rcheck changes

R/CohortCharacterizationDiagnostics.R

@@ -82,10 +82,10 @@ getCohortCharacteristics <- function(connectionDetails = NULL,
     covariates <- featureExtractionOutput$covariates %>%
       dplyr::rename("cohortId" = "cohortDefinitionId") %>%
       dplyr::left_join(populationSize, by = "cohortId", copy = TRUE) %>%
-      dplyr::mutate(p = sumValue / populationSize)
+      dplyr::mutate(p = .data$sumValue / populationSize)
 
     if (nrow(covariates %>%
-               dplyr::filter(p > 1) %>%
+               dplyr::filter(.data$p > 1) %>%
                dplyr::collect()) > 0) {
       stop(
         paste0(
@@ -96,10 +96,10 @@ getCohortCharacteristics <- function(connectionDetails = NULL,
     }
 
     covariates <- covariates %>%
-      dplyr::mutate(sd = sqrt(p * (1 - p))) %>%
-      dplyr::select(-p) %>%
+      dplyr::mutate(sd = sqrt(.data$p * (1 - .data$p))) %>%
+      dplyr::select(-"p") %>%
       dplyr::rename("mean" = "averageValue") %>%
-      dplyr::select(-populationSize)
+      dplyr::select(-"populationSize")
 
     if (FeatureExtraction::isTemporalCovariateData(featureExtractionOutput)) {
       covariates <- covariates %>%
@@ -113,13 +113,13 @@ getCohortCharacteristics <- function(connectionDetails = NULL,
         )
 
       tidNaCount <- covariates %>%
-        dplyr::filter(is.na(timeId)) %>%
+        dplyr::filter(is.na(.data$timeId)) %>%
         dplyr::count() %>%
         dplyr::pull()
 
       if (tidNaCount > 0) {
         covariates <- covariates %>%
-          dplyr::mutate(timeId = if_else(is.na(.data$timeId), -1, .data$timeId))
+          dplyr::mutate(timeId = dplyr::if_else(is.na(.data$timeId), -1, .data$timeId))
       }
     } else {
       covariates <- covariates %>%
@@ -144,9 +144,9 @@ getCohortCharacteristics <- function(connectionDetails = NULL,
     dplyr::pull(dplyr::count(featureExtractionOutput$covariatesContinuous)) > 0) {
     covariates <- featureExtractionOutput$covariatesContinuous %>%
       dplyr::rename(
-        mean = averageValue,
-        sd = standardDeviation,
-        cohortId = cohortDefinitionId
+        mean = "averageValue",
+        sd = "standardDeviation",
+        cohortId = "cohortDefinitionId"
       )
     covariatesContinuous <- covariates
     if (FeatureExtraction::isTemporalCovariateData(featureExtractionOutput)) {
@@ -162,13 +162,13 @@ getCohortCharacteristics <- function(connectionDetails = NULL,
         )
 
       tidNaCount <- covariates %>%
-        dplyr::filter(is.na(timeId)) %>%
+        dplyr::filter(is.na(.data$timeId)) %>%
         dplyr::count() %>%
         dplyr::pull()
 
       if (tidNaCount > 0) {
         covariates <- covariates %>%
-          dplyr::mutate(timeId = if_else(is.na(.data$timeId), -1, .data$timeId))
+          dplyr::mutate(timeId = dplyr::if_else(is.na(.data$timeId), -1, .data$timeId))
       }
     } else {
       covariates <- covariates %>%
@@ -235,7 +235,7 @@ executeCohortCharacterization <- function(connection,
   startCohortCharacterization <- Sys.time()
   subset <- subsetToRequiredCohorts(
     cohorts = cohorts %>%
-      dplyr::filter(cohortId %in% instantiatedCohorts),
+      dplyr::filter(.data$cohortId %in% instantiatedCohorts),
     task = task,
     incremental = incremental,
     recordKeepingFile = recordKeepingFile

R/CohortRelationship.R

@@ -85,7 +85,7 @@ runCohortRelationshipDiagnostics <-
 
     timePeriods <- relationshipDays %>%
       dplyr::distinct() %>%
-      dplyr::arrange(startDay, endDay) %>%
+      dplyr::arrange(.data$startDay, .data$endDay) %>%
       dplyr::mutate(timeId = dplyr::row_number())
 
     ParallelLogger::logTrace("   - Creating Andromeda object to collect results")
@@ -161,12 +161,12 @@ runCohortRelationshipDiagnostics <-
     resultsInAndromeda$cohortRelationships <-
       resultsInAndromeda$cohortRelationships %>%
       dplyr::inner_join(resultsInAndromeda$timePeriods, by = "timeId") %>%
-      dplyr::select(-timeId) %>%
+      dplyr::select(-"timeId") %>%
       dplyr::arrange(
-        cohortId,
-        comparatorCohortId,
-        startDay,
-        endDay
+        .data$cohortId,
+        .data$comparatorCohortId,
+        .data$startDay,
+        .data$endDay
       )
     resultsInAndromeda$timePeriods <- NULL
 
