I really should talk about {capsule} #rstats

This is a post I’ve been meaning to write for some time now about {capsule}.

{capsule} provides alternative workflows to {renv} for establishing and working with controlled package libraries in R. It also uses an renv.lock so it is compatible with {renv} - you can switch between doing things the {capsule} way and the {renv} or vice versa at any time.

Introducing an R package I wrote nearly three years ago

Carefully curating a controlled package environment the {renv} way can be kind of a chore. You have to init the {renv} and then be very diligent in what you install into the project library - because the project library will be “shapshotted” to create the renv.lock. {renv} provides tools to help you keep the library nice and pruned. But you have to remember to use them. And there are some challenging edge cases - particularly around ‘dev’ packages or packages that play no role in the project but aid the process of developing it.

It’s not that the {renv} way is wrong - it’s clearly not - because it’s pretty much how other languages do it, but other languages are different from R and have different developer cultures. Importantly I think R users are much more prone to interactive REPL driven development and this, in turn, lends itself to the use of interactive development tools like: {datapasta}, {ggannotate}, {esquisse} as well as other more traditional dev tools like {styler}, {lintr}. If you use VSCode you will also have packages that support the extension experience like {languageserver} and {httpgd}.

You can install these packages into the project library and by default {renv} won’t snapshot them - but then you have to go make sure your dev dependencies are up to date each time you context switch to a new project - boring! They also contaminate the project library in subtle ways: installing a new dev package might force an update of a controlled package that would have otherwise been unnecessary - should that change be reflected in the lockfile?

It all just feels a bit hard for something tangential to getting the actual work done. The hardness of keeping a clean lockfile magnifies when you have a whole team having at it with all their personal dependencies. In my case at one time we had 3 distinct text editor / IDE workflows being used within the team, not to mention each person’s preferences for RStudio addins. Some people are more onboard with putting up with some added resistance in their dev workflow than others - again the dev culture thing plays an important role as to how functional or otherwise this setup is.

Added resistance can also show up elsewhere like the time to install the local project library. And where this hurts is that this added resistance created by {renv} makes people less likely to use it. “Aww this is just an ad hoc thing, I don’t wanna jump through the hoops”. We all know by now what happens to “ad hoc things” in Data Science Land.

{capsule} provides some new lockfile workflows that are much lazier and therefore perhaps a bit easier to get adopted as standard practice for a team.

The laziest possible workflow

You’ve created a pipeline and it’s spat out the output you wanted. You didn’t use a project library to do it. You can add an renv.lock based on the project dependencies and versions in your main library with a one-liner capsule::capshot(). You commit that lockfile because one day you might need it, but you didn’t have any added resistance to your dev workflow!

In the future if you do need to run the project against the old package versions you can fire off: capsule::run(targets::tar_make()) or capsule::run(source("main.R")) or capsule::run(rmarkdown::render("report.Rmd")) etc. This will create the local project library from the lockfile and run the R command you specified against that local library. You don’t get switched into the {renv} library - so you’re not immediately flying blind without your dev tools!

Ahah but what if there’s a bug you say?! You can temporarily switch your REPL over to an R session running aginst the project library with capule::repl(). You won’t have your dev tools in this REPL though, so it may be worth doing renv::init() and going full {renv} with your dev tools installed. It’s your choice and you only pay that price if you need to.

A workflow for the whingers

Here’s a scenario that might be familiar: you have a team of people rapidly smashing out a project together. Maybe it’s a Shiny app. Someone is building the datasets, someone is wiring up the GUI, someone is making the visualisations etc. With many people contributing to an early stage project there is really no such thing as a ‘controlled package environment’.

Often issues encountered on the project force changes to internal infrastructure packages, so these are constantly updated. New packages flow into the project - particularly thanks to the GUI and vis people - they’re just going wild trying to make things look great. You all need to stay synced up with packages otherwise every time you pull changes the thing darn thing isn’t going to run.

