The document discusses the importance of reproducible research, specifically in the context of using Docker and R. It highlights the advantages of reproducibility such as improved quality of work, easier publication processes, and greater collaboration. Additionally, it provides insights into tools like the 'containerit' package, which simplifies Docker usage for R projects, enhancing documentation and system dependencies management.

1. Reproducible Research in R
with Docker
Daniel Nüst | University of Münster | @nordholmen
MünsteR Meetup, Sep 2017
https://www.meetup.com/Munster-R-Users-Group/events/241108949/

2. Agenda
Motivation
Docker & Rocker
containerit
2

3. Why should I care about reproducible research?
(an opinionated view)
Improve quality of your work today
Existence of your work tomorrow: journal requirements 2020+
Societal challenges… Who knows the Oxford Dictionaries word of the year
2016?
https://en.oxforddictionaries.com/word-of-the-year/word-of-the-year-2016
3

4. J. Leek’s tidypvals
https://simplystatistics.org/2017/07/26/announcing-the-tidypvals-package/
4

5. “Tradition” of notebooks in lab work, e.g. in
chemistry
(analog and digital)
Open Notebook Science
(https://en.wikipedia.org/wiki/Open_notebook_science)
No comparable tradition and education in
younger and mostly digital geostatistics,
GIS, ...
https://twitter.com/wellcometrust/status/49632
3565239955456
5
Lab notebooks
https://www.google.de/search?q=chemistry+l
ab+notebook&safe=off&tbm=isch
https://en.wikipedia.org/wiki/File:Studies_of_the_Arm
_showing_the_Movements_made_by_the_Biceps.jp
g

6. R Markdown a.k.a. .Rmd
http://rmarkdown.rstudio.com/
Based on Mark DOWN
https://daringfireball.net/projects/markdown/syntax
6

7. 7
#1: reproducibility helps to avoid disaster
#2: reproducibility makes it easier to write papers
#3: reproducibility helps reviewers see it your way
#4: reproducibility enables continuity of your work
#5: reproducibility helps to build your reputation

8. 8

9. ERC creation process
❏ Submit workspace to publication platform
❏ Publication platform…
❏ extracts metadata
❏ executes analysis
❏ check output vs. upload (syntax)
❏ capture runtime environment
(manifest + image) 9

10. 10
Slide by Docker inventor & Docker, Inc. CTO Solomon Hykes, DockerCon 2014

11. https://docs.docker.com/engine/docker-overview/ 11
science
data science
research
reproducibility
replication
package &
separate
applications and
their dependencies
for cloud
infrastructures

12. https://docs.docker.com/engine/docker-overview/ 12
science
data science
research
reproducibility
replication
package &
separate
applications and
their
dependencies for
cloud
infrastructures
Iconsbysynonymsof(CC-BY)

13. Docker basics
Dockerfile
ENV
RUN
CMD
Docker
Image
pause
stop/kill
start
logs
cp
exec
rm
stats
build
Docker CLI
run Docker
Container
Docker Engine
Docker Registry
run

14. 14
Docker for Data Science
(all the Docker advantages… write once, biz ops, cloud, etc.)
Reproducibility through controlled working environment
Project separation + don’t clutter dev machine
Environment (re)creation, documentation
Adopt good practices on the way
Easy collaboration
Easy transition from testing to production

15. 15
https://hub.docker.com/r/rocker/rstudio/
Base containers (r-base, r-devel, r-ver, ..)
Use case containers (r-devel-ubsan-clang, ..)
Stacks (tidyverse, geospatial, ..)
docker run -it -p 8787:8787 rocker/rstudio
http://localhost:8787/ (rstudio/rstudio)
Rocker: https://github.com/rocker-org

16. rocker/r-ver and other base images
https://github.com/rocker-org/rocker#base-docker-containers
16

17. rocker/geospatial and other use cases
https://github.com/rocker-org/rocker#versioned-stack-builds-on-r-ver
17

18. rocker/geospatial
https://hub.docker.com/r/rocker
/geospatial/
Opinionated view, not based
on CRAN task views
18

