From b0a023d8834622447000122cb7027a811fde5a3b Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Fri, 3 Nov 2023 16:54:51 -0400 Subject: [PATCH 01/16] Draft paper for JOSS --- paper/paper.bib | 50 ++++++++++++++++++++++++++++++++++ paper/paper.md | 72 +++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 122 insertions(+) create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..c06fa947 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,50 @@ +@article{Harris2019, + title = {The REDCap consortium: Building an international community of software platform partners}, + journal = {Journal of Biomedical Informatics}, + volume = {95}, + pages = {103208}, + year = {2019}, + issn = {1532-0464}, + doi = {https://doi.org/10.1016/j.jbi.2019.103208}, + url = {https://www.sciencedirect.com/science/article/pii/S1532046419301261}, + author = {Paul A. Harris and Robert Taylor and Brenda L. Minor and Veida Elliott and Michelle Fernandez and Lindsay O'Neal and Laura McLeod and Giovanni Delacqua and Francesco Delacqua and Jacqueline Kirby and Stephany N. Duda}, + keywords = {Medical informatics, Electronic data capture, Clinical research, Translational research}, + abstract = {The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the “REDCap Consortium” model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.} +} + +@article{Harris2009, + title = {Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support}, + journal = {Journal of Biomedical Informatics}, + volume = {42}, + number = {2}, + pages = {377-381}, + year = {2009}, + issn = {1532-0464}, + doi = {https://doi.org/10.1016/j.jbi.2008.08.010}, + url = {https://www.sciencedirect.com/science/article/pii/S1532046408001226}, + author = {Paul A. Harris and Robert Taylor and Robert Thielke and Jonathon Payne and Nathaniel Gonzalez and Jose G. Conde}, + keywords = {Medical informatics, Electronic data capture, Clinical research, Translational research}, + abstract = {Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.} +} + +@article{Wickham2014, + title={Tidy Data}, + volume={59}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v059i10}, + doi={10.18637/jss.v059.i10}, + abstract={A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.}, + number={10}, + journal={Journal of Statistical Software}, + author={Wickham, Hadley}, + year={2014}, + pages={1–23} +} + +@Manual{r_citation, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2020}, + url = {https://www.R-project.org/}, +} \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..53496839 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,72 @@ +--- +title: 'REDCapTidieR: Extracting complex REDCap databases into tidy tables' +tags: + - R + - REDCap + - data management +authors: + - name: Richard Hanna + orcid: 0009-0005-6496-8154 + equal-contrib: true + affiliation: "1" + - name: Ezra Porter + orcid: 0000-0002-4690-8343 + equal-contrib: true + affiliation: "1" + - name: Stephany Romero + equal-contrib: true + affiliation: "1" + - name: Paul Wildenhain + equal-contrib: true + affiliation: "6" + - name: Wiliam Beasley + orcid: 0000-0002-5613-5006 + equal-contrib: true + affiliation: "7" + - name: Stephan Kadauke + orcid: 0000-0003-2996-8034 + equal-contrib: true + affiliation: "1, 2, 3, 4, 5" +affiliations: + - name: Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania + index: 1 + - name: Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania + index: 2 + - name: Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania + index: 3 + - name: Division of Transfusion Medicine, Children's Hospital of Philadelphia, Pennsylvania + index: 4 + - name: Division of Pathology Informatics, Children's Hospital of Philadelphia, Pennsylvania + index: 5 + - name: Division of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania + index: 6 + - name: Department of Pediatrics, The University of Oklahoma Health Sciences Center, College of Medicine, Oklahoma City, Oklahoma, USA + - index: 7 +date: XX November 2023 +bibliography: paper.bib +--- + +# Summary + +Capturing and storing electronic data is of utmost importance in the research world, yet often becomes a burden to the researchers themselves. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust and convenient front-end interface that can support data of any type, including data requiring compliance with standards for protected information. To get a sense of its scope and impact, as of 2023 the REDCap Consortium has almost 3 million users spanning over 150 countries. + +For many researchers who use REDCap, the R language is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "Block Matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the Block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving researchers and analysts with the arduous task of reformatting the matrix into something more readable. + +To address these challenges, we developed `REDCapTidieR` as an open source R package [@r_citation] that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to swiftly begin querying their data without the need for intricate data parsing processes. + +# Installation + +`REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems. + +# Conflict of interest + +This package was developed by the [Children’s Hospital of +Philadelphia](https://www.chop.edu) Cell and Gene Therapy Informatics +Team to support the needs of the [Cellular Therapy and Transplant +Section](https://www.chop.edu/centers-programs/cellular-therapy-and-transplant-section). +The development was funded using the following sources: + +- *Stephan Kadauke Start-up funds.* Stephan Kadauke, PI, CHOP, 2018-2024 + +- *CHOP-based GMP cell manufacturing (MFG) for CAR T clinical trials*. + Stephan Grupp, PI; Stephan Kadauke, co-PI, CHOP, 2021-2023 From 0aa18f6544e6a09f32572a0ab0551396f424d996 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Mon, 6 Nov 2023 10:12:02 -0500 Subject: [PATCH 02/16] Add statement of need to paper.md --- paper/paper.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 53496839..4c554d8c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -48,16 +48,24 @@ bibliography: paper.bib # Summary -Capturing and storing electronic data is of utmost importance in the research world, yet often becomes a burden to the researchers themselves. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust and convenient front-end interface that can support data of any type, including data requiring compliance with standards for protected information. To get a sense of its scope and impact, as of 2023 the REDCap Consortium has almost 3 million users spanning over 150 countries. +Capturing and storing electronic data is integral in the research world, yet often becomes a burden to the researchers themselves. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. -For many researchers who use REDCap, the R language is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "Block Matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the Block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving researchers and analysts with the arduous task of reformatting the matrix into something more readable. +For many researchers who use REDCap, the R language [@r_citation] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "Block Matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the Block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving and analysts with the arduous task of reformatting the matrix for usability. -To address these challenges, we developed `REDCapTidieR` as an open source R package [@r_citation] that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to swiftly begin querying their data without the need for intricate data parsing processes. +To address these challenges, we developed `REDCapTidieR` as an open source R package that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to quickly query their data without the need for intricate data parsing processes. + +# 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 from the introduction 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 studies like clinical trials, 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. + +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. # Installation `REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems. +# Acknowledgements + # Conflict of interest This package was developed by the [Children’s Hospital of From dfdb8ba7b4ce156b3d04cab526b26c064ac88bb1 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Mon, 6 Nov 2023 10:28:04 -0500 Subject: [PATCH 03/16] Draft comparison table for paper.md --- paper/paper.