Referencias

Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. Proc. 20th Int. Conf. Very Large Data Bases, VLDB, 1215, 487–499.

Al-Monawer, N., Davoodi, M., & Qi, L. (2021). Brand and quality effects on introduction of store brand products. Journal of Retailing and Consumer Services, 61, 102507. https://doi.org/https://doi.org/10.1016/j.jretconser.2021.102507

Alonso, J. C. (2021). Una introducción a los loops en r (y algunas alternativas). Universidad Icesi. https://ideas.repec.org/p/col/000559/019408.html

Alonso, J. C. (2022). Empezando a transformar bases de datos con r y dplyr. Universidad Icesi. https://doi.org/10.18046/EUI/bda.h.2

Alonso, J. C. (2024). Introducción al modelo clásico de regresión para científico de datos en r. Universidad Icesi. https://doi.org/https://doi.org/10.18046/EUI/bda.h.4

Alonso, J. C., & Carabali, J. A. (2019). Breve tutorial para visualizar y calcular métricas de redes (grafos) en r (para económisas). Universidad Icesi.

Alonso, J. C., & Largo, M. F. (2023). Empezando a visualizar datos con r y ggplot2. (2. ed.). Universidad Icesi. https://doi.org/10.18046/EUI/bda.h.3.2

Alonso, J. C., & Ocampo, M. P. (2022). Empezando a usaR: Una guía paso a paso. Universidad Icesi. https://doi.org/doi.org/10.18046/EUI/bda.h.1

Arboleda, A. M., & Alonso, J. C. (2016). Estimación de un modelo econométrico para determinar el efecto de acciones de marketing en ventas de productos de cuidado personal en Colombia. Revista de Métodos Cuantitativos Para La Economía y La Empresa, 22, Páginas 230 a 249. https://doi.org/10.46661/revmetodoscuanteconempresa.2349

Arboleda, A. M., & Arce-Lopera, C. (2015). Quantitative analysis of product categorization in soft drinks using bottle silhouettes. Food Quality and Preference, 45, 1–10. https://doi.org/https://doi.org/10.1016/j.foodqual.2015.04.006

CBR, S. W. (1998). URBAN MYTH DISPROVED: BEER AND DIAPERS DON’t MIX. https://techmonitor.ai/technology/urban_myth_disproved_beer_and_diapers_dont_mix

Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., & Borges, B. (2021). Shiny: Web application framework for r. https://CRAN.R-project.org/package=shiny

Choi, P. (2018). Why do certain products influence grocery store choice? The role of anchor products and their relationships with other store choice factors: An abstract. In N. Krey & P. Rossi (Eds.), Back to the future: Using marketing basics to provide customer value (pp. 249–249). Springer International Publishing.

Contemporary Analysis, C. (2022). Diapers, beer, and data science in retail. https://canworksmart.com/diapers-beer-retail-predictive-analytics/

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org

Drèze, X., & Hoch, S. J. (1998). Exploiting the installed base using cross-merchandising and category destination programs. International Journal of Research in Marketing, 15(5), 459–471. https://doi.org/https://doi.org/10.1016/S0167-8116(98)00017-2

Egbeola, S. (2023, June 29). The bread basket bakery — analysis project. Medium. https://medium.com/@samuelegbeola/the-bread-basket-bakery-analysis-project-f4543275ef52

Hahsler, M. (2017). ArulesViz: Interactive visualization of association rules with R. R Journal, 9(2), 163–175. https://doi.org/10.32614/RJ-2017-047

Hahsler, M., Chelluboina, S., Hornik, K., & Buchta, C. (2011). The arules r-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12, 1977–1981. https://jmlr.csail.mit.edu/papers/v12/hahsler11a.html

Hery, H., & Widjaja, A. E. (2024). Analysis of apriori and FP-growth algorithms for market basket insights: A case study of the bread basket bakery sales. Journal of Digital Marketing and Digital Currency, 1(1), 63–74. https://doi.org/10.47738/jdmdc.v1i1.2

Kocas, C., Pauwels, K., & Bohlmann, J. D. (2018). Pricing best sellers and traffic generators: The role of asymmetric cross-selling. Journal of Interactive Marketing, 41(1), 28–43. https://doi.org/10.1016/j.intmar.2017.09.001

Kotler, P., & Armstrong, G. (2012). Marketing (10th ed.). Pearson Educación.

lukeA. (2017). Item frequency plots from object of class transactions in ggplot2. https://stackoverflow.com/questions/43500577/item-frequency-plots-from-object-of-class-transactions-in-ggplot2

Madsen, M. (2017). Beer, dispers and correlation: A tale of ambiguity. http://download.1105media.com/tdwi/Remote-assets/Events/2017/Boston/MarkMadsen_Beer-and-Diapers.pdf

Mittal, V. (2018). The Bread Basket [Data set]. https://www.kaggle.com/datasets/mittalvasu95/the-bread-basket

Oliveira, A. (2018). Bakery (market basket analysis). https://www.kaggle.com/aboliveira/bakery-market-basket-analysis

Online Retail. (2015). UCI Machine Learning Repository.

Pascucci, F., Nardi, L., Marinelli, L., Paolanti, M., Frontoni, E., & Gregori, G. L. (2022). Combining sell-out data with shopper behaviour data for category performance measurement: The role of category conversion power. Journal of Retailing and Consumer Services, 65, 102880. https://doi.org/https://doi.org/10.1016/j.jretconser.2021.102880

Power, D. J. (2002). Ask dan! http://www.dssresources.com/newsletters/66.php

R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/

Rooderkerk, R. P., & Lehmann, D. R. (2021). Incorporating consumer product categorizations into shelf layout design. Journal of Marketing Research, 58(1), 50–73. https://doi.org/10.1177/0022243720964127

Sievert, C. (2020). Interactive web-based data iczation with r, plotly, and shiny. Chapman; Hall/CRC. https://plotly-r.com

Swoyer, S. (2016). Beer and diapers: The impossible correlation. https://tdwi.org/articles/2016/11/15/beer-and-diapers-impossible-correlation.aspx

Vaidyanathan, R., Xie, Y., Allaire, J., Cheng, J., Sievert, C., & Russell, K. (2022). Htmlwidgets: HTML widgets for r. https://github.com/ramnathv/htmlwidgets

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wickham, H., François, R., Henry, L., & Müller, K. (2021). Dplyr: A grammar of data manipulation. https://CRAN.R-project.org/package=dplyr