se presenta la salida real de los pedidos a lo largo del primer semestre del 2022
Realdeparture <- data.frame(performance$Date,performance$Real.departure)
colnames(Realdeparture)<-c('Date','Real.departure')
ggplot(data = Realdeparture, aes (x=Date, y=Real.departure)) +
geom_bar(stat = "identity", fill="#7AC5CD") + scale_fill_grey() + # Add bars to the plot
labs(title = "Real departure", # Add a title
x = "2022")
Graficos de dispersión
Validando las diversas variables en la base de datos y observando su distribucion, se decidio utilizar la variable de diferencia (entre la llegada real y la salida real) para observar su comportamiento y ver la comparación.
#Histograma
hist(performance$Diference,main="Histograma",xlab="Delivery diference",col='#FF7F24')
#Diagrama de dispersion
qqnorm(performance$Diference, main="Grafica de dispersion", ylab="Delivery diference",col='#FF7F24')
qqline(performance$Diference, col='#FF7F24')
#Graficos de normalidad
plot_normality(performance,Diference, col='#FF7F24')
Insights
-MAHLE es el unico cliente con la mayor frecuencia de 20 como estimado de llegada para los pedidos
-El estimado real de los pedidos tiene una mediana mucho mayor que la llegada real.
-La salida real vs la llegada real tiene una diferencia observable en los graficos.
-Entre Febrero-Marzo 2022 fue el pico con mayores pedidos para los clientes.
-La diferencia entre la llegada y salida de pedidos sigue una distribucion normal