Academic literature on the topic 'R programming language'

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Journal articles on the topic "R programming language"

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Povitukhin, Sergei Alekseevich, and Elena Nikolaevna Guseva. "Choosing a programming language for high school students." Development of education 6, no. 3 (2023): 40–47. http://dx.doi.org/10.31483/r-107327.

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Currently, special attention is paid to the content of the training of schoolchildren in computer science. Interest in software development and code writing methods is growing every year. This is due to the change in methods and means of training in the field of writing program code associated with the emergence and development of new paradigms and programming languages. The purpose of the study is to substantiate the choice of a programming language for the preparation of high school students in the field of programming and solving the tasks of the Unified State Exam in computer science. Mate
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Brennan, Paul. "Data visualization with the programming language R." Biochemist 43, no. 5 (2021): 8–14. http://dx.doi.org/10.1042/bio_2021_174.

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Data visualization is an extremely valuable skill in science, finance and journalism. Learning to program will help reproducible data analysis and will increase the different types of visualization that can be generated. The statistical programming language R is a very useful programming language. The R community is friendly, supportive and very diverse including students, academics, health scientists, journalists and professional data scientists. An experience of R or another programming language such as Python or JavaScript will improve your science and your employment opportunities in and o
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Giorgi, Federico M., Carmine Ceraolo, and Daniele Mercatelli. "The R Language: An Engine for Bioinformatics and Data Science." Life 12, no. 5 (2022): 648. http://dx.doi.org/10.3390/life12050648.

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The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the cur
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Staples, April, and Jordan R. Wagge. "Learning R Programming Language With Psi Chi." Psi Chi Journal of Psychological Research 25, no. 1 (2020): 85–86. http://dx.doi.org/10.24839/2325-7342.jn25.1.85.

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Briz-Redón, Álvaro, and Ángel Serrano-Aroca. "Novel pedagogical tool for simultaneous learning of plane geometry and R programming." Research Ideas and Outcomes 4 (April 5, 2018): e25485. https://doi.org/10.3897/rio.4.e25485.

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Programming a computer is an activity that can be very beneficial to undergraduate students in terms of improving their mental capabilities, collaborative attitudes and levels of engagement in learning. Despite the initial difficulties that typically arise when learning to program, there are several well-known strategies to overcome them, providing a very high benefit-cost ratio to most of the students. Moreover, the use of a programming language usually raises the interest of students to learn any specific concept, which has caused that many teachers around the world employ a programming lang
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Briz-Redón, Álvaro, and Ángel Serrano-Aroca. "Novel pedagogical tool for simultaneous learning of plane geometry and R programming." Research Ideas and Outcomes 4 (April 5, 2018): e25485. http://dx.doi.org/10.3897/rio.4.e25485.

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Programming a computer is an activity that can be very beneficial to undergraduate students in terms of improving their mental capabilities, collaborative attitudes and levels of engagement in learning. Despite the initial difficulties that typically arise when learning to program, there are several well-known strategies to overcome them, providing a very high benefit-cost ratio to most of the students. Moreover, the use of a programming language usually raises the interest of students to learn any specific concept, which has caused that many teachers around the world employ a programming lang
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Sinha, Sharad, and Clint P. George. "Artificial intelligence for all using R programming language." AI Matters 5, no. 4 (2020): 10–13. http://dx.doi.org/10.1145/3375637.3375642.

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Susko, Alexander Q., and Zachary T. Brym. "An Introduction to R Statistical Computing for Horticultural Science." HortTechnology 26, no. 5 (2016): 588–91. http://dx.doi.org/10.21273/horttech03339-16.

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We present the format for a workshop on introductory computer programming, which was held at the 2015 American Society for Horticultural Science (ASHS) Annual Conference in New Orleans, LA. The main workshop objective was to familiarize attendees with basic computer programming, including data structures, data management, and data analysis. The workshop used the general programming language R, though the concepts and principles presented are transferable across programming languages. Given the increased importance of statistical analysis in the agricultural sciences, the workshop was well atte
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Grzanek, Konrad. "ChR: Dynamic Functional Constraints Checking in R." Journal of Applied Computer Science Methods 9, no. 1 (2017): 65–78. http://dx.doi.org/10.1515/jacsm-2017-0004.

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Abstract Dynamic typing of R programming language may issue some quality problems in large scale data-science and machine-learning projects for which the language is used. Following our efforts on providing gradual typing library for Clojure we come with a package chR - a library that offers functionality of run-time type-related checks in R. The solution is not only a dynamic type checker, it also helps to systematize thinking about types in the language, at the same time offering high expressivenes and full adherence to functional programming style.
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Wiejak, Tomasz, and Jakub Smołka. "Performance of machine learning tools. Comparve analysis of libraries in interpreted and compiled programming languages." Journal of Computer Sciences Institute 33 (December 30, 2024): 339–45. https://doi.org/10.35784/jcsi.6589.

