## Why I don’t use the Tidyverse

There seems to be some revolution going on in the R sphere… people seem to be jumping at what is commonly known as the tidyverse, a collection of packages developed and maintained by the Chief Scientist of RStudio, Hadley Wickham.

In this post, I explain what the tidyverse is and why I resist using it, so read on!
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## Create realistic-looking Islands with R

Modern movies use a lot of mathematics for their computer animations and CGI effects, one of them is what is known under the name fractals.

In this post, we will use this technique to create islands with coastlines that look extraordinarily realistic. If you want to do that yourself read on!
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## Learning Data Science: Sentiment Analysis with Naive Bayes

As we have already seen in former posts simple methods can be surprisingly successful in yielding good results (see e.g Learning Data Science: Predicting Income Brackets or Teach R to read handwritten Digits with just 4 Lines of Code).

If you want to learn how some simple mathematics, known as Naive Bayes, can help you find out the sentiment of texts (in this case movie reviews) read on!
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## Learning R: Data Wrangling in Password Hacking Game

Data Scientists know that about 80% of a Data Science project consists of preparing the data so that they can be analyzed. Building Machine Learning models is the fun part that only comes afterwards!

This process is called Data Wrangling (or Data Munging). If you want to use some Base R data wrangling techniques in a fun game to hack a password read on!
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## Teach R to see by Borrowing a Brain

It has been an old dream to teach a computer to see, i.e. to hold something in front of a camera and let the computer tell you what it sees. For decades it has been exactly that: a dream – because we as human beings are able to see, we just don’t know how we do it, let alone be precise enough to put it into algorithmic form.

Enter machine learning!
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## Data Science on Rails: Analyzing Customer Churn

Customer Relationship Management (CRM) is not only about acquiring new customers but especially about retaining existing ones. That is because acquisition is often much more expensive than retention. In this post, we learn how to analyze the reasons of customer churn (i.e. customers leaving the company). We do this with a very convenient point-and-click interface for doing data science on top of R, so read on!
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## Extracting basic Plots from Novels: Dracula is a Man in a Hole

In 1965 the University of Chicago rejected Kurt Vonnegut’s college thesis, which claimed that all stories shared common structures, or “shapes”, including “Man in a Hole”, “Boy gets Girl” and “Cinderella”. Many years later the then already legendary Vonnegut gave a hilarious lecture on this idea – before continuing to read on please watch it here (about 4 minutes):
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## Understanding Blockchain Technology by building one in R

By now you will know that it is a good tradition of this blog to explain stuff by rebuilding toy examples of it in R (see e.g. Understanding the Maths of Computed Tomography (CT) scans, So, what is AI really? or Google’s Eigenvector… or how a Random Surfer finds the most relevant Webpages). This time we will do the same for the hyped Blockchain technology, so read on!
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## Finding free Science Books from Springer

Today the biggest book fair of the world starts again in Frankfurt, Germany. I thought this might be a good opportunity to do you some good!

Springer is one of the most renowned scientific publishing companies in the world. Normally, their books are quite expensive but also in the publishing business Open Access is a megatrend.

If you want to use R in a little fun project to find the latest additions of open access books to their program read on!
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## Cambridge Analytica: Microtargeting or How to catch voters with the LASSO

The two most disruptive political events of the last few years are undoubtedly the Brexit referendum to leave the European Union and the election of Donald Trump. Both are commonly associated with the political consulting firm Cambridge Analytica and a technique known as Microtargeting.

If you want to understand the data science behind the Cambridge Analytica/Facebook data scandal and Microtargeting (i.e. LASSO regression) by building a toy example in R read on!
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