@@ -209,21 +209,21 @@ executeCohortRelationshipDiagnostics <- function(connection,
   startCohortRelationship <- Sys.time()
 
   allCohortIds <- cohortDefinitionSet %>%
-    dplyr::select(cohortId, checksum) %>%
+    dplyr::select("cohortId", "checksum") %>%
     dplyr::rename(
-      targetCohortId = cohortId,
-      targetChecksum = checksum
+      targetCohortId = "cohortId",
+      targetChecksum = "checksum"
     ) %>%
     dplyr::distinct()
   combinationsOfPossibleCohortRelationships <- allCohortIds %>%
     tidyr::crossing(allCohortIds %>%
       dplyr::rename(
-        comparatorCohortId = targetCohortId,
-        comparatorChecksum = targetChecksum
+        comparatorCohortId = "targetCohortId",
+        comparatorChecksum = "targetChecksum"
       )) %>%
-    dplyr::filter(targetCohortId != comparatorCohortId) %>%
-    dplyr::arrange(targetCohortId, comparatorCohortId) %>%
-    dplyr::mutate(checksum = paste0(targetChecksum, comparatorChecksum))
+    dplyr::filter(.data$targetCohortId != .data$comparatorCohortId) %>%
+    dplyr::arrange(.data$targetCohortId, .data$comparatorCohortId) %>%
+    dplyr::mutate(checksum = paste0(.data$targetChecksum, .data$comparatorChecksum))
 
   subset <- subsetToRequiredCombis(
     combis = combinationsOfPossibleCohortRelationships,
@@ -247,15 +247,15 @@ executeCohortRelationshipDiagnostics <- function(connection,
       (nrow(combinationsOfPossibleCohortRelationships) - (
         nrow(
           combinationsOfPossibleCohortRelationships %>%
-            dplyr::filter(targetCohortId %in% c(subset$targetCohortId))
+            dplyr::filter(.data$targetCohortId %in% c(subset$targetCohortId))
         )
       )) > 0) {
       ParallelLogger::logInfo(
         sprintf(
           " - Skipping %s combinations in incremental mode because these were previously computed.",
           nrow(combinationsOfPossibleCohortRelationships) - nrow(
             combinationsOfPossibleCohortRelationships %>%
-              dplyr::filter(targetCohortId %in% c(subset$targetCohortId))
+              dplyr::filter(.data$targetCohortId %in% c(subset$targetCohortId))
           )
         )
       )

R/ConceptSets.R

@@ -121,10 +121,10 @@ combineConceptSetsFromCohorts <- function(cohorts) {
   checkmate::reportAssertions(errorMessage)
   checkmate::assertDataFrame(
     x = cohorts %>% dplyr::select(
-      cohortId,
-      sql,
-      json,
-      cohortName
+      "cohortId",
+      "sql",
+      "json",
+      "cohortName"
     ),
     any.missing = FALSE,
     min.cols = 4,
@@ -321,7 +321,7 @@ getCodeSetIds <- function(criterionList) {
     return(NULL)
   } else {
     return(dplyr::tibble(domain = names(criterionList), codeSetIds = codeSetIds)
-    %>% dplyr::filter(!is.na(codeSetIds)))
+    %>% dplyr::filter(!is.na(.data$codeSetIds)))
   }
 }
 
@@ -333,7 +333,7 @@ exportConceptSets <- function(cohortDefinitionSet, exportFolder, minCellCount, d
   # Save concept set metadata ---------------------------------------
   conceptSetsExport <- makeDataExportable(
     x = conceptSets %>%
-      dplyr::select(-uniqueConceptSetId) %>%
+      dplyr::select(-"uniqueConceptSetId") %>%
       dplyr::distinct(),
     tableName = "concept_sets",
     minCellCount = minCellCount,
@@ -664,7 +664,7 @@ runConceptSetDiagnostics <- function(connection,
               )
               return(tidyr::tibble())
             }
-            primaryCodesetIds <- primaryCodesetIds %>% dplyr::filter(domain %in%
+            primaryCodesetIds <- primaryCodesetIds %>% dplyr::filter(.data$domain %in%
               c(domains$domain %>% unique()))
             if (nrow(primaryCodesetIds) == 0) {
               warning(
@@ -943,8 +943,8 @@ runConceptSetDiagnostics <- function(connection,
           .data$conceptId
         ) %>%
         dplyr::summarise(
-          conceptCount = max(conceptCount),
-          conceptSubjects = max(conceptSubjects)
+          conceptCount = max(.data$conceptCount),
+          conceptSubjects = max(.data$conceptSubjects)
         ) %>%
         dplyr::ungroup()
       data <- makeDataExportable(

