Data package builds are controlled using the datapackager.yml file.
This file is created in the package source tree when the user creates a package using datapackage_skeleton().
It is automatically populated with the names of the code_files and data_objects the passed in to datapackage_skeleton.
datapackager.yml file.The structure of a correctly formatted datapackager.yml file is shown below:
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ objects: cars_over_20
+ render_root:
+ tmp: '450393'The main section of the file is the configuration: section.
It has three properties:
+files:
The files (R or Rmd) to be processed by DataPackageR. They are processed in the order shown. Users running multi-script workflows with dependencies between the scripts need to ensure the files are processed in the correct order.
Here subsetCars.Rmd is the only file to process. The name is transformed to an absolute path within the package.
Each file itself has just one property:
+ + +enabled: A logical yes, no flag indicating whether the file should be rendered during the build, or whether it should be skipped. This is useful for ‘turning off’ long running processing tasks if they have not changed. Disabling processing of a file will not overwrite existing documentation or data objecs created during previous builds.objects:
render_root:
The directory where the Rmd or R files will be rendered. Defaults to a randomly named subdirectory of tempdir(). Allows workflows that use multiple scripts and create file system artifacts to function correctly by simply writing to and reading from the working directory.
The structure of the YAML is simple enough to understand but complex enough that it can be a pain to edit by hand.
+DataPackageR provides a number of API calls to construct, read, modify, and write the yaml config file.
+construct_yml_configMake an r object representing a YAML config file.
+The YAML config shown above was created by:
+ +yml_findRead a yaml config file from a package path into an r object.
+Read the YAML config file from the mtcars20 example.
yml_list_objectsList the objects in a config read by yml_find.
yml_list_filesList the files in a config read by yml_find.
yml_disable_compileDisable compilation of named files in a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: no
+ objects: cars_over_20
+ render_root:
+ tmp: '912178'yml_enable_compileEnable compilation of named files in a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ objects: cars_over_20
+ render_root:
+ tmp: '912178'yml_add_filesAdd named files to a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ anotherFile.Rmd:
+ enabled: yes
+ objects: cars_over_20
+ render_root:
+ tmp: '912178'yml_add_objectsAdd named objects to a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ anotherFile.Rmd:
+ enabled: yes
+ objects:
+ - cars_over_20
+ - another_object
+ render_root:
+ tmp: '912178'yml_remove_filesRemove named files from a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ objects:
+ - cars_over_20
+ - another_object
+ render_root:
+ tmp: '912178'yml_remove_objectsRemove named objects from a config read by yml_find.
configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ objects: cars_over_20
+ render_root:
+ tmp: '912178'yml_writeWrite a modified config to its package path.
+The yml_oneobj read by yml_find() carries an attribute that is the path to the package. The user doesn’t need to pass a path to yml_write if the config has been read by yml_find.
This vignette demonstrates how to use DataPackageR to build a data package.
+DataPackageR aims to simplify data package construction.
+It provides mechanisms for reproducibly preprocessing and tidying raw data into into documented, versioned, and packaged analysis-ready data sets.
+Long-running or computationally intensive data processing can be decoupled from the usual R CMD build process while maintinaing data lineage.
In this vignette we will subset and package the mtcars data set.
We’ll set up a new data package based on mtcars example in the README. The datapackage_skeleton() API is used to set up a new package. The user needs to provide:
library(DataPackageR)
+
+# Let's reproducibly package up
+# the cars in the mtcars dataset
+# with speed > 20.
+# Our dataset will be called cars_over_20.
+
+# Get the code file that turns the raw data
+# to our packaged and processed analysis-ready dataset.
+processing_code <-
+ system.file("extdata",
+ "tests",
+ "subsetCars.Rmd",
+ package = "DataPackageR")
+
+# Create the package framework.
+DataPackageR::datapackage_skeleton(name = "mtcars20",
+ force = TRUE,
+ code_files = processing_code,
+ r_object_names = "cars_over_20",
+ path = tempdir()
+ #dependencies argument is empty
+ #raw_data_dir argument is empty.
+ ) This has created a datapackage source tree named “mtcars20” (in a temporary directory). For a real use case you would pick a path on your filesystem where you could then initialize a new github repository for the package.
