Get R check passing

.github/workflows/R_CMD_check_Hades.yaml

@@ -48,9 +48,10 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
-      - uses: r-lib/actions/setup-r@v1
+      - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
+          use-public-rspm: true
 
       - uses: r-lib/actions/setup-tinytex@v1
 

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: Capr
 Title: Cohort Definition Application Programming
-Version: 2.0.1
+Version: 2.0.2
 Authors@R: c(
-    person("Martin", "Lavallee", , "mdlavallee92@gmail.com", role = c("aut", "cre")),
+    person("Martin", "Lavallee", , "adam.black@odysseusinc.com", role = c("aut", "cre")),
     person("Adam", "Black", , "black@ohdsi.org", role = "aut")
   )
 Description: Provides a programming language for defining OHDSI cohort definitions in R to use in studies for Observational 
@@ -11,9 +11,9 @@ Description: Provides a programming language for defining OHDSI cohort definitio
     'Capr' functions can be used to create, save, and load component parts to a cohort definition allowing 
     R programmers to easily reuse cohort logic. 'Capr' provides tools to create a large number of OHDSI cohorts 
     programmatically while also helping bridge the gap between human readable descriptions of clinical phenotypes 
-    and their computational implmentation. 
+    and their computational implementation. 
 License: Apache License (>= 2)
-URL: https://ohdsi.github.io/Capr, https://github.com/OHDSI/Capr
+URL: https://ohdsi.github.io/Capr, https://github.com/OHDSI/Capr/
 BugReports: https://github.com/OHDSI/Capr/issues
 Encoding: UTF-8
 RoxygenNote: 7.2.3

NAMESPACE

@@ -3,14 +3,13 @@
 S3method(compile,Cohort)
 export("%>%")
 export(age)
-export(as.json)
 export(atLeast)
 export(atMost)
 export(attrition)
 export(bt)
 export(censoringEvents)
 export(cohort)
-export(compile.Cohort)
+export(compile)
 export(condition)
 export(conditionEra)
 export(continuousObservation)
@@ -68,6 +67,7 @@ export(withAtMost)
 export(writeCohort)
 export(writeConceptSet)
 exportMethods("==")
+exportMethods(compile)
 import(dplyr)
 importFrom(generics,compile)
 importFrom(magrittr,"%>%")

NEWS.md

@@ -1,3 +1,7 @@
+Capr 2.0.2
+==========
+- fix tests for CRAN
+
 Capr 2.0.1
 ==========
 - additions and corrections to vignettes and documentation

R/attributes-op.R

@@ -350,6 +350,7 @@ setMethod("nbt", "Date", function(x, y) {
 #' Function to create age attribute
 #' @param op   an opAttribute object that is either numeric or integer that defines the logical
 #'             operation used to determine eligible patient age
+#' @return An age attribute that can be used in a cohort definition
 #' @export
 age <- function(op) {
 

R/cohort.R

@@ -47,7 +47,6 @@ setClass("CohortExit",
 )
 
 ## CohortEra----
-
 setClass("CohortEra",
          slots = c(
            eraDays = "integer",
@@ -115,6 +114,7 @@ entry <- function(...,
 #'
 #' @param ... Capr groups
 #' @param expressionLimit how to limit initial events per person either First, All, or Last
+#' @return A cohort attrition object that can be used in a cohort definition
 #' @export
 attrition <- function(..., expressionLimit = c("First", "All", "Last")) {
 
@@ -126,11 +126,10 @@ attrition <- function(..., expressionLimit = c("First", "All", "Last")) {
 
 }
 
-
-
 #' Function that creates a cohort exit object
 #' @param endStrategy the endStrategy object to specify for the exit
 #' @param censor the censoring criteria to specify for the exit
+#' @return A cohort exit object that can be used in a cohort definition
 #' @export
 exit <- function(endStrategy, censor = NULL){
   if (is.null(censor)) {
@@ -318,50 +317,19 @@ toCirce <- function(cd) {
   return(cdCirce)
 }
 
+#' Compile a Capr object to json
+#'
+#' @param object A Capr object such as a cohort, list of cohorts, or concept set.
+#' @param ... Arguments passed on to jsonlite::toJSON.
+#' e.g. `pretty = TRUE` for nicely formatted json.
+#'
+#' @return The json representation of the Capr object
+#' @export
+setGeneric("compile", function(object, ...) { standardGeneric("compile") })
 