R/ExportCharacterization.R

@@ -31,30 +31,30 @@ exportCharacterization <- function(characteristics,
   } else if (dplyr::pull(dplyr::count(characteristics$covariateRef)) > 0) {
     characteristics$filteredCovariates <-
       characteristics$covariates %>%
-      dplyr::filter(mean >= minCharacterizationMean) %>%
+      dplyr::filter(.data$mean >= minCharacterizationMean) %>%
       dplyr::mutate(databaseId = !!databaseId) %>%
       dplyr::left_join(counts,
         by = c("cohortId", "databaseId"),
         copy = TRUE
       ) %>%
       dplyr::mutate(
         mean = dplyr::if_else(
-          mean != 0 & mean < minCellCount / as.numeric(cohortEntries),
-          -minCellCount / as.numeric(cohortEntries),
-          mean
+          .data$mean != 0 & .data$mean < minCellCount / as.numeric(.data$cohortEntries),
+          -minCellCount / as.numeric(.data$cohortEntries),
+          .data$mean
         ),
         sumValue = dplyr::if_else(
-          sumValue != 0 & sumValue < minCellCount,
+          .data$sumValue != 0 & .data$sumValue < minCellCount,
           -minCellCount,
-          sumValue
+          .data$sumValue
         )
       ) %>%
-      dplyr::mutate(sd = dplyr::if_else(mean >= 0, sd, 0)) %>%
+      dplyr::mutate(sd = dplyr::if_else(mean >= 0, .data$sd, 0)) %>%
       dplyr::mutate(
-        mean = round(mean, digits = 4),
-        sd = round(sd, digits = 4)
+        mean = round(.data$mean, digits = 4),
+        sd = round(.data$sd, digits = 4)
       ) %>%
-      dplyr::select(-cohortEntries, -cohortSubjects) %>%
+      dplyr::select(-"cohortEntries", -"cohortSubjects") %>%
       dplyr::distinct() %>%
       makeDataExportable(
         tableName = "temporal_covariate_value",

R/IncidenceRates.R

@@ -218,7 +218,7 @@ computeIncidenceRates <- function(connection,
   startIncidenceRate <- Sys.time()
   subset <- subsetToRequiredCohorts(
     cohorts = cohorts %>%
-      dplyr::filter(cohortId %in% instantiatedCohorts),
+      dplyr::filter(.data$cohortId %in% instantiatedCohorts),
     task = "runIncidenceRate",
     incremental = incremental,
     recordKeepingFile = recordKeepingFile

R/InclusionRules.R

@@ -27,7 +27,7 @@ getInclusionStats <- function(connection,
   ParallelLogger::logInfo("Fetching inclusion statistics from files")
   subset <- subsetToRequiredCohorts(
     cohorts = cohortDefinitionSet %>%
-      dplyr::filter(cohortId %in% instantiatedCohorts),
+      dplyr::filter(.data$cohortId %in% instantiatedCohorts),
     task = "runInclusionStatistics",
     incremental = incremental,
     recordKeepingFile = recordKeepingFile

R/Incremental.R

@@ -125,11 +125,11 @@ recordTasksDone <-
         as.character(recordKeeping$timeStamp)
       if ("cohortId" %in% colnames(recordKeeping)) {
         recordKeeping <- recordKeeping %>%
-          dplyr::mutate(cohortId = as.double(cohortId))
+          dplyr::mutate(cohortId = as.double(.data$cohortId))
       }
       if ("comparatorId" %in% colnames(recordKeeping)) {
         recordKeeping <- recordKeeping %>%
-          dplyr::mutate(comparatorId = as.double(comparatorId))
+          dplyr::mutate(comparatorId = as.double(.data$comparatorId))
       }
       idx <- getKeyIndex(list(...), recordKeeping)
       if (length(idx) > 0) {

R/Private.R

@@ -142,28 +142,28 @@ makeDataExportable <- function(x,
 
   fieldsInDataModel <- resultsDataModel %>%
     dplyr::filter(.data$tableName == !!tableName) %>%
-    dplyr::pull(columnName) %>%
+    dplyr::pull(.data$columnName) %>%
     SqlRender::snakeCaseToCamelCase() %>%
     unique()
 
   requiredFieldsInDataModel <- resultsDataModel %>%
     dplyr::filter(.data$tableName == !!tableName) %>%
-    dplyr::filter(isRequired == "Yes") %>%
-    dplyr::pull(columnName) %>%
+    dplyr::filter(.data$isRequired == "Yes") %>%
+    dplyr::pull(.data$columnName) %>%
     SqlRender::snakeCaseToCamelCase() %>%
     unique()
 
