README
================

# DataPackageR

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

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## Installation

You can install the latest
[CRAN](https://cran.r-project.org/package=DataPackageR) release of
DataPackageR with:

``` r
install.packages("DataPackageR")
```

You can install the latest development version of DataPackageR from
[GitHub](https://github.com/ropensci/DataPackageR) with:

``` r
library(remotes)
remotes::install_github("ropensci/DataPackageR")
```

## What problems does DataPackageR tackle?

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

- Perform data analysis
- Write a report
- Publish a paper
- Share data with colleagues and collaborators
- Save time in the future when you return to this project but have
  forgotten all about what you did.

### 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.

- **Reproducibility.**

  As described [elsewhere](https://github.com/ropensci/rrrpkg),
  packaging your data promotes reproducibility. R’s packaging
  infrastructure promotes unit testing, documentation, a reproducible
  build system, and has many other benefits. Coopting it for packaging
  data sets is a natural fit.

- **Collaboration.**

  A data set packaged in R is easy to distribute and share among
  collaborators, and is easy to install and use. All the hard work
  you’ve put into documenting and standardizing the tidy data set comes
  right along with the data package.

- **Documentation.**

  R’s package system allows us to document data objects. What’s more,
  the `roxygen2` package makes this very easy to do with [markup
  tags](https://r-pkgs.org/data.html). That documentation is the
  equivalent of a data dictionary and can be extremely valuable when
  returning to a project after a period of time.

- **Convenience.**

  Data pre-processing can be time consuming, depending on the data type
  and raw data sets may be too large to share conveniently in a packaged
  format. Packaging and sharing the small, tidied data saves the users
  computing time and time spent waiting for downloads.

## Challenges

- **Package size limits.**

  R packages have a 10MB size limit, at least on
  [CRAN](https://cran.r-project.org/web/packages/policies.html).
  Bioconductor
  [ExperimentHub](https://contributions.bioconductor.org/data.html#data)
  may be able to support larger data packages.

  Sharing large volumes of raw data in an R package format is still not
  ideal, and there are public biological data repositories better suited
  for raw data: e.g., [GEO](https://www.ncbi.nlm.nih.gov/geo/),
  [SRA](https://www.ncbi.nlm.nih.gov/sra),
  [ImmPort](https://www.immport.org/),
  [ImmuneSpace](https://immunespace.org/),
  [FlowRepository](http://flowrepository.org/).

  Tools like [datastorr](https://github.com/traitecoevo/datastorr) can
  help with this and we hope to integrate this into DataPackageR in the
  future.

- **Manual effort**

  There is still a substantial manual effort to set up the correct
  directory structures for an R data package. This can dissuade many
  individuals, particularly new users who have never built an R package,
  from going this route.

- **Scale**

  Setting up and building R data packages by hand is a workable solution
  for a small project or a small number of projects, but when dealing
  with many projects each involving many data sets, tools are needed to
  help automate the process.

## DataPackageR

DataPackageR provides a number of benefits when packaging your data.

- It aims to automate away much of the tedium of packaging data sets
  without getting too much in the way, and keeps your processing
  workflow reproducible.

- It sets up the necessary package structure and files for a data
  package.

- It allows you to keep the large, raw data and only ship the packaged
  tidy data, saving space and time consumers of your data set need to
  spend downloading and re-processing it.

- It maintains a reproducible record (vignettes) of the data processing
  along with the package. Consumers of the data package can verify how
  the processing was done, increasing confidence in your data.

- It automates construction of the documentation and maintains a data
  set version and an md5 fingerprint of each data object in the package.
  If the data changes and the package is rebuilt, the data version is
  automatically updated.

