From 786d87a96f96e66d24bad12fbcd24970bdd85142 Mon Sep 17 00:00:00 2001 From: Admin_mschuemi Date: Tue, 3 Sep 2024 06:09:30 -0400 Subject: [PATCH] Rerunning vignettes on DataBricks --- extras/MultiAnalysesVignetteDataFetch.R | 27 ++++++++---------- extras/SingleStudyVignetteDataFetch.R | 35 +++++++++++------------- inst/doc/MultipleAnalyses.pdf | Bin 457566 -> 452200 bytes inst/doc/SingleStudies.pdf | Bin 539820 -> 536126 bytes vignettes/MultipleAnalyses.Rmd | 4 +-- vignettes/MultipleAnalyses.pdf | Bin 0 -> 452465 bytes vignettes/SingleStudies.Rmd | 5 ++-- 7 files changed, 32 insertions(+), 39 deletions(-) create mode 100644 vignettes/MultipleAnalyses.pdf diff --git a/extras/MultiAnalysesVignetteDataFetch.R b/extras/MultiAnalysesVignetteDataFetch.R index 485d89d..2adc4c4 100644 --- a/extras/MultiAnalysesVignetteDataFetch.R +++ b/extras/MultiAnalysesVignetteDataFetch.R @@ -16,21 +16,19 @@ # This code should be used to fetch the data that is used in the vignettes. library(SelfControlledCaseSeries) -options(andromedaTempFolder = "d:/andromedaTemp") +options(andromedaTempFolder = "e:/andromedaTemp") -connectionDetails <- DatabaseConnector::createConnectionDetails( - dbms = "redshift", - connectionString = keyring::key_get("redShiftConnectionStringOhdaMdcd"), - user = keyring::key_get("redShiftUserName"), - password = keyring::key_get("redShiftPassword") +connectionDetails <- createConnectionDetails( + dbms = "spark", + connectionString = keyring::key_get("databricksConnectionString"), + user = "token", + password = keyring::key_get("databricksToken") ) -cdmDatabaseSchema <- "cdm_truven_mdcd_v3038" -cohortDatabaseSchema <- "scratch_mschuemi" -cohortTable <- "sccs_vignette" -options(sqlRenderTempEmulationSchema = NULL) -cohortTable <- "sccs_vignette" -cdmVersion <- "5" -outputFolder <- "d:/temp/sccsVignette2" +cdmDatabaseSchema <- "merative_mdcr.cdm_merative_mdcr_v3045" +cohortDatabaseSchema <- "scratch.scratch_mschuemi" +cohortTable <- "sccs_vignette" +options(sqlRenderTempEmulationSchema = "scratch.scratch_mschuemi") +outputFolder <- "e:/temp/sccsVignette2" # Create cohorts --------------------------------------------------------------- @@ -258,7 +256,6 @@ runSccsAnalyses( exposureTable = "drug_era", outcomeDatabaseSchema = cohortDatabaseSchema, outcomeTable = cohortTable, - cdmVersion = cdmVersion, outputFolder = outputFolder, combineDataFetchAcrossOutcomes = TRUE, exposuresOutcomeList = exposuresOutcomeList, @@ -333,7 +330,7 @@ DatabaseConnector::disconnect(connection) # Launch Shiny app ------------------------------------------------------------- library(dplyr) -outputFolder <- "d:/temp/sccsVignette2" +outputFolder <- "e:/temp/sccsVignette2" databaseFile <- file.path(outputFolder, "export", "SccsResults.sqlite") connectionDetails <- DatabaseConnector::createConnectionDetails( dbms = "sqlite", diff --git a/extras/SingleStudyVignetteDataFetch.R b/extras/SingleStudyVignetteDataFetch.R index 361a03e..0624f23 100644 --- a/extras/SingleStudyVignetteDataFetch.R +++ b/extras/SingleStudyVignetteDataFetch.R @@ -16,20 +16,19 @@ # This code should be used to fetch the data that is used in the vignettes. library(SelfControlledCaseSeries) -options(andromedaTempFolder = "d:/andromedaTemp") - -folder <- "d:/temp/vignetteSccs" -connectionDetails <- DatabaseConnector::createConnectionDetails( - dbms = "redshift", - connectionString = keyring::key_get("redShiftConnectionStringOhdaMdcd"), - user = keyring::key_get("redShiftUserName"), - password = keyring::key_get("redShiftPassword") +options(andromedaTempFolder = "e:/andromedaTemp") + +folder <- "e:/temp/vignetteSccs" +connectionDetails <- createConnectionDetails( + dbms = "spark", + connectionString = keyring::key_get("databricksConnectionString"), + user = "token", + password = keyring::key_get("databricksToken") ) -cdmDatabaseSchema <- "cdm_truven_mdcd_v2565" -cohortDatabaseSchema <- "scratch_mschuemi" -cohortTable <- "sccs_epistaxis" -cdmVersion <- "5" -options(sqlRenderTempEmulationSchema = NULL) +cdmDatabaseSchema <- "merative_mdcr.cdm_merative_mdcr_v3045" +cohortDatabaseSchema <- "scratch.scratch_mschuemi" +cohortTable <- "sccs_vignette" +options(sqlRenderTempEmulationSchema = "scratch.scratch_mschuemi") # Create cohorts --------------------------------------------------------------- connection <- DatabaseConnector::connect(connectionDetails) @@ -73,8 +72,7 @@ sccsData <- getDbSccsData(connectionDetails = connectionDetails, exposureIds = aspirin, studyStartDates = "20100101", studyEndDates = "21000101", - maxCasesPerOutcome = 100000, - cdmVersion = cdmVersion) + maxCasesPerOutcome = 100000) saveSccsData(sccsData, file.path(folder, "data1.zip")) sccsData <- loadSccsData(file.path(folder, "data1.zip")) sccsData @@ -191,8 +189,7 @@ sccsData <- getDbSccsData(connectionDetails = connectionDetails, exposureIds = aspirin, maxCasesPerOutcome = 100000, studyStartDates = c("20100101", "20220101"), - studyEndDates = c("20191231", "21001231"), - cdmVersion = cdmVersion) + studyEndDates = c("20191231", "21001231")) saveSccsData(sccsData, file.path(folder, "data2.zip")) sccsData <- loadSccsData(file.path(folder, "data2.zip")) studyPop <- createStudyPopulation(sccsData = sccsData, @@ -300,8 +297,7 @@ sccsData <- getDbSccsData(connectionDetails = connectionDetails, exposureIds = c(), maxCasesPerOutcome = 100000, studyStartDates = c("19000101", "20220101"), - studyEndDates = c("20191231", "21001231"), - cdmVersion = cdmVersion) + studyEndDates = c("20191231", "21001231")) saveSccsData(sccsData, file.path(folder, "data3.zip")) sccsData <- loadSccsData(file.path(folder, "data3.zip")) @@ -313,6 +309,7 @@ studyPop <- createStudyPopulation(sccsData = sccsData, naivePeriod = 180) covarAllDrugs <- createEraCovariateSettings(label = "Other exposures", + includeEraIds = c(), excludeEraIds = aspirin, stratifyById = TRUE, start = 1, diff --git a/inst/doc/MultipleAnalyses.pdf b/inst/doc/MultipleAnalyses.pdf index 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We'll use these later to tell R where the data in CDM format live, where we want to store the (outcome) cohorts, and what version CDM is used. Note that for Microsoft SQL Server, databaseschemas need to specify both the database and the schema, so for example `cdmDatabaseSchema <- "my_cdm_data.dbo"`. @@ -320,7 +319,6 @@ referenceTable <- runSccsAnalyses( exposureTable = "drug_era", outcomeDatabaseSchema = cohortDatabaseSchema, outcomeTable = cohortTable, - cdmVersion = cdmVersion, outputFolder = outputFolder, combineDataFetchAcrossOutcomes = TRUE, exposuresOutcomeList = exposuresOutcomeList, diff --git a/vignettes/MultipleAnalyses.pdf b/vignettes/MultipleAnalyses.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ae484f4afc706b8120f5d3c70fbb55a3d3583a92 GIT binary patch literal 452465 zcmb@u1yo&IvNepmyK^8&a1IW^-95My+}&M6aF-Aqg1ZC~G`PEaaEIUl{*(0W|E81s zy6<~mGX@979=rCcy`*Z+xoRCsMNx4EAR{{h<=9mB90Cx)46rk@MBwEGFsT5TBc2$8O6C^>}JxJy^9{DSL(Aab`$PPAScEu)vnFqx%Ln(r1>f(6<8C$;MMA zUCe$AyY-=@Q6AnswgSq$uLg>*FX1_Kmq?npNrmK|_HxXX4C1}vW(HiPm_4%PfI(yE zV*eq7W@5fl`i_AzdJ=8kveesr!?I3|2@kOv3=4|MrA%BZ2a+wyLvXu+NRd!RN%Y82 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1, @@ -646,7 +647,7 @@ We can visualize which age ranges are covered by each subject's observation time ```{r eval=FALSE} plotAgeSpans(studyPop) ``` -```{r echo=FALSE,message=FALSE} +```{r echo=FALSE,message=FALSE,warnings=FALSE} if (folderExists) { plotAgeSpans(studyPop) }

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