The learning curve might be steeper than with other software, but with R, you can save all the steps you used to go from the data to the results. So, if you want to redo your analysis because you collected more data, you don’t have to remember which button you clicked in which order to obtain your results, you just have to run your script again.
Working with scripts makes the steps you used in your analysis clear, and the code you write can be inspected by someone else who can give you feedback and spot mistakes.
Working with scripts forces you to have deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.
Reproducibility is when someone else (including your future self) can obtain the same results from the same dataset when using the same analysis.
R integrates with other tools to generate manuscripts from your code. If you collect more data, or fix a mistake in your dataset, the figures and the statistical tests in your manuscript are updated automatically.
An increasing number of journals and funding agencies expect analyses to be reproducible, knowing R will give you an edge with these requirements.
With 6,000+ packages that can be installed to extend its capabilities, R provides a framework that allows you to combine analyses across many scientific disciplines to best suit the analyses you want to use on your data. For instance, R has packages for image analysis, GIS, time series, population genetics, and a lot more.
The skills you learn with R scale easily with the size of your dataset. Whether your dataset has hundreds or millions of lines, it won’t make much difference to you.
R is designed for data analysis. It comes with special data structures and data types that make handling of missing data and statistical factors convenient.
R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.
The plotting functionalities in R are endless, and allow you to adjust any aspect of your graph to convey most effectively the message from your data.
Thousands of people use R daily. Many of them are willing to help you through mailing lists and stack overflow.
Anyone can inspect the source code to see how R works. Because of this transparency, there is less chance for mistakes, and if you (or someone else) find some, you can report and fix bugs.