Actividad 3 y 4 + ShinyApp- Modúlo 4

Regresión lineal

Renta de bicis

1. Importar base de datos

#file.choose()
library(openxlsx)
bicis <- read.xlsx("/Users/chucotamez/Downloads/rentadebicis.xlsx")

2. Entender base de datos

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

summary(bicis)

##       hora            dia              mes              año      
##  Min.   : 0.00   Min.   : 1.000   Min.   : 1.000   Min.   :2011  
##  1st Qu.: 6.00   1st Qu.: 5.000   1st Qu.: 4.000   1st Qu.:2011  
##  Median :12.00   Median :10.000   Median : 7.000   Median :2012  
##  Mean   :11.54   Mean   : 9.993   Mean   : 6.521   Mean   :2012  
##  3rd Qu.:18.00   3rd Qu.:15.000   3rd Qu.:10.000   3rd Qu.:2012  
##  Max.   :23.00   Max.   :19.000   Max.   :12.000   Max.   :2012  
##     estacion     dia_de_la_semana     asueto         temperatura   
##  Min.   :1.000   Min.   :1.000    Min.   :0.00000   Min.   : 0.82  
##  1st Qu.:2.000   1st Qu.:2.000    1st Qu.:0.00000   1st Qu.:13.94  
##  Median :3.000   Median :4.000    Median :0.00000   Median :20.50  
##  Mean   :2.507   Mean   :4.014    Mean   :0.02857   Mean   :20.23  
##  3rd Qu.:4.000   3rd Qu.:6.000    3rd Qu.:0.00000   3rd Qu.:26.24  
##  Max.   :4.000   Max.   :7.000    Max.   :1.00000   Max.   :41.00  
##  sensacion_termica    humedad       velocidad_del_viento
##  Min.   : 0.76     Min.   :  0.00   Min.   : 0.000      
##  1st Qu.:16.66     1st Qu.: 47.00   1st Qu.: 7.002      
##  Median :24.24     Median : 62.00   Median :12.998      
##  Mean   :23.66     Mean   : 61.89   Mean   :12.799      
##  3rd Qu.:31.06     3rd Qu.: 77.00   3rd Qu.:16.998      
##  Max.   :45.45     Max.   :100.00   Max.   :56.997      
##  rentas_de_no_registrados rentas_de_registrados rentas_totales 
##  Min.   :  0.00           Min.   :  0.0         Min.   :  1.0  
##  1st Qu.:  4.00           1st Qu.: 36.0         1st Qu.: 42.0  
##  Median : 17.00           Median :118.0         Median :145.0  
##  Mean   : 36.02           Mean   :155.6         Mean   :191.6  
##  3rd Qu.: 49.00           3rd Qu.:222.0         3rd Qu.:284.0  
##  Max.   :367.00           Max.   :886.0         Max.   :977.0

Observaciones 1. Los días únicamente llegan hasta el 19 y no 31.

3.Generar la regresión lineal

regresion <- lm(rentas_totales ~ hora + dia + mes + año + estacion + dia_de_la_semana + sensacion_termica + humedad + velocidad_del_viento, data = bicis)

summary(regresion)

## 
## Call:
## lm(formula = rentas_totales ~ hora + dia + mes + año + estacion + 
##     dia_de_la_semana + sensacion_termica + humedad + velocidad_del_viento, 
##     data = bicis)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -304.52  -93.34  -27.52   61.53  648.27 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -1.662e+05  5.495e+03 -30.246  < 2e-16 ***
## hora                  7.733e+00  2.070e-01  37.356  < 2e-16 ***
## dia                   3.925e-01  2.481e-01   1.582 0.113735    
## mes                   1.009e+01  1.672e+00   6.035 1.64e-09 ***
## año                   8.264e+01  2.732e+00  30.254  < 2e-16 ***
## estacion             -8.030e+00  5.144e+00  -1.561 0.118567    
## dia_de_la_semana      5.270e-01  6.792e-01   0.776 0.437775    
## sensacion_termica     6.183e+00  1.692e-01  36.551  < 2e-16 ***
## humedad              -2.123e+00  7.870e-02 -26.973  < 2e-16 ***
## velocidad_del_viento  6.121e-01  1.773e-01   3.452 0.000559 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.7 on 10876 degrees of freedom
## Multiple R-squared:  0.3889, Adjusted R-squared:  0.3884 
## F-statistic: 769.2 on 9 and 10876 DF,  p-value: < 2.2e-16

