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Contributing to the manydata universe of packages with manypkgs

manypkgs has package related functions that help developers contribute to the ecosystem of packages by helping with the set-up of packages consistent with manydata. The function setup_package(), for example, establishes many of the required files and folder structures for a manydata-consistent data package. It requires that the package name starts with the prefix ‘many’ as well as that the name of one author is declared. For author’s name, please declare surname(s) first and then given name(s) separated by commas. The function accepts ORCID numbers instead of author names as well.

manypkgs::setup_package("manystates", name = "Hollway, James")

For adding on package contributors or authors to packages later on, use the add_author() function. This function also accepts ORCID numbers instead of author names.

manypkgs::add_author(name = "Sposito, Henrique")

For automatically updating packages for changes in templates, documentation, and file structure, contributors can use the update_package() function.


Collecting data

For data collection and loading into a new package with manypkgs, the function import_data() creates a data-raw folder into the package, copies the raw data into the folder and provides a script to facilitate data loading. The function requires that a name for the dataset - two dimensional data sheet being imported - is declared, in upper case letters, as well as the name of the issue-domain, in lower case letters, the dataset belongs. Note that for transparency and citation purposes, contributors need to provide a .bib file alongside their data in the corresponding data-raw subfolder. The domain issue name will be used to connect two-dimensional datasets into three-dimensional databases that resembles a data cube (a list object where entries are tibbles).

manypkgs::import_data("COW", "states")

Correcting data

Once a dataset is collected with import_data(), it needs to be corrected so that it can be connected into a database. manydata contains several functions geared towards data wrangling. These include, for example, the transmutate() function which is between dplyr’s transmute and mutate but that returns only the mutated variables and none of the variables used in the mutations. Furthermore, standardise_title() function capitalises all words in a string and removes unnecessary symbols to enable comparison. Finally, manydata data preparation workflows also leverage {messydates} to correct uncertain dates. These include, for example, the messydates::make_messydate() function which creates nested vectors of dates for vague date inputs into a range of dates.

COW <- manydata::transmutate(ID = stateabb,
                   Beg = messydates::make_messydate(styear, stmonth, stday),
                   End = messydates::make_messydate(endyear, endmonth, endday),
                   Label = manypkgs::standardise_titles(statenme),
                   COW_Nr = manypkgs::standardise_titles(as.character(ccode)))

Contributors should make sure the variables names in the datasets are in line with manydata ecosystem. Datasets part of the same database should have similar variables which allows to build up the ‘datacube’ for comparison.

Connecting data

Once the dataset has been corrected in manydata, the cleaned dataset can be loaded with manypkgs into the package using the export_data() function which take as inputs the dataset name and the database name as a string. Corrected datasets can be connected to form a three dimensional database that resembles a data cube. The website source of the data should be indicated in the third argument of the function. The function creates a database in a data folder to store corrected datasets, while providing a citation template and a test template. The citation template helps cite the source of the raw data and and describe the variables in the corrected dataset. The test template makes sure the corrected dataset is consistent with manypkgs guidelines.

manypkgs::export_data(COW, "states", URL = "")

The test template is meant to ensure consistency for the corrected dataset. If these tests fail, users can go back to correcting the data and then re-run export_data().