Análisis comparativo de herramientas web para la generación de visualizaciones interactivas: Chart.js, Google Charts y Highcharts

Luis René Quisaguano Collaguazo; Gladys Geoconda Esquivel Paula; Edwin Fabricio Damacela Calero; Edison Isaias Pallo Cuchiparte

doi:10.70577/ASCE/578.597/2025

Authors

Luis René Quisaguano Collaguazo Universidad Técnica De Cotopaxi https://orcid.org/0000-0003-1345-0898
Gladys Geoconda Esquivel Paula Universidad Técnica De Cotopaxi https://orcid.org/0009-0002-3715-7776
Edwin Fabricio Damacela Calero Universidad Técnica De Cotopaxi https://orcid.org/0009-0002-7126-5316
Edison Isaias Pallo Cuchiparte Universidad Técnica De Cotopaxi https://orcid.org/0009-0005-2992-5693

DOI:

https://doi.org/10.70577/ASCE/578.597/2025

Keywords:

Administration; Chart.js; comparison; Google Charts; web tools; Highcharts; interactive visualizations

Abstract

Visualizing data changes the way people perceive information, as transforming data into graphics makes it easier to understand. The objective of the research was to conduct a comparative analysis of web tools designed to generate interactive visualizations. A non-experimental design was used, with a descriptive scope, analytical method, and qualitative-quantitative approach. Three popular interactive data visualization tools were compared: Chart.js, Google Charts, and Highcharts. A bibliographic review was conducted and a survey was administered. The study variables were: a) Ease of use and implementation, b) Variety of chart types, c) Technical performance, d) Customization and extensibility, and e) Community, documentation, and support. Finally, comparative tables were developed. When comparing web tools, it was found that each tool has its degree of quality, efficiency and usefulness, however, ease of use, variety of chart types, technical performance, customization, extensibility, depends on the type of license, being higher in the commercial version (Highcharts) and lower in the free options, this result does not influence the user community, since most prefer the free access options even when there are certain limitations (Chart.js and Google Charts).

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References

Agosti, M., Crivellari, F., Di Nunzio, G., y Gabrielli, S. (2010). Understanding user requirements and preferences for a digital library Web portal. International Journal on Digital Libraries, 11(1), 225-238. https://doi.org/10.1007/s00799-011-0075-7 DOI: https://doi.org/10.1007/s00799-011-0075-7

Aksoy, F., Özdem, M., y Daş, R. (2025). A Perspective View on Data Visualization Libraries Used in Data Analytics. European Journal of Technique (EJT), 15(1), 81-96. https://doi.org/10.36222/ejt.1616824

Alomari, H., Vendome, C., y Rizkallah, L. (2023). A Comprehensive Evaluation Framework of Software Visualizations Effectiveness. IEEE Transactions on Visualization and Computer Graphics, 30(9), 6056-6074. https://doi.org/10.1109/TVCG.2023.3321211 DOI: https://doi.org/10.1109/TVCG.2023.3321211

Baziyad, H., Kayvanfar, V., y Kinra, A. (2024). A bibliometric analysis of data-driven technologies in digital supply chains. Supply Chain Analytics, 6(1), e100067. https://doi.org/10.1016/j.sca.2024.100067 DOI: https://doi.org/10.1016/j.sca.2024.100067

Boström, F., Dahlberg, A., y Linderoth, W. (2022). A performance investigation into JavaScript visualization libraries with the focus on render time and memory usage: A performance measurement of different libraries and statistical charts. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1679885&dswid=-9232

Cantrell, S., Walker, B., y Moseng, O. (2021). Highcharts sonification studio: una herramienta de sonificación de datos en línea, de código abierto, extensible y accesible. http://hdl.handle.net/1853/66348

Chandra, T., y Dwivedi, A. (2022). Data visualization: Existing tools and techniques. In Advanced data mining tools and methods for social computing, 1(2), 177-217. https://doi.org/10.1016/B978-0-32-385708-6.00017-5 DOI: https://doi.org/10.1016/B978-0-32-385708-6.00017-5

Diesh, C., Stevens, G., Xie, P., De Jesus Martinez, T., Hershberg, E., Leung, A., y Holmes, I. H. (2023). JBrowse 2: a modular genome browser with views of synteny and structural variation. Genome biology, 24(1), 74. DOI: https://doi.org/10.1186/s13059-023-02914-z

Duarte, D. (2023). AutoVizuA11y: A tool to automate accessibility in data visualizations for screen reader users. Master's thesis. Universidade de Lisboa, Portugal.

