Update roxygen2 comments, unit tests and fix errors

R/GetCovariates.R

@@ -136,7 +136,6 @@ getDbCovariateData <- function(connectionDetails = NULL,
                                cohortIds = c(-1),
                                rowIdField = "subject_id",
                                covariateSettings,
-
                                exportToTable = FALSE,
                                createTable = exportToTable,
                                dropTableIfExists = exportToTable,
@@ -146,7 +145,6 @@ getDbCovariateData <- function(connectionDetails = NULL,
                                targetCovariateRefTable = NULL,
                                targetAnalysisRefTable = NULL,
                                targetTimeRefTable = NULL,
-
                                aggregated = FALSE,
                                minCharacterizationMean = 0,
                                tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
@@ -350,15 +348,11 @@ getDbCovariateData <- function(connectionDetails = NULL,
           cdmVersion = cdmVersion,
           rowIdField = rowIdField,
           covariateSettings = covariateSettings[[i]],
-
           targetCovariateTable = targetCovariateTable,
           targetCovariateContinuousTable = targetCovariateContinuousTable,
           targetCovariateRefTable = targetCovariateRefTable,
           targetAnalysisRefTable = targetAnalysisRefTable,
           targetTimeRefTable = targetTimeRefTable,
-          dropTableIfExists = FALSE, # can remove this input
-          createTable = FALSE, # can remove this input
-
           aggregated = aggregated,
           minCharacterizationMean = minCharacterizationMean
         )

R/GetCovariatesFromOtherCohorts.R

@@ -29,6 +29,7 @@
 #'                               a permanent table in the \code{targetDatabaseSchema} or a temp table. If
 #'                               it is a temp table, do not specify \code{targetDatabaseSchema}.
 #'                               
+#' @param targetCovariateContinuousTable (Optional) The name of the table where the resulting continuous covariates should be stored.
 #' @param targetCovariateRefTable (Optional) The name of the table where the covariate reference will be stored.
 #'                               
 #' @param targetAnalysisRefTable (Optional) The name of the table where the analysis reference will be stored.
@@ -48,6 +49,7 @@ getDbCohortBasedCovariatesData <- function(connection,
                                            cdmVersion = "5",
                                            rowIdField = "subject_id",
                                            covariateSettings,
+                                           targetDatabaseSchema = NULL,
                                            targetCovariateTable = NULL,
                                            targetCovariateContinuousTable = NULL,
                                            targetCovariateRefTable = NULL,