The contents of mtcars20 are:
Registered S3 method overwritten by 'dplyr':
+ method from
+ as.data.frame.tbl_df tibble
+Registered S3 methods overwritten by 'ggplot2':
+ method from
+ [.quosures rlang
+ c.quosures rlang
+ print.quosures rlang
+ levelName
+1 mtcars20
+2 ¦--data
+3 ¦--data-raw
+4 ¦ °--subsetCars.Rmd
+5 ¦--datapackager.yml
+6 ¦--DESCRIPTION
+7 ¦--inst
+8 ¦ °--extdata
+9 ¦--man
+10 ¦--R
+11 °--Read-and-delete-meYou should fill out the DESCRIPTION file to describe your data package. It contains a new DataVersion string that will be automatically incremented when the data package is built if the packaged data has changed.
The user-provided code files reside in data-raw. They are executed during the data package build process.
A datapackager.yml file is used to configure and control the build process.
The contents are:
+configuration:
+ files:
+ subsetCars.Rmd:
+ enabled: yes
+ objects: cars_over_20
+ render_root:
+ tmp: '411610'The two main pieces of information in the configuration are a list of the files to be processed and the data sets the package will store.
+This example packages an R data set named cars_over_20 (the name was passed in to datapackage_skeleton()). It is created by the subsetCars.Rmd file.
The objects must be listed in the yaml configuration file. datapackage_skeleton() ensures this is done for you automatically.
DataPackageR provides an API for modifying this file, so it does not need to be done by hand.
+Further information on the contents of the YAML configuration file, and the API are in the YAML Configuration Details
+Raw data (provided the size is not prohibitive) can be placed in inst/extdata.
The datapackage_skeleton() API has the raw_data_dir argument, which will copy the contents of raw_data_dir (and its subdirectories) into inst/extdata automatically.
In this example we are reading the mtcars data set that is already in memory, rather than from the file system.
As stated in the README, in order for your processing scripts to be portable, you should not use absolute paths to files. DataPackageR provides an API to point to the data package root directory and the inst/extdata and data subdirectories. These are useful for constructing portable paths in your code to read files from these locations.
For example: to construct a path to a file named “mydata.csv” located in inst/extdata in your data package source tree:
DataPackageR::project_extdata_path("mydata.csv") in your R or Rmd file. This would return: e.g., /var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpjpZUX9/mtcars20/inst/extdata/mydata.csvSimilarly:
+DataPackageR::project_path() constructs a path to the data package root directory. (e.g., /var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpjpZUX9/mtcars20)DataPackageR::project_data_path() constructs a path to the data package data subdirectory. (e.g., /var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T//RtmpjpZUX9/mtcars20/data)Raw data sets that are stored externally (outside the data package source tree) can be constructed relative to the project_path().
If your processing scripts are Rmd files, the usual yaml header for rmarkdown documents should be present.
+If you have Rmd files, you can still include a yaml header, but it should be commented with #' and it should be at the top of your R file. For example, a test R file in the DataPackageR package looks as follows:
#'---
+#'title: Sample report from R script
+#'author: Greg Finak
+#'date: August 1, 2018
+#'---
+data <- runif(100)This will be converted to an Rmd file with a proper yaml header, which will then be turned into a vignette and indexed in the built package.
+Once the skeleton framework is set up,
+# Run the preprocessing code to build cars_over_20
+# and reproducibly enclose it in a package.
+DataPackageR:::package_build(file.path(tempdir(),"mtcars20"))
+
+✔ Setting active project to '/private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20'
+✔ 1 data set(s) created by subsetCars.Rmd
+• cars_over_20
+☘ Built all datasets!
+Enter a text description of the changes for the NEWS.md file.
+
+✔ Creating 'vignettes/'
+✔ Creating 'inst/doc/'
+First time using roxygen2. Upgrading automatically...
+Updating roxygen version in /private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20/DESCRIPTION
+Writing NAMESPACE
+Loading mtcars20
+Writing mtcars20.Rd
+Writing cars_over_20.Rd
+
+ checking for file ‘/private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20/DESCRIPTION’ ...
+
+✔ checking for file ‘/private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20/DESCRIPTION’
+
+
+─ preparing ‘mtcars20’:
+
+
+ checking DESCRIPTION meta-information ...
+
+✔ checking DESCRIPTION meta-information
+
+
+─ 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
+
+
+ NB: this package now depends on R (>= 3.5.0)
+
+
+ WARNING: Added dependency on R >= 3.5.0 because serialized objects in serialize/load version 3 cannot be read in older versions of R. File(s) containing such objects: 'mtcars20/data/cars_over_20.rda'
+
+
+─ building 'mtcars20_1.0.tar.gz'
+
+
+
+
+Next Steps
+1. Update your package documentation.
