diff --git a/paper/images/REDCapTidieR JOSS.png b/paper/images/REDCapTidieR JOSS.png index ba1ce878..a8672535 100644 Binary files a/paper/images/REDCapTidieR JOSS.png and b/paper/images/REDCapTidieR JOSS.png differ diff --git a/paper/paper.md b/paper/paper.md index d599763c..781b6238 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -56,19 +56,19 @@ To address these challenges, we developed `REDCapTidieR` as an open source R pac # Statement of Need -As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 countries. REDCap databases exhibit significant variation in complexity, ranging from simple tables with easily identifiable records to more challenging scenarios where pinpointing a unique identifier is harder. This complexity often arises in databases that make use of "repeating instruments" and "repeating events." For an in-depth exploration of this concept, refer to the [`REDCapTidieR` documentation](https://chop-cgtinformatics.github.io/REDCapTidieR/articles/diving_deeper.html#longitudinal-redcap-projects). Fundamentally, repeating events and instruments support longitudinal studies, where subjects may have distinct timelines with varying levels of record granularity. This is where the flattening of the database into the block matrix becomes a pain point for analysts. +As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 countries. REDCap databases exhibit significant variation in complexity, ranging from simple tables with easily identifiable records to more challenging scenarios where pinpointing a unique identifier is harder. This complexity often arises in databases that make use of "repeating instruments" and "repeating events." These concepts are explored in depth in the [`REDCapTidieR` documentation](https://chop-cgtinformatics.github.io/REDCapTidieR/articles/diving_deeper.html#longitudinal-redcap-projects), but fundamentally repeating events and instruments support longitudinal studies where subjects may have distinct timelines with varying levels of record granularity. Repeating instruments and events are unavoidable for most clinical trial studies and reformatting the data that belongs to them from the flattened block matrix can be a major pain point for analysts. -While there are a few existing REDCap tools for R documented by [`REDCap-tools`](https://redcap-tools.github.io/projects/), `REDCapTidieR` occupies a unique space by providing analysts with an opinionated framework that quickly prepares them for data analysis. Although some of the aforementioned tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) packages, `REDCapTidieR` is unique in how it restructures the block matrix into a format that is easily interpretable within the user's programmatic environment. Of the tools available, `REDCapTidieR` is the only one that fundamentally restructures the block matrix in its entirety. +While there are a few existing REDCap tools for R documented by [`REDCap-tools`](https://redcap-tools.github.io/projects/), `REDCapTidieR` occupies a unique space by providing analysts with an opinionated framework that quickly prepares them for data analysis. Although some of the aforementioned tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) packages, `REDCapTidieR` is unique in how it restructures the block matrix into a format that is easily interpretable within the user's programmatic environment. Of the tools available, `REDCapTidieR` is the only one that fundamentally restructures the block matrix in its entirety and subsequently the only one that gives a definitive tidy solution to the problem of repeating instruments and events. REDCapTidieR was developed with deployment in production environments as a key consideration. To ensure the utmost confidence in the handling of user data, we've implemented an extensive test suite that exhibits 98% code coverage, as of the package's version 1.0 release. Ample documentation is accessible through a collection of package vignettes and articles, offering detailed insights into the opinionated framework, design structure, and a comprehensive glossary of terms associated with the REDCapTidieR package. These considerations have earned the package an [OpenSSF Best Practices certification](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open source projects that meet stringent criteria for delivering high-quality and secure software. -| Package | Data Export Support | Data Import Support | Data Manipulation | Data Tidying | Production Capability | -|-------------|---------------------|---------------------|-------------------|--------------| --------------------- | -| redcapAPI | x | x | | | | -| REDCapR | x | x | | | x | -| tidyREDCap | x | | x | | | -| REDCapDM | x | | x | | | -| REDCapTidieR| x | | x | x | x | +| Package | Data Export Support | Data Import Support | Data Manipulation | Tidy Reformatting | Production Ready | +|-------------|---------------------|---------------------|-------------------|-------------------| ---------------- | +| redcapAPI | x | x | | | | +| REDCapR | x | x | | | x | +| tidyREDCap | x | | x | | | +| REDCapDM | x | | x | | | +| REDCapTidieR| x | | x | x | x | Table 1: Comparative breakdown of the landscape for REDCap tools in R.