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The article compares machine learning tools using the example of several popular programming languages. Existing tools in the following programming languages were tested and compared with each other: Python, Java, R, Julia, C#. For the needs of article, algorithms were created in each studied language, operating on the same test set and using algorithms from the same group. The collected results included the program's running time, number of lines of code and accuracy of trained model. Based on the obtained data, conclusions were drawn that interpreted language libraries in terms of creating m
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Dissertations / Theses on the topic "R programming language"

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Pellerin, Clément. "Taskell : a concurrent constraint programming language." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61089.

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Taskell is an instance of the concurrent constraint programming framework cc. The framework is parameterized by a choice of constraint system. The constraint system of Taskell is the set of finite trees with equality. The choice of constraint system makes Taskell similar to concurrent logic programming languages. When computing with partial information the notion of reading and writing memory becomes incoherent. The framework replaces these operations by ask and tell respectively. We hope to understand this new paradigm by studying implementations of cc languages. Taskell is a parallel impleme
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Xie, Jiantao 1979. "Text operators in a relational programming language." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82451.

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JRelix is an implementation of a relational database system which provides a significantly powerful database programming language and which is especially adept with complex data. This thesis documents an enhancement of JRelix which provides intuitive descriptions and efficient manipulations for textual information in the database.<br>Coupled with the relational and domain algebra in JRelix, the new database system supports rapid textual information retrieval, flexible text mining, structured text schema discovery, relational operations on text, transformation between text and relation,
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Aslam, Toheed. "AspectMatlab: an aspect-oriented scientific programming language." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=92378.

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There has been relatively little work done in the compiler research community for incorporating aspect-oriented features in scientific and dynamic programming languages.<br>MATLAB is a dynamic scientific programming language that is commonly used by scientists because of its convenient and high-level syntax for arrays, the fact that type declarations are not required, and the availability of a rich set of application libraries. This thesis introduces a new aspect-oriented scientific language, AspectMatlab.<br>AspectMatlab introduces key aspect-oriented features in a way that is both accessible
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Germain, Renaud. "Implementation of a dependently typed functional programming language." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=92350.

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Ding, Weiren. "Selsyn-C : a self-synchronizing parallel programming language." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22494.

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In thesis thesis we report the design and implementation of a new self-scheduling parallel programming language, SELSYN-C. As parallel processors become more accessible to a broad range of programmers, the development of simple to use and effective programming languages becomes increasingly important. Our approach to the challenge of parallel programming language design and implementation is two-fold: (1) the design of simple extensions to C that are both easy to use for the programmer, and useful for effective compilation, and (2) the design of efficient and effective scheduling strategies th
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El-Kays, Ahmad Ziad. "Implementing event handlers in a database programming language." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23884.

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This thesis introduces active database concepts to a relational database system. Event handlers are introduced as user defined procedures. An event is the procedure call by the system, triggered in our case by an update statement.<br>Event handlers for the Relix (Relational database on UNIX) update statement have been implemented. These event handlers are of two types: the first type is that which runs before an update statement. The second type runs after an update statement. Also, event handlers have states. An event handler can be either active or inactive. Event handlers are executed when
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Sutyanyong, Nattavut. "Procedural abstraction in a relational database programming language." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=55531.

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This thesis introduces the notion of procedural abstraction in a relational database system. Procedures are treated as special forms of relations, and called computations.<br>Like relations, a computation is defined over a set of attributes. Subset of attributes can be defined as input attributes and the remaining attributes are output. Beyond the notion of procedures that a procedure can have only one set of input and output parameters, computations are symmetric: a computation may have a number of different subsets of input attributes.<br>Computations can be recursive and called by other com
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Chen, YuLing 1970. "A GIS editor for a database programming language /." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31209.

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Geographical Information Systems (GIS) have become a more and more important application of database systems. Most general-purpose database systems do not contain a graphical display interface which is indispensable in GIS applications. This thesis presents the design and implementation of a GIS editor (Geditor) for a relational database programming language. It builds a graphical map display interface into the database language and integrates a set of GIS functions.<br>Two interfaces are built in Geditor. One is with the database programmer and the other is with the GIS End-User. The former i
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Lui, Rebecca. "Implementation of procedures in a database programming language." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=27373.

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This thesis documents the design and implementation of procedures in a database programming language. The purpose of this thesis is to integrate procedure facilities into an existing relational database system.<br>A relation is defined over a set of attributes. Given the values of a subset of attributes as input, a selection operation looks up the relation and outputs the values of the remaining attributes. Our proposed procedure construct supports this concept: a procedure is defined over a set of parameters, and the procedure can be invoked with different subsets of input parameters. This is
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Chiama, Jared Alungo. "Text operations for a relational database programming language." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22726.