19. https://hub.docker.com/r/rocker/rstudio/
docker run --rm -it -p 8787:8787 rocker/rstudio
http://localhost:8787/ (rstudio/rstudio)
Great example: https://github.com/benmarwick/1989-excavation-report-Madjebebe
docker run --rm -it -p 8787:8787 benmarwick/mjb1989excavationpaper
http://localhost:8787/ (rstudio/rstudio)
19

20. RStudio Desktop vs. rocker/rstudio
No functional difference, “Desktop” version ist just a lightweight browser wrapper
(https://rpubs.com/jmcphers/rstudio-architecture)
$ docker run -d -p 8787:8787 rocker/rstudio
$ docker ps
20

21. https://bioconductor.org/help/docker/
21https://hub.docker.com/u/bioconductor/

22. 22
containerit
https://github.com/
o2r-project/
containerit
http://o2r.info/2017/05/30/
containerit-package/

23. Packaging interactive session
23
> library(containerit); library("gstat"); library("sp")
> data(meuse)
> coordinates(meuse) = ~x+y
> data(meuse.grid)
> gridded(meuse.grid) = ~x+y
> v <- variogram(log(zinc)~1, meuse)
> m <- fit.variogram(v, vgm(1, "Sph", 300, 1))
> plot(v, model = m)
> dockerfile_object <- dockerfile()
INFO [2017-07-05 11:20:54] Trying to determine system
requirements for the package(s) 'sp, gstat, zoo,
futile.logger, xts, lambda.r, spacetime, futile.options,
FNN, intervals, lattice' from sysreq online DB
INFO [2017-07-05 11:21:03] Adding CRAN packages: sp,
gstat, zoo, futile.logger, xts, lambda.r, spacetime,
futile.options, FNN, intervals, lattice
INFO [2017-07-05 11:21:03] Created Dockerfile-Object
based on sessionInfo
> print(dockerfile_object)
FROM rocker/r-ver:3.4.1
LABEL maintainer="daniel"
RUN ["install2.r", "-r 'https://cloud.r-project.org'", "sp",
"gstat", "zoo", "futile.logger", "xts", "lambda.r",
"spacetime", "futile.options", "FNN", "intervals",
"lattice"]
WORKDIR /payload/
CMD ["R"]
> str(dockerfile_object, max.level = 2)
Formal class 'Dockerfile' [..] with 4 slots
..@ image :Formal class 'From' [..] with 2
slots
..@ maintainer :Formal class 'Label' [..] with 2 slots
..@ instructions :List of 2
..@ cmd :Formal class 'Cmd' [..] with 2
slots

24. Packaging a script w/ sysreqs dependency resolving
library(rgdal); require(maptools)
nc <- rgdal::readOGR(system.file("shapes/",
package="maptools"), "sids", verbose = FALSE)
proj4string(nc) <- CRS("+proj=longlat
+datum=NAD27")
plot(nc)
summary(nc)
> scriptCmd <- CMD_Rscript("demo.R")
> dockerfile_object <- dockerfile(
from = "~/Documents/2017_useR/demo.R",
cmd = scriptCmd)
# curl https://sysreqs.r-hub.io/pkg/
rgdal,sp,lattice/linux-x86_64-debian-gcc
# ["libgdal-dev", "libproj-dev", "gdal-bin"]
> print(dockerfile_object)
FROM rocker/r-ver:3.4.0
LABEL maintainer="daniel"
RUN export DEBIAN_FRONTEND=noninteractive; apt-get
-y update
&& apt-get install -y gdal-bin
libgdal-dev
libproj-dev
RUN ["install2.r", "-r 'https://cloud.r-
project.org'", "rgdal", "sp", "lattice"]
WORKDIR /payload/
COPY [".", "."]
CMD ["R", "--vanilla", "-f",
"containerit_1a977e2dcdea.R"]
24

25. Running the container
> write(dockerfile_object)
INFO [2017-07-06 10:10:05] Writing dockerfile to
/home/daniel/Documents/2017_useR/Dockerfile
$ docker build -t user2017demo .
Sending build context to Docker daemon 6.054MB
Step 1/7 : FROM rocker/r-ver:3.4.1
3.4.1: Pulling from rocker/r-ver
c75480ad9aaf: Pull complete
[...]
The following additional packages will be installed:
[...]
* installing *source* package ‘foreign’ ...
[...]
Successfully built e30936ac8687
Successfully tagged user2017demo:latest
25
$ docker run -it user2017demo
R version 3.4.1 (2017-06-30) -- "Single Candle"
Copyright (C) 2017 The R Foundation for Statistical
Computing
Platform: x86_64-pc-linux-gnu (64-bit)
[..]
> library(rgdal); require(maptools)
Loading required package: sp
> nc <- rgdal::readOGR(system.file("shapes/",
package="maptools"), "sids", verbose = FALSE)
[...]
> summary(nc)
Object of class SpatialPolygonsDataFrame
Coordinates:
min max
x -84.32385 -75.45698
y 33.88199 36.58965
Is projected: FALSE
[...]

