I’ve been interested in building a web app using R for a while, but never put any time into it until I was informed of the Shiny package.  It looked too easy, so I absolutely had to try it out.

First you need to install the package from the command line .

options(repos=c(RStudio="http://rstudio.org/_packages", getOption("repos"))) install.packages("shiny")

The version in the tutorial uses a two R files, won the front end(ui.R) and the other being the server side (server.R).  However, I wanted a refresher in HTML, so I built it with one HTML and one R file.  The structure is defined here.

For the data, I’m using MLS (home sales) data for Philadelphia, which I’ve been sitting on for quite a while.  The thought behind the app is to be able to examine monthly average listing prices to monthly average prices, and in the end it turned out rather well.

At first, I had the server.R try to do everything within the reactive function, but it  literally took minutes to load a new graph. The solution was to run the data through the following and just have the reactive function call from the data frames:

##raw data d ##convert to numeric from factored variable zip<-as.numeric(as.character(d$area)) #round the listing date to first of the month listed<-paste(format(as.POSIXlt(d$listdate), format="%Y-%m"), "01", sep="-") #round the sales date to first of the month solded<-paste(format(as.POSIXlt(d$solddate), format="%Y-%m"), "01", sep="-") #create the time period period<-seq(as.Date("2010-02-01"), length=24, by="1 month") #create empty data frame for average monthly listing and sales data listing<-data.frame(period=period) sales<-data.frame(period=period) ##list of all zip codes in Philly that we'll examine a<-list("19103","19111","19114","19115","19116","19119","19120", "19124","19125","19128","19130","19131","19134","19135","19136", "19138","19142","19143","19144","19145","19146&quo