Submitting Author: Erik Bülow (@eribul)
Repository: https://github.com/eribul/coder
Version submitted: 0.11.9
Editor: @noamross
Reviewer 1: @zabore
Reviewer 2: @dgrtwo
Archive: TBD
Version accepted: TBD

• Paste the full DESCRIPTION file inside a code block below:

Package: coder
Type: Package
Title: Deterministic Categorization of Items Based on External Code Data
Version: 0.11.9
Authors@R: person("Erik", "Bulow", email = "[email protected]",
                  role = c("aut", "cre"),
                  comment = c(ORCID = "0000-0002-9973-456X"))
Description: 
  Fast categorization of items based on external code data identified by 
  regular expressions.
  It is easy to introduce new classification schemes (by 'classcodes' objects) or  
  to use default schemes included in the package. Use cases includes patient 
  categorization based on co-morbidity indices such as Charlson, Elixhauser, 
  RxRisk V, or the co-morbidity-polypharmacy score (CPS), as well as adverse 
  events after arthroplasty surgery.
License: MIT + file LICENSE
Depends: R (>= 3.3)
Suggests:
  covr,
  testthat,
  knitr,
  rmarkdown,
  writexl
Imports:
  data.table,
  decoder
LazyData: TRUE
RoxygenNote: 7.1.0
VignetteBuilder: 
  knitr
URL: https://github.com/eribul/coder
BugReports: https://github.com/eribul/coder/issues
Encoding: UTF-8
Language: en-US

Scope

• Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • workflow automataion
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

• Explain how and why the package falls under these categories (briefly, 1-2 sentences):

The aim of the package is to categorize items/individuals/patients from one large data sets, with the help of external code data using a dynamic system of classicifaction schemes based on regular expression. There are no analytical/statistical methods included.

• Who is the target audience and what are scientific applications of this package?

The primary user group might be medical/epidemiological researchers classifying patients by medical scores such as comorbidity indices or adverse events. The package might be used more broadly however due to generic principles.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

There are some R packages with a narrow focus on Charlson and Elixhauser co-morbidity based on ICD-9/10-codes (icd, comorbidity, medicalrisk, comorbidities.icd10 and icdcoder).
icd and comorbidity are both good for their purpose. medicalrisk can be used with ICD-9-CM codes but is not up-to-date with the latest version of ICD-10. comorbidities.icd10 and icdcoder are not actively developed or maintained.
The coder package provides greater flexibility for combining different sets of codes (ICD-8, ICD-9, ICD-9-CM and ICD-10 etc as given by regular expressions, either as included in the package by default, or as provided by the user). All other packages relies on pre-specified (hard-coded) classifications. None of the other packages includes any other classification then Charlson/Elixhauser (such as the RxRisk V classification based on medical ATC codes, which is also included in the coder package by default).

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.

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Confirm each of the following by checking the box.

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This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions, created with roxygen2.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.

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