   primaryKeyInDataModel <- resultsDataModel %>%
     dplyr::filter(.data$tableName == !!tableName) %>%
-    dplyr::filter(primaryKey == "Yes") %>%
-    dplyr::pull(columnName) %>%
+    dplyr::filter(.data$primaryKey == "Yes") %>%
+    dplyr::pull(.data$columnName) %>%
     SqlRender::snakeCaseToCamelCase() %>%
     unique()
 
   columnsToApplyMinCellValue <- resultsDataModel %>%
     dplyr::filter(.data$tableName == !!tableName) %>%
-    dplyr::filter(minCellCount == "Yes") %>%
-    dplyr::pull(columnName) %>%
+    dplyr::filter(.data$minCellCount == "Yes") %>%
+    dplyr::pull(.data$columnName) %>%
     SqlRender::snakeCaseToCamelCase() %>%
     unique()
 
@@ -311,7 +311,7 @@ getPrefixedTableNames <- function(tablePrefix) {
   return(resultList)
 }
 
-#' Internal utility function for logging execution of variables
+# Internal utility function for logging execution of variables
 timeExecution <- function(exportFolder,
                           taskName,
                           cohortIds = NULL,

R/RunDiagnostics.R

@@ -464,7 +464,7 @@ executeDiagnostics <- function(cohortDefinitionSet,
 
   checkmate::reportAssertions(collection = errorMessage)
   if (!is.null(cohortIds)) {
-    cohortDefinitionSet <- cohortDefinitionSet %>% dplyr::filter(cohortId %in% cohortIds)
+    cohortDefinitionSet <- cohortDefinitionSet %>% dplyr::filter(.data$cohortId %in% cohortIds)
   }
 
   if (nrow(cohortDefinitionSet) == 0) {
@@ -667,8 +667,8 @@ executeDiagnostics <- function(cohortDefinitionSet,
 
   if (nrow(cohortCounts) > 0) {
     instantiatedCohorts <- cohortCounts %>%
-      dplyr::filter(cohortEntries > 0) %>%
-      dplyr::pull(cohortId)
+      dplyr::filter(.data$cohortEntries > 0) %>%
+      dplyr::pull(.data$cohortId)
     ParallelLogger::logInfo(
       sprintf(
         "Found %s of %s (%1.2f%%) submitted cohorts instantiated. ",

R/Shiny.R

@@ -104,19 +104,16 @@ launchDiagnosticsExplorer <- function(sqliteDbPath = "MergedCohortDiagnosticsDat
       tablePrefix = tablePrefix,
       cohortTableName = cohortTableName,
       databaseTableName = databaseTableName,
-      enableAnnotation = enableAnnotation,
       enableAuthorization = FALSE
     )
-
-    options("enableCdAnnotation" = enableAnnotation)
     on.exit(rm("shinySettings", envir = .GlobalEnv))
   } else {
     checkmate::assertFileExists(shinyConfigPath)
     options("CD-shiny-config" = normalizePath(shinyConfigPath))
     on.exit(options("CD-shiny-config" = NULL))
   }
 
-  if (!"OhdsiShinyModules" %in% as.data.frame(installed.packages())$Package) {
+  if (!"OhdsiShinyModules" %in% as.data.frame(utils::installed.packages())$Package) {
     remotes::install_github("OHDSI/OhdsiShinyModules")
   }
 
@@ -267,6 +264,7 @@ createDiagnosticsExplorerZip <- function(outputZipfile = file.path(getwd(), "Dia
 #' @param appDir                optional - directory to use to copy files for deployment. If you use a consistent dir
 #'                              other internal options can change.
 #' @param  useRenvironFile      logical - not recommended, store db credentials in .Renviron file
+#' @param shinyDirectory        (optional) Directyory shiny app code lives. Use this if you wish to modify the explorer
 #' @param ...                   other parameters passed to rsconnect::deployApp
 deployPositConnectApp <- function(appName,
                                   appDir = tempfile(),
@@ -285,15 +283,15 @@ deployPositConnectApp <- function(appName,
                                   useRenvironFile = FALSE,
                                   ...) {
 
-  if (!"rsconnect" %in% as.data.frame(installed.packages())$Package) {
+  if (!"rsconnect" %in% as.data.frame(utils::installed.packages())$Package) {
     install.packages("rsconnect")
   }
 
-  if (!"yaml" %in% as.data.frame(installed.packages())$Package) {
+  if (!"yaml" %in% as.data.frame(utils::installed.packages())$Package) {
     install.packages("yaml")
   }
 
-  if (!"OhdsiShinyModules" %in% as.data.frame(installed.packages())$Package) {
+  if (!"OhdsiShinyModules" %in% as.data.frame(utils::installed.packages())$Package) {
     remotes::install_github("OHDSI/OhdsiShinyModules")
   }