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unpivotr:Unpivot Complex and Irregular Data Layouts

Tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.

Maintained by Duncan Garmonsway. Last updated 2 months ago.

excelpivot-tablesspreadsheet

186 stars 10.35 score 368 scripts 3 dependents

cran

preregr:Specify (Pre)Registrations and Export Them Human- And Machine-Readably

Preregistrations, or more generally, registrations, enable explicit timestamped and (often but not necessarily publicly) frozen documentation of plans and expectations as well as decisions and justifications. In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The 'preregr' package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an 'HTML' file) as well as human-readable embedded data (using 'JSON'), as well as importing such exported (pre)registration specifications from such embedded 'JSON'.

Maintained by Gjalt-Jorn Peters. Last updated 2 years ago.

3.98 score

cran

justifier:Human and Machine-Readable Justifications and Justified Decisions Based on 'YAML'

Leverages the 'yum' package to implement a 'YAML' ('YAML Ain't Markup Language', a human friendly standard for data serialization; see <https:yaml.org>) standard for documenting justifications, such as for decisions taken during the planning, execution and analysis of a study or during the development of a behavior change intervention as illustrated by Marques & Peters (2019) <doi:10.17605/osf.io/ndxha>. These justifications are both human- and machine-readable, facilitating efficient extraction and organisation.

Maintained by Gjalt-Jorn Peters. Last updated 2 years ago.

3.18 score

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