Wages Shiny App Presentation

Gaurav Garg (gaurav_garg@yahoo.com)
Jul 26th, 2015

Building a Shiny App

The Wage Explorer is a Shiny App, deployed on the Shiny server. https://kinatus.shinyapps.io/Project

library(ISLR)
library(ggplot2)

Wage dataset in the ISLR package has Wage and other data for a group of 3000 workers in the Mid-Atlantic region. The app showcases the following aspects.

How to create a simple Shiny App
Creating a Plot from the Wages data
Using the Inputs and Outputs to build interactive User Experience
Integrating Calculations within the HTML page

Plot from Wages data

The Wage Exploer app uses the qplot() to visualize the data from the dataset. We will use the colour parameter in the qplot() to give user some options.

plot of chunk unnamed-chunk-2

Lets see what the plot should look like with datapoints coloured by the education.

User Interface - Inputs and Outputs

I use the radioButtons() in Shiny inputs to set which column should be used for the colour parameter. in UI.r, variable varGroupBy is assigned 1 (no Group By), 2 (Job classification) or 3 (education).

Similarly, variable varRegressionLines is assigned a TRUE/FALSE value from the checkbox to add regression lines to the plot.

#sample input value. These values should be set in UI.R
varGroupBy <- 2
varRegressionLines <- TRUE

# Conditional code in Server.R
if(varGroupBy == 1) # no grouping selected in the UI
       { p <- qplot(age,wage,data=Wage)}
if (varGroupBy == 2) # Group the datapoints by Job Class
        {p <- qplot(age,wage, colour=jobclass,data=Wage)}
if (varGroupBy == 3) # Group the datapoints by Education
        {p <- qplot(age,wage,colour=education,data=Wage)}

# did the user select the option the add the regression lines?                        
if (varRegressionLines)
        p <- p+geom_smooth(method='lm',formula=y~x)

p

plot of chunk unnamed-chunk-3

Calculation Piece

Course requires use to take an input, do some calculations and display the result back in the HTML. I use the SampleSize from the slider input to calculation the training set size.

#UI.r will set the value of SampleSize
sampleSize <- 1500

# Calculate the training set size. By default the value of sampleSize variable is set to 1500
paste("You have selected sample size of ",sampleSize,
                      " which results in a training set of", 
                      round((sampleSize/nrow(Wage)*100),2),
                      "%.")

[1] "You have selected sample size of  1500  which results in a training set of 50 %."