Please… Draw me a project

Licence CC-BY-SA

👋 Hello from Nancy!

Guten Tag, R-Ladies Vienna!

Today:
Managing your R project

I will share about some tools and tips!

Recent work

Current volunteering

My slidedeck is online 😁

🔗 https://rladies-vienna-maelle.netlify.app/

Goals of my talk

That everyone learns at least one new thing. 😁

⚒️ Basic principles for R projects ;

⚒️ How to protect your projects from external changes;

⚒️ What structure for your project ;

⚒️ How to run your project.

Why give advice on projects?

Why listen to me
on this topic?

😦 Not regularly in charge of analyses.

😄 I keep up-to-date with R news.

Some basics

Main source of advice : Jenny Bryan!

“Everything I know is from @JennyBryan.” —@sharlagelfand #mood #rstudioconf

— Kara Woo (@kara_woo) January 30, 2020

As an aside, Sharla Gelfand’s presentation

Don’t repeat yourself, talk to yourself! Repeated reporting in the R universe

Sharla too is a great source of good ideas! 💐

Main source of advice : Jenny Bryan!

Logo by Martin Monkman.

Your project, a garden?

Yes, but an independent unit!

A project, a folder!

The Kei-tora Gardening Contest is an annual event put on by the Japan Federation of Landscape Contractors to see who can build the best looking garden on the back of a Kei truck. Here are some of the winners from the past 12 years. https://t.co/IpkRiALn7a pic.twitter.com/vep4K0VZt6

— Doctor Popular 💉 💉 🎉 (@DocPop) June 10, 2021

🔥

If the first line of your script is …

🔥 setwd("C:/users/you/a/path/specific/to/you);

🔥 or rm(list = ls());

Jenny Bryan will come into your office and set your computer on fire. 😱

Tweet

File paths relative
to the project root

NO:

readr::read_csv("/home/maelle/Documents/rladies/rladies-vienna/data/cool-stuff.csv")

File paths relative
to the project root

Illustration by Allison Horst.

Package here
by Kirill Müller

• .Rproj or an empty file called .here at the project root.

• Paths are defined relative to that.

here::here()

[1] "/home/maelle/Documents/rladies/rladies-vienna"

here::here("data")

[1] "/home/maelle/Documents/rladies/rladies-vienna/data"

Package here
by Kirill Müller

readr::read_csv(here::here("data", "cool-stuff.csv"))

Always start fresh

• Re-start R regularly and without fear!

• NEVER save and re-load .RData!

“Project-oriented workflow”

🌹 Blog post by Jenny Bryan

🌹 A way of life, hum, work!

✨ usethis::create_project() ✨

The name of your roses

That which we call a rose by any other name would smell as sweet

Shakespeare in Romeo and Juliet. 🌹

Naming your files

Tips by Jenny Bryan

• Machine readable (no accent);

• Human readable (informative about the content);

• Work well with default ordering (YYYY-MM-DD rather than DD-MM-YYYY).

Naming things

I didn't expect programming to involve so much time studying a thesaurus #namingThings

— Jenny Bryan (@JennyBryan) September 1, 2021

Your project over time

🌻 BACKUP! 🌻

Version control

Your project will evolve, how to keep track of changes to be able to come back?

Version control

• Dates in filenames

• or version control.

Learning git: not easy, but worth the effort! Not R but useful for R.

Being able to try things out, come back, understand past changes.

What is git?

XKCD by Randall Munroe.

What is git?

Drawing by Damien Cornu. ❤️

Some git commands roughly explained

• git add to start tracking a file (.gitignore to list files to always ignore) ;
• git commit to save a change in the history ;
• git pull / git push to download / upload the local version from / to the remote version (e.g. GitHub);
• git checkout -b to create a “branch.”

Where to use
git commands?

My preferences 😁

• Packages usethis (usethis::use_git(), usethis::use_github(), etc.) and gert (gert::git_push()) to stay in R ;
• git window in RStudio to click not too far away from R ;
• Command line for commands copy-pasted from Stack Overflow ;
• A graphical interface for better visualization? E.g. GitKraken, VSCODE.

