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pharmaverseraw

Test raw data for use within the pharmaverse family of packages

https://github.com/pharmaverse/pharmaverseraw

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
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    Academic publication links
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    Committers with academic emails
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.7%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Test raw data for use within the pharmaverse family of packages

Basic Info
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 3
  • Releases: 0
Created about 1 year ago · Last pushed 11 months ago
Metadata Files
Readme Changelog License Codeowners

README.md

pharmaverseraw pharmaverse sdtm hex

CRAN status

Raw data for domains in the pharmaversesdtm package

Purpose {#purpose}

To provide raw datasets which can be used to generate SDTM datsets in the pharmaversesdtm package. The raw dataset does not align with any EDC (Electronic Data Capture) systems, meaning that are EDC agnostic. The raw datasets are also data standards agnostic, meaning some of the raw datasets are in CDASH (Clinical Data Acquisition Standards Harmonization) and some are not in CDASH format. We have created such examples to showcase the edc and standards agnostic features of sdtm.oak. The annotated case report forms corresponding to the raw datasets are also present in the inst\acrf folder.

Installation {#installation}

The package is available from CRAN and can be installed by running install.packages("pharmaverseraw"). To install the latest development version of the package directly from GitHub use the following code:

``` r if (!requireNamespace("remotes", quietly = TRUE)) { install.packages("remotes") }

remotes::install_github("pharmaverse/pharmaverseraw", ref = "main") # This command installs the latest development version directly from GitHub. ```

Data Sources {#data-sources}

Raw datasets are created based upon the SDTM domains in pharmaversesdtm package.

Naming Conventions {#naming}

Datasets are named following the associated SDTM domain names with a "raw" appended. For example, the raw data used to create subject disposition DS domain is named as "dsraw".

How To Update {#how-to-update}

Firstly, make a GitHub issue in {pharmaverseraw} with the planned updates. Then there are two main ways to extend the test data: either by adding new datasets or extending existing datasets with new records/variables. Whichever method you choose, it is worth noting the following:

  • Programs that generate raw data are stored in the data-raw/ folder.
  • Each of these programs is written as a standalone R script: if any packages need to be loaded for a given program, then call library() at the start of the program (but please do not call library(pharmaverseraw)).
  • When you have created a program in the data-raw/ folder, you need to run it as a standalone R script, in order to generate a raw dataset that will become part of the {pharmaverseraw} package, but you do not need to build the package.
  • Following best practice, each dataset is stored as a .rda file whose name is consistent with the name of the dataset, e.g., dataset xx_raw is stored as xx_raw.rda. The easiest way to achieve this is to use usethis::use_data(xx_raw)
  • The programs in data-raw/ are stored within the {pharmaverseraw} GitHub repository, but they are not part of the {pharmaverseraw} package--the data-raw/ folder is specified in .Rbuildignore.
  • When you run a program that is in the data-raw/ folder, you generate a dataset that is written to the data/ folder, which will become part of the {pharmaverseraw} package.
  • The names and sources of raw datasets are specified in R/*.R, for the purpose of generating documentation in the man/ folder.
  • The generated raw datasets should adhere with the structure and content in the corresponding sdtm domain in pharmaversesdtm package.

Adding New Raw Datasets

  • Create a program in the data-raw/ folder, named <name>.R, where <name> should follow the naming convention, to generate the raw data and output <name>.rda to the data/ folder.
    • Note that no personal data should be used as part of this package, even if anonymized.
  • Run the program.
  • Reflect this update by documenting the dataset in the R/*.R file.
  • Run devtools::document() in order to update NAMESPACE and update the .Rd files in man/.
  • Add your GitHub handle to .github/CODEOWNERS.
  • Update NEWS.md.

Updating Existing SDTM Datasets

  • Locate the existing program <name>.R in the data-raw/ folder, update it accordingly.
  • Reflect this update by updating the corresponding script in the R/*.R file.
  • Run the program, and output updated <name>.rda to the data/ folder.
  • Run devtools::document() in order to update NAMESPACE and update the .Rd files in man/.
  • Add your GitHub handle to .github/CODEOWNERS.
  • Update NEWS.md.

Owner

  • Name: pharmaverse
  • Login: pharmaverse
  • Kind: organization
  • Location: Switzerland

GitHub Events

Total
  • Issues event: 10
  • Watch event: 1
  • Issue comment event: 1
  • Member event: 3
  • Push event: 41
  • Pull request review comment event: 4
  • Pull request review event: 5
  • Pull request event: 10
  • Create event: 8
Last Year
  • Issues event: 10
  • Watch event: 1
  • Issue comment event: 1
  • Member event: 3
  • Push event: 41
  • Pull request review comment event: 4
  • Pull request review event: 5
  • Pull request event: 10
  • Create event: 8

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 30
  • Total Committers: 5
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.367
Past Year
  • Commits: 30
  • Committers: 5
  • Avg Commits per committer: 6.0
  • Development Distribution Score (DDS): 0.367
Top Committers
Name Email Commits
Shiyu Chen c****u@h****m 19
Adam Foryś a****s@g****m 6
Rammprasad Ganapathy g****d@g****m 3
Ross Farrugia 8****a 1
Edoardo Mancini e****i@r****m 1
Committer Domains (Top 20 + Academic)
roche.com: 1 gene.com: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 5
  • Total pull requests: 5
  • Average time to close issues: 3 days
  • Average time to close pull requests: 1 day
  • Total issue authors: 3
  • Total pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.2
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 5
  • Pull requests: 5
  • Average time to close issues: 3 days
  • Average time to close pull requests: 1 day
  • Issue authors: 3
  • Pull request authors: 3
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.2
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ShiyuC (3)
  • rammprasad (2)
  • rossfarrugia (1)
Pull Request Authors
  • ShiyuC (5)
  • rammprasad (4)
  • manciniedoardo (2)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • cran 466 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 2
  • Total maintainers: 1
cran.r-project.org: pharmaverseraw

Raw Data for 'pharmaversesdtm' Package

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 466 Last month
Rankings
Dependent packages count: 26.5%
Dependent repos count: 32.6%
Average: 48.6%
Downloads: 86.7%
Maintainers (1)
ganapathy.rammprasad@gene.com
Last synced: 11 months ago

Dependencies

.github/workflows/common.yml actions
DESCRIPTION cran
  • R >= 4.1 depends
  • admiral >= 1.0.0 imports
  • admiraldev >= 1.0.0 imports
  • dplyr >= 1.0.5 imports
  • hms >= 0.5.3 imports
  • lifecycle >= 0.1.0 imports
  • lubridate >= 1.7.4 imports
  • magrittr >= 1.5 imports
  • purrr >= 0.3.3 imports
  • rlang >= 0.4.4 imports
  • stringr >= 1.4.0 imports
  • tidyr >= 1.0.2 imports
  • tidyselect >= 1.1.0 imports
  • DT * suggests
  • diffdf * suggests
  • htmltools * suggests
  • knitr * suggests
  • methods * suggests
  • pharmaversesdtm >= 0.2.0 suggests
  • reactable * suggests
  • readxl * suggests
  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests
  • tibble * suggests