I have some C++ code that I intend to export into my r package using Rcpp. However, this code links to fftw3 via
#include <fftw3.h>
at the top of the file. When I try to compile this code, I unsurprisingly get the error
fatal error: 'fftw3.h' file not found
What is the proper way to link to this file so that it will be available upon compilation of my package? I know that a Makevars file can generally be used to link to system libraries but since this library is external I'm not sure what to do.
Thanks,
Eric.
Please see the Rcpp vignette Rcpp libraries -- which is also this arXiv paper.
There are many examples among the 2400+ CRAN packages using Rcpp. My hunch would probably be to look at what I contributed to the nloptr package -- even though that is a more complicated scheme where we allow use either a system library if present (could be the case with fftw3 too) or downloand and build.
Rcpp has been used a lot to build such glue. The most common, and simplest , approach is to look for pkg-config and query it for headers and libraries. Please give that a shot (with some looking around CRAN or GitHub for examples).
Edit: There is also an (old) fftw3 package by Gabor at his previous employer's GitHub org as well as another CRAN package fftwtools (which, if memory served, I helped once too but I don't recall now what for).
Related
I have some C++ code that I intend to export into my r package using Rcpp. However, this code links to fftw3 via
#include <fftw3.h>
at the top of the file. When I try to compile this code, I unsurprisingly get the error
fatal error: 'fftw3.h' file not found
What is the proper way to link to this file so that it will be available upon compilation of my package? I know that a Makevars file can generally be used to link to system libraries but since this library is external I'm not sure what to do.
Thanks,
Eric.
Please see the Rcpp vignette Rcpp libraries -- which is also this arXiv paper.
There are many examples among the 2400+ CRAN packages using Rcpp. My hunch would probably be to look at what I contributed to the nloptr package -- even though that is a more complicated scheme where we allow use either a system library if present (could be the case with fftw3 too) or downloand and build.
Rcpp has been used a lot to build such glue. The most common, and simplest , approach is to look for pkg-config and query it for headers and libraries. Please give that a shot (with some looking around CRAN or GitHub for examples).
Edit: There is also an (old) fftw3 package by Gabor at his previous employer's GitHub org as well as another CRAN package fftwtools (which, if memory served, I helped once too but I don't recall now what for).
I am creating my first package, which shall be installed through Github. I thought that Rtools was needed only for the person creating it. However, people that tried to install it using Github were asked to update Rtools. Is this really necessary?
Doing some research, I found this: https://community.rstudio.com/t/missing-rtools-should-i-be-worried/27817
One of the answers says the following:
"This means that if you are going to install packages that need
compilation, you also have to install Rtools in your system. "
This is the repo with the package: https://github.com/datazoompuc/PNAD_Covid/tree/master/R/datazoom_pnad_covid
What does this actually mean? How do I know that my package needs compilation?
I thought that Rtools was needed only for the person creating it.
Yes, if and only if you distribute it as a binary. Then the creator uses Rtools to compile and link, and the user just installs, and enjoys.
That is how CRAN works as CRAN compiles for Windows users.
GitHub, however, is foremost a source repository so the installation from GitHub is using a source mode ... and every user will need to compile, and hence have Rtools. (Unless the package and all its depedencies are R-code only.)
You can also have a package repository on GitHub using e.g. the drat package to create it, but that is getting us a little further from the original question.
Your package "needs compilation" — i.e. needs Rtools to install from source (on Windows) — if it contains C or Fortran components, i.e. if you have anything in the src/ directory of your package ...
If you, the package author, don't know if you have C or Fortran code as part of your package, then you almost certainly don't.
It's quite possible that devtools is being overzealous, i.e. detecting that users have a not-most-current Rtools and suggesting (requiring??) that they update it, even though it's not needed for this installation.
When I use a package in R I install it and use it with loading it. Now what if I add a package which uses another package? Is this package automatically downloaded and loaded too? Or is it in general forbidden for a R package to use another package? I don't think that.
Suppose I want to publish a R package. Within my code, can I use functions from other packages and install and load these packages? Or how does this work when I need functions from other packages? Do I have to implement a message that this and that package is needed and that the user has to install and load it prior to it and I need to implement error catching functions in case the package cannot be found on the pc system?
When I want to publish a R package, can I use/call Java code within my package/code?
For a package which was already published - so let's take just as an example the fGarch package - I would like to see the complete code. How can I see this? I know that R is open source and I think it is more or less possible to just enter a function empty and get the code displayed, but sometimes this does not work and especially my question is: Is there a way I can look into the whole code of the package?
For a package which was already published, is it possible to see and look into all files which were submitted? So like a repository as git where all files are submitted - the code itself and further files which are needed like description files or whatever - and I can see these files and look into them?
Furthermore regarding this post here and hiding functions: Is there code in a R package which I cannot see as an end user? This refers also to my previous question, how can I or which way can I see the whole code in a R package?
I guess you have a few different questions here. Let's take them in the order you asked them:
What if I add a package which uses another package? Is this package automatically downloaded and loaded too? Or is it in general forbidden for a R package to use another package?
It is certainly not forbidden for an R package to use another R package. In fact, the majority of R packages rely on other packages.
The source code for each R package must include a text-based DESCRIPTION file in the root directory. In this file you will find (among other things) a "Depends" field, and an "Imports" field. Together, these two fields list all the other packages required to use this package. If a user doesn't already have these other packages installed in their local library, R will install them automatically when it installs the requested package.
If your package lists a dependency in "Depends", then the dependency package is attached whenever your package is attached. Thus if you looked at the source code for a package called "foo" and you see that its DESCRIPTION file contains the line
Depends: bar,
you know that when you call library(foo) in your R console, you have effectively done library(bar); library(foo)
This isn't always ideal. The package foo might only need a couple of functions from package bar, and bar might contain some other functions whose names could clash with other commonly used functions. Therefore, in general, if you are writing a package and you only want to use a few functions from another package, it would be better to use "Imports" rather than "Depends" to limit the number of unnecessary symbols being added to your user's search path.