Resources about git

• Excuse Me, Do You Have a Moment to Talk About Version Control?, Jenny Bryan.

• Happy Git and GitHub for the useR, Jenny Bryan, the STAT 545 TAs, Jim Hester.

• Reflections on 4 months of GitHub: my advice to beginners, Suzan Baert.

Until now, what we learnt from Jenny Bryan…

• Isolate R projects, re-start R regularly.

• Name files well.

• Use version control.

More wisdom
by Jenny Bryan et al

🌹 Good enough practices in scientific computing Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, et al. (2017) Good enough practices in scientific computing. PLOS Computational Biology 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510

🌹 What They Forgot to Teach You About R, Jenny Bryan, Jim Hester.

Protect your project from external changes

A scary story…

• You write beautiful data wrangling with package::my_favorite_function()…

• Now you go and update that package and realize my_favorite_function() is gone!

For good reasons but your script is now broken! 😱

Store project dependencies

Encapsulate your project! 🎉

Important tool : renv by Kevin Ushey!

Successor of packrat.

renv in three steps

• renv::init()

• Install and remove packages as usual. Regularly renv::snapshot(). Metadata of dependencies stored in renv.lock. 🔒

• Your colleague who inherits your project runs renv::restore().

Even more encapsulating

🐳 Docker? 🔒 R version, operating system, in short everything.

• Introduction by Colin Fay;

• “Introduction to using Docker for reproducibility in R” by Malindrie Dharmaratne at R-Ladies Brisbane. Video recording, materials.

Protect your project from external changes

List dependencies of the project.

The easiest way to get started is to start using renv.

What file structure
for your project?

What’s in your project?

• Data or the code to get them from a database or a remote resource;

• Some code munging and analysing them;

• Some output that could be a graph, a report etc.

What file structure
for your project?

• Consistent.

• Automatic creation.

• … Package or not?

projectTemplate

By Kenton White.

Make an R package?

• To store functions and data used across projects: YES!!!

  E.g. follow Shel Kariuki’s tutorial given at R-Ladies Nairobi.

• To e.g. store an RStudio project template or re-create a folder structure: YES!!!
• To store your project i.e. a project as a package : maybe.

Analysis project = 📦

• Dependencies in DESCRIPTION,

• Functions in R/ with documentation in man/,

• Data in data/ (or data-raw/),

• Analyses as vignettes (R Markdown),

• Informative README.

Analysis project = 📦?

🏄‍♂️ Re-use or refresh your package development skills,

🏄‍♂️ Re-use tools made for package development (like devtools and usethis).

“Research compendium.” Packaging Data Analytical Work Reproducibly Using R (and Friends), Ben Marwick, Carl Boettiger & Lincoln Mullen (2018), The American Statistician, 72:1, 80-88, DOI: <10.1080/00031305.2017.1375986>

Project as package, specific tools

📦 rrtools by Ben Marwick. Set up a compendium!

📦 holepunch by Karthik Ram. One click and the reader gets to play with your code! (🤫 holepunch works without the compendium structure as well.)

🚀 R-universe by Jeroen Ooms at rOpenSci to publish your analyses.

Project as a package: no?

Project as an R package: An okay idea by Miles McBain.

What file structure
for your project?

As you wish 😉 (as your team wishes) but

• Basic structure consistent over time ;

• Automatic creation.

Make reproducibility ✨ easier ✨.

How to run
your project?

Running the project?

How do you go from resources and scripts to the analysis output (e.g. report, figures)?

🤸

How to run
your project?

Maybe you only need the knit button if your project is an R Markdown report?

Maybe you need something more complex?

Let’s discuss two cases

• Optimize a pipeline.

• Track versions of an analysis (input and output).

Let’s discuss two cases

• Optimize a pipeline. 📦 targets maintained by Will Landau.

