# copy_to with overwrite=T does not work on Oracle. Global temp tables need to be truncated before being dropped. # test_that("Oracle dplyr works", { # skip_if_not("OracleODBC-19" %in% odbc::odbcListDataSources()$name) # skip_on_ci() # need development version of dbplyr # # con <- DBI::dbConnect(odbc::odbc(), "OracleODBC-19") # # cdm <- CDMConnector::cdm_from_con( # con = con, # cdm_schema = "CDMV5" # ) # # df <- cdm$observation_period %>% # dplyr::mutate(new_date = !!asDate(observation_period_start_date)) %>% #as.Date translation is incorrect # head() %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # df <- cdm$person %>% # # dplyr::left_join(cdm$observation_period, by = "person_id") %>% # this fails # dplyr::left_join(cdm$observation_period, by = "person_id", x_as = "x", y_as = "y") %>% # head() %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # # df <- cdm$person %>% # dplyr::slice_sample(n = 100) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # cdm$person %>% # dplyr::select(year_of_birth, month_of_birth, day_of_birth) %>% # dplyr::mutate(dob = as.Date(paste0( # .data$year_of_birth, "/", # .data$month_of_birth, "/", # .data$day_of_birth # ))) %>% # dbplyr::sql_render() # # DBI::dbDisconnect(con) # }) # # # # Test dbplyr quantile translation ----- # # # test_that("quantile translation works on postgres", { # skip_if(Sys.getenv("CDM5_POSTGRESQL_USER") == "") # skip("manual test") # # con <- DBI::dbConnect(RPostgres::Postgres(), # dbname = Sys.getenv("CDM5_POSTGRESQL_DBNAME"), # host = Sys.getenv("CDM5_POSTGRESQL_HOST"), # user = Sys.getenv("CDM5_POSTGRESQL_USER"), # password = Sys.getenv("CDM5_POSTGRESQL_PASSWORD")) # # # # cdm <- cdm_from_con(con, cdm_schema = Sys.getenv("CDM5_POSTGRESQL_CDM_SCHEMA")) # # # fails # # df <- cdm$drug_exposure %>% # # dplyr::select(drug_concept_id, days_supply) %>% # # dplyr::group_by(drug_concept_id) %>% # # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # # dplyr::collect() # # # # expect_s3_class(df, "data.frame") # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # test_that("quantile translation works on sql server", { # skip_if(Sys.getenv("CDM5_SQL_SERVER_USER") == "") # skip("manual test") # # con <- DBI::dbConnect(odbc::odbc(), # Driver = "ODBC Driver 18 for SQL Server", # Server = Sys.getenv("CDM5_SQL_SERVER_SERVER"), # Database = Sys.getenv("CDM5_SQL_SERVER_CDM_DATABASE"), # UID = Sys.getenv("CDM5_SQL_SERVER_USER"), # PWD = Sys.getenv("CDM5_SQL_SERVER_PASSWORD"), # TrustServerCertificate = "yes", # Port = 1433) # # cdm <- cdm_from_con(con, cdm_schema = c("CDMV5", "dbo")) # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # # fails # # df <- cdm$drug_exposure %>% # # dplyr::select(drug_concept_id, days_supply) %>% # # dplyr::group_by(drug_concept_id) %>% # # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # # dplyr::collect() # # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # test_that("quantile translation works on redshift", { # skip_if(Sys.getenv("CDM5_REDSHIFT_USER") == "") # skip("manual test") # # con <- DBI::dbConnect(RPostgres::Redshift(), # dbname = Sys.getenv("CDM5_REDSHIFT_DBNAME"), # host = Sys.getenv("CDM5_REDSHIFT_HOST"), # port = Sys.getenv("CDM5_REDSHIFT_PORT"), # user = Sys.getenv("CDM5_REDSHIFT_USER"), # password = Sys.getenv("CDM5_REDSHIFT_PASSWORD")) # # cdm <- cdm_from_con(con, cdm_schema = Sys.getenv("CDM5_REDSHIFT_CDM_SCHEMA")) # # # df <- cdm$drug_exposure %>% # # dplyr::select(drug_concept_id, days_supply) %>% # # dplyr::group_by(drug_concept_id) %>% # # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # # dplyr::collect() # # # # expect_s3_class(df, "data.frame") # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # test_that("quantile translation works on Oracle", { # skip_on_ci() # skip_on_cran() # skip_if_not("OracleODBC-19" %in% odbc::odbcListDataSources()$name) # skip("manual test") # # cdm_schema <- Sys.getenv("CDM5_ORACLE_CDM_SCHEMA") # con <- DBI::dbConnect(odbc::odbc(), "OracleODBC-19") # # cdm <- cdm_from_con(con, cdm_schema = cdm_schema) # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # test_that("quantile translation works on Spark", { # skip_if_not("Databricks" %in% odbc::odbcListDataSources()$name) # skip("manual test") # # con <- DBI::dbConnect(odbc::odbc(), dsn = "Databricks", bigint = "numeric") # # cdm <- cdm_from_con(con, cdm_schema = "omop531") # # # df <- cdm$drug_exposure %>% # # dplyr::select(drug_concept_id, days_supply) %>% # # dplyr::group_by(drug_concept_id) %>% # # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # # dplyr::collect() # # # # expect_s3_class(df, "data.frame") # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # test_that("quantile translation works on duckdb", { # skip_if_not(rlang::is_installed("duckdb")) # skip_if_not(eunomia_is_available()) # skip("manual test") # # con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir()) # # cdm <- cdm_from_con(con, cdm_schema = "main") # # # df <- cdm$drug_exposure %>% # # dplyr::select(drug_concept_id, days_supply) %>% # # dplyr::group_by(drug_concept_id) %>% # # dplyr::mutate(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # # dplyr::distinct(drug_concept_id, q05_days_supply) %>% # # dplyr::collect() # # # expect_s3_class(df, "data.frame") # # df <- cdm$drug_exposure %>% # dplyr::select(drug_concept_id, days_supply) %>% # dplyr::group_by(drug_concept_id) %>% # dplyr::summarise(q05_days_supply = quantile(.data$days_supply, 0.05, na.rm = T)) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con, shutdown = TRUE) # }) # # # test_that("Oracle inSchema works", { # skip_if_not("OracleODBC-19" %in% odbc::odbcListDrivers()) # # con <- DBI::dbConnect(odbc::odbc(), "OracleODBC-19") # cdm_schema <- Sys.getenv("CDM5_ORACLE_CDM_SCHEMA") # # person <- dplyr::tbl(con, inSchema(cdm_schema, "PERSON", dbms(con))) %>% # dplyr::rename_all(tolower) # # observation_period <- dplyr::tbl(con, inSchema(cdm_schema, "OBSERVATION_PERIOD", dbms(con))) %>% # dplyr::rename_all(tolower) # # df <- dplyr::inner_join(person, observation_period, by = "person_id") %>% # head(2) %>% # dplyr::collect() # # expect_s3_class(df, "data.frame") # # DBI::dbDisconnect(con) # }) # # # TODO not operator on a column does not work on sqlserver # dplyr::tbl(attr(cdm, "dbcon"), inSchema(attr(cdm, "write_schema"), # tempName, # dbms = dbms(con))) %>% # dplyr::filter(!.data$is_excluded)