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/paper/paper.md b/paper/paper.md index 4c554d8c..8882e06b 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -60,6 +60,14 @@ As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 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. +| Package | Functions for Data Exportation | Functions for Data Importation | Data Processing | +|-------------|-------------------------------|-------------------------------|----------------| +| redcapAPI | x | x | | +| REDCapR | x | x | | +| tidyREDCap | x | | x | +| REDCapDM | x | | x | +| REDCapTidieR| x | | x | + # Installation `REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems. From 89f0b6842b02e4287353dff3f63a4f50e2beca88 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Tue, 7 Nov 2023 10:28:09 -0500 Subject: [PATCH 04/16] Update paper.md --- paper/paper.md | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 8882e06b..5770b43f 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -58,15 +58,21 @@ To address these challenges, we developed `REDCapTidieR` as an open source R pac 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 from the introduction 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 studies like clinical trials, 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. -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. - -| Package | Functions for Data Exportation | Functions for Data Importation | Data Processing | -|-------------|-------------------------------|-------------------------------|----------------| -| redcapAPI | x | x | | -| REDCapR | x | x | | -| tidyREDCap | x | | x | -| REDCapDM | x | | x | -| REDCapTidieR| x | | x | +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. + +| Package | Data Export Support | Data Import Support | Data Manipulation | Data Tidying | +|-------------|--------------------|--------------------|------------------|--------------| +| redcapAPI | x | x | | | +| REDCapR | x | x | | | +| tidyREDCap | x | | x | | +| REDCapDM | x | | x | | +| REDCapTidieR| x | | x | x | + +# Design + +Transformation of the Block Matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/), is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data, metadata, or useful information about that instrument itself. Below, we offer a conceptual model of how `REDCapTidieR` transforms the Block Matrix into the supertibble: + +> Image to be provided # Installation From d71ba53e10e5d0bf6a79d0ac83cd70b5723ca10b Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Wed, 8 Nov 2023 10:23:23 -0500 Subject: [PATCH 05/16] Add conceptual model image --- paper/images/REDCapTidieR JOSS.png | Bin 0 -> 149893 bytes paper/paper.md | 12 +++++++----- 2 files changed, 7 insertions(+), 5 deletions(-) create mode 100644 paper/images/REDCapTidieR JOSS.png diff --git a/paper/images/REDCapTidieR JOSS.png b/paper/images/REDCapTidieR JOSS.png new file mode 100644 index 0000000000000000000000000000000000000000..ba1ce8788cc1bbdcf30eb5e45b67ea036f3cf021 GIT binary patch literal 149893 zcmeFZXIPWz);5f~t$^rQKoGDOy3(YpjG`h%dJBjMNFekYN*tx=pkM=p01@fECX@hC zA~4h-q(CSUkd_bvgisQad^h9lnc2_tynFxo-uFAc<9PFj0}^sy*SgoW*16VsuKT&E zv4QYD@qGdU0>Za%{cI*6up28Nu;cNbUBF+;?)L`^2s{?J{qr@8P{;X<-R~?3qqP|1 zae8o-v 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[REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. -For many researchers who use REDCap, the R language [@r_citation] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "Block Matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the Block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving and analysts with the arduous task of reformatting the matrix for usability. +For many researchers who use REDCap, the R language [@r_citation] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "block matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving and analysts with the arduous task of reformatting the matrix for usability. To address these challenges, we developed `REDCapTidieR` as an open source R package that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to quickly query their data without the need for intricate data parsing processes. # 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 from the introduction 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 studies like clinical trials, 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 from the introduction 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 studies like clinical trials, 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. -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. | Package | Data Export Support | Data Import Support | Data Manipulation | Data Tidying | |-------------|--------------------|--------------------|------------------|--------------| @@ -70,9 +70,11 @@ While there are a few existing REDCap tools for R documented by [`REDCap-tools`] # Design -Transformation of the Block Matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/), is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data, metadata, or useful information about that instrument itself. Below, we offer a conceptual model of how `REDCapTidieR` transforms the Block Matrix into the supertibble: +Transformation of the block matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/), is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data (a "data tibble"), metadata, or useful information about that instrument itself. -> Image to be provided +Unlike the block matrix, which combines all columns for record identification into one table, `REDCapTidieR` separates instruments so that only the variables necessary for identification of a record within the instrument are included in each data tibble. Below we provide a sample model that compares the standard output from a REDCap database with non-repeating and repeating instruments to one post-processed through `REDCapTidieR`. + +![Conceptual Model](/paper/images/REDCapTidieR%20JOSS.png) # Installation From 7473dc2aaa52911f43a9f94d4d93902f47891a68 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Wed, 8 Nov 2023 12:10:38 -0500 Subject: [PATCH 06/16] Address CRAN comment with link in vignette --- cran-comments.md | 2 +- vignettes/REDCapTidieR.Rmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cran-comments.md b/cran-comments.md index 4caad87f..1a8df51e 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -2,7 +2,7 @@ Thank you for taking the time to review this submission and please reach out to either Stephan Kadauke, Richard Hanna, or Ezra Porter for any questions, comments, or concerns. -This submission confirms the stability of the package and updates it to 1.0 with a couple of minor updates that coincide with other dependencies. +This submission confirms the stability of the package and updates it to 1.0 with a couple of minor updates that coincide with other dependencies. It also addresses a request to fix a URL that gets missed in `.Rbuildignore` for an article. ## New Features diff --git a/vignettes/REDCapTidieR.Rmd b/vignettes/REDCapTidieR.Rmd index 04f9fbec..884c4b0b 100644 --- a/vignettes/REDCapTidieR.Rmd +++ b/vignettes/REDCapTidieR.Rmd @@ -26,7 +26,7 @@ redcap_uri <- Sys.getenv("REDCAP_URI") token <- Sys.getenv("SUPERHEROES_REDCAP_API") ``` -[REDCap](https://www.project-redcap.org/) is an electronic data capture software that is widely used in the academic research community. The [REDCapR](https://ouhscbbmc.github.io/REDCapR/) package streamlines calls to the REDCap API from an R environment. One of REDCapR's main uses is to [import](glossary.html#import) [records](glossary.html#record) from a REDCap [project](glossary.html#project). This works well for simple projects, however becomes [ugly](diving_deeper.html) when complex databases that include [longitudinal](glossary.html#longitudinal-project) structure and/or [repeating](glossary.html#repeating) [instruments](glossary.html#instrument) are involved. +[REDCap](https://www.project-redcap.org/) is an electronic data capture software that is widely used in the academic research community. The [REDCapR](https://ouhscbbmc.github.io/REDCapR/) package streamlines calls to the REDCap API from an R environment. One of REDCapR's main uses is to [import](glossary.html#import) [records](glossary.html#record) from a REDCap [project](glossary.html#project). This works well for simple projects, however becomes ugly when complex databases that include [longitudinal](glossary.html#longitudinal-project) structure and/or [repeating](glossary.html#repeating) [instruments](glossary.html#instrument) are involved. The REDCapTidieR package aims to make the life of analysts who deal with complex REDCap databases easier. It builds upon **REDCapR** to make its output **tidier**. Instead of [one large data frame that contains all the data from your project](glossary.html#block-matrix), you get to work with a set of [tidy](glossary.html#tidy) [tibbles](glossary.html#tibble), one for each REDCap [instrument](glossary.html#instrument). From 52156ad38252c4ef854de5a13dde46be87052812 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Wed, 8 Nov 2023 15:29:12 -0500 Subject: [PATCH 07/16] Tidy up draft, add to Design section --- .Rbuildignore | 1 + paper/paper.bib | 41 +++++++++++++++++++++++++++++++++++------ paper/paper.md | 11 +++++++++-- 3 files changed, 45 insertions(+), 8 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index 1e67d4e3..87ca042c 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -15,6 +15,7 @@ ^cran-comments\.md$ README.html$ +paper/ pkgdown/ utility/ lastMiKTeXException/ diff --git a/paper/paper.bib b/paper/paper.bib index c06fa947..3c6da259 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -41,10 +41,39 @@ @article{Wickham2014 } @Manual{r_citation, - title = {R: A Language and Environment for Statistical Computing}, - author = {{R Core Team}}, - organization = {R Foundation for Statistical Computing}, - address = {Vienna, Austria}, - year = {2020}, - url = {https://www.R-project.org/}, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2020}, + url = {https://www.R-project.org/}, +} + +@Manual{redcapr_cit, + title = {REDCapR: Interaction Between R and REDCap}, + author = {{Beasley, Will}}, + organization = {Biomedical and Behavioral Methodology Core (University of Oklahoma Health Sciences Center)}, + address = {Oklahoma City, Oklahoma}, + year = {2022}, + url = {https://cran.r-project.org/web/packages/REDCapR/index.html}, +} + +@Manual{redcapapi_cit, + title = {redcapAPI: Interface to 'REDCap'}, + author = {{Garbett, Shawn}}, + organization = {Vanderbilt Biostatistics}, + address = {Nashville, Tennessee}, + year = {2023}, + url = {https://cran.r-project.org/web/packages/redcapAPI/index.html}, +} + +@Article{tidyverse_cit, + title = {Welcome to the {tidyverse}}, + author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani}, + year = {2019}, + journal = {Journal of Open Source Software}, + volume = {4}, + number = {43}, + pages = {1686}, + doi = {10.21105/joss.01686}, } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index bb1b67c0..33259bc7 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -50,13 +50,13 @@ bibliography: paper.bib Capturing and storing electronic data is integral in the research world, yet often becomes a burden to the researchers themselves. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. -For many researchers who use REDCap, the R language [@r_citation] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "block matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the block Matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving and analysts with the arduous task of reformatting the matrix for usability. +For many researchers who use REDCap, the R language [@r_citation] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) [@redcapr_cit] and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) [@redcapapi_cit] packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "block matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. Fundamentally, the block matrix breaks these tidy principles by obscuring the primary keys that identify individual records, leaving analysts with the arduous task of reformatting the matrix for usability. To address these challenges, we developed `REDCapTidieR` as an open source R package that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to quickly query their data without the need for intricate data parsing processes. # 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 from the introduction 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 studies like clinical trials, 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." 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. 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. @@ -75,6 +75,9 @@ Transformation of the block matrix into a friendlier structure is carried out by Unlike the block matrix, which combines all columns for record identification into one table, `REDCapTidieR` separates instruments so that only the variables necessary for identification of a record within the instrument are included in each data tibble. Below we provide a sample model that compares the standard output from a REDCap database with non-repeating and repeating instruments to one post-processed through `REDCapTidieR`. ![Conceptual Model](/paper/images/REDCapTidieR%20JOSS.png) +Figure 1: Comparative model showing REDCap API export formats between the default behavior and `REDCapTidieR`. + +In this example, the supertibble displays three REDCap database instruments, with one repeating and two non-repeating. Below, one of each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work. # Installation @@ -82,6 +85,10 @@ Unlike the block matrix, which combines all columns for record identification in # Acknowledgements +`REDCapTidieR` is made possible in large part thanks to the `REDCapR` and `tidyverse` [@tidyverse_cit] packages. + +The authors would also like to give special thanks to Will Beasley, Paul Wildenhain, and Jan Marvin for their feedback and support in development. + # Conflict of interest This package was developed by the [Children’s Hospital of From afed5ac432125c0504e0994cae3c593a36bb3ba2 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Mon, 13 Nov 2023 10:35:17 -0500 Subject: [PATCH 08/16] Apply feedback :pencil: - reference vignettes - reference integration with labelled, openxlsx2, skimr - reference openssf - production deployment nod - table update - new citations --- paper/paper.bib | 52 ++++++++++++++++++++++++++++++++++++++----------- paper/paper.md | 20 ++++++++++++------- 2 files changed, 54 insertions(+), 18 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 3c6da259..593f11ec 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -50,21 +50,42 @@ @Manual{r_citation } @Manual{redcapr_cit, - title = {REDCapR: Interaction Between R and REDCap}, - author = {{Beasley, Will}}, - organization = {Biomedical and Behavioral Methodology Core (University of Oklahoma Health Sciences Center)}, - address = {Oklahoma City, Oklahoma}, - year = {2022}, - url = {https://cran.r-project.org/web/packages/REDCapR/index.html}, + title = {REDCapR: Interaction Between R and REDCap}, + author = {Will Beasley}, + year = {2023}, + note = {https://ouhscbbmc.github.io/REDCapR/, https://github.com/OuhscBbmc/REDCapR, https://www.ouhsc.edu/bbmc/, https://project-redcap.org}, } @Manual{redcapapi_cit, - title = {redcapAPI: Interface to 'REDCap'}, - author = {{Garbett, Shawn}}, - organization = {Vanderbilt Biostatistics}, - address = {Nashville, Tennessee}, + title = {{redcapAPI}: Accessing data from REDCap projects using the API}, + author = {Benjamin Nutter and Stephen Lane}, year = {2023}, - url = {https://cran.r-project.org/web/packages/redcapAPI/index.html}, + doi = {10.5281/zenodo.11826}, + note = {R package version 2.8.0}, + url = {https://github.com/nutterb/redcapAPI/wiki}, +} + +@Manual{labelled_cit, + title = {labelled: Manipulating Labelled Data}, + author = {Joseph Larmarange}, + year = {2023}, + note = {R package version 2.12.0}, + url = {https://larmarange.github.io/labelled/}, +} + +@Manual{openxlsx2_cit, + title = {openxlsx2: Read, Write and Edit 'xlsx' Files}, + year = {2023}, + note = {R package version 1.1}, + url = {https://janmarvin.github.io/openxlsx2/}, +} + +@Manual{skimr_cit, + title = {skimr: Compact and Flexible Summaries of Data}, + author = {Elin Waring and Michael Quinn and Amelia McNamara and Eduardo {Arino de la Rubia} and Hao Zhu and Shannon Ellis}, + year = {2023}, + note = {R package version 2.1.5, https://github.com/ropensci/skimr/}, + url = {https://docs.ropensci.org/skimr/ (website)}, } @Article{tidyverse_cit, @@ -76,4 +97,13 @@ @Article{tidyverse_cit number = {43}, pages = {1686}, doi = {10.21105/joss.01686}, +} + +@Misc{openssf_cit, + title = {OpenOpen Source Security Foundation_2023}, + url={https://openssf.org/}, + journal={Open Source Security Foundation}, + publisher={The Linux Foundation}, + year={2023}, + month={Oct} } \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md index 33259bc7..d599763c 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -60,13 +60,17 @@ As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 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. -| Package | Data Export Support | Data Import Support | Data Manipulation | Data Tidying | -|-------------|--------------------|--------------------|------------------|--------------| -| redcapAPI | x | x | | | -| REDCapR | x | x | | | -| tidyREDCap | x | | x | | -| REDCapDM | x | | x | | -| REDCapTidieR| x | | x | x | +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 | + +Table 1: Comparative breakdown of the landscape for REDCap tools in R. # Design @@ -79,6 +83,8 @@ Figure 1: Comparative model showing REDCap API export formats between the defaul In this example, the supertibble displays three REDCap database instruments, with one repeating and two non-repeating. Below, one of each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work. +Additionally, REDCapTidieR comes equipped with features that address common requirements of analysts. Seamless integration with the `labelled` [@labelled_cit] package facilitates effortless application of variable labels to both data and metadata. An extension utilizing the `skimr` [@skimr_cit] package provides comprehensive metric summaries of metadata for exported REDCap databases. Lastly, through an extension leveraging the `openxlsx2` [@openxlsx2_cit] package, users can easily export REDCapTidieR data tibbles to individual XLSX sheets. + # Installation `REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems. 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z?o-p;{aEkM-UT{y)3cmE-IM=z!^8h-!2e&YiXZZu?0za5!r)(Zz>~1X@Wc35f9`*JHw|}Dy~5M zpFI)NSm($kIpm*|Ldn)-qi`DWPqsdOx8{w39rf>@e_w%r?G?D5Fe==zv`u!&CZeSt P`L9ozo;gl5c8mHCZ~6@S 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. 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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 more than 150 countries. REDCap databases exhibit significant variation in complexity, ranging from simple tables with easily identifiable records to more comprehensive builds 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 simply put 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 block matrix can be a major 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 to more comprehensive builds where determining unique record identifiers becomes challenging. 