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In this thesis we introduce a method of storing static text data, and algorithms for operations on the data, in a relational database programming language.<br>We introduce a text data type, and implement relational algebra operations on the text data type. Our method stores the text data in text files external to the relations, and maintains pointers to the text data within the relations. Our algorithms minimize accesses to the actual text data so as to maintain the efficiency of database operations.<br>We also implement a text processing mechanism where a text script can be joined to a relati
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Books on the topic "R programming language"

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Gentleman, Robert. R programming for bioinformatics. CRC Press, 2008.

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Gentleman, Robert. R programming for bioinformatics. CRC Press, 2008.

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Lawrence, Michael. Programming graphical user interfaces in R. CRC Press, 2012.

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Smith, D. M. (David M.) and R. Development Core Team, eds. An introduction to R: Notes on R, a programming environment for data analysis and graphics. 2nd ed. Dept. of Statistics and Mathematics, Wirtschaftsuniversität Wien, 2008.

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Matzke, Bernd. ABAP/4, programming the SAP R/3 system. Addison-Wesley, 1997.

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John, Verzani, ed. Programming graphical user interfaces with R. Taylor & Francis, 2012.

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Crawley, Michael J. The R Book. John Wiley & Sons, Ltd., 2007.

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Ferryman, Patty. R Programming Tutorial : Understand the R Programming Language As a Beginner: R Language Quick Study. Independently Published, 2021.

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Nady, Mohsen. Introduction to R Programming Language. Arcler Education Inc, 2021.

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Beginning R: The Statistical Programming Language. Wrox, 2012.

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Book chapters on the topic "R programming language"

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Tuimala, Jarno, and Aleksi Kallio. "R, Programming Language." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_619.

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Van der Elst, Wim. "The R Programming Language." In Regression-Based Normative Data for Psychological Assessment. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50951-3_2.

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Berry, Kenneth J., Kenneth L. Kvamme, Janis E. Johnston, and Paul W. Mielke,. "The R Programming Language." In Permutation Statistical Methods with R. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74361-1_2.

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Araz, Özgür M., and David L. Olson. "R Programming Language and RStudio." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-2.

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Mailund, Thomas. "Components of a Programming Language." In Domain-Specific Languages in R. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3588-1_3.

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Pathak, Manas A. "Overview of the R Programming Language." In Beginning Data Science with R. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12066-9_2.

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Desjardins, Christopher D., and Okan Bulut. "Introduction to the R Programming Language." In Handbook of Educational Measurement and Psychometrics Using R. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b20498-1.

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Heesen, Bernd. "Basics of the R Programming Language." In Artificial Intelligence and Machine Learning with R. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-45392-3_5.

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Ågren Thuné, Anders, Kazutaka Matsuda, and Meng Wang. "Reconciling Partial and Local Invertibility." In Programming Languages and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57267-8_3.

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AbstractInvertible programming languages specify transformations to be run in two directions, such as compression/decompression or encryption/decryption. Two key concepts in invertible programming languages are partial invertibility and local invertibility. Partial invertibility lets invertible code be parameterized by the results of non-invertible code, whereas local invertibility requires all code to be invertible. The former allows for more flexible programming, while the latter has connections to domains such as low-energy computing and quantum computing. We find that existing approaches l
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Morandat, Floréal, Brandon Hill, Leo Osvald, and Jan Vitek. "Evaluating the Design of the R Language." In ECOOP 2012 – Object-Oriented Programming. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31057-7_6.

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Conference papers on the topic "R programming language"

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Igor Banjac, Igor, Dragan Vukmirović, and Marina Jovanović Milenković. "Big Data and the R Programming Language." In 36. mednarodna konferenca o razvoju organizacijskih znanosti, Portorož, Slovenija / 36th International Conference on Organizational Science Development, Portorož, Slovenia. Univerzitetna založba Univerze v Mariboru / University of Maribor Press, 2017. http://dx.doi.org/10.18690/978-961-286-020-2.3.

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Atchison, Abigail, Haley Anderson, Christina Berardi, et al. "A topic analysis of the R programming language." In ICSE '18: 40th International Conference on Software Engineering. ACM, 2018. http://dx.doi.org/10.1145/3183440.3195087.

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Wagner, Christian, Simon Miller, and Jonathan M. Garibaldi. "A fuzzy toolbox for the R programming language." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007743.

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Pavlenko, Liliia, Maksym Pavlenko, Vitalii Khomenko, and Vitaliy Mezhuyev. "Application of R Programming Language in Learning Statistics." In Symposium on Advances in Educational Technology. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010928500003364.

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Stadler, Lukas, Adam Welc, Christian Humer, and Mick Jordan. "Optimizing R language execution via aggressive speculation." In SPLASH '16: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. ACM, 2016. http://dx.doi.org/10.1145/2989225.2989236.

