Articles by INWT-Blog-RBloggers

Data Visualization in R vs. Python

December 23, 2019 | INWT-Blog-RBloggers

A decisive step in the data science process is communicating the results of your analysis. As a data scientist, you are often tasked with presenting these results to people with little or no statistical background, making it important to be able to present the content clearly and understandably. It is ...
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Shiny Modules

July 16, 2019 | INWT-Blog-RBloggers

Tidiness is half the life .. this is a German saying that you might not necessarily have to live. While it becomes essential in programming, at least in my opinion. Because when you do not invest a little time into the order and structure of your projects, the time you spend ...
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rsync as R package

May 21, 2019 | INWT-Blog-RBloggers

In this article we present our R package rsync, which serves as an interface between R and the popular Linux command line tool rsync. Originally rsync is an open source tool for efficiently synchronizing files. Published by Paul Mackerras and Andrew Tridgell under the GNU General Public License, it allows ...
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Using Modules in R

May 7, 2019 | INWT-Blog-RBloggers

When a code base grows we may think of using several files first and then source them. Functions, of course, are rightfully advocated to new R users, and are the essential building block. Packages are then, already, the next level of abstraction we have to offer. With the modules package ...
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Cluster Analysis – Part 2: Hands On

November 21, 2018 | INWT-Blog-RBloggers

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 ...
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Cluster Analysis – Part 1: Introduction

November 6, 2018 | INWT-Blog-RBloggers

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 ...
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Optimize your R Code using Memoization

October 11, 2018 | INWT-Blog-RBloggers

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 ... [Read more...]

Introducing the Kernel Heaping Package III

September 25, 2018 | INWT-Blog-RBloggers

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 ...
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