We will consider the data from the article Getting what you pay for: the debate over equity in public school expenditures. We can load the data as follows:
load("labs/sat.Rdata")We should always begin with a data exploration. In order to make this proceed faster in lab, below is an embedded graph that will allow you to explore the relationships of all the variables in the lab.
To get the shiny below to run, replace the header at the top with this:
---
title: "Lab 3 - Linear Regression Final Model"
author: "Your Name"
output:
html_document
runtime: shiny
---then run the document and your code below will allow you to explore the data more.
library(shiny)
library(ggplot2)
data <- sat
shinyApp(
# Define UI for iris application
shinyUI(pageWithSidebar(
# Application title
headerPanel(""),
# Sidebar with controls to select the variable to plot against mpg
# and to specify whether outliers should be included
sidebarPanel(
selectInput("variable", "First variable:",
list(
"expend", "ratio", "salary", "frac", "verbal", "math", "sat"
)),
selectInput("variable2", "Second variable:",
list(
"expend", "ratio", "salary", "frac", "verbal", "math", "sat"
))
),
mainPanel(
h3(textOutput("caption")),
plotOutput("plot")
)
)),
# Define server logic required to plot variables
shinyServer(function(input, output) {
# Create a reactive text
text <- reactive({
paste(input$variable, 'versus', input$variable2)
})
formulaText <- reactive({
paste(input$variable, "~", input$variable2)
})
# Return as text the selected variables
output$caption <- renderText({
text()
})
# Generate a plot of the requested variables
output$plot <- renderPlot({
p <- ggplot(data, aes_string(x=input$variable, y=input$variable2)) + geom_point()
print(p)
})
}),
options = list(height = 600)
)