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 simply put 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 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 returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, `REDCapTidieR` is unique in how it restructures the block matrix into an easily interpretable format 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 production environment deployment as a key consideration. To ensure package stability, 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. The package was also developed in alignment with the [OpenSSF Best Practices Badging program](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], certifying open source projects adhering to criteria for delivering high-quality, secure software. +REDCapTidieR was developed with production environment deployment in mind. To ensure package stability, we've implemented an extensive test suite that exhibits 98% code coverage as of the 1.0 version 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. The package was also developed in alignment with the [OpenSSF Best Practices Badging program](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], certifying open source projects adhering to criteria for delivering high-quality, secure software. | Package | Data Export Support | Data Import Support | Data Manipulation | Tidy Reformatting | Production Ready | |-------------|---------------------|---------------------|-------------------|-------------------| ---------------- | @@ -74,14 +74,14 @@ Table 1: Comparative breakdown of the landscape for REDCap tools in R. # Design -Transformation of the block matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/) [@tibble_cit], is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data (a nested "data tibble"), metadata, or useful information about that instrument itself. +Transformation of the block matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/) [@tibble_cit], is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data (i.e. a nested "data tibble"), metadata, or useful information about that instrument itself. -Unlike the block matrix, which combines all columns for record identification into one table, `REDCapTidieR` separates instruments into individual data tibbles so that only the variables necessary for identification of a record within the instrument are included. Below, we provide a model that compares the standard output from a REDCap database with non-repeating and repeating instruments to one post-processed through `REDCapTidieR` using a sample of the open source [Superhero Database](https://www.superherodb.com/). +Unlike the block matrix, which combines all columns for record identification into one table, `REDCapTidieR` separates instruments into individual data tibbles to include only the variables necessary for identification of a record within the instrument. Below, we provide a model that compares the standard output from a REDCap database with non-repeating and repeating instruments to one post-processed through `REDCapTidieR` using a sample of the open source [Superhero Database](https://www.superherodb.com/). ![Conceptual Model](/paper/images/REDCapTidieR%20JOSS%20-%20Superheroes.png) Figure 1: Comparative model showing REDCap API export formats between the default behavior and `REDCapTidieR` -In this example, the supertibble displays two REDCap database instruments, with demographic heroes information as non-repeating and corresponding hero powers as repeating. Below, one of each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work. +In this example, the supertibble displays two REDCap database instruments, with demographic heroes information as non-repeating and corresponding hero powers as repeating. Each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work. Additionally, REDCapTidieR comes equipped with features that address common requirements of analysts. Seamless integration with the `labelled` [@labelled_cit] package facilitates effortless application of variable labels to both data and metadata. An extension utilizing the `skimr` [@skimr_cit] package provides comprehensive metric summaries of metadata for exported REDCap databases. Lastly, through an extension leveraging the `openxlsx2` [@openxlsx2_cit] package, users can easily export REDCapTidieR data tibbles to individual XLSX sheets. 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z?<`VB6)oRu?pn5t$gnsWtJXzLmuWi^+pE)uU41LLZ^eIw7MX0@JuI2=zx+MRII)EL zob2$`b%aJ8<*u23YPa@01o=ubL(ZMyi{YZ=N-a(JVukOLZmuQ&_$~^*OZWf3{z>% diff --git a/paper/paper.bib b/paper/paper.bib index 41a7f663..6b34eb3b 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -129,4 +129,25 @@ @Misc{openssf_cit publisher={The Linux Foundation}, year={2023}, month={Oct} -} \ No newline at end of file +} + +@Manual{redcapr_write_cit, + title = {Writing to a REDCap Project}, + author = {Will Beasley and Raymond Balise}, + year = {2023}, + url = {https://ouhscbbmc.github.io/REDCapR/articles/workflow-write.html} +} + +@Manual{redcaptidier_pkgdown_cit, + title = {REDCapTidieR}, + author = {Richard Hanna and Ezra Porter and Stephan Kadauke}, + url = {https://chop-cgtinformatics.github.io/REDCapTidieR/index.html}, + year = {2023} +} + +@Manual{superheroes_cit, + title = {Superhero Database}, + url = {https://www.superherodb.com/}, + author = {Jeroen ter Lingen}, + year = {2023} +} diff --git a/paper/paper.html b/paper/paper.html new file mode 100644 index 00000000..6a22b3d0 --- /dev/null +++ b/paper/paper.html @@ -0,0 +1,700 @@ + + + + + + + + + + + + + +REDCapTidieR: Extracting complex REDCap databases into tidy tables + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/paper/paper.md b/paper/paper.md index 055862f2..dc76308a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -7,26 +7,26 @@ tags: authors: - name: Richard Hanna orcid: 0009-0005-6496-8154 - equal-contrib: true + equal-contrib: false affiliation: "1" - name: Ezra Porter orcid: 0000-0002-4690-8343 - equal-contrib: true + equal-contrib: false affiliation: "1" - name: Stephany Romero - equal-contrib: true + equal-contrib: false affiliation: "1" - name: Paul Wildenhain - equal-contrib: true + equal-contrib: false affiliation: "6" - name: Wiliam Beasley orcid: 0000-0002-5613-5006 - equal-contrib: true + equal-contrib: false affiliation: "7" - name: Stephan Kadauke orcid: 0000-0003-2996-8034 - equal-contrib: true - affiliation: "1, 2, 3, 4, 5" + equal-contrib: false + affiliation: "2, 3, 4, 5" affiliations: - name: Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania index: 1 @@ -48,23 +48,25 @@ bibliography: paper.bib # Summary -Capturing and storing electronic data is integral in the research world, yet often becomes a burden to the researchers themselves. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] alleviates this problem by offering a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. +Capturing and storing electronic data is integral in the research world. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] offers a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. -For many researchers who use REDCap, the R programming language [@r_cit] is a powerful tool for extracting and analyzing their data. To take advantage of REDCap's REST API, the [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) [@redcapr_cit] and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) [@redcapapi_cit] packages allow R users to extract data directly into their programming environment. The default extraction structure for a given REDCap database is referred to as the "block matrix," and is a singular, unwieldy, and "untidy" data table. The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for standard mapping and structuring of data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. The block matrix structure breaks these tidy principles by obscuring the primary keys that identify individual records, leaving analysts with the arduous task of reformatting the matrix for usability. +Many REDCap users use the R programming language [@r_cit] to extract and analyze their data. The [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) [@redcapr_cit] and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) [@redcapapi_cit] packages allow R users to extract data directly into their programming environment. While this works well for simple REDCap databases, it becomes cumbersome for complex databases, because the REDCap API returns a single table with rows that represent different levels of granularity, termed the "block matrix". The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for a standardized data structure where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. The block matrix structure is inconsistent with this standard. -To address these challenges, we developed `REDCapTidieR` as an open source R package that transforms the standard REDCap output into a format that adheres to tidy data principles. `REDCapTidieR` has the potential to save organizations and research staff immeasurable amounts of time, allowing them to quickly query their data without the need for intricate data parsing processes. +We provide the open-source `REDCapTidieR` package as a solution for this problem. `REDCapTidieR` leverages the `REDCapR` package to query data and metadata through the REDCap API and returns a tidy data structure that is easy to explore in the RStudio Data Viewer, aiding users in data exploration. `REDCapTidieR` also provides utility functions to extract data from individual instruments, add summary statistics, and export collaborator-friendly Excel documents. We have developed and extensive automated test suite to ensure that `REDCapTidieR` can be used with any REDCap database of any complexity. However, we believe that researchers and analysts who work with complex databases, such as those used for longitudinal clinical trials, will benefit the most by eliminating the need for data parsing and pre-processing. # 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 to more comprehensive builds where determining unique record identifiers becomes challenging. 