polewtrax.blogg.se - Creating an r package

A minimal example, but complete with respect to the topics that we are going to cover, might be praise, or its furious newborn little brother curser. The following content will be clearer if you have at least some exposure to Continuous Integration / Continuous Development concepts. Therefore, this hands-on guide introduces the essential tools and best practices to build an R package from scratch. Packaging our code addresses all of the above, because it’s easy for collaborators to install a package as any other third-party library, we can easily write a manual for our code, and we will be able to re-use many functions we defined previously, just by importing it with library('mynewpackage').

for our next project, we will most likely start from scratch again even if we are going to do something similar.

it is easy to make well-hidden mistakes that will possibly come out after months, which is rarely pleasant.

it is hard to share our code with collaborators, who might have a hard time understanding what is happening.

After a while, our code can get easily out of hand because the scripts are too many (and/or too long), and we might face problems such as: In a Data Science project, the most common approach is to write a sequence of scripts to explore the data, develop statistical models and show the results.