Writing an R package vignette that reads in an example file? - r

I'm trying to write a vignette for a package in R. I've been following a tutorial from Vanderbilt University as well as the offical documentation.
I made a .Rnw Sweave file and put it into a subdirectory inst/doc inside my package. Inside the same subdirectory inst/doc, I put a folder example containing some example text files. My package has a function myparser(path) that I want to demonstrate in the vignette; myparser(path) creates several data frames by reading in the text files inside the folder with absolute path name path.
Then I checked the package using R CMD CHECK, and got this error:
* checking running R code from vignettes ...
‘mypackage-vignette.Rnw’ using ‘UTF-8’ ... failed
ERROR
Errors in running code in vignettes:
when running code in ‘mypackage-vignette.Rnw’
...
> library(mypackage)
Loading required package: ggplot2
> myparser("/example/")
Warning in file(file, "rt") :
cannot open file '/example/': No such file or directory
When sourcing ‘mypackage-vignette.R’:
Error: cannot open the connection
Execution halted
I see my attempt to use a relative pathway to the folder didn't work (probably should have been obvious to me), but I'm still not sure how to fix this situation. I don't want to replace path with an absolute pathway to the folder on my computer, because then the vignette's code won't be reproducible on other people's computers.
How can I include the example files in the package so that the vignette's code is reproducible? Am I even approaching this problem in the right way?
(Sorry this question isn't itself more reproducible!)

You can use system.file('doc', 'example', package = 'mypackage') to refer to that directory, because R will install the package before building vignettes, as you can see when you run R CMD build mypackage.

Related

R package vignettes

I am a little confused as to why there are multiple possible locations for "vignettes" in an R package. I don't understand which locations are used for what and when. For example:
devtools::use_vignettes()
creates a vignettes folder under the root of the package
devtools::build_vignettes()
creates a inst/doc folder that gets promoted to the root at build
pkgdown::build_site()
creates a docs folder.
As background:I have read H.Wickhams R packages book and I have created several packages using the first option and all things have behaved well. I would have users install from github using:
devtools::install_github(pkg,build_vignettes=TRUE)
Now, I have just started to contribute in the joint development of a package in which the first and third option have been used. I have noticed that the .rmd file in the vignettes folder is the same as the index.html file in the docs folder. Does pkgdown copy from the vignettes folder?
Also for this package when i install from github (with build_vignettes=TRUE) i get an error saying installation failed because the doc/index.html path couldn't be found. Now why would that happen?
Vignettes development
There is only one place to put raw vignettes, it is in the vignette directory at the root. This is the place where you write your Rmd file with text and code examples, when developing your package.
Build vignettes for your users
When you build your vignettes, the Rmd file will be knit. The resulting html file, the raw Rmd file and the extraction of the R code will be three files saved in the inst/doc directory. This is what will be kept in the package installation. This is what users will be able to read.
{pkgdown}
{pkgdown} is using your Rmd files of the vignette directory to knit html files so that it can build a website for your package. It also build a page for the list of functions and a index from the Readme file that is also used for your git repository. This is not supposed to stay in the R package, and not accessible to the users. This is to present your package on the Internet.
Conclusion
Hence, when you develop, you only write your Rmd vignette in the vignette directory. The others will automatically keep what they need.