+ - Edit the documentation.R file in the package source data-raw subdirectory 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] "/private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20_1.0.tar.gz"When you build a package in interactive mode, you will be prompted to input text describing the changes to your data package (one line).
+These will appear in the NEWS.md file in the following format:
+DataVersion: xx.yy.zz
+========
+A description of your changes to the package
+
+[The rest of the file]If the processing script is time consuming or the data set is particularly large, then R CMD build would run the code each time the package is installed. In such cases, raw data may not be available, or the environment to do the data processing may not be set up for each user of the data. DataPackageR decouples data processing from package building/installation for data consumers.
DataPackageR uses the futile.logger package to log progress.
If there are errors in the processing, the script will notify you via logging to console and to /private/tmp/Test/inst/extdata/Logfiles/processing.log. Errors should be corrected and the build repeated.
If everything goes smoothly, you will have a new package built in the parent directory.
+In this case we have a new package mtcars20_1.0.tar.gz.
The pacakge source directory changes after the first build.
+ levelName
+1 mtcars20
+2 ¦--data
+3 ¦ °--cars_over_20.rda
+4 ¦--data-raw
+5 ¦ ¦--documentation.R
+6 ¦ ¦--subsetCars.R
+7 ¦ °--subsetCars.Rmd
+8 ¦--DATADIGEST
+9 ¦--datapackager.yml
+10 ¦--DESCRIPTION
+11 ¦--inst
+12 ¦ ¦--doc
+13 ¦ ¦ ¦--subsetCars.html
+14 ¦ ¦ °--subsetCars.Rmd
+15 ¦ °--extdata
+16 ¦ °--Logfiles
+17 ¦ ¦--processing.log
+18 ¦ °--subsetCars.html
+19 ¦--man
+20 ¦ ¦--cars_over_20.Rd
+21 ¦ °--mtcars20.Rd
+22 ¦--NAMESPACE
+23 ¦--NEWS.md
+24 ¦--R
+25 ¦ °--mtcars20.R
+26 ¦--Read-and-delete-me
+27 °--vignettes
+28 °--subsetCars.Rmd After the first build, the R directory contains mtcars.R that has autogenerated roxygen2 markup documentation for the data package and for the packaged data cars_over20.
The processed Rd files can be found in man.
The autogenerated documentation source is in the documentation.R file in data-raw.
You should update this file to properly document your objects. Then rebuild the documentation:
+document(file.path(tempdir(),"mtcars20"))
+
+✔ Setting active project to '/private/var/folders/jh/x0h3v3pd4dd497g3gtzsm8500000gn/T/RtmpjpZUX9/mtcars20'
+Updating mtcars20 documentation
+Loading mtcars20
+[1] TRUEThis is done without reprocessing the data.
+You should update the documentation in R/mtcars.R, then call package_build() again.
The package source also contains files in the vignettes and inst/doc directories that provide a log of the data processing.
When the package is installed, these will be accessible via the vignette() API.
The vignette will detail the processing performed by the subsetCars.Rmd processing script.
The data set documentation will be accessible via ?cars_over_20, and the data sets via data().
# Let's use the package we just created.
+install.packages(file.path(tempdir(),"mtcars20_1.0.tar.gz"), type = "source", repos = NULL)
+if(!"package:mtcars20"%in%search())
+ attachNamespace('mtcars20') #use library() in your code
+data("cars_over_20") # load the data
+
+cars_over_20 # now we can use it.
+ 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.
+
+vignettes <- vignette(package = "mtcars20")
+vignettes$results
+ Package
+Topic "mtcars20"
+ LibPath
+Topic "/Library/Frameworks/R.framework/Versions/3.6/Resources/library"
+ Item Title
+Topic "subsetCars" "A Test Document for DataPackageR (source, html)"Your downstream data analysis can depend on a specific version of the data in your data package by testing the DataVersion string in the DESCRIPTION file.
+We provide an API for this:
+# We can easily check the version of the data
+DataPackageR::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") #If this fails, execution stops
+ #and provides an informative error.Version 1.12.0 has moved away from controlling the build process using datasets.R and an additional masterfile argument.