4.Ajustar el modelo

regresion <- lm(rentas_totales ~ hora + mes + año + sensacion_termica + humedad + velocidad_del_viento, data = bicis)

summary(regresion)

## 
## Call:
## lm(formula = rentas_totales ~ hora + mes + año + sensacion_termica + 
##     humedad + velocidad_del_viento, data = bicis)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -308.60  -93.85  -28.34   61.05  648.09 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -1.662e+05  5.496e+03 -30.250  < 2e-16 ***
## hora                  7.734e+00  2.070e-01  37.364  < 2e-16 ***
## mes                   7.574e+00  4.207e-01  18.002  < 2e-16 ***
## año                   8.266e+01  2.732e+00  30.258  < 2e-16 ***
## sensacion_termica     6.172e+00  1.689e-01  36.539  < 2e-16 ***
## humedad              -2.121e+00  7.858e-02 -26.988  < 2e-16 ***
## velocidad_del_viento  6.208e-01  1.771e-01   3.506 0.000457 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.7 on 10879 degrees of freedom
## Multiple R-squared:  0.3886, Adjusted R-squared:  0.3883 
## F-statistic:  1153 on 6 and 10879 DF,  p-value: < 2.2e-16

5. Construir modelo predictivo

datos <- data.frame(hora=11.54, mes=1:12, año=2013, sensacion_termica=23.66, humedad=61.89,velocidad_del_viento=12.799)

predict(regresion,datos)

##        1        2        3        4        5        6        7        8 
## 273.6001 281.1738 288.7475 296.3213 303.8950 311.4687 319.0424 326.6161 
##        9       10       11       12 
## 334.1898 341.7635 349.3372 356.9110

Renta de casas

1. Importar base de datos

#file.choose()
library(openxlsx)
bd <- read.xlsx("/Users/chucotamez/Downloads/casadatos.xlsx")

2. Entender base de datos

summary(bd)

##   Observation      Dist_Taxi      Dist_Market    Dist_Hospital  
##  Min.   :  1.0   Min.   :  146   Min.   : 1666   Min.   : 3227  
##  1st Qu.:237.0   1st Qu.: 6477   1st Qu.: 9367   1st Qu.:11302  
##  Median :469.0   Median : 8228   Median :11149   Median :13189  
##  Mean   :468.4   Mean   : 8235   Mean   :11022   Mean   :13091  
##  3rd Qu.:700.0   3rd Qu.: 9939   3rd Qu.:12675   3rd Qu.:14855  
##  Max.   :932.0   Max.   :20662   Max.   :20945   Max.   :23294  
##                                                                 
##      Carpet         Builtup        Parking          City_Category     
##  Min.   :  775   Min.   :  932   Length:905         Length:905        
##  1st Qu.: 1317   1st Qu.: 1579   Class :character   Class :character  
##  Median : 1478   Median : 1774   Mode  :character   Mode  :character  
##  Mean   : 1511   Mean   : 1794                                        
##  3rd Qu.: 1654   3rd Qu.: 1985                                        
##  Max.   :24300   Max.   :12730                                        
##  NA's   :7                                                            
##     Rainfall       House_Price       
##  Min.   :-110.0   Min.   :  1492000  
##  1st Qu.: 600.0   1st Qu.:  4623000  
##  Median : 780.0   Median :  5860000  
##  Mean   : 786.9   Mean   :  6083992  
##  3rd Qu.: 970.0   3rd Qu.:  7200000  
##  Max.   :1560.0   Max.   :150000000  
## 

library(dplyr)
count(bd, Parking, sort=TRUE)

##        Parking   n
## 1         Open 355
## 2 Not Provided 225
## 3      Covered 184
## 4   No Parking 141

count(bd, City_Category, sort=TRUE)

##   City_Category   n
## 1         CAT B 351
## 2         CAT A 320
## 3         CAT C 234

Observaciones: 1. El precio de la casa está con datos atípicos. 2. Rainfall tiene valores negativos. 3. Carpet tiene 7 NA’s.

