I'm writing a R package which begins to grow in size, and so would really appreciate to use a custom structure in folders pkg/R/ and (especially) in pkg/src/.
For example, let's say I have two families of algorithms of some type A, and some functions of type B, and a main entry point. Ideally R/ or src/ folders would be organized as follow:
typeA/
algorithms1/
algo11.ext
...
algorithms2/
algo21.ext
...
typeB/
function1.ext
...
main.ext
with "ext" in {R,cpp,c,f,...}, and potentially two files having the same name.
Is it possible ? If yes, how can I do that ?
Thanks in advance !
[2012-12-31] EDIT: an idea would be to write a few scripts - maybe inside another R package - to (un)flatten a structured package for tests or diffusion. But there is probably a better solution, so I will wait a bit.
As the 'Writing R extensions' manual indicates here, a Makevars file under pkg/src allows to have nested subfolders for C/C++/Fortran code. (See e.g. RSiena package).
However, I didn't find anything concerning a custom structure in pkg/R. So I wrote a little package (usable, although needing improvements) which accomplish the following tasks:
Load/Unload a package having (potentially) nested folders under pkg/R
Launch R and/or C unit tests on it [basic framework, to be replaced (e.g. RUnit and check)]
Export the package to be CRAN-compatible (flatten R code, generate Makevars file)
I will link it here if it reaches a publishable state. (For the moment I could send it by email).
The official package documentation https://cran.r-project.org/doc/manuals/r-devel/R-exts.html, section 1.1.5 contains this quote:
The R and man subdirectories may contain OS-specific subdirectories named unix or windows.
I've tried creating a simple test package with subdirectories in R-3.5.1 and it did not work properly.
Nor devtools::load_all() nor R CMD build successfully exported code from subdirectories in R.
Related
I have already made a simple R package (pure R) to solve a problem with brute force then I tried to faster the code by writing the Rcpp script. I wrote a script to compare the running time with the "bench" library. now, how can I add this script to my package? I tried to add
#'#importFrom Rcpp cppFunction
on top of my R script and inserting the Rcpp file in the scr folder but didn't work. Is there a way to add it to my r package without creating the package from scratch? sorry if it has already been asked but I am new to all this and completely lost.
That conversion is actually (still) surprisingly difficult (in the sense of requiring more than just one file). It is easy to overlook details. Let me walk you through why.
Let us assume for a second that you started a working package using the R package package.skeleton(). That is the simplest and most general case. The package will work (yet have warning, see my pkgKitten package for a wrapper than cleans up, and a dozen other package helping functions and packages on CRAN). Note in particular that I have said nothing about roxygen2 which at this point is a just an added complication so let's focus on just .Rd files.
You can now contrast your simplest package with one built by and for Rcpp, namely by using Rcpp.package.skeleton(). You will see at least these differences in
DESCRIPTION for LinkingTo: and Imports
NAMESPACE for importFrom as well as the useDynLib line
a new src directory and a possible need for src/Makevars
All of which make it easier to (basically) start a new package via Rcpp.package.skeleton() and copy your existing package code into that package. We simply do not have a conversion helper. I still do the "manual conversion" you tried every now and then, and even I need a try or two and I have seen all the error messages a few times over...
So even if you don't want to "copy everything over" I think the simplest way is to
create two packages with and without Rcpp
do a recursive diff
ensure the difference is applied in your original package.
PS And remember that when you use roxygen2 and have documentation in the src/ directory to always first run Rcpp::compileAttributes() before running roxygen2::roxygenize(). RStudio and other helpers do that for you but it is still easy to forget...
I need to "industrialize" an R code for a data science project, because the project will be rerun several times in the future with fresh data. The new code should be really easy to follow even for people who have not worked on the project before and they should be able to redo the whole workflow quite quickly. Therefore I am looking for tips, suggestions, resources and best-practices on how to achieve this objective.
Thank you for your help in advance!
You can make an R package out of your project, because it has everything you need for a standalone project that you want to share with others :
Easy to share, download and install
R has a very efficient documentation system for your functions and objects when you work within R Studio. Combined with roxygen2, it enables you to document precisely every function, and makes the code clearer since you can avoid commenting with inline comments (but please do so anyway if needed)
You can specify quite easily which dependancies your package will need, so that every one knows what to install for your project to work. You can also use packrat if you want to mimic python's virtualenv
R also provide a long format documentation system, which are called vignettes and are similar to a printed notebook : you can display code, text, code results, etc. This is were you will write guidelines and methods on how to use the functions, provide detailed instructions for a certain method, etc. Once the package is installed they are automatically included and available for all users.
The only downside is the following : since R is a functional programming language, a package consists of mainly functions, and some other relevant objects (data, for instance), but not really scripts.
More details about the last point if your project consists in a script that calls a set of functions to do something, it cannot directly appear within the package. Two options here : a) you make a dispatcher function that runs a set of functions to do the job, so that users just have to call one function to run the whole method (not really good for maintenance) ; b) you make the whole script appear in a vignette (see above). With this method, people just have to write a single R file (which can be copy-pasted from the vignette), which may look like this :
library(mydatascienceproject)
library(...)
...
dothis()
dothat()
finishwork()
That enables you to execute the whole work from a terminal or a distant machine with Rscript, with the following (using argparse to add arguments)
Rscript myautomatedtask.R --arg1 anargument --arg2 anotherargument
And finally if you write a bash file calling Rscript, you can automate everything !
Feel free to read Hadley Wickham's book about R packages, it is super clear, full of best practices and of great help in writing your packages.