-# cs1 <- cs(descendants(exclude(436665),440383,442306,4175329))
-# cs2 <- cs(descendants(exclude(436665),440383,4175329))
-
-
-# #' @export
-# setMethod("as.list", "Cohort", function (x, ...) {
-#
-#   # entry events
-#   entryConceptSets <- purrr::map(x@entry@entryEvents, collectConceptSets)
-#
-#   # re-number concept sets
-#   allConceptSets <- entryConceptSets # need to add criteria concept sets
-#
-#   r <- dedupConceptSets(allConceptSets)
-#   lookup <- r$lookup
-#   uniqueConceptSets <- purrr::map(r$uniqueConceptSets, function(x) {
-#     x <- as.list(x)
-#     x$id <- unname(lookup[x$id])
-#     x
-#   })
-#
-#   cohortList <- list(
-#     ConceptSets = uniqueConceptSets,
-#     PrimaryCriteria = list(CriteriaList = purrr::map(x@entry@entryEvents, ~lst(!!.@domain := list(CodesetId = .@conceptSet@id))),
-#        ObservationWindow = list(priorDays = x@entry@observationWindow@priorDays, postDays = x@entry@observationWindow@postDays),
-#        PrimaryCriteriaLimit = list(Type = x@entry@primaryCriteriaLimit)
-#     ),
-#     QualifiedLimit = list(Type = x@entry@qualifiedLimit),
-#     ExpressionLimit = list(Type = x@attrition@expressionLimit),
-#     InclusionRules = x@attrition@rules, # TODO use map(rules, as.list)
-#     CensoringCriteria = x@exit@censor,
-#     CollapseSettings = list(collapseType = "ERA", EraPad = x@era@eraDays),
-#     CensorWindow = list() # TODO implement censor window
-#   )
-#
-#   cohortList$PrimaryCriteria$CriteriaList <- purrr::map(cohortList$PrimaryCriteria$CriteriaList,
-#                                                         function(criteria) {
-#                                                           criteria[[1]]$CodesetId <- unname(lookup[criteria[[1]]$CodesetId])
-#                                                           criteria
-#                                                         })
-#   cohortList
-# })
-
+compile.Cohort <- function(object, ...) {
+  as.character(jsonlite::toJSON(toCirce(object), auto_unbox = TRUE, ...))
+}
 
 #' Compile a Capr cohort to json
 #'
@@ -370,26 +338,32 @@ toCirce <- function(cd) {
 #' e.g. `pretty = TRUE` for nicely formatted json.
 #'
 #' @return The json representation of Capr cohorts
-#' @export
 #' @importFrom generics compile
 #' @exportS3Method compile Cohort
+#' @export
+#' @rdname compile-methods
 #' @examples
 #' \dontrun{
 #' ch <- cohort(condition(cs(1,2)))
 #' compile(ch)
 #' }
-compile.Cohort <- function(object, ...) {
-  as.character(jsonlite::toJSON(toCirce(object), auto_unbox = TRUE, ...))
-}
-
 setMethod("compile", "Cohort", compile.Cohort)
 
+compile.ConceptSet <- function(object, ...) {
+  x <- list(items = lapply(object@Expression, as.list))
+  as.character(jsonlite::toJSON(x, auto_unbox = TRUE, ...))
+}
 
-#' @rdname as.json
-#' @aliases as.json,Cohort-method
-setMethod("as.json", "Cohort", function(x, pretty = TRUE, ...) {
-  as.character(jsonlite::toJSON(toCirce(x), auto_unbox = TRUE, pretty = pretty, ...))
-})
+#' Compile a Capr Concept Set to json
+#'
+#' @export
+#' @rdname compile-methods
+#' @param object A Capr Concept Set created with `cs`
+#' @param ... Arguments passed on to jsonlite::toJSON.
+#' e.g. `pretty = TRUE` for nicely formatted json.
+#'
+#' @return The json representation of Capr cohorts
+setMethod("compile", "ConceptSet", compile.ConceptSet)
 
 setMethod("show", "Cohort", function(object) {
   # TODO make this pretty on the console
@@ -400,9 +374,7 @@ setMethod("show", "Cohort", function(object) {
 #'
 #' @param x A Capr cohort
 #' @param path The name of the file to create
-#'
 #' @export
-#'
 #' @examples
 #' \dontrun{
 #' cs1 <- cs(descendants(exclude(436665),440383,442306,4175329))
@@ -415,9 +387,6 @@ writeCohort <- function(x, path) {
   checkmate::assertClass(x, "Cohort")
   checkmate::assertCharacter(path, len = 1, min.chars = 1, pattern = "\\.json$")
 
