Workshop on Visual Data Analysis with R Program
Kismiantini Kismiantini, Universitas Negeri Yogyakarta, Indonesia
Rosita Kusumawati, Universitas Negeri Yogyakarta, Indonesia
Retno Subekti, Universitas Negeri Yogyakarta, Indonesia
Ezra Putranda Setiawan, Universitas Negeri Yogyakarta, Indonesia
Bayutama Isnaini, Universitas Negeri Yogyakarta, Indonesia
Indira Ihnu Brilliant, Universitas Negeri Yogyakarta, Indonesia
Abstract
Statistics data analysis generally focuses more on mathematical procedures than visual. Visual analysis is very useful for research and this is still very limited to study at Universitas Mercu Buana Yogyakarta, so the UNY Statistics lecturer’s service activity is holding visual data analysis workshop with the R program, where this program is open source and is complete for visual analysis. The material for this activity is about procedures and uses for visual data analysis, introduction to the R program, data management with the R program, visual data analysis for group descriptions and comparisons, and visual data analysis for relationships between variables. Evaluation of participants' ability to understand the material is measured through 14 questions with four Likert Scale responses. Based on 40 questionnaires, 27,86% answered "Strongly Agree", 71,96% "Agree", and 0,18% "Disagree" regarding understanding and applying visual data analysis techniques with the R program. Therefore, it can be concluded that the majority of participants could understand the workshop material and follow the training well.
Keywords
Full Text:
PDFReferences
Arnold, J. (2021). ggthemes: Extra themes, scales and geoms for 'ggplot2'. R package version 4.2.4,.
Boehmke, C. B. (2016). Data Wrangling with R. Springer.
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4),745-766. https://doi.org/10.1080/10618600.2017.1384734
Erickson, B. H., Nosanchuk, T. A. (1981). Memahami data: statistika untuk ilmu sosial (Terjemahan oleh Sembiring, R.K., dan Malo, M). Jakarta, LP3ES.
Grolemund, G., Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3), 1-25. URL https://www.jstatsoft.org/v40/i03/.
Hehman, E., Xie S. Y. (2021). Doing Better Data Visualization. Advances in Methods and Practices in Psychological Science, 4(4). doi:10.1177/25152459211045334
Kandel, S., Heer, J., Plaisant, C., Kennedy, J., Ham, F. v., Richie, N. H., Buono, P. (2011). Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization, 10(4), 271-288. DOI: https://doi.org/10.1177/1473871611415994
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Ali, M., Kamaleldin, W., & Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014. DOI: https://doi.org/10.1155/2014/712826
Midway, S. R. (2020). Principles of Effective Data Visualization. Patterns, 1(9). https://doi.org/10.1016/j.patter.2020.100141.
Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics Anxiety: Nature, etiology, antecedents, effects,and treatments--a comprehensive review of the literature. Teaching in higher education, 8(2),195-209.https://doi.org/10.1080/1356251032000052447
Pebesma, E. (2018). Simple features for R: Standardized support for spatial vector data. The R Journal, 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Setiawan, E. P. (2019). Analisis muatan literasi statistika dalam buku teks matematika Kurikulum 2013. Pythagoras: Jurnal Matematika dan Pendidikan Matematika, 14(2), 163-177. https://doi.org/10.21831/pg.v14i2.28558
Setiawan, E.P. & Sukoco, H. (2021). Exploring first year university students’ statistical literacy: a case on describing and visualizing data. Journal on Mathematics Education, 12(3), 427-448. http://doi.org/10.22342/jme.12.3.13202.427-448
Signorell, A. et mult. al. (2022). DescTools: Tools for descriptive statistics. R package version 0.99.47.
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag.
Wickham, H., François, R., Henry, L., & Müller, K. (2022). dplyr: A grammar of data manipulation. R package version 1.0.10, .
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., and Vasilakos, A. V.(2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231-1247. https://doi.org/10.1016/j.ijinfomgt.2016.07.009
Yusuf, Y., Suyitno, H., & Sukestiyarno, Y. L. (2019). The influence of statistical anxiety on statistic reasoning of pre-service mathematics teachers. Bolema: Boletim de Educação Matemática, 33, 694-706. https://doi.org/10.1590/1980-4415v33n64a12
DOI: https://doi.org/10.21831/jpmmp.v8i2.71583
Refbacks
- There are currently no refbacks.
![Creative Commons License](http://licensebuttons.net/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Accepted and published papers will be freely accessed in this website and the following abstracting & indexing databases:
Supervised by:
![RJI Main logo](https://assets.relawanjurnal.id/logo/rji.png)