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 simply put 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 block matrix can be a major pain point for analysts. +As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 countries. REDCap databases range from single-instrument projects to complex builds that use both repeating instruments and repeating events. These data structures are needed to capture multiple items related to a specific visit, such as concomitant medications, or events that cannot be planned ahead of time, such as adverse events. -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 returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, `REDCapTidieR` is unique in how it restructures the block matrix into an easily interpretable format 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. +REDCap databases that contain repeating events and instruments require significant manual pre-processing, a major pain point for researchers and analysts. This is because the REDCap API returns a single table (Figure 1) that includes data from instruments that record data at different levels of granularity. -REDCapTidieR was developed with production environment deployment in mind. To ensure package stability, we've implemented an extensive test suite that exhibits 98% code coverage as of the 1.0 version 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. The package was also developed in alignment with the [OpenSSF Best Practices Badging program](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], certifying open source projects adhering to criteria for delivering high-quality, secure software. +While there are a few existing REDCap tools (Table 1), `REDCapTidieR` occupies a unique space by providing analysts with a framework returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, only `REDCapTidieR` restructures the block matrix into an easy to use format. + +`REDCapTidieR` is built with production readiness in mind. It builds upon REDCapR, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. | Package | Data Export Support | Data Import Support | Data Manipulation | Tidy Reformatting | Production Ready | -|-------------|---------------------|---------------------|-------------------|-------------------| ---------------- | -| redcapAPI | x | x | | | | +|-------------|---------------------|---------------------|-------------------|-------------------|------------------| +| redcapAPI | x | x | | | x | | REDCapR | x | x | | | x | | tidyREDCap | x | | x | | | | REDCapDM | x | | x | | | @@ -74,36 +76,28 @@ Table 1: Comparative breakdown of the landscape for REDCap tools in R. # Design -Transformation of the block matrix into a friendlier structure is carried out by `REDCapTidieR` through a series of complex operations that result in the "supertibble." The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/) [@tibble_cit], is presented as a table where each row corresponds to a REDCap instrument and each column corresponds to either that instrument's post-processed data (i.e. a nested "data tibble"), metadata, or useful information about that instrument itself. +The `REDCapTidieR::read_redcap()` function leverages `REDCapR` to make API calls to query the data and metadata of a REDCap project and returns the supertibble (Figure 1). The supertibble, named after the [`tibble` package](https://tibble.tidyverse.org/) [@tibble_cit], is an alternative presentation of the data in which multiple tables are linked together in a single object in a fashion consistent with tidy data principles. -Unlike the block matrix, which combines all columns for record identification into one table, `REDCapTidieR` separates instruments into individual data tibbles to include only the variables necessary for identification of a record within the instrument. Below, we provide a model that compares the standard output from a REDCap database with non-repeating and repeating instruments to one post-processed through `REDCapTidieR` using a sample of the open source [Superhero Database](https://www.superherodb.com/). +![The REDCapTidieR Supertibble](images/Figure1.png) -![Conceptual Model](/paper/images/REDCapTidieR%20JOSS%20-%20Superheroes.png) -Figure 1: Comparative model showing REDCap API export formats between the default behavior and `REDCapTidieR` +Figure 1: The REDCapTidieR Supertibble. The "Superhero" database[@superheroes_cit] contains two instruments, one nonrepeating and one repeating. A. The REDCap API returns a "Block Matrix". Note an abundance of `NA` values, which do not represent missing values but rather fields that do not apply due to the data structure. B. The `read_redcap()` function returns a "Supertibble". Note that each row represents one instrument, identified by the `redcap_form_name` column. The `redcap_data` column is a list column that links to tibbles containing the data from a specific instrument. Since each instrument has a consistent granularity, these tibbles can be tidy. Two data tibbles are shown, one from a nonrepeating and one from a repeating instrument. Note the differences in granularity between the instruments. -In this example, the supertibble displays two REDCap database instruments, with demographic heroes information as non-repeating and corresponding hero powers as repeating. Each of these instrument types is expanded to show how `REDCapTidieR` separates these instruments into their own tabular list elements structured with only the identifiers necessary to pinpoint a specific record. This format makes tables easily joinable by analysts for whatever operations they may need later in their work. +`REDCapTidieR` provides utility functions to work with the supertibble, all designed to work with the R pipe `|>`. The `extract_tibble()` function takes a supertibble object and returns a specific data tibble. The `make_labelled()` function leverages the `labelled` package [@labelled_cit] to apply variable labels to the supertibble. The `add_skimr_metadata()` function uses the `skimr` package [@skimr_cit] to add summary statistics. Using the `write_redcap_xlsx()` function, which leverages the `openxlsx2` [@openxlsx2_cit] package, users can easily export an the supertibble into a collaborator-friendly Excel document, in which each Excel sheet contains the data for an instrument. -Additionally, REDCapTidieR comes equipped with features that address common requirements of analysts. Seamless integration with the `labelled` [@labelled_cit] package facilitates effortless application of variable labels to both data and metadata. An extension utilizing the `skimr` [@skimr_cit] package provides comprehensive metric summaries of metadata for exported REDCap databases. Lastly, through an extension leveraging the `openxlsx2` [@openxlsx2_cit] package, users can easily export REDCapTidieR data tibbles to individual XLSX sheets. +`REDCapTidieR` cannot be used to write data to a REDCap project. We refer the reader to an excellent guide of how to accomplish this using `REDCapR` [@redcapr_write_cit]. # Installation -`REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and has been tested for functionality on all major operating systems. +`REDCapTidieR` is available on [GitHub](https://github.com/CHOP-CGTInformatics/REDCapTidieR) and [CRAN](https://cran.r-project.org/web/packages/REDCapTidieR/index.html) and works on all major operating systems. # Acknowledgements -`REDCapTidieR` is made possible in large part thanks to the `REDCapR` and `tidyverse` [@tidyverse_cit] packages. +We would like to thank Will Beasley, Paul Wildenhain, and Jan Marvin for their feedback and support in development. -The authors would also like to give special thanks to Will Beasley, Paul Wildenhain, and Jan Marvin for their feedback and support in development. +This package was developed by the [Cell and Gene Therapy Informatics Team](https://www.chop.edu/centers-programs/cell-and-gene-therapy-informatics-team/our-team) of the [Children’s Hospital of Philadelphia](https://www.chop.edu). # Conflict of interest -This package was developed by the [Children’s Hospital of -Philadelphia](https://www.chop.edu) Cell and Gene Therapy Informatics -Team to support the needs of the [Cellular Therapy and Transplant -Section](https://www.chop.edu/centers-programs/cellular-therapy-and-transplant-section). -The development was funded using the following sources: - -- *Stephan Kadauke Start-up funds.* Stephan Kadauke, PI, CHOP, 2018-2024 +The authors declare no financial conflicts of interest. -- *CHOP-based GMP cell manufacturing (MFG) for CAR T clinical trials*. - Stephan Grupp, PI; Stephan Kadauke, co-PI, CHOP, 2021-2023 +# References From 67847a50b27270e9982f5f1a71ec75987ab4c848 Mon Sep 17 00:00:00 2001 From: Stephan Kadauke Date: Thu, 23 Nov 2023 10:26:40 -0500 Subject: [PATCH 13/16] Incorporate Stephany's suggestions --- paper/paper.bib | 1 + paper/paper.html | 162 +++++++++++++++++++++++------------------------ paper/paper.md | 28 ++++---- 3 files changed, 94 insertions(+), 97 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 6b34eb3b..9a96da61 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -53,6 +53,7 @@ @Manual{redcapr_cit title = {REDCapR: Interaction Between R and REDCap}, author = {Will Beasley}, year = {2023}, + url = {https://ouhscbbmc.github.io/REDCapR/}, note = {https://ouhscbbmc.github.io/REDCapR/, https://github.com/OuhscBbmc/REDCapR, https://www.ouhsc.edu/bbmc/, https://project-redcap.org}, } diff --git a/paper/paper.html b/paper/paper.html index 6a22b3d0..ced32ac5 100644 --- a/paper/paper.html +++ b/paper/paper.html @@ -391,27 +391,23 @@

