Multiple Names for Datasets in R Packages

30 January 2021

“I am known by many names, but you may call me… Tim.” - Graham Chapman

Last year1 the {mangoTraining} package got a fair bit of much-needed TLC, and we discovered a problem which at first glance didn’t seem too tricky to solve, but which ended up leading down a very interesting rabbit hole.

The package is primarily a “data package”, i.e. a package which exists purely to provide datasets to users, and contains nearly 30 datasets of various sizes which are used in Mango’s training courses and referred to in books such as “R in 24 Hours”.

We tend to teach the latest best practices, so our training courses are updated on a regular basis.2 Unfortunately once a physical book has been released into the wild, it’s not so easy to update it in the same way; so whenever we make changes to our material, we have to be careful not to break backwards compatibility!

This is how we stumbled upon the “multiple names for datasets in R packages” problem. Suppose we have a dataset in {mangoTraining} which was historically called demoData, but we now favour snake_case rather than lowerCamelCase in our recent material: how can we also use the name demo_data to refer to the dataset, so that we can use the new name in our new material without breaking compatibility with existing material?

And since we generally want to keep packages as small as possible3: can we do this without including multiple copies of the same dataset in the package?

This allows us to set out three requirements which cover what we’re trying to achieve:

  1. The package must contain only a single copy of the full dataset
  2. The dataset must be accessible using at least two different names
  3. Each name must work correctly with data(), i.e. data("x", package = "pkg") must load an object called x into the session

Adding more data files

I’m going to start by creating a new package, and then and immediately adding a dataset which I would like to include - which is incredibly simple these days, thanks to the marvellous {usethis} package!

 # ... and then once the new session has opened:
 diamonds <- data("diamonds", package = "ggplot2")

Following this, we can see that our dataset has been saved into the data/ directory as a .rda file4, meaning our users can load this dataset into their session using data("diamonds", package = "datapkg").

The name of the file has been created, quite sensibly, from the name of the R object we saved into it. Renaming the file isn’t a good idea, because the object it contains will still have the old name! For example, if we manually renamed data/diamonds.rda to data/diamantes.rda and rebuilt/reinstalled our package, then if we ran

data("diamantes", package = "datapkg")

we would get our data loaded into our session; BUT it would still be in an object called diamonds! This is because .rda files store an entire R environment, possibly containing multiple objects: when we load that environment into our session, all the objects are just dumped into the session, exactly as they were when they were saved.5

So if we want a different name for our dataset, we actually need to rename the object itself before saving it:

diamantes <- diamonds

This is all useful context, but we haven’t actually satisfied our first requirement yet! Our users can now get either diamonds or diamantes from our package; but that’s because the package contains the same dataset twice, in two separate .rda files (diamonds.rda and diamantes.rda). What we would ideally like to do is to have multiple names pointing towards the same underlying dataset, so that we only have to have one copy of that dataset in our package but it can be called in multiple ways.

Multiple names for functions

It’s really easy to do something along those lines with functions, because we can store a function in a variable and then just reassign that variable to our heart’s content.6

To prove the point:

f <- function(x, y) x+y
g <- f
function(x, y) x+y

This can be really useful when you want to implement a piece of functionality in just one place, but then to allow the user to access the same functionality via more than one name. Here’s an example from {tibble}: the “type check” function for tibble objects is named is_tibble(), in line with tidyverse naming conventions; but the authors also provide an alias, is.tibble(), which matches base R naming conventions (is.numeric(), etc) in anticipation of users trying that pattern first.

is.tibble <- function(x) {
  deprecate_warn("2.0.0", "is.tibble()", "is_tibble()")


You can see this in the context of the source code here. If a user calls is.tibble(), the responsibility is passed straight on to is_tibble() after a nudge towards the “preferred” tidyverse syntax.

Multiple names for datasets?

Let’s review again the key points we would like to achieve:

  1. The package must contain only a single copy of the full dataset
  2. The dataset must be accessible using at least two different names
  3. Each name must work correctly with data(), i.e. data("x", package = "pkg") must load an object called x into the session

That third point is quite important for the usability of the package. In fact, if we spend some time thinking about the third point here, we actually get a big push in the right direction!

From the Details section of the documentation for data():

Currently, four formats of data files are supported:

  1. files ending ‘.R’ or ‘.r’ are source()d in, with the R working directory changed temporarily to the directory containing the respective file. (data ensures that the utils package is attached, in case it had been run via utils::data.)

  2. files ending ‘.RData’ or ‘.rda’ are load()ed. …

We’re familiar with the second format, .rda, but maybe we didn’t know about the first! So it looks like we might be able to create a .R file which somehow loads the dataset which we have already saved elsewhere…

Let’s make a file called data/diamantes.R, which - as we’ve just learned! - will be run using source() if our user calls data("diamantes", package = "datapkg").

Now all we have to do is figure out how to “copy” our diamonds dataset into an object called diamantes.

Let’s naively try a similar approach to the one we’ve seen used for functions. I’ll add the following line to data/diamantes.R:

diamantes <- diamonds

But attempting to build the package quickly results in a nasty error…

==> Rcmd.exe INSTALL --no-multiarch --with-keep.source datapkg

* installing to library 'C:/.../R/win-library/4.0'
* installing *source* package 'datapkg' ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** byte-compile and prepare package for lazy loading
Error in eval(exprs[i], envir) : object 'diamonds' not found
Error: unable to load R code in package 'datapkg'
Execution halted
ERROR: lazy loading failed for package 'datapkg'
* removing 'C:/.../R/win-library/4.0/datapkg'
* restoring previous 'C:/.../R/win-library/4.0/datapkg'

Exited with status 1.

