Interactive applications based on the shiny/r package to explain statistical concepts
A literature systematic mapping
Keywords:
ICT, Teaching statistics, Interactive applications, Shiny/R, Systematic mappingAbstract
Shiny is an application for R software that allows the creation of interfaces for users without programming knowledge. In this work we use a systematic mapping method for the collection, analysis, and extraction of information in publications that indicate the use of Shiny to explain statistical concepts. Among the conclusions, it is found that Shiny is used as a tool for carrying out academic experiences, as well as a means for solving problems in the areas of education and natural and life sciences, addressing statistical topics related to pre-inferential statistics and inferential statistics, among others.
References
Arnholt, A. T. (2019). Using a Shiny app to teach the concept of power. Teaching Statistics, 41(3), 79-84.
Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., & Horton, N. J. (2014). R Markdown: Integrating a reproducible analysis tool into introductory statistics. arXiv preprint arXiv:1402.1894.
Beeley, C. (2016). Web application development with R using Shiny. Packt Publishing Ltd.
Berg, A. (2021). Bayesian explorations with dice. Teaching Statistics, 43(3), 114-123.
Boza Carreño, Á., Tirado Morueta, R., y Guzmán Franco, M. D. (2010). Creencias del profesorado sobre el significado de la tecnología en la enseñanza: influencia para su inserción en los centros docentes andaluces. Revista electrónica de investigación y Evaluación educative.
Chance, B. & Rossman, A. (2006). Using Simulation to Teach and Learn Statistics. In Proceedings of the Seventh International Conference on Teaching Statistics (pp. 1-6). Voorburg, The Netherlands: International Statistical Institute.
Chang, W., Cheng, J., Allaire, JJ, Xie, Y. & McPherson, J. (2021). shiny: Web Application Framework for R. R package version 1.7.1.
Disponible en https://cran.r-project.org/web/packages/shiny/index.html
Das, K. (2019). Role of ICT for Better Mathematics Teaching. Shanlax International Journal of Education, 7(4), 19-28.
Depaoli, S., Winter, S. D., & Visser, M. (2020). The importance of prior sensitivity analysis in Bayesian statistics: demonstrations using an interactive Shiny App. Frontiers in psychology, 11, 608045.
Di Iorio, J., & Vantini, S. (2021). How to Get Away with Statistics: Gamification of Multivariate Statistics. Journal of Statistics and Data Science Education, 29(3), 241-250.
Fernández, Z. y Neri, C. (2013). Estudiantes universitarios, TICS y aprendizaje. Anuario de Investigaciones, 20(3), 153-158.
Fawcett, L. (2018). Using interactive shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education, 26(1), 2-16.
Garfield, J. & Ben-Zvi, D. (2008). Developing Students’ Statistical Reasoning: Connecting Research and Teaching Practice, Kluwer Academic Publishers.
Gopinath, P. P., Parsad, R., Joseph, B., & Adarsh, V. S. (2021). grapesAgri1: collection of shiny apps for data analysis in agriculture. Journal of Open Source Software, 6(63), 3437
Harraway (2012). Learning Statistics Using Motivational Videos, Real Data and Free Soft- ware, Technology Innovations in Statistics Education, 6(1). https://doi.org/10.5070/T561000186
Johnson O, Fronterre C, Diggle PJ, Amoah B, Giorgi E (2021) MBGapp: A Shiny application for teaching model-based geostatistics to population health scientists. PLoS ONE 16(12): e0262145. https://doi.org/10.1371/journal.pone.0262145
LaMar, M., & Donovan, S. (2017). Building a gateway between classrooms and data science using QUBESHub. Gateways, 23-25.
Mairing, J. P. (2020). The Effect of Advance Statistics Learning Integrated Minitab and Excel with Teaching Teams. International Journal of Instruction, 13(2), 139-150.
Manjunath, M., Zhang, Y., Yeo, S. H., Sobh, O., Russell, N., Followell, C., ... & Song, J. S. (2017). ClusterEnG: An interactive educational web resource for clustering big data. bioRxiv, 120915.
