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...

In the first part of Introducing the Kernelheaping Package I showed how to compute and plot kernel density estimates on rounded or interval censored data using the Kernelheaping package. Now, let’s make a big leap forward to the 2-dimensional case. Interval censoring can be generalised to rectangles or alternatively ...

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: library(ggplot2) library(MASS) ...

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 ...

This is a reproduction of the (simple) bar plot of chapter 6.1.1 in Datendesign mit R with ggplot2. To download the data you can use the following lines: dir.create("data") writeLines("*", "data/.gitignore") download.file("http://www.datendesign-r.de/alle_daten.zip", "data/alle_daten.zip") unzip("data/alle_daten....

Which layout of an advertisement leads to more clicks? Would a different color or position of the purchase button lead to a higher conversion rate? Does a special offer really attract more customers – and which of two phrasings would be better? For a long time, people have trusted their gut ...

At the moment I try to improve my knowledge about functional programming in R. Luckily there are some explanations on the topic in the web (adv-r and Cartesian Faith). Beginning to (re)discover the usefulness of closures, I remember some (at first sight) very strange behaviour. Actually it is consistent ...

In Part 1 and Part 2 of this series, we scraped plane crash data from the web and complemented it with the geocoordinates of departure, crash and intended arrival location. In this third part, we will visualise the data on a map using the leaflet package. library("leaflet") library("geosphere") Again we ...

This is the second part of our series about plane crash data. To execute the code below, you'll first need to execute the code from the first part of this series to obtain the prepared plane crash dataset. In this part I'd like to get the geocoordinates from the Google ...

Have you ever tried to find your way around in the file structure of an already existing project? To separate relevant from obsolete files in a historically grown directory? To find out in which order existing scripts should be executed? ...

Several months ago I stumbled across the Kaggle data set Airplane Crashes Since 1908. Since I couldn't find the data source, I searched the web for historical plane crash data and quickly found the web page http://www.planecrashinfo.com. On this site you can find various tables inside tables with ...

Everyone who has children or deep down still is one himself might had the pleasure to find himself in a toy store's Lego counter once. Since it is my son's favourite father-son-activity to build Lego stuff I did so several times in the last year. Now as I stood in ...