Der Beitrag beschreibt die persönlichen Gründe für meine intensive Beschäftigung mit R und Data Science. Ich erwähne einige Eckpunkte dieser Exkursion und berichte über Erfolge und Misserfolge meiner Aktivitäten. Meine Pläne machen deutlich, dass Modellierungen bzw. Machine Learning mit R auch weiterhin eine wichtige zukünftige Rolle für mich einnehmen werden. Letztlich mache ich den Zusammenhang meiner Data Science Aktivitäten mit dem weiterhin stark geprägten didaktischen Forschungsrahmen deutlich.
Der Beitrag ist ein Interview mit Michael Rundel. Michael ist Lehrer an einem Realgymnasium in Wien und unterrichtet Physik, Informatik und Mediendesign. Er berichtet über sein neues – mit bookdown erstelltes – Physikbuch.
Damit stellt dieser Beitrag – zu den bereits veröffentlichten 7-teiligen Tutorial zum CrossMedia-Publishing (CMP-01 bis CMP-07) – nun eine praktische Umsetzung für diese Technologie im Schulbereich vor.
Seit mehreren Monaten arbeite ich an einem neuen persönlichen Webauftritt von mir. Der Beitrag erläutert die Motive, diskutiert die Pläne und verlinkt zu den bereits im Ansatz fertigen Webseiten.
Nach meinen drei vorigen Beiträgen zu RWordPress nun ein weiteres Beispiel, wie Digitalisierung Forschungsprozesse transformieren kann. Der Beitrag zeigt wie sich in derProgrammiersprache R – mit der Hilfe einiger Erweiterungen – nicht nur Daten (statistisch) verarbeiten lassen, sondern Kommentare als auch die Interpretation in einem Arbeitsprozess publiziert werden kann. Die damit verbundene Strategie heißt Literate Programming und bedeutet, dass Dokumentation als auch der Quelltext des Programms in einer gemeinsamen Datei zugänglich sind.
This is another follow-up article from previous posts Publishing R Statistics directly into WordPress and imgur.com website. This is very important because otherwise the transfer to WordPress would encrypt the pictures to an awful peace of code. An example how this looks can be seen from my first trial.
This is the follow-up article from the previous post Publishing R Statistics directly into WordPress. This time I will explain in more detail how to apply the different packages. There is a companion webpage where you can see the content of the different program chunks. You will see how to post text, graphic, uploading files, setting categories and tags, fill in the excerpt and providing a thumbnail.
In contrast to the previous post I have now establised the facility to upload graphics resulted from R calculation automatically to the imgur.com website. This is very important because otherwise the transfer to WordPress would encrypt the pictures to an awful peace of code. An example how this looks can be seen from my first trial.
This post is an experiment: Writing a program in R and publishing directly the results into Wordpress. This experiment is part of my ongoing effort of the last few months to collect material for a new book. I want to describe new working procedures in scientific research which are — thanks to progress in digitalization like open source tools, open data — now feasible. My focus on these new work flows are guided on the one hand by the goal to improve reproducibility of all research phases and on the other hand to facilitate research procedures in closing the digital gaps between different research tasks.
The book „Efficient R Programming: A Practical Guide to Smarter Programming“ by Colin Gillespie & Robing Lovelace is a collection of very important resources. It is not only helpful for the advanced data scientist but also the intermediate and even R beginner can benefit a great deal from the book. It covers important R related everyday subjects and activities. Each self-contained chapter starts with 5 top five tips and discusses practical issues. The book is rich in internet references, which I have collected and presented in 10 link lists, divided by the book chapters.
With December last year, I started to learn data science using R. As a self-determined learner, I had not only to find resources, that are adequate to my learning goals and status of competencies but also to plan and organize my spare time for my self-organized continuing education.This article reports about my strange learning behavior and closes with 3 tips on R learning resources for the beginner, suggesting the most minimal learning set for the prospective data scientist.
„Hands-On Programming with R“ by Garrett Grolemund is a very gentle introduction to programming with R. It starts with explaining the installation and the R user interface and covers step by step more advances topics. It is is not a programming book per se, as you will learn how to use R for data science. If you want to learn R from scratch and you are a newcomer to programming, then this is the book I would like warmly to recommend!
Yesterday I have experimented with R packages for generating Twitter Word clouds. In this post, I will give some hints how to proceed. I will also refer to my GitHub repository, where you can find the complete program code. I have added some examples in generating all the twitter clouds for all member of the IBM staff with a Twitter account, for the department and the university account.
Der Beitrag stellt Wordclouds vor, die sich aus den Twitter-Accounts der Mitarbeiter/innen am IMB ergeben. Ich habe das nicht nur aus Spielerei gemacht, sondern weil ein Studierender aus unserem Lehrgang Personalmanagement und Kompetenzentwicklung mit Neuen Medien die Nutzung von Social Media bei Personalentwicklungsfirmen als Masterthese untersuchen möchte. – Das Bild zeigt meine eigene Twitter-Wordcloud.
In the first episode about my personal learning experiences, I criticized that rigid curricula are not adequate to support experienced learner. This second episode is a follow up of this thought. I discuss the subject in more detail and in a more general way addressing not only R programming and data science but generally all adult learners who have work experiences and want to improve their knowledge and skills in their respective field of expertise. I will also present a model how self-determined learners could better be supported.
This is the first report of a series of personal experiences in learning the statistical programming language R to acquire competencies of a data scientist. In this kick-off article, I will present an example, why cohort learning in the age of individualisation is not appropriate anymore. I will suggest an alternative, present my earned certificates and explain why I interrupted (or aborted?) a nine-course introduction to data science taught by professors of John Hopkins University and offered via the MOOC-platform Coursera.