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Comparison of R and Excel in the Field of data analysis

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Journal of Electronic Research and Application 2024年第3期8卷 178-184页

作者： Jue Wang Syracuse University New York 13244USA

This research paper compares Excel and R language for data analysis and concludes that R language is more suitable for complex data analysis tasks.R language’s open-source nature makes it accessible to everyone,and its powerful data management and analysis tools make it suitable for handling complex data analysis *** is also highly customizable,allowing users to create custom functions and packages to meet their specific ***,R language provides high reproducibility,making it easy to replicate and verify research results,and it has excellent collaboration capabilities,enabling multiple users to work on the same project *** advantages make R language a more suitable choice for complex data analysis tasks,particularly in scientific research and business *** findings of this study will help people understand that R is not just a language that can handle more data than Excel and demonstrate that r is essential to the field of data *** the same time,it will also help users and organizations make informed decisions regarding their data analysis needs and software preferences.

关键词： Excel R language data analysis Open source Compare data management Advantages Disadvantages Function

Enhancing data analysis and Automation: Integrating Python with Microsoft Excel for Non-Programmers

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Journal of Software Engineering and Applications 2024年第6期17卷 530-540页

作者： Osama Magdy Ali Mohamed Breik Tarek Aly Atef Tayh Nour El-Din Raslan Mervat Gheith Software Engineering Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.

关键词： Python End-User Approach Microsoft Excel data analysis Integration Spreadsheet Programming data Visualization

Exploring commercial water electrolyser systems: a data-based analysis of product characteristics

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CLEAN ENERGY 2024年第1期8卷 126-133页

作者： Jin, Xin You, Shi Petersen, Marianne Riofrio, Jonathan Thakur, Soumya Traeholt, Chresten Feng, Zhijian Tech Univ Denmark Dept Wind & Energy Syst DK-2800 Lyngby Denmark Siemens Gamesa Renewable Energy AS DK-7330 Brande Denmark

The urgency for energy transition is evident through the increasing demand for new technologies such as water electrolysers (WEs), which have the potential to generate green hydrogen using renewable electricity. This paper aims to provide a comprehensive overview of the technical capabilities of commercially available WE system products. The analysis is based on publicly accessible data gathered from 28 WE manufacturers worldwide with a total of 186 products, focusing on technology types and various technical characteristics of each WE system, including capacity, footprint, hydrogen output pressure, hydrogen purity and conversion rate. The analysis reveals that the current WE system solutions in the market exhibit diverse and varied characteristics. Further, there is a lack of standardized product specifications adopted by manufacturers. This underscores the urgent need for the development of frameworks and standards. Implementing such standards is crucial for enhancing clarity and understanding, facilitating efficient comparisons and selection processes, and supporting the future advancement of WE technologies and WE-enabled Power-to-X applications on a global scale. A comprehensive overview of the technical capabilities of commercially available water electrolysis system products is presented. The analysis is based on publicly accessible data gathered from manufacturers worldwide, focusing on technology types and technical characteristics, including capacity, conversion rate, footprint, hydrogen output pressure, hydrogen purity and efficiency. Graphical Abstract

关键词： data analysis hydrogen Power-to-X standards water electrolyser system ENERGY

data-driven analysis of chemicals,proteins and pathways associated with peanut allergy:from molecular networking to biological interpretation

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Food Science and Human Wellness 2024年第3期13卷 1322-1335页

作者： Emmanuel Kemmler Julian Braun Florent Fauchère Sabine Dölle-Bierke Kirsten Beyer Robert Preissner Margitta Worm Priyanka Banerjee Institute for Physiology Charité-University Medicine BerlinBerlin 10115Germany Member of the KFO339 FOOD@Berlin 10115Germany Si-M/“Der Simulierte Mensch”a science framework of Technische Universität Berlin and Charité-Universitätsmedizin Berlin Berlin 10115Germany Charité-Universitätsmedizin Berlin corporate member of Freie Universität BerlinHumboldt-Universität zu Berlinand Berlin Institute of HealthBerlin 10115Germany Division of Allergy and Immunology Department of DermatologyVenerology and AllergyCharité-Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlin 10115Germany Pediatrics Campus VirchowCharité-University Medicine BerlinBerlin 10115Germany

