Is it possible to use R package data in testthat tests or run_examples()?

Question

I'm working on developing an R package, using devtools, testthat, and roxygen2. I have a couple of data sets in the data folder (foo.txt and bar.csv).

My file structure looks like this:

/ mypackage     / data         * foo.txt, bar.csv     / inst         / tests             * run-all.R, test_1.R     / man     / R 

I'm pretty sure 'foo' and 'bar' are documented correctly:

    #' Foo data     #'     #' Sample foo data     #'     #' @name foo     #' @docType data     NULL     #' Bar data     #'     #' Sample bar data     #'     #' @name bar     #' @docType data     NULL 

I would like to use the data in 'foo' and 'bar' in my documentation examples and unit tests.

For example, I would like to use these data sets in my testthat tests by calling:

    data(foo)     data(bar)     expect_that(foo$col[1], equals(bar$col[1])) 

And, I would like the examples in the documentation to look like this:

    #' @examples     #' data(foo)     #' functionThatUsesFoo(foo) 

If I try to call data(foo) while developing the package, I get the error "data set 'foo' not found". However, if I build the package, install it, and load it - then I can make the tests and examples work.

My current work-arounds are to not run the example:

    #' @examples     #' \dontrun{data(foo)}     #' \dontrun{functionThatUsesFoo(foo)} 

And in the tests, pre-load the data using a path specific to my local computer:

    foo <- read.delim(pathToFoo, sep="\t", fill = TRUE, comment.char="#")     bar <- read.delim(pathToBar, sep=";", fill = TRUE, comment.char="#"     expect_that(foo$col[1], equals(bar$col[1])) 

This does not seem ideal - especially since I'm collaborating with others - requiring all the collaborators to have the same full paths to 'foo' and 'bar'. Plus, the examples in the documentation look like they can't be run, even though once the package is installed, they can.

Any suggestions? Thanks much.

Answer 1

Importing non-RData files within examples/tests

I found a solution to this problem by peering at the JSONIO package, which obviously needed to provide some examples of reading files other than those of the .RData variety.

I got this to work in function-level examples, and satisfy both R CMD check mypackage as well as testthat::test_package().

(1) Re-organize your package structure so that example data directory is within inst. At some point R CMD check mypackage told me to move non-RData data files to inst/extdata, so in this new structure, that is also renamed.

/ mypackage     / inst         / tests             * run-all.R, test_1.R         / extdata             * foo.txt, bar.csv     / man     / R     / tests         * run-testthat-mypackage.R 

(2) (Optional) Add a top-level tests directory so that your new testthat tests are now also run during R CMD check mypackage.

The run-testthat-mypackage.R script should have at minimum the following two lines:

library("testthat") test_package("mypackage") 

Note that this is the part that allows testthat to be called during R CMD check mypackage, and not necessary otherwise. You should add testthat as a "Suggests:" dependency in your DESCRIPTION file as well.

(3) Finally, the secret-sauce for specifying your within-package path:

barfile <- system.file("extdata", "bar.csv", package="mypackage") bar <- read.csv(barfile) # remainder of example/test code here... 

If you look at the output of the system.file() command, it is returning the full system path to your package within the R framework. On Mac OS X this looks something like:

"/Library/Frameworks/R.framework/Versions/2.15/Resources/library/mypackage/extdata/bar.csv" 

The reason this seems okay to me is that you don't hard code any path features other than those within your package, so this approach should be robust relative to other R installations on other systems.

data() approach

As for the data() semantics, as far as I can tell this is specific to R binary (.RData) files in the top-level data directory. So you can circumvent my example above by pre-importing the data files and saving them with the save() command into your data-directory. However, this assumes you only need to show an example in which the data is already loaded into R, as opposed to also reproducibly demonstrating the upstream process of importing the files.