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Popov, Artem, Dmitrii Orekhov, Denis Litvinov, Nikolay Korolev, and Gleb Morgachev. "Time-Efficient Code Completion Model for the R Programming Language." In Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021). Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.nlp4prog-1.4.

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Chung, Tran Duc, Rosdiazli Ibrahim, Sabo Miya Hassan, and Nurfatihah Syalwiah Rosli. "Fast approach for automatic data retrieval using R programming language." In 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). IEEE, 2016. http://dx.doi.org/10.1109/roma.2016.7847824.

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Chen, Chao, Tajul Rosli Razak, and Jonathan M. Garibaldi. "FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177780.

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Hasan, Forat Falih, and Muhamad Shahbani Abu Bakar. "Data Transformation from SQL to NoSQL MongoDB Based on R Programming Language." In 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2021. http://dx.doi.org/10.1109/ismsit52890.2021.9604548.

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Yurchyshena, Liudmyla, Larysa Shaulska, and Maryna Naumova. "Using R Programming Language to Cluster of Universities Based on Sustainability Potential Indicators." In 2022 12th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2022. http://dx.doi.org/10.1109/acit54803.2022.9913137.

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Reports on the topic "R programming language"

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Nowosad, Jakub. Making Beautiful And Effective Maps In R. Instats Inc., 2023. http://dx.doi.org/10.61700/jjmsseyi5ywsn469.

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The aim of the course is to show you how to use R to make pretty, yet appealing maps using the R programming language. Several R packages related to spatial data processing and visualization will be introduced during the course. The course will teach you how create publication-ready static maps, animated maps, interactive maps, and simple map applications using a mixture of lectures and computer exercises. An official Instats certificate of completion is provided at the conclusion of the seminar, and European PhD students receive 1.5 ECTS Equivalent points.
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Pesotsky, A. A. Shock resistance in conditions of global turbulence: approaches to analysis. Institute for Regional Economic Studies Russian Academy of Science, 2023. http://dx.doi.org/10.52897/tms-2023-27-03.

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The purpose of the study is to analyze the shock impact caused by economic sanctions imposed against the socio–economic system of Russia and its regions using the method of the main components and the programming language R.
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Briggs, Cheryl J., and Samantha Sambado. Data Analysis in R to Gain Insights for Conservation: Examples from Long-Term Ecological Research. American Museum of Natural History, 2025. https://doi.org/10.5531/cbc.ncep.0189.

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The R programming language is a powerful tool for analyzing ecological datasets and gaining valuable insights to inform conservation efforts. This module is designed in two parts to help develop foundational skills for working with ecological data in R. The first part introduces you to R and RStudio, providing a solid foundation for data analysis. The second part focuses on key techniques for data wrangling and visualization. Whether you’re new to R or looking to expand your skills from a fresh perspective, this module offers something for those early in their R journey. Throughout, we will us
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Ricca, Bernard. Nonlinear Time Series Analyses (Part II). Instats Inc., 2024. https://doi.org/10.61700/0emwhpkidie951390.

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This four-day seminar delves into phenomenological modeling of time series, focusing on nonlinear dynamic methodologies such as sparse identification of nonlinear dynamics, dynamic mode decomposition, and hidden Markov models, all implemented using the R programming language. Participants will enhance their research skills by learning to dynamically model, analyze, and interpret time-dependent data, ultimately enabling them to conduct innovative and robust research across various fields.
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Kohler, Karsten. Teaching macroeconomics with an open-source online model simulation in R and Python. The Economics Network, 2024. http://dx.doi.org/10.53593/n3917a.

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Murphy, Joe J., Michael A. Duprey, Robert F. Chew, Paul P. Biemer, Kathleen Mullan Harris, and Carolyn Tucker Halpern. Interactive Visualization to Facilitate Monitoring Longitudinal Survey Data and Paradata. RTI Press, 2019. http://dx.doi.org/10.3768/rtipress.2019.op.0061.1905.

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Surveys often require monitoring during data collection to ensure progress in meeting goals or to evaluate the interim results of an embedded experiment. Under complex designs, the amount of data available to monitor may be overwhelming and the production of reports and charts can be costly and time consuming. This is especially true in the case of longitudinal surveys, where data may originate from multiple waves. Other such complex scenarios include adaptive and responsive designs, which were developed to act on the results of such monitoring to implement prespecified options or alternatives
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Further Topics in R. Instats Inc., 2024. http://dx.doi.org/10.61700/2s7qtk9jgs1e91093.

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The 'Further Topics in R' workshop by the Centre for Applied Statistics Courses (CASC) at University College London is a comprehensive seminar designed to enhance your R programming skills, widely used in academic research and across various professional disciplines. This course introduces a number of topics in R for those already familiar with the basics of the programming language, including some of the more advanced R code structures such as conditional commands, loops and writing functions.
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