Hash Me If You Can

We are living in the era of Big Data but the problem of course is that the bigger our data sets become the slower even simple search operations get. I will now show you a trick that is the next best thing to magic: building a search function that practically doesn’t slow down even for large data sets… in base R!
Continue reading “Hash Me If You Can”

Understanding the Maths of Computed Tomography (CT) scans


Noseman is having a headache and as an old-school hypochondriac he goes to see his doctor. His doctor is quite worried and makes an appointment with a radiologist for Noseman to get a CT scan.
Continue reading “Understanding the Maths of Computed Tomography (CT) scans”

Clustering the Bible


During this time of year, there is obviously a lot of talk about the Bible. As most people know the New Testament comprises four different Gospels written by anonymous authors 40 to 70 years after Jesus’ supposed crucifixion. Unfortunately we have lost all of the originals but only retained copies of copies of copies (and so on) which date back hundreds of years after they were written in all kinds of different versions (renowned Biblical scholar Professor Bart Ehrmann states that there are more versions of the New Testament than there are words in the New Testament). Just as a fun fact: there are many more Gospels but only those four were included in the official Bible.
Continue reading “Clustering the Bible”

Learning R: A gentle Introduction to Higher-Order Functions


Have you ever thought about why the definition of a function in R is different from many other programming languages? The part that causes the biggest difficulties (especially for beginners of R) is that you state the name of the function at the beginning and use the assignment operator – as if functions were like any other data type, like vectors, matrices or data frames…
Continue reading “Learning R: A gentle Introduction to Higher-Order Functions”

Intuition for Principal Component Analysis (PCA)


Principal Component Analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.

PCA is a concept that is traditionally hard to grasp so instead of giving you the n’th mathematical derivation I will provide you with some intuition.
Continue reading “Intuition for Principal Component Analysis (PCA)”