Post on 22-Jan-2018
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R software development How to write and maintain 30K+ LOC in R and
survive?
Wit Jakuczun, WLOG Solutions
2017-06-20
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World of analytics has changed.
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4000x4 elastic-net models (CV-5) for 45Kx10K datasetin 1,5 minute!
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Join 21st centuRy today!
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What is R?
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Dynamically interpreted general programming language
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Stable open-source productdeveloped by R Foundation
since ~1995 year.
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Created for data analysis.
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flights %>%
group_by(year, month, day) %>%
select(arr_delay, dep_delay) %>%
summarise(
arr = mean(arr_delay, na.rm = TRUE),
dep = mean(dep_delay, na.rm = TRUE)
) %>%
filter(arr > 30 | dep > 30)
z <- scaled_input %>%
layer_convolution2D(c(5,5), 32, pad = TRUE) %>%
layer_max_pooling(c(3,3), c(2,2)) %>%
layer_convolution2D(c(3,3), 48) %>%
layer_max_pooling(c(3,3), c(2,2)) %>%
layer_convolution2D(c(3,3), 64) %>%
layer_dense(96) %>%
layer_dropout(0.5) %>%
layer_dense(num_output_classes, activation = activation_softmax())
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R is a community.
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CRAN10K+ packages
Githubmore and more
popular
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http://githut.info
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R is really popular
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Tiobe Index, 2017
Estimated 2M+ users all over the world.
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Sounds like python?
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RPackage reticulate
PythonPackage rpy2
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R Software DevelopmentWhat is large scale?
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R software development vs
R scripting
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Large scale ~ 10K+ LOCSmall scale ~ 1K LOC
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CRAN (MRAN) Github Other
R environment
Installed packages
Local CRANSource code repo
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CRAN (MRAN) Github Other
R environment
Installed packages
Local CRANSource code repo
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R Software DevelopmentBest practices by WLOG
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Always make final test from command line.
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Rscript my_script.R
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Put all logic into packages.
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Package help system
Package dependency
system
External data in packages
Vignettes Tests
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Use any source code version control system.
Yes, even if you are working alone. :)
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print is not for logging.
Forbidden
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logging::loginfo(“Phase 1 passed”)
logging::logdebug(“Iter %d done”, i)
logging::logwarning(“Are you sure?”)
logging::logerror(“I failed :(”)
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Select external packages carefully.And control their versions!
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data.table
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Use configuration files.
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SnapshotDate: 2015-11-01PackagesPath: packagesLocalRepoPath: repositoryScriptPath: executionScriptsProject: XXXZipVersion:Artifacts:
LogLevel: INFOwork_path: ../workdata_path: ../dataexport_path: ../exportN_days: 365solver_max_iterations: 10solver_opt_horizon: 8
PARAMETERS CONFIG
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Use standard project structure.
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Master scripts
Project local packages
Tests
External packages
Logs
Work
Import
Export
Configura
tion
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Automate building, deploying, testing, etc.
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Jenkins exemplary pipeline
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Go to hell :)
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“If you are using R and
you think you’re in hell, this is a map for you.”
Patrick Burns, “R Inferno”, 2011
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Summary
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Seamless integration with existing systems and IT infrastructure
Dev/Test/Prod processes according to current software
development standards
Fast development to production cycle
Continuous integration & deployment
Repositories – models, builds, code,
dependencies, configuration
Controllable distributed job
scheduling
Resource usage monitoring
Secure access control, protected password
repositories
A well deployed R based analytical platform must have the following features
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Wit Jakuczun, PhD
wit.jakuczun@wlogsolutions.com
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WLOG R Suite™Field tested R ecosystem for Enterprise