Miranda Freire, S. (2019). Using Shiny to illustrate the probability density function concept. Teaching Statistics, 41(1), 30-35.
Mochón, J. F. C., Carrillo, J. R., Jiménez, E. S., y López, A. M. (2021). Didáctica de las matemáticas, software libre y desarrollo de recursos mediante Learnr y Shiny. EDUCATECONCIENCIA, 29(31), 101-121.
Moore, T. N., Thomas, R. Q., Woelmer, W. M., & Carey, C. C. (2022). Integrating Ecological Forecasting into Undergraduate Ecology Curricula with an R Shiny Application-Based Teaching Module. Forecasting, 4(3), 604-633.
Murillo, D. A., Gezan, S. A., Heilman, A. M., Walk, T. C., Aparicio, J. S., & Horsley, R. D. (2021). FielDHub: A shiny app for design of experiments in life sciences. The Journal of Open Source Software.
Nolan, D. & Speed, T. P. (2000). Stat Labs: Mathematical Statistics Through Applications, Springer. http://www.stat.Berkeley.edu/users/statlabs/
Panchenko, L. & Khomiak, A. (2020). Education Statistics: Looking for а Case-study for Modelling.
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008, June). Systematic mapping studies in software engineering. In 12th International Conference on Evaluation and Assessment in Software Engineering (EASE) 12 (pp. 1-10).
Pfleeger, S. L. (2005). Soup or art? The role of evidential force in empirical software engineering. IEEE software, 22(1), 66-73.
Potter, G., Wong, J., Alcaraz, I., & Chi, P. (2016). Web application teaching tools for statistics using R and shiny. Technology Innovations in Statistics Education, 9(1).
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Satyahadewi, N., & Perdana, H. (2021, May). Web Application Development for Inferential Statistics using R Shiny. In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020) (pp. 425-429). Atlantis Press.
Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. CRC Press.
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte Carlo simulation. Journal of Statistics Education, 24(3), 136-156.
Stratton, C., Green, J. L., & Hoegh, A. (2021). Not just normal: Exploring power with Shiny apps. Technology Innovations in Statistics Education, 13(1).
Toledo, Á. y Vicencio, I. (2021). El uso de TIC para el aprendizaje de la Estadística: un estudio en universidades chilenas. Educação e ensino na era da informação, 40-58.
Varma, J. R., & Virmani, V. (2017). Shiny alternative for Finance in the Classroom.
Von Borries, G. F., & De Castro Quadros, A. VV. (2022). ROC App: an application to understand roc curves. Brazilian Journal of Biometrics, 40(2).
Wang, S. L., Zhang, A. Y., Messer, S., Wiesner, A., & Pearl, D. K. (2021). Student-Developed Shiny Applications for Teaching Statistics. Journal of Statistics and Data Science Education, 29(3), 218-227.
Wen, J., Li, S., Lin, Z., Hu, Y., & Huang, C. (2012). Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology, 54(1), 41-59.
Wickham, H. (2011). ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics, 3(2), 180-185. https://doi.org/10.1002/wics.147
Wickham, H. (2021). Mastering shiny. “ O’Reilly Media, Inc.”.
Williams, I. J., & Williams, K. K. (2018). Using an R shiny to enhance the learning experience of confidence intervals. Teaching Statistics, 40(1), 24-28.
Wojciechowski, J., Hopkins, A. M., & Upton, R. N. (2015). Interactive pharmacometric applications using R and the shiny package. CPT: pharmacometrics & systems pharmacology, 4(3), 146-159.
Zieffler, A., Park, J., Gareld, J., delMas, R. & Bjornsdottir, A. (2012). The Statistics. Teaching Inventory: A Survey on Statistics Teachers’ Classroom Practices and Beliefs, Journal of Statistics Education, 20(1). https://doi.org/10.1080/10691898.2012.11889632
