scholarly journals 4172 Introduction to R Programming and GitHub: Developing Automated Analysis of Complete Blood Count Data as a Translational Science Undergraduate Project

2020 ◽  
Vol 4 (s1) ◽  
pp. 63-63
Author(s):  
Jeffrey Robinson ◽  
Annica Wayman
Keyword(s):  
Software Development ◽  
Open Source ◽  
Complete Blood Count ◽  
Life Sciences ◽  
Statistical Testing ◽  
Translational Science ◽  
Development Environment ◽  
Individual Parameter ◽  
R Language ◽  
R Programming

OBJECTIVES/GOALS: Introduce students to programming and software development practices in the life sciences by analyzing standard clinical diagnostic bloodwork for differential immune responses. Including lectures and a semester project with the goal of enhancing undergraduate students’ education to prepare them for careers in translational science. METHODS/STUDY POPULATION: The educational content was taught for the first time as a component of the newly developed course BTEC 330 “Software Applications in the Life Sciences” in UMBC’s Translational Life Science Technology (TLST) Bachelor’s degree program at the Universities at Shady Grove campus. Eleven students took the course. All were beginners with no programming background. Lectures provided background on the diagnostic components of the CBC, criteria for differential diagnosis in the clinical setting, and introduction to hematology and flow cytometry, forming underpinnings for interpretation of the CBC results. Weekly computer lab practical sessions provided training fundamentals of R programming language, the R-studio integrated development environment (IDE), and the GitHub.com open-source software development platform. RESULTS/ANTICIPATED RESULTS: The graded assignment consisted of a coding project in which students were each assigned an individual parameter from the CBC results. These include, for example, relative lymphocyte count or hemoglobin readouts. Students each created their own R-language script using R-studio, with functional code which: 1) Read in data from a file provided, 2) Performed statistical testing, 3) Read out statistical results as text, and charts as image files, 4) “Diagnosed” individuals in the dataset as being inside or outside the clinical normal range for that parameter. Each student also registered their own GitHub account and published their open-source code. Grading was performed on code functionality by downloading each student repository and running the code with the instructor as an outside developer using the resource. DISCUSSION/SIGNIFICANCE OF IMPACT: In this curriculum, students with no background in programming learned to code a basic R-language script and use GitHub to automate interpretation of CBC results. With advanced automation now becoming commonplace in translational science, such course content can provide introductory level of literacy in development of clinical informatics software.

A Constructivist View of Knowledge Management in Open Source Virtual Communities

2011 ◽  
pp. 290-308 ◽  
Author(s):  
Sulayman K. Sowe ◽  
Athanasis Karoulis ◽  
Ioannis Stamelos
Keyword(s):  
Knowledge Management ◽  
Learning Environment ◽  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Learning Context ◽  
Open Source Software Development ◽  
Development Environment ◽  
Constructivist View ◽  
Real Life Situation

This chapter addresses a learning environment that is manifested in the domain offree/open source software development. It provides the base for the emergence,development, interactions, and management of a novel learning environment bytaking a constructivist view of knowledge management. The learning activities ofan online collaborative effort of a loosely and geographically disperse communityof individuals is explored by looking at the interactions between members of thecommunity, the tools used to communicate, and the interactions between the mem-bers of the community and the virtual learning context. The learning context asenvisaged here refers to the free/open source software development environment inwhich learning actually takes place. The main focus is on the resources and pur-poseful activities that promote collaborative learning in this context, as well as thetransfer of learning from the virtual setting to the real-life situation by involving ina collaborative activity.

Exploring the Effects of Process Characteristics on Products Quality in Open Source Software Development

2009 ◽  
pp. 3008-3036 ◽  
Author(s):  
Stefan Koch ◽  
Christian Neumann
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Open Source Software Development ◽  
Development Environment ◽  
Product Metrics ◽  
Software Products ◽  
Process Characteristics ◽  
Development Processes ◽  
Process Metrics

There has been considerable discussion on the possible impacts of open source software development practices, especially in regard to the quality of the resulting software product. Recent studies have shown that analyzing data from source code repositories is an efficient way to gather information about project characteristics and programmers, showing that OSS projects are very heterogeneous in their team structures and software processes. However, one problem is that the resulting process metrics measuring attributes of the development process and of the development environment do not give any hints about the quality, complexity, or structure of the resulting software. Therefore, we expanded the analysis by calculating several product metrics, most of them specifically tailored to object-oriented software. We then analyzed the relationship between these product metrics and process metrics derived from a CVS repository. The aim was to establish whether different variants of open source development processes have a significant impact on the resulting software products. In particular we analyzed the impact on quality and design associated with the numbers of contributors and the amount of their work, using the GINI coefficient as a measure of inequality within the developer group.

scholarly journals Overview of R and RStudio

2021 ◽  
pp. 31-47
Author(s):  
Joseph F. Hair ◽  
G. Tomas M. Hult ◽  
Christian M. Ringle ◽  
Marko Sarstedt ◽  
Nicholas P. Danks ◽  
...  
Keyword(s):  
Programming Language ◽  
Graphical User Interface ◽  
Computational Statistics ◽  
Development Environment ◽  
Integrated Development ◽  
Vast Array ◽  
Popular Method ◽  
R Language ◽  
R Programming Language ◽  
R Programming

AbstractComputational statistics is now an increasingly popular method of analysis for researchers that combines a vast array of algorithms, statistical methods, and the power of functional coding. The R programming language, in particular, has benefitted from this development alongside of traditional graphical user interface (GUI) software. Today, it has become the language of choice for empirical researchers. In this chapter, we introduce the R programming language as well as its popular development environment in the form of RStudio. We walk the reader through downloading both the R language and the RStudio integrated development environment (IDE). Then, we discuss the software layout and demonstrate how to interact with the software. Finally, we address creating and managing R projects and scripts, gaining access to documentation and help via various sources. This chapter is not intended as a tutorial on the writing of code in the R programming language. We do, however, provide useful open-source resources for learning R, which can be accessed from the R console RStudio environment.

Exploring the Effects of Process Characteristics on Product Quality in Open Source Software Development

2011 ◽  
pp. 132-159 ◽  
Author(s):  
Stefan Koch ◽  
Christian Neumann
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Open Source Software Development ◽  
Development Environment ◽  
Product Metrics ◽  
Software Products ◽  
Process Characteristics ◽  
Development Processes ◽  
Process Metrics

There has been considerable discussion on the possible impacts of open source software development practices, especially in regard to the quality of the resulting software product. Recent studies have shown that analyzing data from source code repositories is an efficient way to gather information about project characteristics and programmers, showing that OSS projects are very heterogeneous in their team structures and software processes. However, one problem is that the resulting process metrics measuring attributes of the development process and of the development environment do not give any hints about the quality, complexity, or structure of the resulting software. Therefore, we expanded the analysis by calculating several product metrics, most of them specifically tailored to object-oriented software. We then analyzed the relationship between these product metrics and process metrics derived from a CVS repository. The aim was to establish whether different variants of open source development processes have a significant impact on the resulting software products. In particular we analyzed the impact on quality and design associated with the numbers of contributors and the amount of their work, using the GINI coefficient as a measure of inequality within the developer group.

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