This kind of situation is a strong argument for {renv} due to the need to stay in sync to be able run the project. However, if we slightly loosen the meaning of ‘in sync’ we can get away without the resistance of full {renv}. What I mean is: We probably don’t need to all have the exact same package versions. Over a short space of time, the culture of R developers is that packages are backward compatible with prior versions. So it’s probably fine if people are ahead of the lockfile, but less good if they fall behind.

With {capsule} we can have a workflow where we use a lockfile, but no project local library to keep us all as loosely in sync. If someone makes an important change to a package that is a dependency (or one comes down the pipe externally) we definitely need to call capsule::capshot() and commit a new lockfile. But if someone installs the latest version of {ggplot2} the day it drops because they happened not to have it, or they’re into bleeding edge - meh, we probably don’t need to update the lockfile. It’s fine for them to stay ahead of the lockfile until an important change is dropped.

What’s key is that when changes are made to the lockfile, everyone knows about them and updates the necessary packages. To do this you put capusle::whinge() somewhere in the first couple of lines of your project. It produces output like this:

> capsule::whinge()
Warning message:
In capsule::whinge() :
  [{capsule} whinge] Your R library packages are behind the lockfile. Use capsule::dev_mirror_lockfile to upgrade.

Oh but people will ignore the warning! Probably. So you can also do this, which is how my team does it:

> capsule::whinge(stop)
Error in capsule::whinge(stop) : 
  [{capsule} whinge] Your R library packages are behind the lockfile. Use capsule::dev_mirror_lockfile to upgrade.

Now it’s an error. If you’re missing any packages in the lockfile, or you’re behind the lockfile versions the project doesn’t run.

If you get this error you have a one-liner to bring yourself up to the lockfile: capsule::dev_mirror_lockfile() which will make sure you every package in your library is at least the lockfile version. Importantly packages are never downgraded. So that bleeding edge {ggplot2} person can still get those Up To Date endorphins.

I am shocked by how well this workflow works. There’s a possibility of someone using a new package version with breaking changes and forgetting to update the lockfile - and I was fully expecting this to be a big problem… but it just wasn’t. The reason I think it works is that there’s no production environment in the mix here. This is an early-stage development workflow. You’re all constantly updating and running the project, so if it breaks it gets picked up very quickly. There are only ever a few commits you need to look at to see what changed, or a quick question to your teammate who will then slap their forehead and call capsule::capshot() on their machine, commit it, and away you all go.

Finally, we’ve never felt motivated to do this, but you can tighten things up a lot with something like this:


If all your package versions meet or exceed the lockfile, proceed immediately to creating a new lockfile, then run the project. This was actually why I created capsule::capshot(), it’s designed as a very fast ‘in-pipeline’ lockfile creator.


{capsule} is a package I wrote nearly 3 years ago for my team as our main R package dependency management tool. I was always hesitant to promote it because I wasn’t confident my ideas would translate well outside our team, and I also wasn’t confident I wouldn’t balls up someone’s project and they’d curse me forevermore.

It’s fairly battle-hardened now though. It has a niche little fan base of people who saw it in my {drake} post and liked it. I assume a lot of this is due to fact that there’s just less to it than {renv}. I was fairly stoked to find out it was used in the national COVID modelling pipelines in at least two countries recently. It kind of excels in that fast-paced “need something now, but can’t get all these collaborators up to speed on {renv} workflow” zone.

What I’ve learned over the last couple of years is that there isn’t ‘one workflow that fits all’ in this space. It’s about who is on the team and what that team is doing. Workflows that are standard for software engineers are sometimes hard to sell to analytic or scientific collaborators who have a different relationship with their software tool. As I have said many times in the past: If you want people to do something important, ergonomics is key! Success is much more likely with something that feels like it is “for them” rather than something that is “for someone else but we just have to live with it”. And I’m not saying I have the answer for your team, but it might be worth a look if {renv} isn’t clicking.