3. Limpiar base de datos

# ¿Cuántos NA tengo en la base de datos?
sum(is.na(bd))

## [1] 7

# ¿Cuántos NA tengo por variable?
sapply(bd, function(x) sum(is.na(x)))

##   Observation     Dist_Taxi   Dist_Market Dist_Hospital        Carpet 
##             0             0             0             0             7 
##       Builtup       Parking City_Category      Rainfall   House_Price 
##             0             0             0             0             0

# Eliminar NA
bd <- na.omit(bd)

# Eliminar registro del precio atípico
bd <- bd[bd$House_Price<12000000,]

# Eliminar registro de Rainfall negativo
bd <- bd[bd$Rainfall>=0,]

#Gráficas
boxplot(bd$House_Price, horizontal = TRUE)

4. Generar regresión lineal

regresion2 <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital  + Carpet + Builtup + Parking + City_Category + Rainfall, data=bd)

summary(regresion2)

## 
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + 
##     Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3572009  -800792   -65720   761534  4401585 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          5.599e+06  3.672e+05  15.246  < 2e-16 ***
## Dist_Taxi            3.009e+01  2.682e+01   1.122   0.2622    
## Dist_Market          1.285e+01  2.081e+01   0.618   0.5370    
## Dist_Hospital        4.864e+01  3.008e+01   1.617   0.1062    
## Carpet              -7.997e+02  3.476e+03  -0.230   0.8181    
## Builtup              1.339e+03  2.901e+03   0.462   0.6444    
## ParkingNo Parking   -6.040e+05  1.389e+05  -4.348 1.53e-05 ***
## ParkingNot Provided -4.924e+05  1.235e+05  -3.988 7.22e-05 ***
## ParkingOpen         -2.632e+05  1.126e+05  -2.338   0.0196 *  
## City_CategoryCAT B  -1.877e+06  9.598e+04 -19.554  < 2e-16 ***
## City_CategoryCAT C  -2.890e+06  1.059e+05 -27.300  < 2e-16 ***
## Rainfall            -1.175e+02  1.550e+02  -0.758   0.4484    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1222000 on 884 degrees of freedom
## Multiple R-squared:  0.5007, Adjusted R-squared:  0.4945 
## F-statistic: 80.58 on 11 and 884 DF,  p-value: < 2.2e-16

5. Ajustar el modelo

regresion2 <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital  + Carpet + Builtup + Parking + City_Category + Rainfall, data=bd)

summary(regresion2)

## 
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + 
##     Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3572009  -800792   -65720   761534  4401585 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          5.599e+06  3.672e+05  15.246  < 2e-16 ***
## Dist_Taxi            3.009e+01  2.682e+01   1.122   0.2622    
## Dist_Market          1.285e+01  2.081e+01   0.618   0.5370    
## Dist_Hospital        4.864e+01  3.008e+01   1.617   0.1062    
## Carpet              -7.997e+02  3.476e+03  -0.230   0.8181    
## Builtup              1.339e+03  2.901e+03   0.462   0.6444    
## ParkingNo Parking   -6.040e+05  1.389e+05  -4.348 1.53e-05 ***
## ParkingNot Provided -4.924e+05  1.235e+05  -3.988 7.22e-05 ***
## ParkingOpen         -2.632e+05  1.126e+05  -2.338   0.0196 *  
## City_CategoryCAT B  -1.877e+06  9.598e+04 -19.554  < 2e-16 ***
## City_CategoryCAT C  -2.890e+06  1.059e+05 -27.300  < 2e-16 ***
## Rainfall            -1.175e+02  1.550e+02  -0.758   0.4484    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1222000 on 884 degrees of freedom
## Multiple R-squared:  0.5007, Adjusted R-squared:  0.4945 
## F-statistic: 80.58 on 11 and 884 DF,  p-value: < 2.2e-16

6. Construir modelo predictivo

datos <- data.frame(Dist_Taxi=8278, Dist_Market=16251, Dist_Hospital=13857, Carpet=1455, Builtup=1764, Parking="Covered", City_Category="CAT A", Rainfall=390)

predict(regresion2,datos)