26. Running container with data in plain R with harbor
https://github.com/nuest/containerit/blob/79f8832975e00c84cdcc665df0c2846d834e27c5/demo/fullstack.R
> write.csv(file = “dataset.csv”, x = cars)
> dataset <- read.csv("dataset.csv")
> model <- lm(log(dist) ~ log(speed),
data = dataset)
> summary(model)
> cmd <- CMD_Rscript("script.R")
> df <- containerit::dockerfile(from = workspace,
cmd = cmd,
r_version = "3.3.3",
copy = "script_dir")
> write(df)
/tmp/Rtmpeachap/
├── dataset.csv
├── Dockerfile
└── script.R
> harbor::docker_cmd(harbor::localhost, "build",
arg = workspace,
docker_opts = c("-t", "fullstack-r-demo")
capture_text = TRUE
)
> harbor::docker_run(image = "fullstack-r-demo")
R version 3.3.3 (2017-03-06) -- "Another Canoe"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
[...]
> dataset <- read.csv("dataset.csv")
> model <- lm(log(dist) ~ log(speed), data = dataset)
> summary(model)
Call:
lm(formula = log(dist) ~ log(speed), data = dataset)
Residuals:
Min 1Q Median 3Q Max
-1.00215 -0.24578 -0.02898 0.20717 0.88289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.7297 0.3758 -1.941 0.0581 .
26

27. Running container with data in plain R with harbor
https://github.com/nuest/containerit/blob/79f8832975e00c84cdcc665df0c2846d834e27c5/demo/fullstack.R
> write.csv(file = “dataset.csv”, x = cars)
> dataset <- read.csv("dataset.csv")
> model <- lm(log(dist) ~ log(speed),
data = dataset)
> summary(model)
> cmd <- CMD_Rscript("script.R")
> df <- containerit::dockerfile(from = workspace,
cmd = cmd,
r_version = "3.3.3",
copy = "script_dir")
> save(df)
/tmp/Rtmpeachap/
├── dataset.csv
├── Dockerfile
└── script.R
> harbor::docker_cmd(harbor::localhost, "build",
arg = workspace,
docker_opts = c("-t", "fullstack-r-
demo"),
capture_text = TRUE
)
> harbor::docker_run(image = "fullstack-r-demo")
27

28. More
Labels for metadata
devtools session information (install from git under dev.)
Custom base images
Docker vs. R
http://bit.ly/docker-r
Boettiger, Carl. 2015. “An Introduction
to Docker for Reproducible Research,
with Examples from the R
Environment.” ACM SIGOPS
Operating Systems Review 49
(January): 71–79.
doi:10.1145/2723872.2723882 28

29. Limitations
No shell, no fun
Windows :-(
image size
Versioning
How to access files/plots from a container?
29

30. Summary
Docker is a great tool for data science, reproducible research, consulting, …
Be “tidy” outside of your R Markdown
containerit makes Docker easier
(DRY, less copy&paste, best practices, automatic system dependencies)
Benefits from Rocker (MRAN by default, …), harbor, ...
Alternatives / potential for combination:
package management locally (packrat, pkgsnap, switchr/GRANBase)
or
remotely (MRAN timemachine/checkpoint), or install specific versions
from
30

31. https://github.com/o2r-project/containerit/projects/1
Outlook containerit
Support o2r’s ERC creation service
Get feedback
Singularity
OCI/acbuild
CRAN
Docker + R paper for RJournal?
Package rplumber / jug web apps
Versioned system libs (sf::sf_extSoftVersion()) 31

32. Thanks!
What are your questions?
32
@o2r_project
github.com/o2r-project
o2r.info