Finally {capsule} isn’t exactly a competitor to {renv}. It’s not fair to pitch it like that. It uses some {renv} functions under the hood. It wouldn’t have been possible if Kevin Ushey hadn’t been extremely accomodating in {renv} with some changes that enabled {capsule} to work. I think of it more as a ‘driver’ for {renv} that you can switch out at any time for the standard {renv} experience.

A bit of how and why regarding making vector tiles and mapping them with #rstats #rspatial


Data Scientists: Switch Your Deskop To Linux

Many years ago now I told a class of summer semester students that one of the lowest effort, highest reward things they could do to prepare themselves for working on big data problems was to build familiarity with Linux, the operating system of the cloud. This is probably one of the most prophetic things I have ever said. This was back before Kubernetes existed, and if Docker existed, I’d certainly never seen it used.

I advised them to try switching their personal laptop OS to Linux.

I think this is still decent advice for all Data Scientists today. Linux know-how is a great value add for teams that need to scale up themselves - that don’t have the support (or don’t have priority or quality support) from dedicated cloud infrastructure teams.

If you are confident with the Linux ecosystem, you’re not dependent someone else to ‘productionise’ your work. You can cede as much or as little of that as you want.

It’s also a way easier sell these days. I mean, I play Steam games without a hitch on my personal laptop running Linux. Steam Games! What times we live in!

In the weirdest twist of fate, Microsoft Windows is now a strong contender as a desktop OS for those who want to build Linux skills with the safety net of a commercial OS. The Windows Subsystem for Linux ‘just works’ pretty well. Especially when you combine it with VSCode.

On the Apple side of the fence there look to be some cool projects that are aiming to create a decent Linux experience on the proprietary Apple chips. This is definitely worth looking into if you’re one of the, what seems like, 95% of Data Scientists that favour working on a Mac.


Made with #rstats {rdeck} #notgenerative

A tip for installing #rstats {arrow} from binary on Linux

The Apache Arrow project has a handy guide for cutting down R package installation time on Linux: https://cran.r-project.org/web/packages/arrow/vignettes/install.html

But the RSPM suggestion didn’t work for me:

install.packages("arrow", repos = "https://packagemanager.rstudio.com/cran/__linux__/focal/latest")
Installing package into '/home/ubuntu/R/x86_64-pc-linux-gnu-library/4.1'
(as 'lib' is unspecified)
trying URL 'https://packagemanager.rstudio.com/cran/__linux__/focal/latest/src/contrib/arrow_7.0.0.tar.gz'
Content type 'binary/octet-stream' length 4572465 bytes (4.4 MB)
downloaded 4.4 MB

* installing *source* package 'arrow' ...
** package 'arrow' successfully unpacked and MD5 sums checked
** using staged installation
*** Found local C++ source: 'tools/cpp'
*** Building libarrow from source
    For a faster, more complete installation, set the environment variable NOT_CRAN=true before installing
    See install vignette for details:
**** arrow  
PKG_LIBS=-L/tmp/RtmphYTlJ7/R.INSTALL14fae676c8045/arrow/libarrow/arrow-7.0.0/lib -larrow_dataset -lparquet -larrow -larrow /usr/lib/x86_64-linux-gnu/libbz2.so -pthread -larrow_bundled_dependencies -lz -llz4 -lzstd -larrow -larrow_bundled_dependencies -larrow_dataset -lparquet -lssl -lcrypto -lcurl


This is definitely not a binary package! What’s more, this is pretty consistent with the experience I’ve always had with the RSPM: I’ve never had it successfully serve me a binary package, it has always sent me something that needs compilation. The epic compilation time of {arrow} motivated me to look into this though, and this is what I found: https://community.rstudio.com/t/unable-to-install-binary-packages-from-packagemanager-rstudio-com-on-linux/82161

