Abstract: This paper describes a series of short experiments collecting and analyzing ADS B data using IoT (Internet of Things) devices. The collection is performed using a Raspberry Pi single board computer, an RTL-SDR radio, custom software written for the project, and the open source dump1090 software program. This Raspberry Pi/RTL-SDR/dump1090 combination is used to collect ADS-B data. The data is captured and archived using a software program that reads the ADS-B data in real time as it is received by the RTL-SDR radio and subsequently output by the dump1090 program. The captured data is written to a series of flat files. Subsequent analytics and analysis is performed using the R programming language and the R Studio environment. The paper describes a subset of the R code that that was used in the analysis. All of the custom software used in this project is freely available at GitHub (see References section).

Abstract: In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known results from the semi-supervised learning literature.

Abstract: Utilize R to uncover hidden patterns in your Big DataAbout This BookPerform computational analyses on Big Data to generate meaningful resultsGet a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketWho This Book Is ForThis book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows.It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.What You Will LearnLearn about current state of Big Data processing using R programming language and its powerful statistical capabilitiesDeploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving mannerApply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usageExplore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platformIn DetailBig Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing.The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.Style and approachThis book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets.

Abstract: The development of big data analytics in recent years has enabled wide use of analysis tools such as R in various industries including robotics and automation. Its most recent reported application for data analysis is limited to only a small dataset and the processing time was not reported. Thus, this work proposes a fast approach for automatic data retrieval using R, a powerful programming language for statistical and big data analysis. The developed algorithm performs data retrieval using a source file and a map file as inputs and produces a desired output file. Based on the experiment results on a real dataset, significantly low processing time can be achieved using the approach. In addition, the developed algorithm is general and thus can be applied in various similar data retrieval applications such as pooling sub-dataset in manufacturing environment involving the use of robotics and automation.

Abstract: Galaxy provides a web-based platform for interactive, large-scale data analyses, which integrates bioinformatics tools written in a variety of languages. A substantial number of these tools are written in the R programming language, which enables powerful analysis and visualization of complex data. The Bioconductor Project provides access to these open source R tools and currently contains over 1200 R packages. While some R/Bioconductor tools are currently available in Galaxy, scientific research communities would benefit greatly if they were integrated on a larger scale. Tool development in Galaxy is an early entry point for Galaxy developers, biologists, and bioinformaticians, who want to make their work more accessible to a larger community of scientists. Here, we present a guide and best practices for R/Bioconductor tool integration into Galaxy. In addition, we introduce new functionalities to existing software that resolve dependency issues and semi-automate generation of tool integration components. With these improvements, novice and experienced developers can easily integrate R/Bioconductor tools into Galaxy to make their work more accessible to the scientific community.

Abstract: Author(s): Boettiger, C; Chamberlain, S; Vos, R; Lapp, H | Abstract: NeXML is a powerful and extensible exchange standard recently proposed to better meet the expanding needs for phylogenetic data and metadata sharing. Here we present the RNeXML package, which provides users of the r programming language with easy-to-use tools for reading and writing NeXML documents, including rich metadata, in a way that interfaces seamlessly with the extensive library of phylogenetic tools already available in the r ecosystem. Wherever possible, we designed RNeXML to map NeXML document contents, whose arrangement is influenced by the format's XML Schema definition, to their most intuitive or useful representation in r. To make NeXML's powerful facility for recording semantically rich machine-readable metadata accessible to r users, we designed a functional programming interface to it that hides the semantic web standards leveraged by NeXML from r users who are unfamiliar with them. RNeXML can read any NeXML document that validates, and it generates valid NeXML documents from phylogeny and character data in various r representations in use. The metadata programming interface at a basic level aids fulfilling data documentation best practices, and at an advanced level preserves NeXML's nearly limitless extensibility, for which we provide a fully working demonstration. Furthermore, to lower the barriers to sharing well-documented phylogenetic data, RNeXML has started to integrate with taxonomic metadata augmentation services on the web, and with online repositories for data archiving. RNeXML allows r's rich ecosystem to read and write data in the NeXML format through an interface that is no more involved than reading or writing data from other, less powerful data formats. It also provides an interface designed to feel familiar to r programmers and to be consistent with recommended practices for r package development, yet that retains the full power for users to add their own custom data and metadata to the phylogenies they work with, without introducing potentially incompatible changes to the exchange standard.

Abstract: The Engineer School of Eibar initiated the Grade of Engineering in Renewable Energies four years ago. This pioneering educational project has shown many challenges to the teachers of the new grade. Among the different software skills used in this project, R programming language has been a very important one because of its capacity for spatio-temporal analysis and graphical visualization of wind energy and wave energy potential. A quarter of the subject's program in Wind Energy and Ocean Energy has been used via Problem Based Learning for the application of statistical calculus with R. The aim of this contribution is to show some paradigmatic problems solved by the students and the results obtained. Finally, the opinion of the students about the use of R and its learning potentiality have been gathered and analysed.

Abstract: Paper presents the results of Remote Laboratory services application into Embedded Systems Education, developed under Embedded Computer Engineering Learning Platform FP7 project. This paper reports on results of performed Remote Laboratory evaluation in Warsaw University of Technology, where we introduce the new learning model in Digital System Design course. Students got an introduction to Xilinx software environment, exercises laboratory example of fundamentals in VHDL programming language and introduction to different digital logic circuits and their operation. The paper presents the analysis and results of performed evaluation based on Computer System Usability Questionnaire. For data analysis R programming language and software environment for statistical computing and graphics was used.

Abstract: In this short article I introduce the knotR package, which creates two dimensional knot diagrams optimized for visual appearance using the R programming language. The knotR package is a systematic R-centric suite of software for the creation of production-quality artwork of knot diagrams, released under GPL2.

Abstract: The article presents ready-to-use database containing a set of objective measures and Mean Opinion Scores (MOS) for the set of 40 video sequences. The format of published data enables to use a wide range of software. Additionally scripts for R (a programming language and software environment for statistical computing and graphics) are published, to use the database in that environment.

Abstract: The main goal of the work presented in this paper was to develop a set of algorithms which allows to predict what will be the probability ratio of acquisition of the items form the given database. To fulfill this goal, the appropriate statistical methods were developed, mainly using R programming language. In order to apply the specific statistical methods, the appropriate database preprocessing was implemented.

Abstract: In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes and comment on their use and implementation. We then show, using several code examples, how the package can be used to replicate well-known results from the semi-supervised learning literature.