A app to regress and visualise, using the mtcars dataset

regression_app.R

library(dotwhisker)
library(tidyverse)


shinyApp(

ui <- fluidPage(

  sidebarLayout(

    sidebarPanel(
         
         sliderInput(inputId = "carb", label = "carb", step = 1, ticks = FALSE,
                     value = c(min(mtcars$carb, na.rm = TRUE), 
                               max(mtcars$carb, na.rm = TRUE)), 
                     min = min(mtcars$carb, na.rm = TRUE), 
                     max = max(mtcars$carb, na.rm = TRUE)),
         
         sliderInput(inputId = "cyl", label = "cyl", step = 1, ticks = FALSE,
                     value = c(min(mtcars$cyl, na.rm = TRUE), 
                               max(mtcars$cyl, na.rm = TRUE)), 
                     min = min(mtcars$cyl, na.rm = TRUE), 
                     max = max(mtcars$cyl, na.rm = TRUE)),
         
         sliderInput(inputId = "disp", label = "disp", step = 1, ticks = FALSE,
                     value = c(min(mtcars$disp, na.rm = TRUE), 
                               max(mtcars$disp, na.rm = TRUE)), 
                     min = min(mtcars$disp, na.rm = TRUE), 
                     max = max(mtcars$disp, na.rm = TRUE)),
         
         sliderInput(inputId = "drat", label = "drat", step = 1, ticks = FALSE,
                     value = c(min(mtcars$drat, na.rm = TRUE), 
                               max(mtcars$drat, na.rm = TRUE)), 
                     min = min(mtcars$drat, na.rm = TRUE), 
                     max = max(mtcars$drat, na.rm = TRUE)),
         
         sliderInput(inputId = "gear", label = "gear", step = 1, ticks = FALSE,
                     value = c(min(mtcars$gear, na.rm = TRUE), 
                               max(mtcars$gear, na.rm = TRUE)), 
                     min = min(mtcars$gear, na.rm = TRUE), 
                     max = max(mtcars$gear, na.rm = TRUE)),
         
         
         sliderInput(inputId = "hp", label = "hp", step = 1, ticks = FALSE,
                     value = c(min(mtcars$hp, na.rm = TRUE), 
                               max(mtcars$hp, na.rm = TRUE)), 
                     min = min(mtcars$hp, na.rm = TRUE), 
                     max = max(mtcars$hp, na.rm = TRUE)),
         
         sliderInput(inputId = "qsec", label = "qsec", step = 1, ticks = FALSE,
                     value = c(min(mtcars$qsec, na.rm = TRUE), 
                               max(mtcars$qsec, na.rm = TRUE)), 
                     min = min(mtcars$qsec, na.rm = TRUE), 
                     max = max(mtcars$qsec, na.rm = TRUE)),
         
         sliderInput(inputId = "wt", label = "wt", step = 1, ticks = FALSE,
                     value = c(min(mtcars$wt, na.rm = TRUE), 
                               max(mtcars$wt, na.rm = TRUE)), 
                     min = min(mtcars$wt, na.rm = TRUE), 
                     max = max(mtcars$wt, na.rm = TRUE))
      
    ),

    mainPanel(plotOutput("regressionPlot"))
  )
),

  
  server = function(input, output) {
    
      
      output$regressionPlot = renderPlot({
      
      # filter the dataset with input from sliders
      df <- mtcars %>%
        filter(carb >= input$carb[1] & carb <= input$carb[2],
               cyl >= input$cyl[1] & cyl <= input$cyl[2],
               disp >= input$disp[1] & disp <= input$disp[2],
               drat >= input$drat[1] & drat <= input$drat[2],
               gear >= input$gear[1] & gear <= input$gear[2],
               hp >= input$hp[1] & hp <= input$hp[2],
               qsec >= input$qsec[1] & qsec <= input$qsec[2],
               wt >= input$wt[1] & wt <= input$wt[2]
               )
      
      # run the regression model
      model <- lm(mpg ~ carb + cyl + disp + drat + gear + hp + qsec + wt, 
                  data = df)
      
      # plot the results
      dwplot(model, dot_args = list(size = 3.2), whisker_args = list(size = 1.5)) +
        theme_bw() +
        theme(legend.position = "none",
              axis.text=element_text(size = 14),
              axis.title=element_text(size = 14)) +
        geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
        ggtitle("Predicting Gas Mileage") +
        xlab("\nCoefficient")
    
  })
    
  }

)