R check for a package: optimize the way DLLs are built and checked

I want to optimize my process for building a package. I have in pckgname/src some fortran code (f90):
pckgname/src/FortranFile1.f90
pckgname/src/FortranFile2.f90
I am under RStudio. When I build the package, it creates the src-i386 and src-x64 folders, inside which executable files in .o are produced
pckgname/src-i386/FortranFile1.o
pckgname/src-i386/FortranFile2.o
pckgname/src-x64/FortranFile1.o
pckgname/src-x64/FortranFile2.o
then dll files are produced into each of these folders from the .o files:
pckgname/src-i386/dllname.dll
pckgname/src-x64/dllname.dll
thereafter if I want to check the code successfully, I need to manually copy paste the dll into these two folders (in the previous version of the question i wrote code instead of dll which might have led to misunderstandings)
pckgname/inst/libs/x64/dllname.dll
pckgname/libs/X64/dllname.dll
My question is: is it normal that I have to do this or is there a shorter way without having to copy paste by hand dllname.dll
into these two folders? It could be indeed a source of error.
NB: If i don't copy the dlls into the said folders I get the following error messages (translated from the French):
Error in inDL(x, as.logical(local), as.logical(now), ...) :
impossible to load shared object 'C:/Users/username/Documents/pckgname/inst/libs/x64/dllname.dll':
LoadLibrary failure: The specified module can't be found
Error in inDL(x, as.logical(local), as.logical(now), ...) :
impossible to load shared object 'C:/Users/username/Documents/pckgname/libs/x64/dllname.dll':
LoadLibrary failure: The specified module can't be found.
[...]
`cleanup` is deprecated
The short answer
Is it normal that I have to do this?
No. If path/to/package is the directory you are developing your package in, and you have everything set up for your package to call your Fortran subroutines correctly (see "The long answer"), you can run
R CMD build path/to/package
at the command prompt, and a tarball will be constructed for you with everything in the right place (note you will need Rtools for this). Then you should be able to run
R CMD check packagename_versionnumber.tar.gz
from the command prompt to check your package, without any problems (stemming from the .dll files being in the wrong place -- you may have other problems, in which case I would suggest asking a new question with the ERROR, WARNING, or NOTE listed in the question).
If you prefer to work just from R, you can even
devtools::check("path/to/package")
without having to run devtools::build() or R CMD build ("devtools::check()... [b]undles the package before checking it" -- Hadley's chapter on checking; see also Karl Broman's chapter on checking).
The long answer
I think your question has to do with three issues potentially:
The difference between directory structure of packages before and after they're installed. (You may want to read the "What is a package?" section of Hadley's Package structure chapter -- luckily R CMD build at the command prompt or devtools::build() in R will take care of that for you)
Using INSTALL vs. BUILD (from the comments to the original version of this answer)
The proper way to set up a package to call Fortran subroutines.
You may need quite a bit of advice on the process of developing R packages itself. Some good guides include (in increasing order of detail):
Karl Broman's R package primer
Hadley Wickham's R packages
The Writing R Extensions manual
In particular, there are some details about having compiled code in an R package that you may want to be aware of. You may want to first read Hadley's chapter on compiled code (Broman doesn't have one), but then you honestly need to read most of the Writing R Extensions manual, in particular sections 1.1, 1.2, 1.5.4, and 1.6, and all of chapters 5 and 6.
In the mean time, I've setup a GitHub repository here that demonstrates a toy example R package FortranExample that shows how to correctly setup a package with Fortran code. The steps I took were:
Create the basic package structure using devtools::create("FortranExample").
Eliminate the "Depends" line in the DESCRIPTION, as it set a dependence on R >= 3.5.1, which will throw a warning in check (I have now also revised the "License" field to eliminate a warning about not specifying a proper license).
Make a src/ directory and add toy Fortran code there (it just doubles a double value).
Use tools::package_native_routine_registration_skeleton("FortranExample") to generate the symbol registration code that I placed in src/init.c (See Writing R Extensions, section 5.4).
Create a nice R wrapper for using .Fortran() to call the Fortran code (placed in R/example_function.R).
In that same file use the #' #useDynLib FortranExample Roxygen tag to add useDynLib(FortranExample) to the NAMESPACE file; if you don't use Roxygen, you can put it there manually (See Writing R Extensions 1.5.4 and 5.2).
Now we have a package that's properly set up to deal with the Fortran code. I have tested on a Windows machine (running Windows 8.1 and R 3.5.1) both the paths of running
R CMD build FortranExample
R CMD check FortranExample_0.0.0.9000.tar.gz
from the command prompt, and of running
devtools::check("FortranExample")
from R. There were no errors, and the only warning was the "License" issue mentioned above.
After cleaning up the after-effects of running devtools::check("FortranExample") (for some reason the cleanup option is now deprecated; see below for an R function to handle this for you inspired by devtools::clean_dll()), I used
devtools::install("FortranExample")
to successfully install the package and tested its function, getting:
FortranExample::example_function(2.0)
# [1] 4
The cleanup function I mentioned is
clean_source_dirs <- function(path) {
paths <- file.path(path, paste0("src", c("", "-i386", "-x64")))
file_pattern <- "\\.o|so|dll|a|sl|dyl"
unlink(list.files(path = paths, pattern = file_pattern, full.names = TRUE))
}
No, it is not normal and there is a solution to this problem. Make use of Makevars.win. The reason for your problem is that .dlls are looking for dependencies in places defined by environment variable PATH and relative paths defined during the linking. Linking is being done when running the command R CMD INSTALL as it is stated in Mingw preferences plus some custom parameters defined in the file Makevars.win (Windows platform dependent). As soon as the resulting library is copied, the binding to the places where dependent .dlls were situated may become broken, so if you put dlls in a place where typically dependent libraries reside, such as, for instance, $(R_HOME)/bin/$(ARCH)/,
cp -f <your library relative path>.dll $(R_HOME)/bin/$(ARCH)/<your library>.dll
during the check R will be looking for your dependencies specifically there too, so you will not miss the dependencies. Very crude solution, but it worked in my case.

