Reproducible: Difference between revisions

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automake() # Create and open <Makefile>
automake() # Create '.repro/Dockerfile_packages',
          #        '.repro/Makefile_Rmds' & 'Dockerfile'
          # and open <Makefile>
 
# Modify <Makefile> by following the console output
# Modify <Makefile> by following the console output
rerun() # and follow the console output
 
# by opening a terminal
rerun() # will inspects the files of a project and suggest a way to
        # reproduce the project. So just follow the console output
        # by opening a terminal and typing
make docker && make -B DOCKER=TRUE
make docker && make -B DOCKER=TRUE
# The above will generate the output
 
# The above will generate the output html file in your browser
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Revision as of 14:07, 11 April 2021

Common Workflow Language (CWL)

R

  • A Reproducible Data Analysis Workflow with R Markdown, Git, Make, and Docker, Slides, Talks & Video. The whole idea is written in an R package repro package. The package create an R project Template where we can use it by RStudio -> New Project -> Create Example Repro Template. Note that the Makefile and Dockerfile can be inferred from the markdown.Rmd file. Four elements
    • Git folder of source code for version control (R project)
    • Makefile. Make is a “recipe” language that describes how files depend on each other and how to resolve these dependencies.
    • Docker software environment (Containerization)
    • RMarkdown (dynamic document generation)
    automake() # Create '.repro/Dockerfile_packages', 
           #        '.repro/Makefile_Rmds' & 'Dockerfile'
           # and open <Makefile>

# Modify <Makefile> by following the console output

rerun() # will inspects the files of a project and suggest a way to 
        # reproduce the project. So just follow the console output
        # by opening a terminal and typing
make docker && make -B DOCKER=TRUE

# The above will generate the output html file in your browser

Rmarkdown

Rmarkdown package

packrat and renv

R packages → packrat/renv

checkpoint

R → Reproducible Research

dockr package

'dockr': easy containerization for R

Docker & Singularity

Docker

targets package

targets: Democratizing Reproducible Analysis Pipelines Will Landau

Snakemake

Papers

High-throughput analysis suggests differences in journal false discovery rate by subject area and impact factor but not open access status

Share your code and data

Misc

  • 4 great free tools that can make your R work more efficient, reproducible and robust
  • digest: Create Compact Hash Digests of R Objects
  • memoise: Memoisation of Functions. Great for shiny applications. Need to understand how it works in order to take advantage. I modify the example from Efficient R by moving the data out of the function. The cache works in the 2nd call. I don't use benchmark() function since it performs the same operation each time (so favor memoise and mask some detail). 
    library(ggplot2) # mpg 
library(memoise) 
plot_mpg2 <- function(mpgdf, row_to_remove) {
  mpgdf = mpgdf[-row_to_remove,]
  plot(mpgdf$cty, mpgdf$hwy)
  lines(lowess(mpgdf$cty, mpgdf$hwy), col=2)
}
m_plot_mpg2 = memoise(plot_mpg2)
system.time(m_plot_mpg2(mpg, 12))
#   user  system elapsed
#  0.019   0.003   0.025
system.time(plot_mpg2(mpg, 12))
#   user  system elapsed
#  0.018   0.003   0.024
system.time(m_plot_mpg2(mpg, 12))
#   user  system elapsed
#  0.000   0.000   0.001
system.time(plot_mpg2(mpg, 12))
#   user  system elapsed
#  0.032   0.008   0.047
And be careful when it is used in simulation.
f <- function(n=1e5) { 
  a <- rnorm(n)
  a
} 
system.time(f1 <- f())
mf <- memoise::memoise(f)
system.time(f2 <- mf())
system.time(f3 <- mf())
all.equal(f2, f3) # TRUE
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