I needed to add this line to my .Rprofile:

options(HTTPUserAgent = sprintf("R/%s R (%s)", getRversion(), paste(getRversion(), R.version$platform, R.version$arch, R.version$os)))

And now I get:

Installing package into ‘/home/ubuntu/R/x86_64-pc-linux-gnu-library/4.1’
(as ‘lib’ is unspecified)
trying URL 'https://packagemanager.rstudio.com/all/__linux__/focal/latest/src/contrib/arrow_7.0.0.tar.gz'
Content type 'binary/octet-stream' length 29595575 bytes (28.2 MB)
downloaded 28.2 MB

* installing *binary* package ‘arrow’ ...
* DONE (arrow)

The downloaded source packages are in

I’ve heard a lot of people talking about Linux binaries via RPSM over the last couple of years, but never mentioned this HTTPUserAgent issue. I suspect there are a lot of people who think they are getting binary installs on their servers that are still doing compilation! Definitely worth checking!

Surprised how well this works over ssh. Faster than local on Windows(!). paint::ipaint() is sort of a terminal-based alternative to #rstats View(). I really wanna rewrite it now making full use of control characters to make the scrolling a bit snappier.

When using VSCode over SSH the auto port forwarding is just so so so much good magic.

If something in your terminal looks like it’s serving something on your remote host, VSCode automatically creates a tunnel for you over the ssh connection and forwards the remote port to local!

On the tightness of loops

A lot of my work is about making tight loops.

Or maybe it’s just that as I gain mastery (slowly) of programming on datasets, the effort I expend is more around the edges of that, and the more I do that the more I understand this is a problem domain in an of itself.

For me, a tight loop is usually made by the scaffolding around the project. Typically this scaffold also made from code like the project. The scaffold’s job is to allow me to observe the effects of the project code that I write rapidly, ideally instantaneously, and to paper over context switching to keep me focussed.

The more rapidly I can get feedback the quicker I can learn if I’m on the right track and correct to eventually complete the task. While context switching and focus are intimately, and invsersely related in my experience.

In R, {targets} is a tool for making tight loops. You change a pipeline, and by the magic of caching, get to observe the effect of that change in the shorest possible time.

Likewise, {rmarkdown} is a tool for making tight loops, but focussed on scientific documents that contain assets built from code. We can change the code that builds the assets, and immediately view the resulting document, without all the slow GUI work of attaching new figures etc.

{testthat} and test suites in general are tools for rapidly collecting vast amounts of feedback about a software project. When adding new code, we can very quickly evaluate if that code has created any problems with existing functionality.

And then there’s the R REPL itself! A spectacular facilitator of tight loops for making graphics, munging data, modelling data etc. I’m supremely spoiled when I use R.

I feel the absence of these loops when they’re missing. The last project I worked on before this extended COVID break was updating a small vector tile server written in Typescript on AWS lambda. I didn’t create the project, and it was initially extremely disorienting trying to get a workflow going.

One thing I knew was that a workflow that was like: Compile to JS, zip code, upload to AWS lambda, test in browser… was not going to work for me. That feels like sludge. I could work like that, but it would cause a lot of bad feelings, and probably take even longer because I’d keep getting distracted between all the context switches.

After some research, I decided to go the “infrastructure as code” route with AWS SAM. By virtue of doing that, I got to run the lambda function locally in a simulated environment, including attaching VSCode’s debugger! That’s pretty tight. I spent probably a week setting that up before even looking into making the changes I needed to make. I think a less experienced me would have felt the pressure to just get in there and start hacking, eager to show progress. But I was able to sell the infrastructure investment to the team with the confidence that it would all be worth it once I had the tight loop rolling.

I see this need of mine cropping up in other places too: I’ve been casually working on my on bespoke keyboard design. Taking what I learned building a Corne, but realising that in a design for my unique and hand measurements.

