I imported the org.ddahl.rscala class into my Scala project and have gotten the minimal example found here: https://dahl.byu.edu/software/rscala/scaladoc/org/ddahl/rscala/RClient.html to run. Now I would like to use an R library embedded in my Scala code if possible; however I cannot find what syntax I would use to import an external library from the rscala package.
Calling from RClient object is not supported, the below does not work:
val R = org.ddahl.rscala.RClient()
R.library("libraryname")
Is this something which is possible to do, or is rscala limited in scope to the methods listed on the scaladocs page?
R.eval("require {library_name}")
Related
I created a local package with personal functions to be easily used within R. One of these is aimed to be used in the lidR package within a wrapper function (i.e. grid_metrics). For this reason I took the scheme of this script as a reference, exporting both the long name (e.g. my_metrics(param1, param2,...)) and the lazy one (e.g. .my_metrics), because I really like its ease of use.
Nevertheless, if I load my package and then call the lazy function
library(mypackage)
test = grid_metrics(las, .my_metrics, 20)
it does not work, so I have to load in memory the function by running its code from the file. At this stage, I can use it in both forms.
Within the NAMESPACE file I can see that both forms are exported so my last guess is that this might be related somehow to lazyeval but I don't get how.
It seems that the problem is related to the DESCRIPTION section in which the lidR package was included. Since when I moved from Imports to Depends the issue is solved.
I am looking for a fast R solution to read a package NAMESPACE file. The solution should contain already preprocessed (and aggregated) records and separated imports and exports.
Unfortunately I can’t use getNamespaceExports("dplyr")/getNamespaceImports("dplyr") as they need the package to be loaded to the R session, which is too slow.
I need a solution which simply process a text from the NAMESPACE file. Any solutions using external packages as well as partial solutions would still be welcome.
The raw data we could grabbed with a call like readLines("https://raw.githubusercontent.com/cran/dplyr/master/NAMESPACE"). roxygen2 generated files are formatted properly, but this will be not true for all manually generated files.
EDIT:
Thanks to Konrad R. answer I could develop such functionality in my new CRAN package - pacs. I recommended to check pacs::pac_namespace function. There is even one function which goes one step further, comparing NAMESPACE files between different package versions pacs::pac_comapre_namespace.
The function is included in R as base::parseNamespaceFile. Unfortunately the function does not directly take a path as an argument. Instead it constructs the path from a package name and the library location. However, armed with this knowledge you should be able to call it; e.g.:
parseNamespaceFile('dplyr', .libPaths()[1L])
EDIT
Somebody has to remember that the whole packages imports (like import(rlang)) have to be still invoked with the same function and the exports for them extracted. Two core elements are using parse on NAMESPACE code and then using the recursive extract function parseDirective.
I need to know how to create a library in Julia and where I must keep it in order to call it later. I come from C and matlab, it seems there is no documentation about pratical programming in Julia.
Thanks
If you are new to Julia, you will find it helpful to realize that Julia has two mechanisms for loading code. Stating you "need to know how to create a library in Julia" would imply you most likely will want to create a Julia module docs and possibly a packagedocs. But the first method listed below may also be useful to you.
The two methods to load code in Julia are:
1. Code inclusion via the include("file_path_relative_to_call_or_pwd.jl")docs
The expression include("source.jl") causes the contents of the file source.jl to be evaluated in the global scope of the module where the include call occurs.
Regarding where the "source.jl" file is searched for:
The included path, source.jl, is interpreted relative to the file where the include call occurs. This makes it simple to relocate a subtree of source files. In the REPL, included paths are interpreted relative to the current working directory, pwd().
Including a file is an easy way to pull code from one file into another one. However, the variables, functions, etc. defined in the included file become part of the current namespace. On the other hand, a module provides its own distinct namespace.
2. Package loading via import X or using Xdocs
The import mechanism allows you to load a package—i.e. an independent, reusable collection of Julia code, wrapped in a module—and makes the resulting module available by the name X inside of the importing module.
Regarding the difference between these two methods of code loading:
Code inclusion is quite straightforward: it simply parses and evaluates a source file in the context of the caller. Package loading is built on top of code inclusion and is quite a bit more complex.
Regarding where Julia searches for module files, see docs summary:
The global variable LOAD_PATH contains the directories Julia searches for modules when calling require. It can be extended using push!:
push!(LOAD_PATH, "/Path/To/My/Module/")
Putting this statement in the file ~/.julia/config/startup.jl will extend LOAD_PATH on every Julia startup. Alternatively, the module load path can be extended by defining the environment variable JULIA_LOAD_PATH.
For one of the simplest examples of a Julia module, see Example.jl
module Example
export hello, domath
hello(who::String) = "Hello, $who"
domath(x::Number) = x + 5
end
and for the Example package, see here.
Side Note There is also a planned (future) library capability similar to what you may have used with other languages. See docs:
Library (future work): a compiled binary dependency (not written in Julia) packaged to be used by a Julia project. These are currently typically built in- place by a deps/build.jl script in a project’s source tree, but in the future we plan to make libraries first-class entities directly installed and upgraded by the package manager.
I have been developing a package with Rcpp for C++ integration. I used RcppExport to make the functions return SEXP objects.
The issue is travis-ci seems to give warnings telling that there are undocumented code objects. (These are cpp functions). However I do not want users to directly access those functions as well.
How can I resolve this issue? How can I document these functions as well?
You seem to have an elementary misunderstanding here.
If your NAMESPACE contains a wildcard 'export all' a la exportPattern("^[[:alpha:]]+") then each global symbol is exported and per R standards that are clearly documented needs a help entry.
One easy fix is NOT to export everything and just write documentation for what you want exported. We sometimes do that and call the Rcpp function something like foo_impl and then have R functions foo (with documentation) call foo_impl. In that case you would just export foo and all is good.
In short, you are confused about R packages and not so much Rcpp. I would recommend downloading the sources of a few (small) Rcpp packages to get a feel for what they do.
I'm new to Rcpp and want to export some C++ class into R. I've install RcppExmples intending to learn some code snippet. But I didn't even know how to use it? help(RcppExamples) only to get:
Description:
This package shows some simple examples for the use of ‘Rcpp’.
Both the older ('classic') and new API are documented.
It can also serve as a working template to create packages that
use ‘Rcpp’ to interface C++ code or libraries.
Where can I get the examples?
The sources for the examples in the package are, well, in the package itself.
This is Rcpp. It works with C++ source code, and R code. You don't use this like a normal package, you create packages with it. From source.
Did you had a look at the manual? I think it's clear from this document. But the DESCRIPTION file says:
Note that the documentation in this package currently does not cover all the features in the package. It is not even close.
So I fear that's all you can get - besides looking at the source code.