Release 1.0.5

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: DrugUtilisation
 Title: Summarise Patient-Level Drug Utilisation in Data Mapped to the OMOP 
     Common Data Model 
-Version: 1.0.4
+Version: 1.0.5
 Authors@R: c(
     person(
         "Martí", "Català", email = "marti.catalasabate@ndorms.ox.ac.uk", 
@@ -49,9 +49,11 @@ Description: Summarise patient-level drug utilisation cohorts using data mapped
 License: Apache License (>= 2)
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Suggests: 
     bit64,
+    CDMConnector (>= 1.4.0),
+    CohortConstructor,
     CohortSurvival,
     covr,
     DBI,
@@ -63,22 +65,23 @@ Suggests:
     here,
     knitr,
     odbc,
+    omock,
     rmarkdown,
     RPostgres,
     scales,
     testthat (>= 3.1.5),
     tibble,
-    visOmopResults (>= 1.0.0)
+    visOmopResults (>= 1.3.0)
 Config/testthat/edition: 3
-Imports: 
-    CDMConnector (>= 1.4.0),
+Imports:
     cli,
     clock,
     CodelistGenerator (>= 3.1.0),
     dplyr,
     glue,
-    omopgenerics (>= 1.0.0),
-    PatientProfiles (>= 1.0.0),
+    lifecycle,
+    omopgenerics (>= 1.3.1),
+    PatientProfiles (>= 1.4.2),
     purrr,
     rlang,
     stringr,

NAMESPACE

@@ -28,7 +28,6 @@ export(generateDrugUtilisationCohortSet)
 export(generateIngredientCohortSet)
 export(groupColumns)
 export(importSummarisedResult)
-export(mockDisconnect)
 export(mockDrugUtilisation)
 export(patternTable)
 export(plotDrugRestart)
@@ -57,7 +56,6 @@ export(tableIndication)
 export(tableProportionOfPatientsCovered)
 export(tableTreatment)
 export(tidy)
-importFrom(PatientProfiles,mockDisconnect)
 importFrom(dplyr,"%>%")
 importFrom(omopgenerics,additionalColumns)
 importFrom(omopgenerics,attrition)

NEWS.md

@@ -1,3 +1,16 @@
+# DrugUtilisation 1.0.5
+
+* Explictly copy data into the database using insertTable by @ablack3 in #727
+* Correct as.numeric of datediff by @catalamarti in #731
+* Add style argument in tables and figures by @catalamarti in #734
+* Call libraries explicitly in vignettes by @catalamarti in #739
+* Include in vignette how to use days prescribed by @catalamarti in #743
+* tests edge case by @catalamarti in #744
+* Support local datasets and standardise testing by @catalamarti in #736
+* Review mock data vignette by @catalamarti in #745
+* add plotIndication params by @ginberg in #740
+* add type and style as NULL to use default values by @catalamarti in #747
+
 # DrugUtilisation 1.0.4
 
 * Fix plotDrugUtilisation combining different cdm_name by @catalamarti

R/addDrugUtilisation.R

@@ -888,7 +888,7 @@ addDrugUseInternal <- function(x,
         dplyr::mutate("time_to_exposure" = dplyr::if_else(
           .data$time_to_exposure <= .data[[indexDate]],
           0L,
-          as.integer(!!CDMConnector::datediff(start = indexDate, end = "time_to_exposure"))
+          as.integer(clock::date_count_between(start = .data[[indexDate]], end = .data$time_to_exposure, precision = "day"))
         ))
     }
     x <- x |>
@@ -912,9 +912,11 @@ addDrugUseInternal <- function(x,
   if (initialQuantity | initialExposureDuration) {
     qs <- c(
       "as.numeric(sum(.data$quantity, na.rm = TRUE))",
-      "max(as.integer(local(CDMConnector::datediff(
-        start = 'drug_exposure_start_date', end = 'drug_exposure_end_date'
-      ))) + 1L, na.rm = TRUE)"
+      "max(as.integer(clock::date_count_between(
+        start = .data$drug_exposure_start_date,
+        end = .data$drug_exposure_end_date,
+        precision = 'day'
+      )) + 1L, na.rm = TRUE)"
     ) |>
       rlang::parse_exprs() |>
       rlang::set_names(c("initial_quantity", "initial_exposure_duration"))
@@ -959,10 +961,10 @@ addDrugUseInternal <- function(x,
             .data[[censorDate]], .data$drug_exposure_end_date
           )
         ) %>%
-        dplyr::mutate("exposed_time" = as.integer(!!CDMConnector::datediff(
-          start = "drug_exposure_start_date",
-          end = "drug_exposure_end_date",
-          interval = "day"
+        dplyr::mutate("exposed_time" = as.integer(clock::date_count_between(
+          start = .data$drug_exposure_start_date,
+          end = .data$drug_exposure_end_date,
+          precision = "day"
         )) + 1L)
     }
     qs <- c(
@@ -1017,8 +1019,10 @@ addDrugUseInternal <- function(x,
           .data[[censorDate]]
         )
       ) %>%
-      dplyr::mutate("exposure_duration" = as.integer(!!CDMConnector::datediff(
-        start = "start_contribution", end = "end_contribution"
+      dplyr::mutate("exposure_duration" = as.integer(clock::date_count_between(
+        start = .data$start_contribution,
+        end = .data$end_contribution,
+        precision = "day"
       )) + 1L) |>
       dplyr::select(-c("start_contribution", "end_contribution")) |>
       dplyr::compute(