Suppose I want to publish a R package. Within my code, can I use functions from other packages and install and load these packages
Yes, you can use functions from other packages. The simplest way to do this is to include the name of the package in the Depends field of your DESCRIPTION file.
However, when using just a few functions from another package inside your own package, best practice is to use the "Imports" field in the DESCRIPTION file, and use a namespace qualifier for the imported function in your actual R code. For example, if you wanted to use ggplot from the ggplot2 package, then inside your function you would call it ggplot2::ggplot rather than just ggplot.
If you publish your package for others to use, the dependencies will be installed automatically along with your package if the user calls install.packages with the default settings. For example, when I did:
install.packages("fGarch")
I got the associated message:
#> also installing the dependencies ‘timeSeries’, ‘fBasics’, ‘fastICA’
Do I have to implement a message that this and that package is needed and that the user has to install and load it prior to it and I need to implement error catching functions in case the package cannot be found on the pc system?
No, not in general. R will take care of this as long as you have listed the correct packages in your DESCRIPTION file.
When I want to publish a R package, can I use/call Java code within my package/code?
R does not have a native Java API, but you can use your own Java code via the rJava package, which you can list as a dependency for your package. However, there are some users who have difficulty getting Java to run, for example business and academic users who may use R but do not have Java installed and do not have admin rights to install it, so this is something to bear in mind when writing a package.
For a package which was already published - so let's take just as an example the fGarch package - I would like to see the complete code. How can I see this?
Every package available for download from CRAN has its source code available. In the case of fGarch, its CRAN page contains a link to the gzipped tarball of the source code. You can download this and use untar in R to review all the source code. Alternatively, many packages will have an easily-found repository on Github or other source-control sites where you can examine the source code via a browser. For example, you can browse the fGarch source on Github here.
For a package which was already published, is it possible to see and look into all files which were submitted? So like a repository as git where all files are submitted - the code itself and further files which are needed like description files or whatever - and I can see these files and look into them?
Yes, you can look at all the sources files for all the packages uploaded to CRAN on Github at the unofficial Github CRAN mirror here
Is there code in a R package which I cannot see as an end user? This refers also to my previous question, how can I or which way can I see the whole code in a R package?
As above, you can get the source code for any package via CRAN or Github. As you said, you can look at the source code for exported functions just by typing the name of that function into R. For unexported functions, you can do the same with a triple colon. For example, ggplot2:::adjust_breaks allows you to see the function body of the unexported function adjust_breaks from ggplot2. There are some complexities when an object-oriented system like S4, ggproto or R6 is used, or when the source code includes compiled C or C++ code, but I haven't come across a situation yet in which I was not able to find the relevant source code after a minute or two with an R console and a good search engine.
In my R package I have C code that uses Intel MKL (and an open source library for C unit testing). I use CMake to build the C code. I also have Rcpp interface code that I use to call the C code from R.
I wanted to know if CRAN would accept this package given that you'd have to have Intel MKL and CMake already installed on your system for it to work?
I'm not too worried about the unit testing, I can always get rid of that, but I definitely need Intel MKL because I'm using linear algebra routines that are specific to MKL e.g. Sparse-Matrix-Dense-Matrix multiply etc.
The reason I need CMake is because currently, that's what I'm using to build a static library from the C code and manipulate the Makevars file in the package's src folder, so that I can link the Rcpp interface code against the C library.
My Makevars looks as follows:
PKG_CPPFLAGS=-I/usr/local/lib/R/site-library/Rcpp/include -I/opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/include -I./C
PKG_LIBS=-L./C/cmake-build-release -lbcd -llog -Wl,--start-group /opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/lib/intel64/libmkl_intel_ilp64.a /opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/lib/intel64/libmkl_sequential.a /opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread -lm -ldl
This works on Ubuntu, but I'm worried about OS X and especially Windows. For example, here someone claims that Intel MKL is not compatible with minGW.
I know that there are many CRAN packages that use either MKL or CMake. But for the ones that I've investiaged, MKL is used as an optional BLAS library and CMake is used to build what's under the inst folder, which did not depend on external libraries. I've looked at writing R extensions and also the CRAN submission page but wasn't able to find find an answer. I thought that since there are more and more people interested in using CMake and MKL with R it would be good to have a SO post.
It seems you are tieing this up from the wrong end.
You have a more narrow and specialised solution you created (MKL, CMake).
Now you are worried that it may be too specialised. You are on the right path. First off, CMake is used by other packages so you could just declare it as a SystemRequirements:. Second, MKL is tougher but ... also truly specialised.
Methinks the general recommendation for a broader CRAN upload would be to
relax your code requirements: use MKL if present, but offer a fallback when not; now you no longer require MKL
ditto for CMake: keep it and depend on it, better still try to not require it as most 15000 other CRAN package no not need it either.
Once you hit these two you're ready for a CRAN upload. If all this seems to onerous, just stick with a GitHub repository or maybe a drat repo.
I am the developer of the following package https://gitlab.inria.fr/gdurif/pCMF and I have a problem: the compiled library is too big.
When I check it with R CMD check, I get the following note regarding the size of the compiled library:
checking installed package size ... NOTE installed size is 31.0Mb sub-directories of 1Mb or more:
libs 30.8Mb
My package is based on C++ code interfaced thanks to the package rcpp and heavily uses the algebra template library Eigen based on the package RcppEigen.
Since Eigen is templated, I think that the compiled library can become large. However, I would like to submit my package on the CRAN and I have no idea at all how I could solve this.
Thanks