Unable to generate help files from R package

I have created a package in R. It is all fully documented and written according to R package guidelines. I have used devtools to generate documentation.
document("/home/rstudio/EndoMineR/")
However when I try to use ?EndoMineR I get the error:
No documentation for ‘EndoMineR’ in specified packages and libraries:
you could try ‘??EndoMineR’
How can I create the help files for my package? What am I likely to be missing?
As additional information, when I click the package name in R studio I get the help files but not if I try ?EndoMineR. Also the .Rd files in the man directory (which I think is what devtools::document() generates) seem to be updating just fine. I assume the ?EndoMiner accesses the man files so I'm not sure why this folder is not accessible (it is top level)

R: instructions for unbundling and using a packrat snapshot

I used packrat (v 0.4.8.-1) to to create a snapshot and bundle of the R package dependencies that go along with the corresponding R code. I want to provide the R code and packrat bundle to others to make the work I am doing (including the R environment) fully reproducible.
I tested unbundling using a different computer from the one I used to write R code and create the bundle. I opened an R code file in R studio, and called library(packrat) to load packrat (also v 0.4.8-1). I then called packrat::unbundle(bundle = "directory", where = "directory"), which unbundled successfully. But subsequently calling packrat::restore() gave me the error "This project has not yet been packified. Run 'packrat::init()' to init packrat". It seems like init() should not be necessary because I am not trying to create a new snapshot, but rather utilize the one in the bundle. The packrat page (https://rstudio.github.io/packrat/) and CRAN provide very little documentation about unbundling to help troubleshoot this, or that I could point users of my code to for instructions (who likely will be familiar with R, but may not have used packrat).
So, can someone please provide clear step-by-step instructions for how users of a bundled snapshot should unbundle, and then use that saved snapshot to run a R code file?
After some experimenting, I found an approach that seems to have worked so far.
I have provided users with three files:
-tar.gz (packrat bundle file)
-unbundle.R (R code file that includes a library statement to load
the packrat library, and the unbundle command for the tar.gz file)
-unbundle_readme.txt
The readme file includes instructions similar to those below, and so far users have been able to run R code using the package dependencies. The readme file tells users about requirements (R, R studio, packrat, R package development prerequisites (Rtools for Windows, XCode for Mac)), and includes output of sessionInfo() to document R package versions that the R code should use after instructions are followed. In the example below 'code_folder' refers to a folder within the tar.gz file that contains R. code and associated input files.
Example unbundle instructions:
Step 1
Save, but do not expand/unzip, the tar file to a directory.
Problems with accessing the saved package dependencies
are more likely when a program other than R or R studio
is used to unbundle the tar file.
If the tar file has already been expanded, re-save the
tar file to a new directory, which should not be a the same
directory as the expanded tar file, or a subdirectory of
the expanded tar file.
Step 2
Save unbundle.R in the same directory as the tar file
Step 3
Open unbundle.R using R studio
Step 4
Execute unbundle.R
(This will create a subfolder ‘code_folder’.
Please note that this step may take 5-15 minutes to run.)
Step 5
Close R studio
Step 6
Navigate to the subfolder ‘cold_folder’
Step 7
Open a R script using R studio
(The package library should correspond to that listed below.
This will indicate R studio is accessing the saved package
dependencies.)
Step 8
Execute the R code, which will utilize the project package library.
After the package library has been loaded using the above
steps, it is not necessary to re-load the package library for each
script. R studio will continue to access the package dependencies
for each script you open within the R studio session. If you
subsequently close R-studio, and then open scripts from within
the unbundle directory, R studio should still access the
dependencies without requiring re-loading of the saved package
snapshot.

creating package

i am creating a package in R language, everything is running properly, but when i run R CMD check , it shows an error message while running examples.. i.e.
"can't open the file." "No such file or directory"
actually my function needs a PubMed text file containing abstracts from the PubMed, i have placed my text file in every sub-directory of my package, but its not working. showing same error again and again.
so please suggest me the right way how to put a text file in a package which can be used by examples to run properly.
i will be very thankful to you.
Usually you put such data in the /inst folder. E.g.:
<packageRoot>/inst/pubmed/myfile
After the package is build you can access the content of this folder from within the package like this:
system.file( "pubmed/myfile", package="<package>" )
See for more information http://cran.r-project.org/doc/manuals/r-release/R-exts.pdf (1.1.5 Data in packages).
I suggest you to use devtools and roxygen2 packages. Basically, you just need to prepare description and .R files.
see more details in this brilliant answer :devtools roxygen package creation and rd documentation

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