Peanut allergy is majorly related to severe food induced allergic *** food including cow's milk,hen's eggs,soy,wheat,peanuts,tree nuts(walnuts,hazelnuts,almonds,cashews,pecans and pistachios),fish and shellfish are responsible for more than 90%of food ***,we provide promising insights using a large-scale data-driven analysis,comparing the mechanistic feature and biological relevance of different ingredients presents in peanuts,tree nuts(walnuts,almonds,cashews,pecans and pistachios)and ***,we have analysed the chemical compositions of peanuts in different processed form raw,boiled and *** the data-driven approach we are able to generate new hypotheses to explain why nuclear receptors like the peroxisome proliferator-activated receptors(PPARs)and its isoform and their interaction with dietary lipids may have significant effect on allergic *** results obtained from this study will direct future experimeantal and clinical studies to understand the role of dietary lipids and PPARisoforms to exert pro-inflammatory or anti-inflammatory functions on cells of the innate immunity and influence antigen presentation to the cells of the adaptive immunity.

关键词： Allergy informatics Knowledge-graph data analysis Food allergy Peroxisome proliferator-activated receptors Fatty acids

data analysis guidelines for single‑cell RNA‑seq in biomedical studies and clinical applications

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军事医学研究:英文版 2023年第4期10卷 529-553页

作者： Min Su Tao Pan Qiu‑Zhen Chen Wei‑Wei Zhou Yi Gong Gang Xu Huan‑Yu Yan Si Li Qiao‑Zhen Shi Ya Zhang Xiao He Chun‑Jie Jiang Shi‑Cai Fan Xia Li Murray J.Cairns Xi Wang Yong‑Sheng Li State Key Laboratory of Reproductive Medicine Nanjing Medical UniversityNanjing 211166China College of Biomedical Information and Engineering the First Affiliated Hospital of Hainan Medical UniversityHainan Medical UniversityHaikou 571199HainanChina College of Bioinformatics Science and Technology Harbin Medical UniversityHarbin 150081HeilongjiangChina Department of Immunology Nanjing Medical UniversityNanjing 211166China Department of Laboratory Medicine Women and Children’s Hospital of Chongqing Medical UniversityChongqing 401174China Baylor College of Medicine HoustonTX 77030USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110GuangdongChina School of Biomedical Sciences and Pharmacy Faculty of Health and Medicinethe University of NewcastleUniversity DriveCallaghanNSW 2308Australia Precision Medicine Research Program Hunter Medical Research InstituteNew Lambton HeightsNSW 2305Australia

The application of single-cell RNA sequencing(scRNA-seq)in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic *** the expansion of capacity for high-throughput scRNA-seq,including clinical samples,the analysis of these huge volumes of data has become a daunting prospect for researchers entering this ***,we review the workflow for typical scRNA-seq data analysis,covering raw data processing and quality control,basic data analysis applicable for almost all scRNA-seq data sets,and advanced data analysis that should be tailored to specific scientific *** summarizing the current methods for each analysis step,we also provide an online repository of software and wrapped-up scripts to support the *** and caveats are pointed out for some specific analysis tasks and *** hope this resource will be helpful to researchers engaging with scRNA-seq,in particular for emerging clinical applications.

关键词： Single-cell RNA-sequencing(scRNA-seq) data analysis Biomedical research Clinical applications

Blockchain data analysis from the Perspective of Complex Networks:Overview

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Tsinghua Science and Technology 2023年第1期28卷 176-206页

作者： Wanshui Song Wenyin Zhang Jiuru Wang Linbo Zhai Pengkun Jiang Shanyun Huang Bei Li School of Information Science and Engineering Shandong Normal UniversityJinan 250307China School of Information Science and Engineering Linyi UniversityLinyi 276005China

Cryptocurrency based on blockchain technology has gradually become a choice for people to invest in,and several users have participated in the accumulation of massive transaction *** transaction records in blockchains and the openness of data provide researchers with opportunities to mine and analyze data in *** modeling and analysis of cryptocurrency transaction records are common methods in blockchain data *** analysis of attribute graphs can provide insights into various economic indicators,illegal activities,and general Internet security,among ***,this article aims to summarize and analyze the literature on cryptocurrency transaction data from the perspective of complex *** provide systematic guidance for researchers,we put forward a blockchain data analysis framework based on the introduction of the relevant background and reviewed the work from five aspects:blockchain data model,data acquisition on blockchains,existing analysis tools,available insights,and common analysis *** each aspect,we introduce the research problems,summarize the methods,and discuss the results and ***,we present future research points and several open questions in the study of cryptocurrency transaction networks.

