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What should you consider when choosing R packages for statistical analysis?

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R is a popular and powerful programming language for statistical analysis, but it also has a huge and diverse ecosystem of packages that can enhance its functionality and usability. However, choosing the right packages for your project can be a daunting task, especially if you are new to R or operations research. In this article, you will learn some tips and criteria to help you select the best R packages for your statistical analysis needs.

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1 Purpose and scope

The first thing you should consider when choosing R packages is the purpose and scope of your project. What kind of statistical analysis do you want to perform? What data sources and formats do you need to work with? What output and visualization options do you prefer? Depending on your answers, you may need different packages that offer specific functions, methods, or tools for your analysis. For example, if you want to do linear programming, you may need the lpSolve package, while if you want to do network analysis, you may need the igraph package.

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Vincent Rodriguez, MS

Energy Analytics Leader | Data Engineering | Data Science | USAF Veteran
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Hopefully this goes without saying: limit loading packages you don't use much and purge packages from scripts when they are no longer needed. Only including packages used in a script ensures there are no compilation errors due to undesired packages, smoother and faster production-level code, and fewer outages due to maintenance. KISS those unnecessary packages good-bye!

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Ryan Hutchings, PhD

User Experience Researcher
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Very sound advice. One common pitfall that I see in code is “over” loading packages. This is most common when loading a set of packages (such as tidyverse or tidymodels) when you only need functions from one package (for example, just filter() and select() from dplyr). These bundled packages take longer to load, and as your code ages, loading them will may produce unnecessary dependency issues. Minimizing calling packages you don’t need will make it easier to update your code over time as the packages you rely on grow and change.

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2 Quality and reliability

The second thing you should consider when choosing R packages is the quality and reliability of the packages. How well are the packages maintained and updated? How thoroughly are the packages tested and documented? How compatible are the packages with your R version and other packages? How widely are the packages used and recommended by other users? These questions can help you assess the quality and reliability of the packages, and avoid potential errors, bugs, or conflicts in your analysis. You can check the quality and reliability of the packages by looking at their CRAN status, GitHub activity, vignettes, manuals, reviews, ratings, or citations.

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Vincent Rodriguez, MS

Energy Analytics Leader | Data Engineering | Data Science | USAF Veteran
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I've used R for many years. The most important feature of any package is to check the scope of its complexity and, if it should contain many dependencies, the likelihood of maintenance being performed on the package. Small, simple packages can stand the test of time regardless of developer support whereas large, complex packages with many dependencies usually breed dependency on other packages and their development and maintenance schedule in addition to their own. The best packages I've used thus far have been from the RStudio/Posit team. This is mainly to do with the ecosystem of packages created and the vested interests of the development team in keeping them current.

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3 Ease of use and learning

The third thing you should consider when choosing R packages is the ease of use and learning of the packages. How intuitive and user-friendly are the packages? How easy are the packages to install and load? How clear and consistent are the packages' syntax and arguments? How helpful and accessible are the packages' help files and examples? How steep is the learning curve of the packages? These questions can help you evaluate the ease of use and learning of the packages, and choose the ones that suit your skill level and preferences. You can test the ease of use and learning of the packages by trying them out in a sample project, or following some tutorials, blogs, or videos.

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Seifullah Bello

Engineer, PE, MEng | AI/ML, Microsoft Certified: Azure Data Scientist Associate | Program Management, PMP
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RStudio comes with its own IDE. Once RStudio is installed, there isn't an additional step of going through the process of searching and eventually installing an IDE. In terms of helpfulness and accessibility, the help page which is easily accessible from the RStudio console provides documentation for all the functions, and methods that can be utilized for statistical, predictive and operations research analysis

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4 Performance and efficiency

The fourth thing you should consider when choosing R packages is the performance and efficiency of the packages. How fast and accurate are the packages in performing your analysis? How much memory and CPU do the packages consume? How scalable and robust are the packages for large or complex data sets? How flexible and customizable are the packages for different scenarios or objectives? These questions can help you measure the performance and efficiency of the packages, and choose the ones that optimize your results and resources. You can compare the performance and efficiency of the packages by using some benchmarks, metrics, or simulations.

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5 Innovation and integration

The fifth thing you should consider when choosing R packages is the innovation and integration of the packages. How novel and advanced are the packages in terms of their features, functions, or methods? How well do the packages integrate with other R packages or external tools or platforms? How often do the packages introduce new or improved versions or options? How responsive and receptive are the packages' developers or contributors to user feedback or requests? These questions can help you appreciate the innovation and integration of the packages, and choose the ones that keep up with the latest trends and standards in your field. You can explore the innovation and integration of the packages by reading their news, blogs, or newsletters, or joining their communities, forums, or webinars.

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6 Ethics and values

The sixth and final thing you should consider when choosing R packages is the ethics and values of the packages. How ethical and responsible are the packages in terms of their data collection, processing, analysis, or presentation? How transparent and accountable are the packages in terms of their assumptions, limitations, or implications? How respectful and inclusive are the packages in terms of their language, design, or representation? How aligned and consistent are the packages with your personal or professional values and goals? These questions can help you reflect on the ethics and values of the packages, and choose the ones that match your principles and expectations. You can learn about the ethics and values of the packages by reading their code of conduct, license, or policy, or contacting their authors, developers, or maintainers.

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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?

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What should you consider when choosing R packages for statistical analysis?

1

2

3

4

5

6

7

1 Purpose and scope

2 Quality and reliability

3 Ease of use and learning

4 Performance and efficiency

5 Innovation and integration

6 Ethics and values

7 Here’s what else to consider

Operations Research

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What should you consider when choosing R packages for statistical analysis?

1

2

3

4

5

6

7

1 Purpose and scope

2 Quality and reliability

3 Ease of use and learning

4 Performance and efficiency

5 Innovation and integration

6 Ethics and values

7 Here’s what else to consider

Operations Research

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