Initially, I was physically laying out the keyboard in KiCad, and doing paper tests on a printed version. Each time I decided to move a key, I had to do a bunch of trigonometry and sometimes propagate that to many affected keys. It got quite cumbersome.

Then I discovered (well re-discovered) Ergogen (thanks Kyle Mitchell) which is a kind of parameterised framework for generating minimal ergonomic keyboard designs from configuration files. Basically AWS SAM for keyboards if you will.

One thing I noticed was that Ergogen doesn’t ship with a quick way to do a complete visualisation of the design. But I was able to close that loop by tacking a little R script on to the build process that read in the outputs as spatial data in {sf} and plotted them with {ggplot2}.

If I have the plot image open in VSCode, it’s automatically refreshed every time I build the config. Tight loop achieved!

A screenshot of my VSCode workspace while working on the keyboard. Keyboard config on left, visualisation on right.

It’s such a powerful concept: investing in the infrastructure of the doing, to boost feedback and minimise context switching. It’s the UI for building the UI. Developer UI. Meta UI?

On reflection, I’ve been into meta UI stuff for a while now. At some level, pretty much all my open source projects are about tightening the loop: Smoothing out annoying snarls that slow down project iteration speed.

Thinking about valuing these things in this context is new for me though.

A bit of whimsy wrapped around tempfile(). I feel like multi-session/multi-project workflows are my new frontier. #rstats


For #rstats #adventofcode day 2 I decided to avoid all string parsing/manipulation/comparisons and use the command as a class to dispatch s3 methods. Is this a good idea? Probably not!

Happy Friday #rstats {targets}/{tflow} users! Added two new addins to help smooth multi-plan workflows: Load target at cursor if found in any store in the _targets.yaml, and tar_make() the active editor plan.


Today’s #rstats hero is @mdneuzerling with {getsysreqs}!


(make your own) Team code commit timeline vis #rstats

Having a second stab at a plot of my team’s commits since it has come to light that an unnamed someone was using a gmail for user.email on most of their work commits:


I also binned the dots, instead of using alpha, and of course, that works a lot better.

Regarding software engineering for data science, I think this highlights some important issues I am going to expand on in an upcoming long-form piece. As a teaser: In a world with so many projects, and code constantly flowing between those projects, are “project-oriented workflows” that end their opinions at the project folder underfitting the needs of Data Science teams?

Make your own

If you’re feeling brave I would love to compare patterns with other teams!

It’s hardly any code (if you have a flat repository structure like mine) thanks to the {gert} package:


scan_dir <- "c:/repos"
repos <- list.dirs(scan_dir, recursive = FALSE)

all_commits <- map_dfr(repos, function(repo) {
  with_dir(repo, {
    branches <- git_branch_list() |> pluck("name")
    repo_commits <- map_dfr(branches, function(branch) {
      commits <- git_log(ref = branch)
      commits$branch <- branch
    repo_commits$repo <- repo

qfes_commits <-
  all_commits |>
  filter(grepl("@qfes|North", author))

duplicates <- duplicated(qfes_commits$commit)

p <-
  qfes_commits |>
  filter(!duplicates) |>
  group_by(repo) |>
  mutate(first_commit = min(time)) |>
  mutate(repo_num = cur_group_id()) |>
  ungroup() |>
  group_by(repo_num, first_commit, week = floor_date(time, "week")) |>
    count = n(),
    .groups = "drop"
  ) |>
    x = week,
    y = fct_reorder(as.character(repo_num), first_commit),
    colour = count
  )) +
  geom_point(size = 2) +
    title = "Data Science Software Engineering: 2312 commits over 96 projects",
    subtitle = "1 Dot = 1 Week's commits for 4x Public Sector Data Scientists",
    y = "project"
  ) +
  scale_colour_viridis_c() +

  device = ragg::agg_png,
  height = 10,
  width = 13

Making short work of format()ting #rstats output

Often when outputting stuff to a package user, the question arises: how much effort could I be bothered to put into formatting the output? The format() function in R has some really nice stuff for this, in particular: alignment.