R/addIntersect.R

@@ -46,10 +46,10 @@
 #' @examples
 #' \donttest{
 #' library(DrugUtilisation)
-#' library(CDMConnector)
 #' library(dplyr, warn.conflicts = FALSE)
+#' library(CDMConnector)
 #'
-#' cdm <- mockDrugUtilisation()
+#' cdm <- mockDrugUtilisation(source = "duckdb")
 #'
 #' indications <- list(headache = 378253, asthma = 317009)
 #' cdm <- generateConceptCohortSet(cdm = cdm,

R/benchmarkDrugUtilisation.R

@@ -30,12 +30,9 @@
 #' @examples
 #' \donttest{
 #' library(DrugUtilisation)
-#' library(CDMConnector)
-#' library(duckdb)
+#' library(omock)
 #'
-#' requireEunomia()
-#' con <- dbConnect(drv = duckdb(dbdir = eunomiaDir()))
-#' cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
+#' cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")
 #'
 #' timings <- benchmarkDrugUtilisation(cdm)
 #'
@@ -72,7 +69,7 @@ benchmarkDrugUtilisation <- function(cdm,
   mainCohort <- paste0(prefix, "main")
   altCohort <- paste0(prefix, "alt")
   inclusion <- paste0(prefix, "inclusion")
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -88,7 +85,7 @@ benchmarkDrugUtilisation <- function(cdm,
       gapEra = 30
     )
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -106,7 +103,7 @@ benchmarkDrugUtilisation <- function(cdm,
       daysPrescribed = TRUE
     )
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -119,7 +116,7 @@ benchmarkDrugUtilisation <- function(cdm,
       requireObservationBeforeDrug(days = 365, name = inclusion) |>
       requirePriorDrugWashout(days = 365, name = inclusion)
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -134,7 +131,7 @@ benchmarkDrugUtilisation <- function(cdm,
       name = altCohort
     )
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -148,7 +145,7 @@ benchmarkDrugUtilisation <- function(cdm,
         gapEra = 30, ingredientConceptId = ingredientConceptId
       )
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -164,7 +161,7 @@ benchmarkDrugUtilisation <- function(cdm,
           indicationCohortName = indicationCohort
         )
     )
-    time <- as.numeric(Sys.time() - t0)
+    time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
     result <- result |>
       dplyr::union_all(dplyr::tibble(task = task, time = time))
   }
@@ -177,7 +174,7 @@ benchmarkDrugUtilisation <- function(cdm,
     cdm[[mainCohort]] |>
       summariseDrugRestart(switchCohortTable = altCohort)
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -189,7 +186,7 @@ benchmarkDrugUtilisation <- function(cdm,
     cdm[[mainCohort]] |>
       summariseProportionOfPatientsCovered()
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -204,7 +201,7 @@ benchmarkDrugUtilisation <- function(cdm,
         window = list(c(-Inf, -1), c(0, 0), c(1, Inf))
       )
   )
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 
@@ -213,7 +210,7 @@ benchmarkDrugUtilisation <- function(cdm,
   benchmarkMessage(task)
   t0 <- Sys.time()
   omopgenerics::dropSourceTable(cdm = cdm, name = dplyr::starts_with(prefix))
-  time <- as.numeric(Sys.time() - t0)
+  time <- as.numeric(difftime(time1 = Sys.time(), time2 = t0, units = "secs"))
   result <- result |>
     dplyr::union_all(dplyr::tibble(task = task, time = time))
 