• Track versions of an analysis (input and output). 📦 orderly maintained by Rich FitzJohn.

targets by Will Landau

• targets deduces relationships between pieces of a project (e.g. if raw data changes, everything needs to be re-done) ;

• targets only performs necessary computation.

targets

At the core of a targets project, the _targets.R file.

• Load packages;

• Load functions (source() scripts from R/ for instance);

• Define targets!

Targets

list(
  tar_target(
    raw_data_file,
    "data/raw_data.csv",
    format = "file"
  ),
  tar_target(
    raw_data,
    read_csv(raw_data_file, col_types = cols())
  ),
  tar_target(
    data,
    raw_data %>%
      filter(!is.na(Ozone))
  ),
  tar_target(hist, create_plot(data)),
  tar_target(fit, biglm(Ozone ~ Wind + Temp, data))
)

How does it run?

• To build, targets::tar_make() (and targets::tar_destroy());

• To understand your pipeline, targets::tar_glimpse() &co.

Visualizing the pipeline

targets::tar_glimpse()

Illustration from targets manual.

How to get started with targets?

• Manual!

• Reproducible Computation at Scale in R with {targets} (Will Landau at RUG Lille).

• Start with a small project (My current level 😅).

The R Targetopia

An R package ecosystem for democratized reproducible pipelines at scale

How to keep up with targets?

• Watch the GitHub repository of targets ;

• Follow Will Landau on Twitter ;

• Subscribe to rOpenSci newsletter ;

• Connect with other users. 😉

orderly by Rich FitzJohn

(Thanks to Rich for answering my questions 🙏)

Different challenge: keep track of everything going into and out of an analysis, at different points in time.

For instance to allow audits in the future.

The analysis can be huge so git isn’t the tool for the job.

orderly

In orderly there are repos and in repos there are reports.

Example with a repo of one report. orderly::orderly_init("blop") then orderly::orderly_new("example", "blop"), some file editing and I get:

blop
├── orderly_config.yml
└── src
    └── example
        ├── orderly.yml
        └── script.R

Configuration src/example/orderly.yml

script: script.R

artefacts:
  - staticgraph:
      description: A graph of things
      filenames: mygraph.png
  - data:
      description: Data that went into the plot
      filenames: mydata.csv

Example script

dat <- data.frame(x = 1:10, y = runif(10))
write.csv(dat, "mydata.csv", row.names = FALSE)

png("mygraph.png")
plot(dat)
dev.off()

How does it run?

id <- orderly::orderly_run("example", root = "blop")

orderly::orderly_commit(id, root = "blop")

How does it run?

⚠️The archive and draft folders can be huge, so back them up with something other than git. ⚠️

How to get started with orderly?

• orderly documentation website is really great!

• Start small to understand how it works (once again, my level 👋).

• Connect with other users.

How to keep up with orderly?

• Watch orderly GitHub repository ;

• Follow the blog of the team developing orderly ;

• Follow Rich FitzJohn on Twitter.

How to run
your project…

• “Only” one or a few R Markdown report(s): knit button?

• Optimize a pipeline : targets.

• Keep track of all versions : orderly, with a backup system in place.

Conclusion

Thank you

Before summing up, thanks to Laura Vana, Annalisa Cadonna, Camilla Damian and Ursula Laa and to all of you! 🙏 ✨

Thanks a lot to Christophe Dervieux for useful feedback on the content of this talk!

How to draw a project?

How to learn how to draw a project?

Some more useful resources:

• Course “Reproducible Research Data and Project Management in R” by Anna Krystalli.

• Good enough practices in scientific computing Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, et al. (2017) Good enough practices in scientific computing. PLOS Computational Biology 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510

• The Turing Way, an open source community-driven guide to reproducible, ethical, inclusive and collaborative data science.

How to draw a project?

🌹 Read everything Jenny Bryan wrote.

🌹 Choose or even create, as a team, the box in which you put and build your project.

🌹 Do not be afraid to renew your toolset over time.

Slides 🔗 https://rladies-vienna-maelle.netlify.app/