R/GetDefaultCovariates.R

@@ -29,19 +29,14 @@
 #'                               be stored. If not provided, results will be fetched to R. The table can be
 #'                               a permanent table in the \code{targetDatabaseSchema} or a temp table. If
 #'                               it is a temp table, do not specify \code{targetDatabaseSchema}.
-#'                               
+#' @param targetCovariateContinuousTable (Optional) The name of the table where the resulting continuous covariates should be stored.
 #' @param targetCovariateRefTable (Optional) The name of the table where the covariate reference will be stored.
 #'                               
 #' @param targetAnalysisRefTable (Optional) The name of the table where the analysis reference will be stored.
 #' @param targetTimeRefTable     (Optional) The name of the table for the time reference
 #' @param minCharacterizationMean The minimum mean value for binary characterization output. Values below this will be cut off from output. This
 #'                                will help reduce the file size of the characterization output, but will remove information
 #'                                on covariates that have very low values. The default is 0.
-#'
-#'                               
-#' @param dropTableIfExists      If targetDatabaseSchema, drop any existing tables. Otherwise, results are merged
-#'                               into existing table data. Overides createTable.
-#' @param createTable            Run sql to create table? Code does not check if table exists.
 #' @template GetCovarParams
 #'
 #' @examples
@@ -59,9 +54,7 @@
 #'   connection = connection,
 #'   cdmDatabaseSchema = "main",
 #'   cohortTable = "cohort",
-#'   covariateSettings = createDefaultCovariateSettings(),
-#'   targetDatabaseSchema = "main",
-#'   targetCovariateTable = "ut_cov"
+#'   covariateSettings = createDefaultCovariateSettings()
 #' )
 #' }
 #' @export
@@ -74,15 +67,12 @@ getDbDefaultCovariateData <- function(connection,
                                       cdmVersion = "5",
                                       rowIdField = "subject_id",
                                       covariateSettings,
-
+                                      targetDatabaseSchema = NULL,
                                       targetCovariateTable = NULL,
                                       targetCovariateContinuousTable = NULL,
                                       targetCovariateRefTable = NULL,
                                       targetAnalysisRefTable = NULL,
                                       targetTimeRefTable = NULL,
-
-                                      dropTableIfExists = FALSE,
-                                      createTable = TRUE,
                                       aggregated = FALSE,
                                       minCharacterizationMean = 0,
                                       tempEmulationSchema = getOption("sqlRenderTempEmulationSchema")) {
@@ -298,6 +288,17 @@ getDbDefaultCovariateData <- function(connection,
         andromedaTableName = "covariateRef",
         snakeCaseToCamelCase = TRUE
       )
+
+      collisions <- covariateData$covariateRef %>%
+          dplyr::filter(collisions > 0) %>%
+          dplyr::collect()
+
+      if (nrow(collisions) > 0) {
+        warning(sprintf(
+          "Collisions in covariate IDs detected for post-coordinated concepts with covariate IDs %s",
+          paste(collisions$covariateId, paste = ", ")
+        ))
+      }      
     } else{
       sql <- "
       INSERT INTO @target_covariate_ref_table(

R/UnitTestHelperFunctions.R

@@ -59,6 +59,7 @@
 #' @param minCharacterizationMean The minimum mean value for binary characterization output. Values below this will be cut off from output. This
 #'                                will help reduce the file size of the characterization output, but will remove information
 #'                                on covariates that have very low values. The default is 0.
+#' @param ...                    Additional arguments, not used.
 #' @return
 #' Returns an object of type \code{covariateData}, containing information on the covariates.
 #'
@@ -71,7 +72,8 @@
                                    rowIdField = "subject_id",
                                    covariateSettings,
                                    aggregated = FALSE,
-                                   minCharacterizationMean = 0) {
+                                   minCharacterizationMean = 0,
+                                   ...) {
   writeLines("Constructing length of observation covariates")
   if (covariateSettings$useLengthOfObs == FALSE) {
     return(NULL)

tests/testthat/setup.R

@@ -2,6 +2,19 @@ library(testthat)
 library(FeatureExtraction)
 library(dplyr)
 
+# AGS: This rJava code block was used to add the Java dependencies to the classpath for testing. It is needed to run the tests on all platforms, but it is not needed when running individual test files in RStudio, which is why it is not included in the helper functions file. If we want to run individual test files, we can use the loadRenderTranslateUnitTestSql function defined below, which also adds the Java dependencies to the classpath if they are not already there.
+# library(rJava)
+# .jinit()
+# jar_dirs <- c(
+#   system.file("java", package = "DatabaseConnector"),
+#   system.file("java", package = "SqlRender"),
+#   system.file("java", package = "FeatureExtraction")
+# )
+# jar_files <- unlist(
+#   lapply(jar_dirs, list.files, pattern = "\\.jar$", full.names = TRUE)
+# )
+# .jaddClassPath(jar_files)
+
 dbms <- getOption("dbms", default = "sqlite")
 message("************* Testing on ", dbms, " *************\n")
 
@@ -98,7 +111,7 @@ checkRemoteFileAvailable <- function(remoteFile) {
   }
   # Then stop if status > 400
   if (httr::http_error(resp)) {
-    message_for_status(resp)
+    httr::message_for_status(resp)
     return(NULL)
   }
   return("success")