I came up with the following function to tap into the FourSquare trending venues API: library("RCurl", "RJSONIO")   foursquare<-function(x,y,z){ w<-paste("https://api.foursquare.com/v2/venues/trending?ll=",x,"&radius=2000&oauth_token=",y,"&v=",z,sep="") u<-getURL(w) test<-fromJSON(u) locationname="" lat="" long="" zip="" herenowcount="" likes="" for(n in 1:length(test$response$venues)) { locationname = test$response$venues]$name lat = test$response$venues]$location$lat long = test$response$venues]$location$lng zip = test$response$venues]$location$postalCode herenowcount<-test$response$venues]$hereNow$count likes<-test$response$venues]$likes$count xb<-as.data.frame(cbind(locationname, lat, long, zip, herenowcount, likes)) } xb$pulled=date() return(xb)

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Unfortunately the RpostgreSQL package (I’m pretty sure other SQL DBs as well) doesn’t have a provision to UPDATE multiple records (say a whole data.frame) at once or allow placeholders making the UPDATE a one row at a time ordeal, so I built a work around hack to do the job in parellel.  The big problem

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Before heading home for the holidays, I had a large data set (1.6 GB with over 1.25 million rows) with columns of text and integers ripped out of the company (Kwelia) Database and put into a .csv file since I was going to be offline a lot over the break. I tried opening the csv file

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My company, Kwelia, is sitting on mountains of data, so I decided to try my hand at mapping.  I have played around with JGR but it’s just too buggy, at least on my mac, so I went looking for other alternatives and found a good write up here.  I decided on mapping prices per sqft

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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")

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The following data was collected during the Presidential Debate on the 22nd of October by tapping into the Facebook social graph API using R. The top three posted links during the debate for each candidate are: Obama- #1     http://bit.ly/QCODJg #2     http://bit.ly/RXstnm #3    http://bit.ly/P8MmJ1 Romney- #1    http://bit.ly/zDdsKf #2    http://bit.ly/SjFbKx

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The following are word clouds of tweets for each candidate from the October 16, 2012 debate with the bigger words the more often they were used in tweets (click on each word cloud to enlarge): And the net-negative posts for each candidate: Please note that the bigger the word is in the word cloud the

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Click on above graph to enlarge. Background The data for this graph was collected automatically every ~60 seconds of the VP debate on 10/11/2012, with an ending aggregate sample size of 363,163 tweets.  From this dataset duplicate tweets were removed (because of bots), which gave a final dataset of 81,124 remaining unique tweets (52,303-Biden, 28,821-Ryan).

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After watching the Presidential debates and hearing all the opinions on how the candidates performed, I got the hair brained idea of creating a simple function that would do automate the pulling down of tweets for each candidate, analyze the positivity or negativity of tweets, and then graph them out. This project turned out to

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For a while now I have been contemplating pulling data from our postgreSQL db directly from R, but just never actually pulled the trigger until today.  What I found was that it was a lot easier than I ever could have imagined.  My laptop was already on the VPN, so I decided to try it

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