So today I’m outputting a list of packages to be updated:

arrow  ->
broom 0.7.7  ->  0.7.9
cachem 1.0.5  ->  1.0.6
cli 3.0.1  ->  3.1.0
crayon 1.4.1  ->  1.4.2
desc 1.3.0  ->  1.4.0
e1071 1.7-8  ->  1.7-9
future 1.22.1  ->  1.23.0
gargle 1.1.0  ->  1.2.0
generics 0.1.0  ->  0.1.1
gert 1.3.2  ->  1.4.1
googledrive 1.0.1  ->  2.0.0
googlesheets4 0.3.0  ->  1.0.0
haven 2.4.1  ->  2.4.3
htmltools  ->  0.5.2
jsonvalidate 1.1.0  ->  1.3.1
knitr 1.34  ->  1.36
lattice 0.20-44  ->  0.20-45
lubridate 1.7.10  ->  1.8.0
lwgeom 0.2-7  ->  0.2-8
mime 0.11  ->  0.12
osmdata  ->  0.1.8
paws.common 0.3.12  ->  0.3.14
pillar 1.6.3  ->  1.6.4
pkgload 1.2.1  ->  1.2.3
qfesdata 0.2.9011  ->  0.2.9030
reprex 2.0.0  ->  2.0.1
rmarkdown 2.9  ->  2.10
roxygen2 7.1.1  ->  7.1.2
RPostgres 1.3.3  ->  1.4.1
rvest 1.0.1  ->  1.0.2
sf 1.0-2  ->  1.0-3
sodium 1.1  ->  1.2.0
stringi 1.7.4  ->  1.7.5
tarchetypes 0.2.0  ->  0.3.2
targets  ->  0.8.1
tibble 3.1.4  ->  3.1.5
tinytex 0.32  ->  0.33
travelr 0.7.5  ->  0.9.1
tzdb 0.1.2  ->  0.2.0
usethis 2.0.1  ->  2.1.3
xfun 0.24  ->  0.27

Made by this code:

      " -> ",
    sep = "\n"

And one thing that would make it look a bit less amateurish is alignment. I laboured over this sort of stuff years ago when I wrote {datapasta} making really hard work of it - it was the source of an infamous recurring bug. This was partly because I didn’t know that if you call format() on a character vector it automatically pads all your strings to the same length:


      " -> ",
    sep = "\n"

Makes the output look like:

arrow     ->
broom         0.7.7       ->  0.7.9
cachem        1.0.5       ->  1.0.6
cli           3.0.1       ->  3.1.0   
crayon        1.4.1       ->  1.4.2
desc          1.3.0       ->  1.4.0
e1071         1.7-8       ->  1.7-9
future        1.22.1      ->  1.23.0
gargle        1.1.0       ->  1.2.0
generics      0.1.0       ->  0.1.1
gert          1.3.2       ->  1.4.1
googledrive   1.0.1       ->  2.0.0
googlesheets4 0.3.0       ->  1.0.0
haven         2.4.1       ->  2.4.3
htmltools     ->  0.5.2
jsonvalidate  1.1.0       ->  1.3.1
knitr         1.34        ->  1.36
lattice       0.20-44     ->  0.20-45
lubridate     1.7.10      ->  1.8.0
lwgeom        0.2-7       ->  0.2-8
mime          0.11        ->  0.12
osmdata   ->  0.1.8
paws.common   0.3.12      ->  0.3.14
pillar        1.6.3       ->  1.6.4
pkgload       1.2.1       ->  1.2.3
qfesdata      0.2.9011    ->  0.2.9030
reprex        2.0.0       ->  2.0.1
rmarkdown     2.9         ->  2.10
roxygen2      7.1.1       ->  7.1.2
RPostgres     1.3.3       ->  1.4.1
rvest         1.0.1       ->  1.0.2
sf            1.0-2       ->  1.0-3
sodium        1.1         ->  1.2.0
stringi       1.7.4       ->  1.7.5
tarchetypes   0.2.0       ->  0.3.2
targets  ->  0.8.1
tibble        3.1.4       ->  3.1.5
tinytex       0.32        ->  0.33
travelr       0.7.5       ->  0.9.1
tzdb          0.1.2       ->  0.2.0   
usethis       2.0.1       ->  2.1.3
xfun          0.24        ->  0.27

Cool hey?