Summary

(Nutter and Lane 2023) packages allow R users to extract data directly into their programming environment. While this works well for simple REDCap databases, it becomes cumbersome for -complex databases, because the REDCap API returns a single table with -rows that represent different levels of granularity, termed the “block -matrix”. The concept of “tidy data” -(Wickham 2014) describes a framework for a -standardized data structure where each variable forms a column, each -observation forms a row, and each type of observational unit forms a -table. The block matrix structure is inconsistent with this -standard.

-

We provide the open-source REDCapTidieR package as a -solution for this problem. REDCapTidieR leverages the -REDCapR package to query data and metadata through the -REDCap API and returns a tidy data structure that is easy to explore in -the RStudio Data Viewer, aiding users in data exploration. -REDCapTidieR also provides utility functions to extract -data from individual instruments, add summary statistics, and export -collaborator-friendly Excel documents. We have developed and extensive -automated test suite to ensure that REDCapTidieR can be -used with any REDCap database of any complexity. However, we believe -that researchers and analysts who work with complex databases, such as -those used for longitudinal clinical trials, will benefit the most by -eliminating the need for data parsing and pre-processing.

+complex databases, because the REDCap API outputs a “block matrix”–a +single table with varied granularity levels, which conflicts with the +“tidy data” framework (Wickham 2014) that +advocates for standardized data organization.

+

To address this, we introduce REDCapTidieR, an +open-source package that streamlines data extraction and restructures it +into an intuitive format compatible with the tidy data principles. This +facilitates seamless data analysis in R, especially for complex +longitudinal studies.

+

While there are several tools available for REDCap data management, +REDCapTidieR introduces a unique solution by transforming the +challenging block matrix into a standardized tidy data structure that we +term the “supertibble”. This approach not only aligns with good data +science practice but also caters to databases of any complexity. By +providing a suite of utility functions to work with the supertibble, +REDCapTidieR provides a complete framework for extracting REDCap data +designed with user-friendliness at its core.

Statement of Need

@@ -436,74 +432,67 @@

Statement of Need

REDCapTidieR restructures the block matrix into an easy to use format.

REDCapTidieR is built with production readiness in mind. -It builds upon REDCapR, which contains an excellent test suite, to make -API calls, and includes an extensive automated test suite and ample -documentation through a pkgdown site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) +It builds upon REDCapR, which contains an excellent test +suite, to make API calls, and includes an extensive automated test suite +and ample documentation through a pkgdown site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) (Hanna, Porter, and Kadauke 2023). It meets the rigorous requirements of the OpenSSF Best Practices Badge (“OpenOpen Source Security Foundation_2023” 2023), which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software.

- +
------+++++ - - - - - + + + + - - - - - + + + + - - - - - + + + + - - - - - + + + + - - - - - + + + + - - - - - + + + +
PackageData Export SupportData Import SupportData ManipulationTidy ReformattingProduction ReadyExports from REDCapImports into REDCapTidy ReformattingExtensive Test Suite
redcapAPIxxxxxx
REDCapRxxxxxx
tidyREDCapxxx
REDCapDMxxx
REDCapTidieRxxxxxxx
@@ -524,32 +513,37 @@

Design

The REDCapTidieR Supertibble
The REDCapTidieR Supertibble
-

Figure 1: The REDCapTidieR Supertibble. The “Superhero” database(Lingen 2023) contains two instruments, one -nonrepeating and one repeating. A. The REDCap API returns a “Block -Matrix”. Note an abundance of NA values, which do not -represent missing values but rather fields that do not apply due to the -data structure. B. The read_redcap() function returns a -“Supertibble”. Note that each row represents one instrument, identified -by the redcap_form_name column. The -redcap_data column is a list column that links to tibbles -containing the data from a specific instrument. Since each instrument -has a consistent granularity, these tibbles can be tidy. Two data -tibbles are shown, one from a nonrepeating and one from a repeating +

Figure 1: The REDCapTidieR supertibble shown in the Data Viewer of +the RStudio IDE. The “Superhero database” (Lingen +2023) contains two instruments, one nonrepeating and one +repeating. A. The REDCap API outputs a “Block Matrix”. Note an abundance +of NA values, which do not represent missing values but +rather fields that do not apply due to the data structure. B. The +read_redcap() function returns a “Supertibble”. Note that +each row represents one instrument, identified by the +redcap_form_name column. The redcap_data +column is a list column that links to tibbles containing the data from a +specific instrument. The Data Viewer allows drilling down into +individual tibbles by clicking on the table icon, allowing for rapid and +intuitive data exploration without any preprocessing. Since each +instrument has a consistent granularity, these tibbles can be tidy. Two +data tibbles are shown, one from a nonrepeating and one from a repeating instrument. Note the differences in granularity between the instruments.

REDCapTidieR provides utility functions to work with the -supertibble, all designed to work with the R pipe |>. -The extract_tibble() function takes a supertibble object -and returns a specific data tibble. The make_labelled() -function leverages the labelled package (Larmarange 2023) to apply variable labels to -the supertibble. The add_skimr_metadata() function uses the -skimr package (Waring et al. -2023) to add summary statistics. Using the -write_redcap_xlsx() function, which leverages the -openxlsx2 (Openxlsx2: Read, -Write and Edit ’Xlsx’ Files 2023) package, users can easily -export an the supertibble into a collaborator-friendly Excel document, -in which each Excel sheet contains the data for an instrument.

+supertibble, all designed to work with the R pipe operator +|>. The extract_tibble() function takes a +supertibble object and returns a specific data tibble. The +make_labelled() function leverages the +labelled package (Larmarange +2023) to apply variable labels to the supertibble. The +add_skimr_metadata() function uses the skimr +package (Waring et al. 2023) to add +summary statistics. Using the write_redcap_xlsx() function, +which leverages the openxlsx2 (Openxlsx2: Read, Write and Edit ’Xlsx’ Files +2023) package, users can easily export an the supertibble into a +collaborator-friendly Excel document, in which each Excel sheet contains +the data for an instrument.

