The R-Podcast is back up and running! In this episode I discuss some useful resources and helpful tips/extensions that have greatly enhanced my work flow in creating reproducible analysis documents via R-Markdown. I also highlight some exciting new endeavors in the R community as well as provide my take ... [Read more...]

In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about implementing user based collaborative filtering approach using R. In this post, I will be explaining about basic implementation of Item based collaborative filtering recommender systems in r. Intuition:Item based ... [Read more...]

by Andrie de Vries We have written on several occasions about AzureML, the Microsoft machine learning studio that is part of the Cortana Analytics suite: Running R in the Azure ML cloud Call R functions from any application with the AzureML package Using miniCRAN in Azure ML In September we ... [Read more...]

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18.11.2015 In the current release of Bio7 2.3  two new plot preferences are available to automatically plot data with the size of the visible display (as an image) or with the size of available ImageSizeX and ImageSizeY R workspace variables. The second option is handy if you transfer images from ImageJ to ... [Read more...]

So this is it !!! In the current tutorial I will show you my entire code that i used to grab the data from web as well as learn to parse the XML data. This tutorial will be a bit longer but since you have already come to this point lets ... [Read more...]

Last week witnessed a number of exciting announcements from the big data and machine learning space. What it shows is …Continue reading → [Read more...]

This document describes several different techniques for including SVG images within a web page and points out the important SVG attributes that control the final appearance of the SVG image within the web page. The document then describes how to … Continue reading → [Read more...]

“Science is reportedly in the middle of a reproducibility crisis.” This is the claim of quite a few these days including an article from ROpenSci which directly references another article by The Conversation. But what is “reproducible research” and how … Continue reading → [Read more...]

One of the neat tools available via a variety of packages in R is the creation of beautiful tables using data frames stored in R. In what follows, I’ll discuss these different options using data on departing flights from Seattle … Continue reading → [Read more...]

SummaryThis post discussed the background, approaches, windows and linux environment setup for my Geocoding task. See more details about the script and workflow in next post. [Read more...]

The New York City Taxi & Limousine Commission has released a staggeringly detailed historical dataset covering over 1.1 billion individual taxi trips in the city from January 2009 through June 2015. Taken as a whole, the detailed trip-level data is more than just a vast list of taxi pickup and drop off coordinates: it’... [Read more...]

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In part one of a series of tutorials, we will develop a Shiny App for performing analysis of academic text from PubMed. There’s no shortage of great tutorials for developing a Shiny App using R, including Shiny’s own tutorial. Here at datascience+ we have a perfect introduction by ... [Read more...]

A new version of RcppAnnoy, our Rcpp-based R integration of the nifty Annoy library by Erik, is now on CRAN. Annoy is a small, fast, and lightweight C++ template header library for approximate nearest neighbours. This release mostly just catches up w... [Read more...]

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Two recent surveys — one based on LinkedIn skills data, and another a direct survey of data miners — show that R remains the most popular software for statistical data analysis. In a study of skills associated with LinkedIn profiles by RJmetrics (and also reported on Forbes), "data analysis" was unsurprisingly the ... [Read more...]

Consider some simulated data __ set.seed(1) __ x=exp(rnorm(100)) Assume that those data are observed i.id. random variables with distribution, with . The natural idea is to consider the maximum likelihood estimator For instance, consider some maximum likelihood estimator, __ library(MASS) __ (F=fitdistr(x,"gamma")) shape rate 1.4214497 0.8619969 (0.1822570) (0.1320717) __ F$estimate[1]+c(... [Read more...]

This post is in response to Michael Harris' Price Action Lab post, where he uses some simple R code to evaluate the asymmetry of returns from the day's close to the following day's open.  I'd like to respond to his 3 notes, which I've included below.The R backtest assumes fractional ... [Read more...]