Publications by INWT-Blog-RBloggers
INWT’s guidelines for R code
“It turns out that style matters in programming for the same reason that it matters in writing. It makes for better reading.“ Douglas Crockford in JavaScript: The Good Parts Why do we need yet another style guide? “The reason to care about a style guide is just one thing: We want that our source code is not only interpretable by a comp...
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Introducing the Kernelheaping Package
In this blog article I’d like to introduce the univariate kernel density estimation for heaped (i.e. rounded or interval censored) data with the Kernelheaping package. It is not unusual to have interval censored data such as in income surveys due to anonymisation or simplification issues. However, a simple task like plotting a density may fail...
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smoothScatter with ggplot2
The motivation for this plot is the function: graphics::smoothScatter, basically a plot of a two dimensional density estimator. In the following I want to reproduce the features with ggplot2. smoothScatter To have some data I draw some random numbers from a two dimensional normal distribution: <pre class ="r"><code>library(ggplot2) library(MA...
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Design Patterns in R
<script type="text/javascript" async src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-MML-AM_CHTML"> </script> These notes are inspired by a talk by Stuart Sierra on Design Patterns in Functional Programming and some thoughts I found on F# for fun an profit and are reflection on how I use different strategies to solve things...
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Introducing the Kernelheaping Package II
In the first part of <a target="_blank" style = "color:#527BBA; text-decoration: underline;" href="https://www.inwt-statistics.com/read-blog/introducing-the-kernelheaping-package-512.html">Introducing the Kernelheaping Package</a> I showed how to compute and plot kernel density estimates on rounded or interval censored data using the Kernelheapin...
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Do GPU-based Basic Linear Algebra Subprograms (BLAS) improve the performance of standard modeling techniques in R?
Introduction The speed or run-time of models in R can be a critical factor, especially considering the size and complexity of modern datasets. The number of data points as well as the number of features can easily be in the millions. Even relatively trivial modeling procedures can consume a lot of time, which is critical both for optimization an...
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Introducing the Kernel Heaping Package III
In the second part of this blog series, I showed how to compute spatial kernel density estimates based on area-level data. The Kernelheaping package also supports boundary-corrected kernel density estimation, which allows us to exclude certain areas, where we know that the density must be zero. One example is estimating the population density wh...
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Optimize your R Code using Memoization
This article describes how you can apply a programming technique, called Memoization, to speed up your R code and solve performance bottlenecks. Wikipedia says: In computing, […] memoisation is an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached res...
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Cluster Analysis – Part 1: Introduction
What is Cluster Analysis? Cluster analysis is a collective term for various algorithms to find group structures in data. The groups are called clusters and are usually not known a priori. In contrast, classification procedures assign the observations to already known groups (e.g., buyers and non-buyers). A classification is often performed with ...
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Cluster Analysis – Part 2: Hands On
<code><pre class="r">library(broom) library(cluster) library(dplyr) library(ggplot2) library(ggdendro) In the first part of this blog series, we examined the theoretical foundations of cluster analysis. In the following article we put the theory into practice using R. For the analysis in R, we will use the variables mpg (fuel consumption in mile...
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