Little critter got his legs…

A quick route to cursor based shortcuts in RStudio

A lot of the automations I rig up in my code editor depend on decting where the cursor is in a document and using that context to perform helpful operations.

The simplest class of these are functions that are executed using the symbol the cursor is “on” as input. Typically this symbol represents an object name and typical usage would be:

  • calling str() on the object to inspect it
  • calling targets::tar_load() on the object to read it from cache into the global environment
  • Search and open the definition or help of that object.

Simple things that help keep my hands on the keyboard and my head in the flow.

Rigging in RStudio

RStudio poses two challenges in setting these types of things up as keyboard shortcuts:

  1. The user is not permitted to create shortcuts to run arbitrary R code.
  2. The RStudio API does not provide a facility for getting the symbol at the cursor.

To solve 1. we can use Garrick Aden-Buie’s {shrtcts} package. To solve 2. there’s a tiny package I wrote called {atcursor}.

Suppose we desire a shortcut to call head() on the object cursor is on. This is how we could rig that up in ~/.shrtcts.R:

#' head() on cursor object
#' head(symbol or selection)
#' @interactive
function()  {
  target_object <- atcursor::get_word_or_selection()
  eval(parse(text = paste0("head(",target_object,")")))

After that we’d:

  1. shrtcts::add_rstudio_shortcuts()
  2. Bind the shortcut to a key. Using Tools -> Modify Keyboard Shortcuts.
  3. Experience the rush of using the shortcut!

Advanced Notes

  • {shrtcts} can also manage the keyboard bindings with an @shortcut tag but add_rstudio_shortcuts() won’t refer to it by default. See the doco if you want to do that.
  • atcursor::get_word_or_selection() will return a symbol the cursor is “insisde” - e.g. on a column inside the span of the string. If the symbol is namespaced the namespace is also returned, e.g: “namespace::symbol”. If the user has made a selection, that is returned, regardless of cursor position.
  • Rather than building text to parse and then eval, sometimes I find it easier to work with expressions. So you coud do like: target_object <- as.symbol(atcursor::get_word_or_selection()) and then build an expression with bquote:


In conclusion

Without getting overly metaphysical: I think these kind of shortcuts make a lot of sense to me because I view the cursor as my avatar in this world of code before me. I navigate that world almost exclusively with keys, so coding is like piloting that little avatar around. To learn about objects or manipulate them, it makes complete sense to cruise up to them and start engaging them in a dialogue of commands, the scope of which is completely unambigous, because my avatar is in the same space as those objects. In this way, my sense of ‘where I am’ in the code is not broken.

Ofcourse it does happen, I have to jump to the console world when I don’t have a binding for what I need to do, but it feels great when I don’t!

If anyone else is down for some command line JSON munging this little tool knocked my socks of this week: stedolan.github.io/jq/



Almost there!

Dispatch your S3 methods off global state like a real crusty wrangler #rstats

Here’s a fun #rstats one from last week:

At my work, we’ve wrapped our database queries for our core datasets in an R package. Last week I needed to implement a second backend for that package such that the same interface could be used to issue fetches against either:

  • an on premises Microsoft SQL Server
  • a set of parquet files stored in AWS S3.