The build process is now controlled via a datapackager.yml configuration file located in the package root directory. (see YAML Configuration Details)
You can migrate an old package by constructing such a config file using the construct_yml_config() API.
# assume I have file1.Rmd and file2.R located in /data-raw,
+# and these create 'object1' and 'object2' respectively.
+
+config <- construct_yml_config(code = c("file1.Rmd", "file2.R"),
+ data = c("object1", "object2"))
+cat(yaml::as.yaml(config))
+configuration:
+ files:
+ file1.Rmd:
+ enabled: yes
+ file2.R:
+ enabled: yes
+ objects:
+ - object1
+ - object2
+ render_root:
+ tmp: '169120'config is a newly constructed yaml configuration object. It can be written to the package directory:
path_to_package <- tempdir() #e.g., if tempdir() was the root of our package.
+yml_write(config, path = path_to_package)Now the package at path_to_package will build with version 1.12.0 or greater.
In versions prior to 1.12.1 we would read data sets from inst/extdata in an Rmd script using paths relative to data-raw in the data package source tree.
For example:
+# read 'myfile.csv' from inst/extdata relative to data-raw where the Rmd is rendered.
+read.csv(file.path("../inst/extdata","myfile.csv"))Now Rmd and R scripts are processed in render_root defined in the yaml config.
To read a raw data set we can get the path to the package source directory using an API call:
+# DataPackageR::project_extdata_path() returns the path to the data package inst/extdata subdirectory directory.
+# DataPackageR::project_path() returns the path to the data package root directory.
+# DataPackageR::project_data_path() returns the path to the data package data subdirectory directory.
+read.csv(
+ DataPackageR::project_extdata_path("myfile.csv")
+ )We can also perform partial builds of a subset of files in a package by toggling the enabled key in the config file.
This can be done with the following API:
+config <- yml_disable_compile(config,filenames = "file2.R")
+yml_write(config, path = path_to_package) # write modified yml to the package.
+configuration:
+ files:
+ file1.Rmd:
+ enabled: yes
+ file2.R:
+ enabled: no
+ objects:
+ - object1
+ - object2
+ render_root:
+ tmp: '169120'Note that the modified configuration needs to be written back to the package source directory in order for the changes to take effect.
+The consequence of toggling a file to enable: no is that it will be skipped when the package is rebuilt, but the data will still be retained in the package, and the documentation will not be altered.
This is useful in situations where we have multiple data sets, and want to re-run one script to update a specific data set, but not the other scripts because they may be too time consuming, for example.
+We may have situations where we have mutli-script pipelines. There are two ways to share data among scripts.
+The yaml configuration property render_root specifies the working directory where scripts will be rendered.
If a script writes files to the working directory, that is where files will appear. These can be read by subsequent scripts.
+A script (e.g., script2.Rmd) running after script1.Rmd can access a stored data object named script1_dataset created by script1.Rmd by calling
script1_dataset <- DataPackageR::datapackager_object_read("script1_dataset").
Passing of data objects amongst scripts can be turned off via:
+package_build(deps = FALSE)
We recommend the following once your package is created.
+You now have a data package source tree.
+git init in the package source root to initialize a new git repository.github. see step 7This will let you version control your data processing code, and provide a mechanism for sharing your package with others.
+For more details on using git and github with R, there is an excellent guide provided by Jenny Bryan: Happy Git and GitHub for the useR and Hadley Wickham’s book on R packages.
+We provide some additional details for the interested.
+DataPackageR calculates an md5 checksum of each data object it stores, and keeps track of them in a file called DATADIGEST.
DataVersion string has been incremented in the DESCRIPTION file.The DATADIGEST file contains the following:
DataVersion: 0.1.0
+cars_over_20: 3ccb5b0aaa74fe7cfc0d3ca6ab0b5cf3The description file has the new DataVersion string.
Package: mtcars20
+Title: What the Package Does (One Line, Title Case)
+Version: 1.0
+Authors@R:
+ person(given = "First",
+ family = "Last",
+ role = c("aut", "cre"),
+ email = "first.last@example.com")
+Description: What the package does (one paragraph).
+License: What license it uses
+Encoding: UTF-8
+LazyData: true
+DataVersion: 0.1.0
+Roxygen: list(markdown = TRUE)
+Date: 2018-10-24
+Suggests:
+ knitr,
+ rmarkdown
+VignetteBuilder: knitr
+RoxygenNote: 6.1.0