One can get lost in the multiple files in the project's folder, so it should be structured properly: link
Naming conventions that I use: first, second.
Set up the random seed, so the outputs should be reproducible.
Documentation is important: you can use the Roxygen skeleton in rstudio (default ctrl+alt+shift+r).
I usually separate the code into smaller, logically cohesive scripts, and use a main.R script, that uses the others.
If you use a special set of libraries, you can consider using packrat. Once you set it up, you can manage the installed project-specific libraries.
I am currently developing an R package and want it to be as clean as possible, so I try to resolve all WARNINGs and NOTEs displayed by devtools::check().
One of these notes is related to some code I use for generating sample data to go with the package:
checking top-level files ... NOTE
Non-standard file/directory found at top level:
'generate_sample_data.R'
It's an R script currently placed in the package root directory and not meant to be distributed with the package (because it doesn't really seem useful to include)
So here's my question:
Where should I put such a file or how do I tell R to leave it be?
Is .Rbuildignore the right way to go?
Currently devtools::build() puts the R script in the final package, so I shouldn't just ignore the NOTE.
As suggested in http://r-pkgs.had.co.nz/data.html, it makes sense to use ./data-raw/ for scripts/functions that are necessary for creating/updating data but not something you need in the package itself. After adding ./data-raw/ to ./.Rbuildignore, the package generation should ignore anything within that directory. (And, as you commented, there is a helper-function devtools::use_data_raw().)
I'm building a package that uses two main functions. One of the functions model.R requires a special type of simulation sim.R and a way to set up the results in a table table.R
In a sharable package, how do I call both the sim.R and table.R files from within model.R? I've tried source("sim.R") and source("R/sim.R") but that call doesn't work from within the package. Any ideas?
Should I just copy and paste the codes from sim.R and table.R into the model.R script instead?
Edit:
I have all the scripts in the R directory, the DESCRIPTION and NAMESPACE files are all set. I just have multiple scripts in the R directory. ~R/ has premodel.R model.R sim.R and table.R. I need the model.R script to use both sim.R and table.R functions... located in the same directory in the package (e.g. ~R/).
To elaborate on joran's point, when you build a package you don't need to source functions.
For example, imagine I want to make a package named TEST. I will begin by generating a directory (i.e. folder) named TEST. Within TEST I will create another folder name R, in that folder I will include all R script(s) containing the different functions in the package.
At a minimum you need to also include a DESCRIPTION and NAMESPACE file. A man (for help files) and tests (for unit tests) are also nice to include.
Making a package is pretty easy. Here is a blog with a straightforward introduction: http://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/
As others have pointed out you don't have to source R files in a package. The package loading mechanism will take care of losing the namespace and making all exported functions available. So usually you don't have to worry about any of this.
There are exceptions however. If you have multiple files with R code situations can arise where the order in which these files are processed matters. Often it doesn't matter or the default order used by R happens to be fine. If you find that there are some dependencies within your package that aren't resolved properly you may be faced with a situation where a custom processing order for the R files is required. The DESCRIPTION file offers the optional Collate field for this purpose. Simply list all your R files in the order they should be processed to satisfy the dependencies.
If all your files are in R directory, any function will be in memory after you do a package build or Load_All.
You may have issues if you have code in files that is not in a function tho.
R loads files in alphabetical order.
Usually, this is not a problem, because functions are evaluated when they are called for execution, not at loading time (id. a function can refer another function not yet defined, even in the same file).
But, if you have code outside a function in model.R, this code will be executed immediately at time of file loading, and your package build will fail usually with a
ERROR: lazy loading failed for package 'yourPackageName'
If this is the case, wrap the sparse code of model.R into a function so you can call it later, when the package has fully loaded, external library too.
If this piece of code is there for initialize some value, consider to use_data() to have R take care of load data into the environment for you.
If this piece of code is just interactive code written to test and implement the package itself, you should consider to put it elsewhere or wrap it to a function anyway.
if you really need that code to be executed at loading time or really have dependency to solve, then you must add the collate line into DESCRIPTION file, as already stated by Peter Humburg, to force R to load files order.
Roxygen2 can help you, put before your code
#' #include sim.R table.R
call roxygenize(), and collate line will be generate for you into the DESCRIPTION file.
But even doing that, external library you may depend are not yet loaded by the package, leading to failure again at build time.
In conclusion, you'd better don't leave code outside functions in a .R file if it's located inside a package.
Since you're building a package, the reason why you're having trouble accessing the other functions in your /R directory is because you need to first:
library(devtools)
document()
from within the working directory of your package. Now each function in your package should be accessible to any other function. Then, to finish up, do:
build()
install()
although it should be noted that a simple document() call will already be sufficient to solve your problem.
Make your functions global by defining them with <<- instead of <- and they will become available to any other script running in that environment.
I'm creating an R package and I need it to include a couple of non R script files which get called by one of my functions. I need these script files to be distributed with the package, naturally. So that leaves me with two questions:
a) In which directory of the package
tree should I place these files? b) Is that location mandatory or just convention?
Do I need to change any other
settings or configurations or will
they just get copied to the
directory mentioned in #1 and then I
can figure out the path using
system.file()?
I've tried to find the answer in the Writing R Extensions document, but it didn't jump out at me. And, of course, I didn't read the whole thing. Am I being too honest here?
I think you want either exec/ at the top-level (even though that is labeled 'still experimental, or subdirectory of inst as everything in inst/ gets copied verbatim into the package.
A quick example from the packages I have expanded in source is gdata which has inst/perl, inst/xls and inst/bin. These you could then call from R itself by computing the path of the installed package using system.file().