R/cohortUtilities.R

@@ -115,8 +115,10 @@ subsetTables <- function(cdm, conceptSet, name, subsetCohort, subsetCohortId) {
       cohort <- cohort %>%
         dplyr::mutate(
           number_exposures = 1L,
-          days_prescribed = as.integer(!!CDMConnector::datediff(
-            "cohort_start_date", "cohort_end_date"
+          days_prescribed = as.integer(clock::date_count_between(
+            start = .data$cohort_start_date,
+            end = .data$cohort_end_date,
+            precision = "day"
           )) + 1L
         ) |>
         erafy(
@@ -199,17 +201,18 @@ erafy <- function(x,
     dplyr::select(dplyr::all_of(c(group, "date_event" = end))) |>
     dplyr::mutate(date_id = 1, !!!newCols)
   if (gap > 0) {
+    gap <- as.integer(gap)
     xend <- xend %>%
-      dplyr::mutate("date_event" = as.Date(!!CDMConnector::dateadd(
-        date = "date_event", number = gap, interval = "day"
+      dplyr::mutate("date_event" = as.Date(clock::add_days(
+        x = .data$date_event, n = .env$gap
       )))
   }
   x <- xstart |>
     dplyr::union_all(xend) |>
     dplyr::group_by(dplyr::across(dplyr::all_of(group))) |>
     dplyr::arrange(.data$date_event, .data$date_id) |>
     dplyr::mutate(era_id = dplyr::if_else(
-      cumsum(.data$date_id) == -1 && .data$date_id == -1, -1L, 0L
+      cumsum(.data$date_id) == -1 & .data$date_id == -1, -1L, 0L
     )) |>
     dplyr::arrange(.data$date_event, .data$date_id, .data$era_id) |>
     dplyr::mutate(era_id = cumsum(.data$era_id)) |>
@@ -223,9 +226,10 @@ erafy <- function(x,
       .groups = "drop"
     )
   if (gap > 0) {
+    gap <- -gap
     x <- x %>%
-      dplyr::mutate(!!end := as.Date(!!CDMConnector::dateadd(
-        date = end, number = -gap, interval = "day"
+      dplyr::mutate(!!end := as.Date(clock::add_days(
+        x = .data[[end]], n = .env$gap
       )))
   }
   x <- x |>

R/dailyDose.R

@@ -27,9 +27,10 @@
       "quantity"
     ) |>
     dplyr::distinct() %>%
-    dplyr::mutate(days_exposed = !!CDMConnector::datediff(
-      start = "drug_exposure_start_date",
-      end = "drug_exposure_end_date"
+    dplyr::mutate(days_exposed = clock::date_count_between(
+      start = .data$drug_exposure_start_date,
+      end = .data$drug_exposure_end_date,
+      precision = "day"
     ) + 1) |>
     dplyr::inner_join(
       drugStrengthPattern(cdm = cdm, ingredientConceptId = ingredientConceptId),
@@ -96,6 +97,10 @@ summariseDoseCoverage <- function(cdm,
   omopgenerics::assertNumeric(x = sampleSize, min = 0, length = 1, null = TRUE, integerish = TRUE)
   omopgenerics::assertCharacter(estimates)
 
+  # insert routes table
+  nmRoutes <- omopgenerics::uniqueTableName()
+  cdm <- omopgenerics::insertTable(cdm = cdm, name = nmRoutes, table = routes)
+
   # get daily dosage
   dailyDose <- cdm[["drug_exposure"]] |>
     dplyr::inner_join(
@@ -109,9 +114,10 @@ summariseDoseCoverage <- function(cdm,
       "drug_concept_id", "drug_exposure_start_date", "drug_exposure_end_date",
       "quantity"
     ) %>%
-    dplyr::mutate(days_exposed = !!CDMConnector::datediff(
-      start = "drug_exposure_start_date",
-      end = "drug_exposure_end_date"
+    dplyr::mutate(days_exposed = clock::date_count_between(
+      start = .data$drug_exposure_start_date,
+      end = .data$drug_exposure_end_date,
+      precision = "day"
     ) + 1) |>
     dplyr::left_join(
       drugStrengthPattern(cdm = cdm, ingredientConceptId = ingredientConceptId),
@@ -123,7 +129,19 @@ summariseDoseCoverage <- function(cdm,
       "drug_concept_id", "daily_dose", "unit", "pattern_id",
       "concept_id" = "ingredient_concept_id"
     ) |>
-    .addRoute() |>
+    dplyr::left_join(
+      cdm[["concept_relationship"]] |>
+        dplyr::select(c("concept_id_1", "concept_id_2", "relationship_id")) |>
+        dplyr::filter(.data$relationship_id == "RxNorm has dose form") |>
+        dplyr::select(-"relationship_id") |>
+        dplyr::rename(
+          "drug_concept_id" = "concept_id_1",
+          "dose_form_concept_id" = "concept_id_2"
+        ),
+      by = "drug_concept_id"
+    ) |>
+    dplyr::left_join(cdm[[nmRoutes]], by = "dose_form_concept_id") |>
+    dplyr::select(-"dose_form_concept_id") |>
     dplyr::left_join(
       cdm[["concept"]] |>
         dplyr::rename("ingredient_name" = "concept_name") |>
@@ -132,6 +150,9 @@ summariseDoseCoverage <- function(cdm,
     ) |>
     dplyr::collect()
 
+  # drop nmRoutes
+  omopgenerics::dropSourceTable(cdm = cdm, name = nmRoutes)
+
   if (!is.null(sampleSize)) {
     dailyDose <- dailyDose |>
       dplyr::group_by(.data$ingredient_name) |>