Duarte, D., Costa, R., Bizarro, P., y Duarte, C. (2024). AutoVizuA11y: A Tool to Automate Screen Reader Accessibility in Charts. In Computer Graphics Forum, 43(3), e15099. https://doi.org/10.1111/cgf.15099 DOI: https://doi.org/10.1111/cgf.15099

Fahmy, N., y Attia, M. A. (2024). Innovative Journalism Practices: Insights for Reporting Science and Health Issues. Palgrave Handbook of Science and Health Journalism, 239-269. https://doi.org/10.1007/978-3-031-49084-2_24 DOI: https://doi.org/10.1007/978-3-031-49084-2_13

García, L. A., OliveiraJr, E., & Morandini, M. (2022). Tailoring the Scrum framework for software development: Literature mapping and feature-based support. Information and Software Technology, 146, 106814. DOI: https://doi.org/10.1016/j.infsof.2021.106814

Igulu, K., Onuodu, F., Chaudhary, R., y Justice, P. (2023). Business Intelligence. In AI-Based Data Analytics. 1st Edition. Auerbach Publications. pp. 49-82. DOI: https://doi.org/10.1201/9781032614083-4

Kato, A., Kisangiri, M., y Kaijage, S. (2021). A review development of digital library resources at university level. Education Research International, 2021(1), e8883483. https://doi.org/10.1155/2021/8883483 DOI: https://doi.org/10.1155/2021/8883483

Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H., y El Omri, A. (2024). A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics, 6(2), e100063. https://doi.org/10.1016/j.sca.2024.100063 DOI: https://doi.org/10.1016/j.sca.2024.100063

Liggesmeyer, P., Barthel, H., Ebert, A., Heidrich, J., Keller, P., Yang, Y., y Wickenkamp, A. (2012). Quality improvement through visualization of software and systems. Quality Assurance and Management, InTech, 315-334. https://doi.org/10.5772/33058 DOI: https://doi.org/10.5772/33058

Naceri, A., Mazzanti, D., Bimbo, J., Tefera, Y., Prattichizzo, D., Caldwell, D., y Deshpande, N. (2021). The vicarios virtual reality interface for remote robotic teleoperation: Teleporting for intuitive tele-manipulation. Journal of Intelligent & Robotic Systems, 101(2), 1-16. https://doi.org/10.1007/s10846-021-01311-7 DOI: https://doi.org/10.1007/s10846-021-01311-7

Persson, J. (2021). Scalability of javascriptlibraries for datavisualization. https://www.diva-portal.org/smash/get/diva2:1574320/FULLTEXT01.pdf

Sacchi, F. (2024). Purché siano accessibili: una desk review dei dati statistici e amministrativi italiani delle persone con disabilità. L'INTEGRAZIONE SCOLASTICA E SOCIALE, (23), 90-105.

Sarker, H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective’. SN Computer Science, 2(5), e377. https://doi.org/10.1007/s42979-021-00765-8. DOI: https://doi.org/10.1007/s42979-021-00765-8

Smatt, C., Pratt, R., Abegaz, T., y Dobbs, A. (2020). Towards Effective Customer Data Visualization: Data-Driven Documents (D3. JS) VS Google Charts. A Journal Of International Academy Of Business Disciplines, 7(3), 207-222.

Verma, S., y Dwivedi, U. (2023). Optimizing Digital Knowledge Repositories: Leveraging Electronic Resources in University Libraries For Enhanced Academic Advantages. Perspectives in Social Work, 37(03), 112-132.