When I first saw the *Computer Algebra System Mathematica* in the nineties I was instantly fascinated by it: you could not just calculate things with it but solve equations, simplify, differentiate and integrate expressions and even solve simple differential equations… not just numerically but *symbolically*! It helped me a lot during my studies at the university back then. Normally you cannot do this kind of stuff with R but fear not, there is, of course, a package for that!

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# Category: R

Posts with R code

## Kalman Filter as a Form of Bayesian Updating

The *Kalman filter* is a very powerful algorithm to optimally include *uncertain information* from a *dynamically changing system* to come up with the best educated guess about the *current state of the system*. Applications include (car) navigation and stock forecasting. If you want to understand how a Kalman filter works and build a toy example in R, read on!

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## Time Series Analysis: Forecasting Sales Data with Autoregressive (AR) Models

Forecasting the future has always been one of man’s biggest desires and many approaches have been tried over the centuries. In this post we will look at a simple statistical method for *time series analysis*, called *AR* for *Autoregressive Model*. We will use this method to predict future sales data and will rebuild it to get a deeper understanding of how this method works, so read on!

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## COVID-19: False Positive Alarm

In this post, we are going to replicate an analysis from the current issue of *Scientific American* about a common mathematical pitfall of Coronavirus antibody tests with R.

Many people think that when they get a positive result of such a test they are immune to the virus with high probability. If you want to find out why nothing could be further from the truth, read on!

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## Learning Data Science: A/B Testing in Under One Minute

Google does it! Facebook does it! Amazon does it for sure!

Especially in the areas of web design and online advertising, everybody is talking about *A/B testing*. If you quickly want to understand what it is and how you can do it with R, read on!

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## Learning R: Build a Password Generator

It is not easy to create secure passwords. The best way is to let a computer do it by randomly combining lower- and upper-case letters, digits and other printable characters.

If you want to learn how to write a small function to achieve that read on!

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## Learning Statistics: Randomness is a Strange Beast

Our intuition concerning *randomness* is, strangely enough, quite limited. While we expect it to behave in certain ways (which it doesn’t) it shows some regularities that have unexpected consequences. In a series of seemingly random posts, I will highlight some of those regularities as well as consequences. If you want to learn something about randomness’ strange behaviour and gain some intuition read on!

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## Lying with Statistics: One Beer a Day will Kill you!

About two years ago the renowned medical journal “The Lancet” came out with the rather sensational conclusion that there is no safe level of alcohol consumption, so every little hurts! For example, drinking a bottle of beer per day (half a litre) would increase your risk of developing a serious health problem within one year by a whopping 7%! When I read that I had to calm my nerves by having a drink!

Ok, kidding aside: in this post, you will learn how to lie with statistics by deviously mixing up *relative* and *absolute* changes in risks, so read on!

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## Learning R: Build *xkcd’s* Star Wars Spoiler Generator

Star Wars is somewhat nerdy, R definitely is… what could be more worthwhile than combining both ðŸ˜‰

This Sunday was Star Wars Day (May the 4th be with you!) and suitable for the occasion we will do a little fun project and implement the following *xkcd* flowchart, which can give us more than 2 million different Star Wars plots.

Even if you are new to R, the used code should be comprehensible, so read on!

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## ZeroR: The Simplest Possible Classifier… or: Why High Accuracy can be Misleading

In one of my most popular posts So, what is AI really? I showed that *Artificial Intelligence (AI)* basically boils down to autonomously learned rules, i.e. *conditional statements* or simply, *conditionals*.

In this post, I create the simplest possible *classifier*, called *ZeroR*, to show that even this classifier can achieve surprisingly high values for *accuracy* (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!

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