R Software Recommendations

The R software is an OpenSource language for statistical analysis. It sets a high standard for being comprehensive (via an extensive array of packages for many specific applications), correct, and easy to use. It is generally considered the standard solution for a broad range of statistical applications. In addition to the software itself, I also highly recommend the IDE RStudio. It provides a highly functional work environment that makes it easy for the statistical to develop and test code, read and organize data, create visual analyses, and to fit a broad range of statistical models. Finally, I recommend that statisticians become familiar with and use the collection of tools for managing and dealing with data that is include in the collection of packages called The Tidyverse.

* R: www.r-project.org
* RStudio: www.rstudio.com
* tidyverse: www.tidyverse.com

Keep your software up to date. New releases are made periodically. Features are added and bugs are fixed. Remember that packages in R may change releases independently of R itself. Use the `update` feature under the `Packages` tab in RStudio to manage packages and be sure you are up to date.

It is very helpful to use tidyverse, which is a collection of packages that provide a unified and comprehensive approach to working with data. If you learn how to use tidyverse your code will be clearer and more efficient. You will be using best practices for data management. You will be part of a larger data science community that is continuously brings new technology to its members.

Use one of the two main notebook formats in RStudio; R Markdown or Quarto. This will allow you to write readable and reproducible code.