30 July 2017
This shiny application is developed within the scope of the final project of the Coursera "Developing Data Products" course.
The project consists of two parts:
- a shiny application deployed on R Studio servers
- a pitch presentation
The code is available on Github: https://github.com/xvalda/Coursera—Data-Products—Course-Project
The shiny application can be viewed here: https://xvalda.shinyapps.io/dataproductscourseproject/
This is a very basic project, proposing to draw a linear regression between one regressor and one outcome.
For each configuration we also display the intercept, the slope and the R-squared value of the linear model.
The user can choose any variable as the outcome or the regressor.
Data We use the mtcars dataset in R.
library(shiny)
shinyUI(fluidPage(
titlePanel(HTML("Linear relations between variables of the mtcars dataset")),
# user inputs requested predictor and outcome
sidebarLayout(
sidebarPanel(
h3("Choose a predictor (X-axis): "),
selectInput("inputPredictor", label = NULL, choices = c("Displacement"="disp", "Cylinders"="cyl",
"Miles per Gallon"="mpg", "Horsepower"="hp", "Rear Axle Ratio"="drat",
"Weight"="wt", "1/4 Mile Time"="qsec", "vs"="vs",
"Transmission"="am", "Gears"="gear", "Carburator"="carb")),
h3("Choose the outcome variable (Y-axis): "),
selectInput("inputOutcome", label = NULL, choices = c("Miles per Gallon"="mpg", "Cylinders"="cyl", "Displacement"="disp", "Horsepower"="hp", "Rear Axle Ratio"="drat", "Weight"="wt", "1/4 Mile Time"="qsec", "vs"="vs", "Transmission"="am", "Gears"="gear", "Carburator"="carb")),
#show in side pane the intercept, slope and R-Squared
h3("Intercept"),
textOutput("interceptOut"),
h3("Slope"),
textOutput("slopeOut"),
h3("R-Squared"),
textOutput("rsquaredtOut")
),
# Plot the liner model
mainPanel(
plotOutput("plotLM")
)
)
))
library(shiny)
data("mtcars")
shinyServer(function(input, output, session) {
# PLOT
fit <- reactive({
lm(mtcars[,input$inputOutcome]~mtcars[,input$inputPredictor])
})
output$plotLM <- renderPlot({
plot(mtcars[,input$inputPredictor], mtcars[,input$inputOutcome],
xlab=input$inputPredictor, ylab=input$inputOutcome, cex = 1.5, pch = 16)
abline(lm(mtcars[,input$inputOutcome]~mtcars[,input$inputPredictor]), col = "red", lwd = 2)
})
output$interceptOut <- renderText({
summary(lm(mtcars[,input$inputOutcome]~mtcars[,input$inputPredictor]))$coef[1,1]
})
output$slopeOut <- renderText({
summary(lm(mtcars[,input$inputOutcome]~mtcars[,input$inputPredictor]))$coef[2,1]
})
output$rsquaredtOut <- renderText({
summary(lm(mtcars[,input$inputOutcome]~mtcars[,input$inputPredictor]))$r.squared
})
})