##       1 
## 7883860

ShinyApp

library(shiny)
library(shinythemes)
shinyApp(

ui <- fluidPage(theme = shinytheme("cerulean"),
                navbarPage("Aplicaciones",
                           tabPanel("Distribución Normal",
                                   sidebarPanel(
                                     tags$h3("Ingresa los siguientes datos:"),
                                     numericInput("x_funcion","x:", 1),
                                     numericInput("promedio1","promedio:", 0),
                                     numericInput("des_est","Desviación Estandar:", 1)
                                     
                                  ),
                                   mainPanel(
                                     h1("Función de Distribución y Densidad Normal"),
                                     plotOutput("densidad"),
                                     plotOutput("distribucion1")
                                   )
                                    ),
                           tabPanel("Regresión Lineal"),
                                  sidebarPanel(
                                     tags$h3("Ingresa los siguientes datos:"),
                                     numericInput("dist_taxi", "Distancia al Taxi:", 0),
                                     numericInput("dist_market", "Distancia al Mercado:", 0),
                                     numericInput("dist_hospital", "Distancia al Hospital:", 0),
                                     numericInput("carpet", "Área de Alfombra:", 0),
                                     numericInput("builtup", "Área Construida:", 0),
                                     selectInput("parking", "Tipo de Estacionamiento:", choices = c("Open", "Not Provided", "No Parking", "Covered")),
                                    selectInput("city_category", "Categoría de Ciudad:", choices = c("CAT A", "CAT B", "CAT C")),
                                    numericInput("rainfall", "Precipitación:", 0),
                                    actionButton("predict_button", "Predecir Precio de Casa")
                                     
                                     ),
                           mainPanel(
                                  h4("Resultado de la Predicción:"),
                                 verbatimTextOutput("prediction_output")
                           ),
                           tabPanel("Tab 3")
                )
),   
server <- function(input, output) {
  output$densidad <- renderPlot({
    x_densidad1 <- seq(input$promedio1-3*input$des_est, input$promedio1+3*input$des_est, length=1000)
    y_densidad1 <- dnorm(x_densidad1,input$promedio1,input$des_est)
    densidad<- plot(x_densidad1, y_densidad1,type="l", lty=1, xlab="x", ylab= "f(x)",main="Función de Densidad de Probabilidad (Normal)", col="skyblue")
    })
  output$distribucion1 <- renderPlot({
    x_distribucion1 <- seq(input$promedio1-3*input$des_est, input$promedio1+3*input$des_est, length=1000)
    y_distribucion1 <- pnorm(x_distribucion1,input$promedio1,input$des_est)
    distribucion1 <-plot(x_distribucion1, y_distribucion1,type="l", lty=1, xlab="x", ylab= "f(x)",main="Función de Distribución de Probabilidad (Normal)", col="red")
    
  })
  
regresion2 <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)

output$prediction_output <- renderText({
    # Crear un nuevo conjunto de datos con los valores ingresados por el usuario
    datos2 <- data.frame(
      Dist_Taxi = input$dist_taxi,
      Dist_Market = input$dist_market,
      Dist_Hospital = input$dist_hospital,
      Carpet = input$carpet,
      Builtup = input$builtup,
      Parking = input$parking,
      City_Category = input$city_category,
      Rainfall = input$rainfall
    )
    
    # Realizar la predicción usando el modelo de regresión
    precio_predicho <- predict(regresion2, newdata = datos2)
    
    # Formatear y mostrar el resultado de la predicción
    paste("El precio predicho de la casa es:", round(precio_predicho, 2))
  })
})  

## Warning: Navigation containers expect a collection of
## `bslib::nav_panel()`/`shiny::tabPanel()`s and/or
## `bslib::nav_menu()`/`shiny::navbarMenu()`s. Consider using `header` or `footer`
## if you wish to place content above (or below) every panel's contents.

## Warning: Navigation containers expect a collection of
## `bslib::nav_panel()`/`shiny::tabPanel()`s and/or
## `bslib::nav_menu()`/`shiny::navbarMenu()`s. Consider using `header` or `footer`
## if you wish to place content above (or below) every panel's contents.

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