As Data Scientists, we often train complex algorithms in order to tackle certain business problems and generate value. These algorithms, however, can take a while to train. Sometimes they take a couple of hours, hours which I’m not going to spend just sitting and waiting. But regularly checking whether ... [Read more...]

This is a short post on how to quickly calculate the Fiedler vector for large graphs with the igraph package.

#used libraries
library(igraph)    # for network data structures and tools
library(microbenchmark)    # for benchmark results

Fiedler Vector with eigen My goto approach at the start was using the eigen() function to compute the whole spectrum of the Laplacian Matrix.

g <- sample_gnp(n = 100,p = 0.1,directed = FALSE,loops = FALSE)
M <- laplacian_matrix(g,sparse = FALSE)
spec <- eigen(M)
comps <- sum(round(spec$values,8)==0)
fiedler <- spec$vectors[,comps-1]

While this is easy ... [Read more...]

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Correspondence Analysis Correspondence analysis is a multivariate statistical technique that summarizes a set of categorical data in a two dimensional form. It’s like the equivalent of Principal Component Analysis but for categorical data. Correspondence analysis is usually applied to contigency tables. In this post, we will apply it to ... [Read more...]

I’ve been measuring my weight almost daily for almost 2 years now; I actually started earlier, but not as consistently. The goal of this blog post is to get re-acquaiented with time series; I haven’t had the opportunity to work with time series for a long time now and ... [Read more...]

Introduction This primer shall consist of two parts and its goal is to provide a walk-through of using resources developed in Java from R. It is structured as more of a “note-to-future-self” rather than a proper educational article, I however hope that some readers may still find it useful. It ... [Read more...]

Introduction This primer shall consist of two parts and its goal is to provide a walk-through of using resources developed in Java from R. It is structured as more of a “note-to-future-self” rather than a proper educational article, I however hope that some readers may still find it useful. It ... [Read more...]

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Something that gets to many a footy fan, is the feeling that your team has won the game in most areas expect on the scoreboard. Thinking about this statement a little bit deeper has the following implication. That there are some areas of the game, that if you win, you ... [Read more...]

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R is primarily a language for working with numbers, but we often need to work with text as well. Whether it's formatting text for reports, or analyzing natural language data, R provides a number of facilities for working with character data. Handling Strings with R, a free (CC-BY-NC-SA) e-book by ... [Read more...]

Immediate and personalized feedback has been central to the learning experience on DataCamp since we launched the first courses. If students submit code that contains a mistake, they are told where they made a mistake, and how they can fix this. You can play around with it in our free ... [Read more...]

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