The idea being that pipelines that we author on our local machines should just work when running on AWS with zero changes to code. We’ll use an environment variable to control which backend our data getting functions target. So:

  • Sys.getenv("QFESDATA_BACKEND") == "analytics" means hit the SQL sever
  • Sys.getenv("QFESDATA_BACKEND") == "aws" means slurp those parquet files

So how to implement switching which methods are dispatched based on an environment variable? Well I definitely don’t want this:

get_oms_responses <- function() {

  if (Sys.getenv("QFESDATA_BACKEND") == "analytics") {
    ... SQL DB stuff
 } else if (Sys.getenv("QFESDATA_BACKEND") == "aws") {
   ... AWS stuff
 ... common stuff

You CAN do that and it will work. But now the different logic for the two backends is kind of tangled together. Say I want to add a different backend in the future, I can’t do that in a way that doesn’t interact with code that is already known to work. Regressions could easily be introduced.

Isolation is what I wanted. The first thing I thought of was S3 methods, since this a bread and butter issue that S3 is designed to solve. But I thought to myself: “If I use S3 I’ll have to change the interface of my functions to refer to an object to be dispatched off.” And I didn’t like that. In other words, this type of thing:

get_oms_responses <- function(backend = "analytics", ...) UseMethod("get_oms_responses", backend)

I’d have to change all the documentation for all the functions to explain the backend arg.

So I went and implemented some complicated metaprogramming thing that detected the method you were calling and recalled a new method with the same arguments pulled from the correct parent environments based on Sys.getenv("QFESDATA_BACKEND"). I felt really smart, but the code was hard to follow, and I had to write a bunch of unit tests to convince myself it worked.

What happened next was that on seeing the code, my colleague, Anthony North, pointed out that S3 method dispatch doesn’t need to dispatch off one of the generic function arguments, it can use any object!


get_oms_responses <- function(backend = "analytics", ...) UseMethod("get_oms_responses", ANYTHING_YOU_WANT_BUCKO)

Or perhaps more pertinently:

get_qfes_backend <- function() { 
  backend <- Sys.getenv("QFESDATA_BACKEND")
  structure(backend, class = backend)

get_oms_responses() UseMethod("get_oms_responses", get_qfes_backend())

get_oms_responses.analytics <- function() {
  ... SQL server stuff

get_oms_responses.aws <- function() {
   ... AWS stuff

I immediately deleted what I had written and switched to this approach. Scary metaprogramming was gone, and I don’t need to unit test S3 method dispatch. It’s working perfectly.

Upon close reading of the S3 documentation, it appears this usecase is covered, barely:

for ‘UseMethod’: an object whose class will determine the method to be dispatched. Defaults to the first argument of the enclosing function.

But I’ve never seen the convenience of using any old object outside the generic function’s arguments discussed before. Quite a handy one!

That @anthonynorth has got us covered #rstats


Today I participated in the first meeting of the #rstats RConsortium working group for R repositories. The path I started on with cranchange lead me to this point, although this group has a much larger scope.

On the CRAN side of things I was encouraged to hear from Michael Lawrence that there is a desire to make change at CRAN including plans create a more informative public web presence, and bring on someone in a Developer Advocate role(!).

One thing I think that is going to be key to positive change is eliciting some clearer sense from CRAN as to what the group’s goals and priories are. For example: What priority is placed on being a Continuous Integration service for R-Core vs a validation and distribution mechanism for a rolling release of R packages?

I have a hunch that some of the inconsistency R users and developers see is due to tension between these types of objectives, but I am keen to learn more from this group.

I am very thankful to the Linux Foundation and RConsortium for facilitating this group, especially Joseph Rickert for leading.

Hadley Wickham’s meeting minutes are accessible from the repository

Ice and fire vibes #notGenerative

Made with #rstats {rdeck}

Project filled weekend! I give you Saturday and Sunday.


#rstats VSCode productivity tip: assign keybindings to workbench.action.terminal.scrollDown and workbench.action.terminal.scrollUp so you can move though console output without having to switch back and forth from the terminal or use your mouse.