[ACCEPTED]-Organizing R Source Code-r

Accepted answer

Score: 45

This question is very closely related to: "How to organize large R programs?"

You 29 should consider creating an R package. You 28 can use the package.skeleton function to start with given 27 a set of R files. I also strongly recommend 26 using roxygen to document the package at the beginning, because 25 it's much more difficult to do it after 24 the fact.

Read "Writing R Extensions". The online book "Statistics 23 with R" has a section on this subject. Also take a look at 22 Creating R Packages: A Tutorial by Friedrich Leisch. Lastly, if you're 21 in NY, come to the upcoming NY use-R group 20 meeting on "Authoring R Packages: a gentle introduction with examples".

Just to rehash some suggestions 19 about good practices:

A package allows you to use R CMD check which is very helpful at catching bugs; separately you can look at using the codetools package.
A package also forces you to do a minimal amount of documentation, which leads to better practices in the long run.
You should also consider doing unit testing (e.g. with RUnit) if you want your code to be robust/maintainable.
You should consider using a style guide (e.g. Google Style Guide).
Use a version control system from the beginning, and if you're going to make your code open source, then consider using github or r-forge.

Edit:

Regarding how do make 18 incremental changes without rebuilding and 17 installing the full package: I find the 16 easiest thing to do is to make changes in 15 your relevant R file and then use the source command 14 to load those changes. Once you load your 13 library into an R session, it will always 12 be lower in the environment (and lower in 11 priority) than the .GlobalEnv, so any changes 10 that you source or load in directly will 9 be used first (use the search command to see this). That 8 way you can have your package underlying 7 and you are overwriting changes as you're 6 testing them in the environment.

Alternatively, you 5 can use an IDE like StatET or ESS. They 4 make loading individual lines or functions 3 out of an R package very easy. StatET is 2 particularly well designed to handle managing 1 packages in a directory-like structure.

Score: 0

this is for benefit of others who are directed 9 to this post upon their search. I too faced 8 exactly same scenario and found no resource 7 which explained it clearly. Here is my attempt 6 to put the solution in a few simple steps:
1) Create 5 a new project directory
2) Create a Package 4 via R studio(same process as above)
3) Keep 3 both in same location(to avoid confusion).
4) Install 2 and load packages: devtools and and roxygen2.
5) use 1 function load_all().

And you are done.

Source: stackoverflow.com