README.md

README

DataPackageR

DataPackageR is used to reproducibly process raw data into packaged, analysis-ready data sets.

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Status Project Status: Active – The project has reached a stable, usable
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Important Note: datapack is a different package that is used to “create, send and load data from common repositories such as DataONE into the R environment”.

This package is for processing raw data into tidy data sets and bundling them into R packages.

What problems does DataPackageR tackle?

You have diverse raw data sets that you need to preprocess and tidy in order to:

Why package data sets?

Definition: A data package is a formal R package whose sole purpose is to contain, access, and / or document data sets.

Challenges.

DataPackageR

DataPackageR provides a number of benefits when packaging your data.

Similar work

There are a number of tools out there that address similar and complementary problems:

See the publication for further discussion.

Installation

You can install the latest version of DataPackageR from github with:

library(devtools)
devtools::install_github("RGLab/DataPackageR")

Blog Post - building packages interactively.

See this rOpenSci blog post on how to build data packages interactively using DataPackageR. This uses several new interfaces: use_data_object(), use_processing_script() and use_raw_dataset() to build up a data package, rather than assuming the user has all the code and data ready to go for datapackage_skeleton().

Example (assuming all code and data are available)

library(DataPackageR)

# Let's reproducibly package up
# the cars in the mtcars dataset
# with speed > 20.
# Our dataset will be called cars_over_20.
# There are three steps:

# 1. Get the code file that turns the raw data
# into our packaged and processed analysis-ready dataset.
# This is in a file called subsetCars.Rmd located in exdata/tests of the DataPackageR package.
# For your own projects you would write your own Rmd processing file.
processing_code <- system.file(
  "extdata", "tests", "subsetCars.Rmd", package = "DataPackageR"
)

# 2. Create the package framework.
# We pass in the Rmd file in the `processing_code` variable and the names of the data objects it creates (called "cars_over_20")
# The new package is called "mtcars20"
datapackage_skeleton(
  "mtcars20", force = TRUE, 
  code_files = processing_code, 
  r_object_names = "cars_over_20", 
  path = tempdir()) 

# 3. Run the preprocessing code to build the cars_over_20 data set 
# and reproducibly enclose it in the mtcars20 package.
# packageName is the full path to the package source directory created at step 2.
# You'll be prompted for a text description (one line) of the changes you're making.
# These will be added to the NEWS.md file along with the DataVersion in the package source directory.
# If the build is run in non-interactive mode, the description will read
# "Package built in non-interactive mode". You may update it later.
package_build(packageName = file.path(tempdir(),"mtcars20"), install = TRUE)

# Update the autogenerated roxygen documentation in data-raw/documentation.R. 
# edit(file.path(tempdir(),"mtcars20","R","mtcars20.R"))

# 4. Rebuild the documentation.
document(file.path(tempdir(),"mtcars20"), install = TRUE)

# Let's use the package we just created.
install.packages(file.path(tempdir(),"mtcars20_1.0.tar.gz"), type = "source", repos = NULL)
library(mtcars20)
data("cars_over_20") # load the data
cars_over_20  # Now we can use it.
?cars_over_20 # See the documentation you wrote in data-raw/documentation.R.

# We have our dataset!
# Since we preprocessed it,
# it is clean and under the 5 MB limit for data in packages.
cars_over_20

# We can easily check the version of the data
data_version("mtcars20")

# You can use an assert to check the data version in  reports and
# analyses that use the packaged data.
assert_data_version(data_package_name = "mtcars20",
                    version_string = "0.1.0",
                    acceptable = "equal")

Reading external data from within R / Rmd processing scripts.

When creating a data package, your processing scripts will need to read your raw data sets in order to process them. These data sets can be stored in inst/extdata of the data package source tree, or elsewhere outside the package source tree. In order to have portable and reproducible code, you should not use absolute paths to the raw data. Instead, DataPackageR provides several APIs to access the data package project root directory, the inst/extdata subdirectory, and the data subdirectory.

# This returns the datapackage source 
# root directory. 
# In an R or Rmd processing script this can be used to build a path to a directory that is exteral to the package, for 
# example if we are dealing with very large data sets where data cannot be packaged.
DataPackageR::project_path()

# This returns the   
# inst/extdata directory. 
# Raw data sets that are included in the package should be placed there.
# They can be read from that location, which is returned by: 
DataPackageR::project_extdata_path()

# This returns the path to the datapackage  
# data directory. This can be used to access 
# stored data objects already created and saved in `data` from 
# other processing scripts.
DataPackageR::project_data_path()

Preprint and publication.

The publication describing the package, (Finak et al., 2018), is now available at Gates Open Research .

The preprint is on biorxiv.

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

References

  1. Gentleman, Robert, and Duncan Temple Lang. 2004. “Statistical Analyses and Reproducible Research.” Bioconductor Project Working Papers, Bioconductor project working papers,. bepress.

  2. Finak G, Mayer B, Fulp W et al. DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis [version 1; referees: 1 approved with reservations]. Gates Open Res 2018, 2:31 (doi: 10.12688/gatesopenres.12832.1)

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ropensci/DataPackageR documentation built on Aug. 27, 2019, 9:57 a.m.