(In a past job interview I failed at explaining how to calculate and interprete ROC curves – so here goes my attempt to fill this knowledge gap.) Think of a regression model mapping a number of features onto a real number … Continue reading → [Read more...]

OpenCPU is (simply put) a server implementing a RESTful web API for remotely executing R functions and retrieving results. In this tutorial I am going to showcase how OpenCPU can be installed on an EC2 instance running Ubuntu 14.04. Python and … Continue reading → [Read more...]

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Wow – what a headline … okay, I admit it’s phrased quite sensational given that it anticipates just one possible interpretation of increasingly more births around summer / autumn compared to in spring … but I guess I just get … Continue reading → [Read more...]

Agglomerative hierarchical clustering is a simple, intuitive and well-understood method for clustering data points. I used it with good results in a project to estimate the true geographical position of objects based on measured estimates. With this tutorial I would … Continue reading → [Read more...]

In this tutorial I am going to describe a straightforward way of how to make use of Twitter’s REST API v1.1. For that purpose I composed a little package (RTwitterAPI), so that requesting data just needs the API URL, the API parameters … Continue reading → [Read more...]

Naturally there are two reasons for why you need to access MongoDB from R: MongoDB is already used for whatever reason and you want to analyze the data stored therein You decide you want store your data in MongoDB instead of … Continue reading → [Read more...]

… or Inferring Identity from Observations Let’s assume the following application: A conservation organisation starts a project to geographically catalogue the remaining representatives of an endangered plant species. For that purpose hikers are encouraged to communicate the location of the plant … Continue reading → [Read more...]

In this text I am going to describe a very straightforward way of how to make use of Twitter’s REST API v1.1. I put some code together for that purpose, so that requesting data just needs the API URL, the API … Continue reading → [Read more...]

This article serves the purpose of illustrating that signal processing with R is possible – thanks to the signal package – and to keep a reference of some of the stuff that I learned at my last edX course. Anyway, I … Continue reading → [Read more...]

Having a habit of compulsively wondering approximately every 34.765th day about how zip compression (bzip2 in this case) might be used to measure information contained in data – this time the question popped up in my head of whether or … Continue reading → [Read more...]

You all know why MapReduce is fancy – so let’s just jump right in. I like researching data and I like to see results fast – does that mean I enjoy the process of setting up a Hadoop cluster? No, … Continue reading → [Read more...]

For the previous article I needed a quick way to figure out if two sets of points are linearly separable. But for crying out loud I could not find a simple and efficient implementation for this task. Except for the perceptron and … Continue reading → The post Testing for Linear Separability ... [Read more...]

I am excited to announce that this is supposed to be my first article published also on r-bloggers.com :) The processing of data needs to take dimensionality into account as usual metrics change their behaviour in subtle ways, which impacts the … Continue reading → The post Impact of Dimensionality on Data ... [Read more...]

The Titanic challenge on Kaggle is about inferring from a number of personal details whether a passenger survived the disaster or did not. I gave two algorithms a try, which are decision trees using R package party and SVMs using … Continue reading → The post Titanic challenge on Kaggle with decision ... [Read more...]

The tf-idf-statistic (“term frequency – inverse document frequency”) is a common tool for the purpose of extracting keywords from a document by not just considering a single document but all documents from the corpus. In terms of tf-idf a word … Continue reading → The post The tf-idf-Statistic For Keyword Extraction appeared first ... [Read more...]

This article is about the “Digit Recognizer” challenge on Kaggle. You are provided with two data sets. One for training: consisting of 42’000 labeled pixel vectors and one for the final benchmark: consisting of 28’000 vectors while labels are not … Continue reading → The post “Digit Recognizer” Challenge on Kaggle using SVM Classification ... [Read more...]

(This article is referring to an initial proof-of-concept version of r-big-pivot) I have to admit that I very much enjoy pivoting through data using Excel. Its pivoting tool is great for getting a quick insight into a data set’s structure … Continue reading → The post Pivoting Data in R Excel-style ... [Read more...]

First of all this text is not just about an intuitive perspective on the beta distribution but at least as much about the idea of looking behind a measured empirical probability and thinking of it as a product of chance itself. … Continue reading → The post An intuitive interpretation of the ... [Read more...]