-  # ParallelLogger::logInfo(
-  #   "Cohort written to", path
-  # )
   toCirce(x) %>%
     jsonlite::write_json(
       path = path,
@@ -440,25 +409,5 @@ generateCaprTemplate <- function(file, .capr) {
 
   #generate cohort from template
   .capr(conceptSet)
-
 }
 
-# writeCohort <- function(x, path, ...) {
-#   checkmate::assertClass(x, "Cohort")
-#   checkmate::assertCharacter(path, len = 1, min.chars = 1, pattern = "\\.json$")
-#   # check that concept set details are filled in
-#   check <- unlist(x$ConceptSets, recursive = TRUE)
-#   if (any(check[grepl( "CONCEPT_NAME|STANDARD_CONCEPT", names(check))] == "")) {
-#     rlang::abort("Concept set details are missing. Fill in concept set details using `getConceptSetDetails()`")
-#   }
-#   jsonlite::write_json(x = as.list(x), path = path, auto_unbox = TRUE, pretty = TRUE, ...)
-# }
-
-
-
-
-
-
-
-
-

R/conceptSet.R

@@ -353,19 +353,6 @@ setMethod("as.list", "ConceptSet", function(x){
                  'expression' = list('items' = lapply(x@Expression, as.list)))
 })
 
-#' Coerce Capr object to json
-#' @param x the capr object
-#' @param pretty a toggle to make the json look nice, part of jsonlite
-#' @param ... additional arguments passes to jsonlite::toJSON
-#' @export
-#' @docType methods
-setGeneric("as.json", function(x, pretty = TRUE, ...)  standardGeneric("as.json"))
-
-setMethod("as.json", "ConceptSet", function(x, pretty = TRUE, ...){
-  items <- list(items = lapply(x@Expression, as.list))
-  jsonlite::toJSON(x = items, pretty = pretty, auto_unbox = TRUE, ...)
-})
-
 #' Save a concept set as a json file
 #'
 #' The resulting concept Set JSON file can be imported into Atlas.
@@ -501,11 +488,9 @@ readConceptSet <- function(path, name, id = NULL) {
       conceptCode = df[["concept_code"]] %||%  df[["concept code"]] %||% "" %>% as.character(),
       domainId = df[["domain_id"]] %||%  df[["domain"]] %||% "" %>% as.character(),
       vocabularyId = df[["vocabulary_id"]] %||%  df[["vocabulary"]] %||% "" %>% as.character(),
-      conceptClassId = df[["concept_class_id"]] %||% "" %>% as.character()
-    ) %>%
-      dplyr::mutate(
-        dplyr::across(.data$conceptName:.data$conceptClassId, ~tidyr::replace_na(.x, "")) #convert na to ""
-      )
+      conceptClassId = df[["concept_class_id"]] %||% "" %>% as.character()) %>%
+      dplyr::mutate_if(is.character, ~tidyr::replace_na(.x, ""))
+
     conceptList <- purrr::pmap(conceptDf, newConcept)
   }
 

R/criteria.R

@@ -97,10 +97,12 @@ exactly <- function(x,
 }
 
 #' Function to enumerate an minimal count of occurrences
+#'
 #' @param x the integer counting the number of occurrences
 #' @param query a query object that provides context to the clinical event of interest
 #' @param aperture an eventAperture object that shows the temporal span where the event is to be observed
 #' relative to the index event
+#' @return A criteria that can be used in a cohort definition
 #' @export
 atLeast <- function(x,
                     query,
@@ -126,6 +128,7 @@ atLeast <- function(x,
 #' @param query a query object that provides context to the clinical event of interest
 #' @param aperture an eventAperture object that shows the temporal span where the event is to be observed
 #' relative to the index event
+#' @param A criteria that can be used in a cohort definition
 #' @export
 atMost <- function(x,
                    query,

R/query.R

@@ -3,7 +3,7 @@
 
 #' An S4 class for a Circe Query
 #'
-#' A query is a medical concept that can be extracted from a database through a 'where' clause in a SQL statement.
+#' A query is a medical concept that can be extracted from a database through a 'where' clause in a 'SQL' statement.
 #' This includes concepts.
 #'
 #' @slot domain The domain to search (e.g. "Condition", "Drug", "Measurement", etc)