Publications by INWT-Blog-RBloggers
Who’s best in class? Comparing forecasting models with a Predictive Analytics Cube (PAC)
The problem of (comparable) forecasting Over the last years, time series forecasting has become a subfield of Data Science where well established statistical and econometric methods mingle with modern ML techniques. However, solid standards with regard to data partitioning or comparable model evaluation have not been established yet. Forecasting...
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100 grams of Lego, please.
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 front of all those Lego boxes studying prices and themes, while my son careful...
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MariaDB monitor
MariaDB is currently the fastest growing open source database solution. It is mainly developed by the MariaDB corporation and is a fork of MySQL. The DBMS MariaDB with its various free features offers much, which MySQL does not provide or only at a cost, e.g. a storage engine for processing huge amounts of data; a database proxy for secure and h...
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Plane Crash Data – Part 1: Web Scraping
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 lots of information on aviation accidents of th...
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A meaningful file structure for R projects
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? To make all this easier, it helps to have a consistent file and folder structure across your projects. You sh...
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Plane Crash Data – Part 2: Google Maps Geocoding API Request
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 Maps Geocoding API for the crash location and the point of departure a...
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Plane Crash Data – Part 3: Visualisation
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. <pre class="r"><code>library("leaflet") library("geosphere")</code> Again we keep only co...
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Promises and Closures in R
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 within the scoping rules of R, but until I...
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Tips for A/B Testing with R
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 feeling to answer these questions. Today all these qu...
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A Not So Simple Bar Plot Example Using ggplot2
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: <pre class ="r"><code>dir.create("data") writeLines("*", "data/.gitignore") download.file("http://www.datendesign-r.de/alle_daten.zip", "data/alle_daten.zip") unzip("data/alle_da...
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