How can you create easy-to-understand data visualizations for Operations Research?

1 Why visualize data?

Data visualization is the process of transforming data into graphical or interactive forms that can reveal patterns, trends, relationships, and insights. This process can help you and your audience explore and analyze data more effectively and efficiently, as well as communicate and present data more clearly and persuasively. Furthermore, data visualization can engage and persuade your audience more emotionally and cognitively, while also supporting and justifying your decisions and recommendations more convincingly.

3 How to use R for data analysis and visualization?

R is a powerful and popular programming language and environment for data analysis and visualization. It has many advantages for OR practitioners, such as being free and open source with a large community of users and developers, as well as a diverse collection of packages and libraries that can handle various types of data and visualization tasks. It also supports reproducible and collaborative research, with tools for documenting and sharing code and results, plus integrates well with other software and platforms. To use R, you need to install R and RStudio, an IDE that makes working with R easier. You should also learn the basics of R syntax, data structures, functions, and packages. There are many online resources and courses available to help you learn R, such as the R Project for Statistical Computing, RStudio, R for Data Science, and DataCamp.

4 How to choose the right visualization techniques and packages in R?

R offers a variety of visualization techniques and packages to help you create visualizations for OR. Base R graphics is the basic and default system for creating simple plots, such as histograms and bar charts. Lattice is a package that provides a high-level framework for creating multi-panel plots, like trellis and contour plots. ggplot2 implements the grammar of graphics to create sophisticated plots, such as line graphs and heat maps. Additionally, Plotly can be used to create interactive and dynamic 3D plots, animations, dashboards, and maps. When selecting the right visualization techniques and packages in R, it is important to consider the characteristics and dimensions of your data, the goals of your visualization, and the preferences and expectations of your audience.

5 How to design and refine your data visualizations in R?

Once you have chosen the appropriate visualization techniques and packages in R, it is necessary to design and refine your data visualizations to make them more effective and appealing. Utilizing appropriate and consistent colors, shapes, sizes, labels, and legends can help encode your data and convey your message. Additionally, using appropriate and consistent scales, axes, grids, and margins can organize and align your data and plots. Utilizing appropriate and consistent fonts, titles, captions, and annotations can provide context and explanation for your data and plots. Moreover, you can use appropriate and consistent layouts, transitions, and interactions to arrange and connect your data and plots. Finally, you can utilize appropriate and consistent formats, resolutions, and file types to export and share your data visualizations. Furthermore, you can use some specific functions in R such as the theme() or scale_*() functions in ggplot2 to modify the appearance of your plots or the plotly_build() or plotly_json() functions in plotly to modify the behavior of your plots. Finally, you can use the ggsave() or htmlwidgets::saveWidget() functions in R to save your plots as images or web pages.

6 How to evaluate and improve your data visualizations in R?

Finally, you need to evaluate and improve your data visualizations in R to ensure that they are accurate, clear, and engaging. You can test and debug your code and plots to check for errors, inconsistencies, and anomalies in your data and visualization. Furthermore, it’s important to review and revise your code and plots to optimize their performance, readability, and maintainability. Additionally, you should seek feedback from peers, clients, or users to assess their understanding, satisfaction, and suggestions for your data and visualization. Lastly, it’s beneficial to compare your code and plots with other examples, best practices, and standards to identify their strengths, weaknesses, and opportunities for improvement.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?