REDCapTidieR cannot be used to write data to a REDCap project. We refer the reader to an excellent guide of how to accomplish this using REDCapR (Beasley and diff --git a/paper/paper.md b/paper/paper.md index dc76308a..b13e99e2 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -19,7 +19,7 @@ authors: - name: Paul Wildenhain equal-contrib: false affiliation: "6" - - name: Wiliam Beasley + - name: William Beasley orcid: 0000-0002-5613-5006 equal-contrib: false affiliation: "7" @@ -50,9 +50,11 @@ bibliography: paper.bib Capturing and storing electronic data is integral in the research world. [REDCap](https://www.project-redcap.org/) [@Harris2009; @Harris2019] offers a secure web application that lets users build databases and surveys with a robust front-end interface that can support data of any type, including data requiring compliance with standards for protected information. -Many REDCap users use the R programming language [@r_cit] to extract and analyze their data. The [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) [@redcapr_cit] and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) [@redcapapi_cit] packages allow R users to extract data directly into their programming environment. While this works well for simple REDCap databases, it becomes cumbersome for complex databases, because the REDCap API returns a single table with rows that represent different levels of granularity, termed the "block matrix". The concept of "[tidy data](https://www.jstatsoft.org/article/view/v059i10)" [@Wickham2014] describes a framework for a standardized data structure where each variable forms a column, each observation forms a row, and each type of observational unit forms a table. The block matrix structure is inconsistent with this standard. +Many REDCap users use the R programming language [@r_cit] to extract and analyze their data. The [`REDCapR`](https://cran.r-project.org/web/packages/REDCapR/index.html) [@redcapr_cit] and [`redcapAPI`](https://cran.r-project.org/web/packages/redcapAPI/index.html) [@redcapapi_cit] packages allow R users to extract data directly into their programming environment. While this works well for simple REDCap databases, it becomes cumbersome for complex databases, because the REDCap API outputs a "block matrix"--a single table with varied granularity levels, which conflicts with the "tidy data" framework [@Wickham2014] that advocates for standardized data organization. -We provide the open-source `REDCapTidieR` package as a solution for this problem. `REDCapTidieR` leverages the `REDCapR` package to query data and metadata through the REDCap API and returns a tidy data structure that is easy to explore in the RStudio Data Viewer, aiding users in data exploration. `REDCapTidieR` also provides utility functions to extract data from individual instruments, add summary statistics, and export collaborator-friendly Excel documents. We have developed and extensive automated test suite to ensure that `REDCapTidieR` can be used with any REDCap database of any complexity. However, we believe that researchers and analysts who work with complex databases, such as those used for longitudinal clinical trials, will benefit the most by eliminating the need for data parsing and pre-processing. +To address this, we introduce `REDCapTidieR`, an open-source package that streamlines data extraction and restructures it into an intuitive format compatible with the tidy data principles. This facilitates seamless data analysis in R, especially for complex longitudinal studies. + +While there are several tools available for REDCap data management, REDCapTidieR introduces a unique solution by transforming the challenging block matrix into a standardized tidy data structure that we term the "supertibble". This approach not only aligns with good data science practice but also caters to databases of any complexity. By providing a suite of utility functions to work with the supertibble, REDCapTidieR provides a complete framework for extracting REDCap data designed with user-friendliness at its core. # Statement of Need @@ -62,15 +64,15 @@ REDCap databases that contain repeating events and instruments require significa While there are a few existing REDCap tools (Table 1), `REDCapTidieR` occupies a unique space by providing analysts with a framework returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, only `REDCapTidieR` restructures the block matrix into an easy to use format. -`REDCapTidieR` is built with production readiness in mind. It builds upon REDCapR, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. +`REDCapTidieR` is built with production readiness in mind. It builds upon `REDCapR`, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. -| Package | Data Export Support | Data Import Support | Data Manipulation | Tidy Reformatting | Production Ready | -|-------------|---------------------|---------------------|-------------------|-------------------|------------------| -| redcapAPI | x | x | | | x | -| REDCapR | x | x | | | x | -| tidyREDCap | x | | x | | | -| REDCapDM | x | | x | | | -| REDCapTidieR| x | | x | x | x | +| Package | Exports from REDCap | Imports into REDCap | Tidy Reformatting | Extensive Test Suite | +|-------------|:-------------------:|:-------------------:|:-----------------:|:--------------------:| +| redcapAPI | x | x | | x | +| REDCapR | x | x | | x | +| tidyREDCap | x | | | | +| REDCapDM | x | | | | +| REDCapTidieR| x | | x | x | Table 1: Comparative breakdown of the landscape for REDCap tools in R. @@ -80,9 +82,9 @@ The `REDCapTidieR::read_redcap()` function leverages `REDCapR` to make API calls ![The REDCapTidieR Supertibble](images/Figure1.png) -Figure 1: The REDCapTidieR Supertibble. The "Superhero" database[@superheroes_cit] contains two instruments, one nonrepeating and one repeating. A. The REDCap API returns a "Block Matrix". Note an abundance of `NA` values, which do not represent missing values but rather fields that do not apply due to the data structure. B. The `read_redcap()` function returns a "Supertibble". Note that each row represents one instrument, identified by the `redcap_form_name` column. The `redcap_data` column is a list column that links to tibbles containing the data from a specific instrument. Since each instrument has a consistent granularity, these tibbles can be tidy. Two data tibbles are shown, one from a nonrepeating and one from a repeating instrument. Note the differences in granularity between the instruments. +Figure 1: The REDCapTidieR supertibble shown in the Data Viewer of the RStudio IDE. The "Superhero database" [@superheroes_cit] contains two instruments, one nonrepeating and one repeating. A. The REDCap API outputs a "Block Matrix". Note an abundance of `NA` values, which do not represent missing values but rather fields that do not apply due to the data structure. B. The `read_redcap()` function returns a "Supertibble". Note that each row represents one instrument, identified by the `redcap_form_name` column. The `redcap_data` column is a list column that links to tibbles containing the data from a specific instrument. The Data Viewer allows drilling down into individual tibbles by clicking on the table icon, allowing for rapid and intuitive data exploration without any preprocessing. Since each instrument has a consistent granularity, these tibbles can be tidy. Two data tibbles are shown, one from a nonrepeating and one from a repeating instrument. Note the differences in granularity between the instruments. -`REDCapTidieR` provides utility functions to work with the supertibble, all designed to work with the R pipe `|>`. The `extract_tibble()` function takes a supertibble object and returns a specific data tibble. The `make_labelled()` function leverages the `labelled` package [@labelled_cit] to apply variable labels to the supertibble. The `add_skimr_metadata()` function uses the `skimr` package [@skimr_cit] to add summary statistics. Using the `write_redcap_xlsx()` function, which leverages the `openxlsx2` [@openxlsx2_cit] package, users can easily export an the supertibble into a collaborator-friendly Excel document, in which each Excel sheet contains the data for an instrument. +`REDCapTidieR` provides utility functions to work with the supertibble, all designed to work with the R pipe operator `|>`. The `extract_tibble()` function takes a supertibble object and returns a specific data tibble. The `make_labelled()` function leverages the `labelled` package [@labelled_cit] to apply variable labels to the supertibble. The `add_skimr_metadata()` function uses the `skimr` package [@skimr_cit] to add summary statistics. Using the `write_redcap_xlsx()` function, which leverages the `openxlsx2` [@openxlsx2_cit] package, users can easily export an the supertibble into a collaborator-friendly Excel document, in which each Excel sheet contains the data for an instrument. `REDCapTidieR` cannot be used to write data to a REDCap project. We refer the reader to an excellent guide of how to accomplish this using `REDCapR` [@redcapr_write_cit]. From 350a0d8ccc34714ae75b62203936b3a9a2c21960 Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Fri, 24 Nov 2023 09:12:15 -0500 Subject: [PATCH 14/16] Update paper.bib --- paper/paper.bib | 35 ++++++++++++----------------------- 1 file changed, 12 insertions(+), 23 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 9a96da61..2fcb258f 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -58,12 +58,12 @@ @Manual{redcapr_cit } @Manual{redcapapi_cit, - title = {{redcapAPI}: Accessing data from REDCap projects using the API}, - author = {Benjamin Nutter and Stephen Lane}, - year = {2023}, - doi = {10.5281/zenodo.11826}, - note = {R package version 2.8.0}, - url = {https://github.com/nutterb/redcapAPI/wiki}, + title = {{redcapAPI}: Accessing data from REDCap projects using the API}, + author = {Benjamin Nutter and Stephen Lane}, + year = {2023}, + doi = {10.5281/zenodo.11826}, + note = {R package version 2.8.0}, + url = {https://github.com/nutterb/redcapAPI/wiki}, } @Manual{redcapdm_cit, @@ -105,17 +105,6 @@ @Manual{skimr_cit url = {https://docs.ropensci.org/skimr/ (website)}, } -@Article{tidyverse_cit, - title = {Welcome to the {tidyverse}}, - author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani}, - year = {2019}, - journal = {Journal of Open Source Software}, - volume = {4}, - number = {43}, - pages = {1686}, - doi = {10.21105/joss.01686}, -} - @Manual{tibble_cit, title = {tibble: Simple Data Frames}, author = {Kirill Müller and Hadley Wickham}, @@ -124,12 +113,12 @@ @Manual{tibble_cit } @Misc{openssf_cit, - title = {OpenOpen Source Security Foundation_2023}, - url={https://openssf.org/}, - journal={Open Source Security Foundation}, - publisher={The Linux Foundation}, - year={2023}, - month={Oct} + title = {OpenOpen Source Security Foundation_2023}, + url={https://openssf.org/}, + journal={Open Source Security Foundation}, + publisher={The Linux Foundation}, + year={2023}, + month={Oct} } @Manual{redcapr_write_cit, From c0cf383e3457861cbdf2e9720fb34fd7ea9c54cd Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Mon, 27 Nov 2023 13:14:54 -0500 Subject: [PATCH 15/16] Minor md fixes :wrench: --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index b13e99e2..ff12ec92 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -62,9 +62,9 @@ As of 2023, the REDCap Consortium boasts nearly 3 million users across over 150 REDCap databases that contain repeating events and instruments require significant manual pre-processing, a major pain point for researchers and analysts. This is because the REDCap API returns a single table (Figure 1) that includes data from instruments that record data at different levels of granularity. -While there are a few existing REDCap tools (Table 1), `REDCapTidieR` occupies a unique space by providing analysts with a framework returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, only `REDCapTidieR` restructures the block matrix into an easy to use format. +While there are a few existing REDCap tools (Table 1), `REDCapTidieR` occupies a unique space by providing analysts with a framework returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, only `REDCapTidieR` restructures the block matrix into an easy to use format. -`REDCapTidieR` is built with production readiness in mind. It builds upon `REDCapR`, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. +`REDCapTidieR` is built with production readiness in mind. It builds upon `REDCapR`, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` [site](https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. | Package | Exports from REDCap | Imports into REDCap | Tidy Reformatting | Extensive Test Suite | |-------------|:-------------------:|:-------------------:|:-----------------:|:--------------------:| From 2db4f66b4fb780a21988e33a97f66b1246dd948b Mon Sep 17 00:00:00 2001 From: Richard Hanna Date: Tue, 28 Nov 2023 10:53:02 -0500 Subject: [PATCH 16/16] Update prod readiness paragraph :wrench: --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index ff12ec92..5f0190ff 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -64,7 +64,7 @@ REDCap databases that contain repeating events and instruments require significa While there are a few existing REDCap tools (Table 1), `REDCapTidieR` occupies a unique space by providing analysts with a framework returns a tidy data structure regardless of the size or complexity of the extracted database. Although some of these tools also offer functions for data processing, such as the [`tidyREDCap`](https://raymondbalise.github.io/tidyREDCap/) [@tidyredcap_cit] and [`REDCapDM`](https://ubidi.github.io/REDCapDM/index.html) [@redcapdm_cit] packages, only `REDCapTidieR` restructures the block matrix into an easy to use format. -`REDCapTidieR` is built with production readiness in mind. It builds upon `REDCapR`, which contains an excellent test suite, to make API calls, and includes an extensive automated test suite and ample documentation through a `pkgdown` [site](https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. +`REDCapTidieR` is built with production readiness in mind. In addition to an extensive 98% coverage test suite, `REDCapTidieR` execution is evaluated against 15 test databases that cover many complex configuration scenarios. It also provides ample documentation through a `pkgdown` [site](https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) [@redcaptidier_pkgdown_cit]. It is also built on top of `REDCapR`, which contains its own extensive test suite, and evaluated against an additional 26 test databases. `REDCapTidieR` meets the rigorous requirements of the [OpenSSF Best Practices Badge](https://www.bestpractices.dev/en/projects/6845) [@openssf_cit], which certifies open-source projects that adhere to criteria for delivering high-quality, robust, and secure software. | Package | Exports from REDCap | Imports into REDCap | Tidy Reformatting | Extensive Test Suite | |-------------|:-------------------:|:-------------------:|:-----------------:|:--------------------:|

