I have developed a R package with many functions that use external files as a parameter. I am documenting the package with examples and when I run the devtools::check() command I get an error because the execution of the examples fail. I think this is caused because I have not stored the external files where it's needed and fail trying to find these files.
I need to know how can I do to document these functions with examples and where do I have to store the external files the functions will use in examples.
Related
I'm experimenting with GitHub and I created a little package for my colleagues to use. They install it with the devtools package and install_github() function directly in R. I also have some example data and a R-Markdown file that shows the usage of all functions in the package and can be published via GitHub Pages.
I would like to know what would be the best practice to enable others to use this example data to learn the package.
I can think of two different options:
Host the data in a separate directory which is not part of the installation and tell people to download it manually or use something like the download.file() function from R at the beginning of the example script to download all data that could be packed into a .zip.
Make the data part of the package installation, however this would require the data to be fairly small which is difficult in my particular case (data is 10MB).
Ideally the examples in the R-documentation (.Rd files in the man folder) could also use the same examples as in the markdown file. also in this case, option (2) seems to be favorable.
Could anybody give me some advice what would be the best way to go, sort of the "industry standard" if there is any.
I am writing a user-friendly function to import Access tables using R. I have found that most of the steps will have to be done outside of R, but I want to keep most of this within the script if possible. The first step is to download a Database driver from microsoft: https://www.microsoft.com/en-US/download/details.aspx?id=13255.
I am wondering if it is possible to download software from inside R, and what function/package I can use? I have looked into download.file but this seems to be for downloading information files rather than software.
Edit: I have tried
install_url(https://download.microsoft.com/download/2/4/3/24375141-E08D-
4803-AB0E-10F2E3A07AAA/AccessDatabaseEngine_X64.exe)
But I get an error:
Downloading package from url: https://download.microsoft.com/download/2/4/3/24375141-E08D-4803-AB0E-10F2E3A07AAA/AccessDatabaseEngine_X64.exe
Installation failed: Don't know how to decompress files with extension exe
I'm working on an R package at work. My package has gotten large enough that I've decided I need some form of repeatable testing. I settled upon using testthat and mockery. I'm not a developer, so this is the first time I'm writing tests at this level.
I deal with a lot of data files and it's very convenient to have functions in my package to help locate files. These functions interact with the file system via calls to dir. For example,
Data from one event can be split over multiple files. If I have file datafile_2017.10.20_12.00.00, I have a function that can find the next file that is part of the same event, i.e. datafile_2017.10.20_12.05.00.
My question is this: what is the best way to test functions like this? My intuition is to avoid using actual files stored somewhere else in my repository because that can fail for a number of reasons, e.g. different paths, different repo states b/w systems. I searched around and it looks like different languages have mocking libraries that allow for mocking directory structures. I haven't found anything like that for R (except for testthatsomemore, but it was removed from CRAN sometime in 2016).
Is there an R package that allows for mocking directory structures? Or am I wrong to move away from storing small test files in my repo?
I'm creating an R package for the handling of a specific dataset that is regularly updated in our organization, but not on a fixed schedule (making it unsuitable for something such as a cronjob). As a result, users must currently run a set of two scripts for data processing before they begin to analyze the data. In converting this set of functions into a package, I'm hoping to alleviate this by having the scripts be called whenever the package is first loaded to R (with analogous functions if people would like to manually check for an update in the middle of a multi-day session).
I've seen ways to deal with compiling external files upon package installation, but nothing on how to get R to run a script whenever the package is loaded (not just installed). Does anyone know if this is possible, and if so, how to do it?
Thanks!
These functions are outlined in the Writing R Extensions Guide, (which, if you're writing a package, you should be reading carefully) specifically section 1.5.3 Load Hooks
You can define an .onLoad function that will be called when you package loads.
In a vein similar to this question: I'm writing a package and am using knitr to write a few documents in inst/doc/. Since I'm using github to host my repo (and I intend to point to people to that repo to get the package), I'm wondering if I should be version controlling my the caches of my various documents.
I ask this question because it's unclear where cache falls in the guidelines provided by this other question (which addresses when certain file types should and shouldn't be in the .gitignore of a repo).
Can anyone shed some light on to how package developers that use knitr and git are handling their caches?
If R CMD check passes without the knitr cache, and I think it would, I wouldn't include them. In fact, I suspect R CMD check would give a note about the cache files being present in the package. I know for LaTeX files, you only want to include the .tex file in the R package and in the version control. The other required files should be automatically generated on install.