关键词： blockchain cryptocurrency transaction record complex network data analysis data mining

Health care data analysis and visualization using interactive data exploration for sportsperson

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Science China(Information Sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big data’s influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics

RFID-based Production data analysis in an IoT-enabled Smart Job-shop

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 128-138页

作者： Kai Ding Pingyu Jiang state key laboratory for manufacturing systems engineering xi’an jiaotong university Xi'an 710054 China

Under industry 4.0, internet of things(IoT), especially radio frequency identification(RFID) technology, has been widely applied in manufacturing environment. This technology can bring convenience to production control and production transparency. Meanwhile, it generates increasing production data that are sometimes discrete, uncorrelated, and hard-to-use. Thus,an efficient analysis method is needed to utilize the invaluable data. This work provides an RFID-based production data analysis method for production control in Io T-enabled smart *** physical configuration and operation logic of Io T-enabled smart job-shop production are firstly described. Based on that,an RFID-based production data model is built to formalize and correlate the heterogeneous production data. Then, an eventdriven RFID-based production data analysis method is proposed to construct the RFID events and judge the process command execution. Furthermore, a near big data approach is used to excavate hidden information and knowledge from the historical production data. A demonstrative case is studied to verify the feasibility of the proposed model and methods. It is expected that our work will provide a different insight into the RFIDbased production data analysis.

关键词： data analysis internet of things(IoT) production control radio frequency identification(RFID) smart jobshop

Gravitational wave astrophysics, data analysis and multimessenger astronomy

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Science China(Physics,Mechanics & Astronomy) 2015年第12期58卷 44-64页

作者： LEE Hyung Mok LE BIGOT Eric-Olivier DU ZhiHui LIN ZhangXi GUO XiangYu WEN LinQing PHUKON Khun Sang PANDEY Vihan BOSE Sukanta FAN Xi-Long HENDRY Martin Department of Physics and Astronomy Seoul National University Research Institute of Information Technology Tsinghua National Laboratory for Information Science and TechnologyTsinghua University Laboratoire Astro Particule et Cosmologie (APC) Universit Paris Diderot-Paris 7 Bc/timent Condorcet Case 7020 75205 Paris Cedex 13 France Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and TechnologyTsinghua University School of Physics The University of Western Australia Department of Physics IIT Kanpur Inter-University Centre for Astronomy and Astrophysics Department of Physics & Astronomy Washington State University School of Physics and Electronics Information Hubei University of Education SUPA School of Physics and Astronomy University of Glasgow

This paper reviews gravitational wave sources and their detection. One of the most exciting potential sources of gravitational waves are coalescing binary black hole systems. They can occur on all mass scales and be formed in numerous ways, many of which are not understood. They are generally invisible in electromagnetic waves, and they provide opportunities for deep investigation of Einstein's general theory of relativity. Sect. 1 of this paper considers ways that binary black holes can be created in the universe, and includes the prediction that binary black hole coalescence events are likely to be the first gravitational wave sources to be detected. The next parts of this paper address the detection of chirp waveforms from coalescence events in noisy *** analysis is computationally intensive. Sect. 2 reviews a new and powerful method of signal detection based on the GPUimplemented summed parallel infinite impulse response filters. Such filters are intrinsically real time alorithms, that can be used to rapidly detect and localise signals. Sect. 3 of the paper reviews the use of GPU processors for rapid searching for gravitational wave bursts that can arise from black hole births and coalescences. In sect. 4 the use of GPU processors to enable fast efficient statistical significance testing of gravitational wave event candidates is reviewed. Sect. 5 of this paper addresses the method of multimessenger astronomy where the discovery of electromagnetic counterparts of gravitational wave events can be used to identify sources, understand their nature and obtain much greater science outcomes from each identified event.

关键词： gravitational waves data analysis multimessenger