Predicting Air Quality Shiny App

This app will allow users to predict ozone levels (the measure used to determine air quality) based on solar radiation levels, wind speed, and temperature values of their choice.

Purpose

The purpose of this application is to allow users to see the difference in predicted air quality when modeling on only one of the aforementioned variables versus accounting for all three.

Data

The data used in this application come from the dataset that comes preloaded in R. For more information on the dataset, see the link below.

https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/airquality

The code shown below contains the linear modeling functions used, assuming that the user chose solar radiation, as well as the reactive functions that predict ozone based on solar radiation alone and on all three variables together.

solarModel <- lm(Ozone ~ Solar.R, data = airquality)
totalModel <- lm(Ozone ~ Solar.R + Wind + Temp, data = airquality)

solarPred <- reactive({
    solarInput <- input$sliderSolar
    predict(solarModel, newdata = data.frame(Solar.R = solarInput))
})
totalPred <- reactive({
    solarInput <- input$sliderSolar
    windInput <- input$sliderWind
    tempInput <- input$sliderTemp
    predict(totalModel, newdata =
                data.frame(Solar.R = solarInput,Wind = windInput, Temp = tempInput))
})