## Blog Post - building packages interactively.

See this [rOpenSci blog
post](https://ropensci.org/blog/2018/09/18/datapackager/) 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

``` r
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())
#> ✔ Creating '/tmp/RtmpT70eUS/mtcars20/'.
#> ✔ Setting active project to "/tmp/RtmpT70eUS/mtcars20".
#> ✔ Creating 'R/'.
#> ✔ Writing 'DESCRIPTION'.
#> Package: mtcars20
#> Title: What the Package Does (One Line, Title Case)
#> Version: 0.0.0.9000
#> Authors@R (parsed):
#>     * First Last <first.last@example.com> [aut, cre]
#> Description: What the package does (one paragraph).
#> License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
#>     license
#> Encoding: UTF-8
#> Roxygen: list(markdown = TRUE)
#> RoxygenNote: 7.3.3
#> ✔ Writing 'NAMESPACE'.
#> ✔ Setting active project to "<no active project>".
#> ✔ Setting active project to "/tmp/RtmpT70eUS/mtcars20".
#> ✔ Added DataVersion string to 'DESCRIPTION'
#> ✔ Creating 'data-raw/'.
#> ✔ Creating 'data/'.
#> ✔ Creating 'inst/extdata/'.
#> ✔ Copied subsetCars.Rmd into 'data-raw'
#> ✔ configured 'datapackager.yml' file

# 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"))
#> 
#> ✔ 1 data set(s) created by subsetCars.Rmd
#> • cars_over_20
#> ☘ Built all datasets!
#> Non-interactive NEWS.md file update.
#> * Added: cars_over_20
#> ✔ Creating 'vignettes/'.
#> ✔ Creating 'inst/doc/'.
#> ℹ Loading mtcars20
#> Writing 'NAMESPACE'
#> Writing 'mtcars20.Rd'
#> Writing 'cars_over_20.Rd'
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#> * checking for file ‘/tmp/RtmpT70eUS/mtcars20/DESCRIPTION’ ... OK
#> * preparing ‘mtcars20’:
#> * checking DESCRIPTION meta-information ... OK
#> * checking for LF line-endings in source and make files and shell scripts
#> * checking for empty or unneeded directories
#> * looking to see if a ‘data/datalist’ file should be added
#> * building ‘mtcars20_1.0.tar.gz’
#> 
#> Next Steps 
#> 1. Update your package documentation.
#>    - Edit the documentation.R file in the package sourcedata-rawsubdirectory and update the roxygen markup. 
#>    - Rebuild the package documentation with document(). 
#> 2. Add your package to source control.
#>    - Call git init . in the package source root directory. 
#>    - git add the package files. 
#>    - git commit your new package. 
#>    - Set up a github repository for your pacakge. 
#>    - Add the github repository as a remote of your local package repository. 
#>    - git push your local repository to gitub.
#> [1] "/tmp/RtmpT70eUS/mtcars20_1.0.tar.gz"

# 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"))
#> ℹ Loading mtcars20
#> [1] TRUE

# Let's use the package we just created.
# During actual use, the temporary library does not need to be specified.
temp_lib <- file.path(tempdir(),"lib")
dir.create(temp_lib)
install.packages(file.path(tempdir(),"mtcars20_1.0.tar.gz"),
                 type = "source", repos = NULL, lib = temp_lib)
library(mtcars20, lib.loc = temp_lib)
data("cars_over_20") # load the data

# We have our dataset!
# Since we preprocessed it,
# it is clean and under the 5 MB limit for data in packages.
cars_over_20
#>    speed dist
#> 44    22   66
#> 45    23   54
#> 46    24   70
#> 47    24   92
#> 48    24   93
#> 49    24  120
#> 50    25   85

?cars_over_20 # See the documentation you wrote in data-raw/documentation.R.

# We can easily check the version of the data
data_version("mtcars20")
#> [1] '0.1.0'

# 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.

``` r
# 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()
```

## Vignettes

[yaml configuration
guide](https://docs.ropensci.org/DataPackageR/articles/YAML_Configuration_Details.html)

[a more detailed technical
vignette](https://docs.ropensci.org/DataPackageR/articles/Using_DataPackageR.html)

## Preprint and publication <a id = "publication"></a>

The publication describing the package, (Finak *et al.*,
2018)<!--@10.12688/gatesopenres.12832.2-->, is now available at [Gates
Open Research](https://gatesopenresearch.org/articles/2-31/v2) .

The preprint is on [bioRxiv](https://doi.org/10.1101/342907).

## Similar work

DataPackageR is for processing raw data into tidy data sets and bundling
them into R packages. (Note:
[datapack](https://github.com/ropensci/datapack) is a **different
package** that is used to “create, send and load data from common
repositories such as DataONE into the R environment”.)

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

- **datastorr** [github repo](https://github.com/traitecoevo/datastorr)

  Simple data retrieval and versioning using GitHub to store data.

  - Caches downloads and uses github releases to version data.
  - Deal consistently with translating the file stored online into a
    loaded data object
  - Access multiple versions of the data at once

  `datastorrr` could be used with DataPackageR to store / access remote
  raw data sets, remotely store / access tidied data that are too large
  to fit in the package itself.

- **fst** [github repo](https://github.com/fstpackage/fst)

  `fst` provides lightning fast serialization of data frames.

- **The modern data package**
  [pdf](https://github.com/noamross/2018-04-18-rstats-nyc/blob/master/Noam_Ross_ModernDataPkg_rstatsnyc_2018-04-20.pdf)

  A presentation from @noamross touching on modern tools for open
  science and reproducibility. Discusses `datastorr` and `fst` as well
  as standardized metadata and documentation.

- **rrrpkg** [github repo](https://github.com/ropensci/rrrpkg)

  A document from rOpenSci describing using an R package as a research
  compendium. Based on ideas originally introduced by Robert Gentleman
  and Duncan Temple Lang (Gentleman and Lang
  (2004)<!--@Gentleman2004-oj-->)

- **template** [github repo](https://github.com/ropensci/rrrpkg)

  An R package template for data packages.

See the [publication](#publication) for further discussion.

## Code of conduct

Please note that this project is released with a [Contributor Code of
Conduct](https://github.com/ropensci/DataPackageR/blob/main/CODE_OF_CONDUCT.md).
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. Gates Open Res 2018, 2:31 (DOI:
    10.12688/gatesopenres.12832.1)

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