+ + + + + + + +
+

Summary

+

Capturing and storing electronic data is integral in the research +world. REDCap (Harris et al. 2009, 2019) offers a secure web +application that lets users build databases and surveys with a robust +front-end interface that can support data of any type, including data +requiring compliance with standards for protected information.

+

Many REDCap users use the R programming language (R Core Team 2020) to extract and analyze their +data. The REDCapR +(Beasley 2023) and redcapAPI +(Nutter and Lane 2023) packages allow R +users to extract data directly into their programming environment. While +this works well for simple REDCap databases, it becomes cumbersome for +complex databases, because the REDCap API returns a single table with +rows that represent different levels of granularity, termed the “block +matrix”. The concept of “tidy data” +(Wickham 2014) describes a framework for a +standardized data structure where each variable forms a column, each +observation forms a row, and each type of observational unit forms a +table. The block matrix structure is inconsistent with this +standard.

+

We provide the open-source REDCapTidieR package as a +solution for this problem. REDCapTidieR leverages the +REDCapR package to query data and metadata through the +REDCap API and returns a tidy data structure that is easy to explore in +the RStudio Data Viewer, aiding users in data exploration. +REDCapTidieR also provides utility functions to extract +data from individual instruments, add summary statistics, and export +collaborator-friendly Excel documents. We have developed and extensive +automated test suite to ensure that REDCapTidieR can be +used with any REDCap database of any complexity. However, we believe +that researchers and analysts who work with complex databases, such as +those used for longitudinal clinical trials, will benefit the most by +eliminating the need for data parsing and pre-processing.

+
+
+

Statement of Need

+

As of 2023, the REDCap Consortium boasts nearly 3 million users +across over 150 countries. REDCap databases range from single-instrument +projects to complex builds that use both repeating instruments and +repeating events. These data structures are needed to capture multiple +items related to a specific visit, such as concomitant medications, or +events that cannot be planned ahead of time, such as adverse events.

+

REDCap databases that contain repeating events and instruments +require significant manual pre-processing, a major pain point for +researchers and analysts. This is because the REDCap API returns a +single table (Figure 1) that includes data from instruments that record +data at different levels of granularity.

+

While there are a few existing REDCap tools (Table 1), +REDCapTidieR occupies a unique space by providing analysts +with a framework returns a tidy data structure regardless of the size or +complexity of the extracted database. Although some of these tools also +offer functions for data processing, such as the tidyREDCap +(Balise et al. 2023) and REDCapDM +(Carmezim et al. 2023) packages, only +REDCapTidieR restructures the block matrix into an easy to +use format.

+

REDCapTidieR is built with production readiness in mind. +It builds upon REDCapR, which contains an excellent test suite, to make +API calls, and includes an extensive automated test suite and ample +documentation through a pkgdown site(https://chop-cgtinformatics.github.io/REDCapTidieR/index.html) +(Hanna, Porter, and Kadauke 2023). It +meets the rigorous requirements of the OpenSSF Best +Practices Badge (“OpenOpen Source +Security Foundation_2023” 2023), which certifies +open-source projects that adhere to criteria for delivering +high-quality, robust, and secure software.

+ ++++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PackageData Export SupportData Import SupportData ManipulationTidy ReformattingProduction Ready
redcapAPIxxx
REDCapRxxx
tidyREDCapxx
REDCapDMxx
REDCapTidieRxxxx
+

Table 1: Comparative breakdown of the landscape for REDCap tools in +R.

+
+
+

Design

+

The REDCapTidieR::read_redcap() function leverages +REDCapR to make API calls to query the data and metadata of +a REDCap project and returns the supertibble (Figure 1). The +supertibble, named after the tibble package +(Müller and Wickham 2023), is an +alternative presentation of the data in which multiple tables are linked +together in a single object in a fashion consistent with tidy data +principles.

+
+The REDCapTidieR Supertibble +
The REDCapTidieR Supertibble
+
+

Figure 1: The REDCapTidieR Supertibble. The “Superhero” database(Lingen 2023) contains two instruments, one +nonrepeating and one repeating. A. The REDCap API returns a “Block +Matrix”. Note an abundance of NA values, which do not +represent missing values but rather fields that do not apply due to the +data structure. B. The read_redcap() function returns a +“Supertibble”. Note that each row represents one instrument, identified +by the redcap_form_name column. The +redcap_data column is a list column that links to tibbles +containing the data from a specific instrument. Since each instrument +has a consistent granularity, these tibbles can be tidy. Two data +tibbles are shown, one from a nonrepeating and one from a repeating +instrument. Note the differences in granularity between the +instruments.

+

REDCapTidieR provides utility functions to work with the +supertibble, all designed to work with the R pipe |>. +The extract_tibble() function takes a supertibble object +and returns a specific data tibble. The make_labelled() +function leverages the labelled package (Larmarange 2023) to apply variable labels to +the supertibble. The add_skimr_metadata() function uses the +skimr package (Waring et al. +2023) to add summary statistics. Using the +write_redcap_xlsx() function, which leverages the +openxlsx2 (Openxlsx2: Read, +Write and Edit ’Xlsx’ Files 2023) package, users can easily +export an the supertibble into a collaborator-friendly Excel document, +in which each Excel sheet contains the data for an instrument.

+

REDCapTidieR cannot be used to write data to a REDCap +project. We refer the reader to an excellent guide of how to accomplish +this using REDCapR (Beasley and +Balise 2023).

+
+
+

Installation

+

REDCapTidieR is available on GitHub +and CRAN +and works on all major operating systems.

+
+
+

Acknowledgements

+

We would like to thank Will Beasley, Paul Wildenhain, and Jan Marvin +for their feedback and support in development.

+

This package was developed by the Cell +and Gene Therapy Informatics Team of the Children’s Hospital of Philadelphia.

+
+
+

Conflict of interest

+

The authors declare no financial conflicts of interest.

+
+
+

References

+
+
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