Note the object 'diamonds' not found!

This is because datasets aren’t like other R objects: as r-pkgs puts it,

Objects in data/ are always effectively exported (they use a slightly different mechanism than NAMESPACE but the details are not important).

That “slightly different mechanism” is our problem - we can’t simply refer to datasets as internal objects within our package! So we’ll have to try a different approach…

Let’s read that again

Remember what the documentation for data() said? (We only looked at it a minute ago!)

We’ve started to take advantage of

1. files ending ‘.R’ or ‘.r’ are source()d in, with the R working directory changed temporarily to the directory containing the respective file

already - and now

2. files ending ‘.RData’ or ‘.rda’ are load()ed

is also going to be helpful, for getting hold of our already-saved data!

Let’s edit data/diamantes.R so that it contains just the following line:


Note that we are using a relative path to diamonds.rda because, as informed by the docs, our working directory is now the directory where data/diamantes.R lives, i.e. data/!

Now our package does build and install, but we do get an ominous warning in the output…

** data
*** moving datasets to lazyload DB
Warning: object 'diamonds' is created by more than one data call
** byte-compile and prepare package for lazy loading

… which makes sense. When we call data("diamantes", package = "datapkg"), we simply end up calling the same thing as data("diamonds", package = "datapkg"), so we end up with an object called diamonds in our session rather than diamantes. We’re closer, but we’re not there yet!

Maybe we can rearrange data/diamantes.R slightly to rename things for us…

diamantes <- diamonds

This seems very close. We get the same warning about diamonds when we rebuild the package, but now when we call data("diamantes", package = "datapkg") we do get an object called diamantes… we just also get one called diamonds! This is because when we source data/diamantes.R, we end up with two objects in the environment at the end of the script (diamonds and diamantes), both of which are then pulled into our R session.

So we can add one more line to fix this problem:

diamantes <- diamonds

And now we have achieved our goal! No more warning message when we build, and both diamonds and diamantes work in the way we wanted.

Now do it in one line

Three lines was too many for you?? Okay - we can replace the current contents of data/diamantes.R with:

diamantes <- local(get(load("diamonds.rda")))

Working from the inside out:

  • load("diamonds.rda") loads diamonds into our environment, and returns a vector of names of all loaded objects, i.e. c("diamonds")
  • get() retrieves the object with a name of "diamonds" from the current environment, since we didn’t provide a different environment via the pos argument - i.e. we now have the actual data frame which the diamonds variable name refers to
  • local() means all of that happened in its own little throwaway environment - so we end up with the data frame object which was returned from get(), and everything else is just thrown away, including the diamonds object which had been created by load()
  • And finally, we assign that data frame into an object called diamantes, which is the only object we created in our main environment and therefore the only object which will be created when we call data("diamantes", package = "datapkg")


Of course, it’s important to document our datasets - again we can refer to the relevant chapter of the ever-so-useful r-pkgs for guidance! The documentation should live in R/data.R: we only have to write one set of documentation, and then we can use the @rdname Roxygen tag to include the other names!

#' Prices of 50,000 round cut diamonds.
#' A dataset containing the prices and other attributes of almost 54,000
#' diamonds.
#' @format A data frame with 53940 rows and 10 variables:
#' \describe{
#'   \item{price}{price, in US dollars}
#'   \item{carat}{weight of the diamond, in carats}
#'   ...
#' }
#' @source \url{}

#' @rdname diamonds


So far I’ve noticed two important things to bear in mind when using this approach:

  • We don’t have to stop at two names - if we want to, we can keep making new data/*.R files which point back to the same .rda file! HOWEVER, we shouldn’t have two files with names which differ only in capitalisation, e.g. diamantes.R and DiaMantes.R - this is not possible on Windows, and not a great idea on other platforms! (In this particular case, we could have diamantes.rda with diamonds.R and DiaMantes.R instead)
  • If you build your package using the --resave-data flag, any data/*.R files will be sourced and resaved as .rda files as part of the build process, rendering all your hard work useless!7

I’d be keen to hear whether anyone else has success with this method too, or if there are better ways to achieve the same thing! We seemed to make it through the CRAN submission process for {mangoTraining} with no problems at all; but I’m curious as to how reproducible this is. In particular I would love to know whether the CRAN builds use --resave-data or not, and whether that would scupper this method for packages where resaving takes us over the package size limit!

  1. Yes, it’s taken me nearly a year to write up this post. Please forgive me, I’ve been very busy surviving. 

  2. Keeping content up to date can be quite a lot of work in itself! So in fact it was only quite recently that we committed 100% to following the tidyverse style guide wherever possible. 

  3. While keeping packages small is a good idea anyway (especially if you’re using version control!), an extra motivating factor here is the 5MB upload limit for CRAN packages as detailed on the CRAN policies page. You can imagine a situation where we want to use 2 different names for a 3MB dataset! 

  4. As we would expect from {usethis}, this completely aligns with the rules and conventions on saving datasets within R packages, set out in r-pkgs and elsewhere. 

  5. It took me a REALLY long time to get my head around this at first - has anyone else had an experience like trying to run diamantes <- data("diamonds"), and then getting really frustrated when diamantes is not the data frame you were expecting? I found that so unintuitive. Actually nowadays when I need to save a single R object, I tend to use .rds rather than .rda for exactly that reason. However, .rda is the convention for R packages, so we’ll stick with it… grrrr… 

  6. This is because functions in R are “first-class objects”, i.e. you can give them a name, save them in variables, pass them into other functions as parameters… I took this for granted until recently, when I found out that you can’t do this so easily in many languages - for example, Java! 

  7. And possibly resulting in two identically-named files, if you had e.g. hello.rda and HeLLO.R