# Copyright 2018 Observational Health Data Sciences and Informatics
#
# This file is part of DistributedRegressionEval
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#' Create the exposure and outcome cohorts
#'
#' @details
#' This function will create the exposure and outcome cohorts following the definitions included in
#' this package.
#'
#' @param connectionDetails An object of type \code{connectionDetails} as created using the
#' \code{\link[DatabaseConnector]{createConnectionDetails}} function in the
#' DatabaseConnector package.
#' @param cdmDatabaseSchema Schema name where your patient-level data in OMOP CDM format resides.
#' Note that for SQL Server, this should include both the database and
#' schema name, for example 'cdm_data.dbo'.
#' @param cohortDatabaseSchema Schema name where intermediate data can be stored. You will need to have
#' write priviliges in this schema. Note that for SQL Server, this should
#' include both the database and schema name, for example 'cdm_data.dbo'.
#' @param cohortTable The name of the table that will be created in the work database schema.
#' This table will hold the exposure and outcome cohorts used in this
#' study.
#' @param oracleTempSchema Should be used in Oracle to specify a schema where the user has write
#' priviliges for storing temporary tables.
#' @param outputFolder Name of local folder to place results; make sure to use forward slashes
#' (/)
#'
#' @export
createCohorts <- function(connectionDetails,
cdmDatabaseSchema,
cohortDatabaseSchema,
cohortTable = "cohort",
oracleTempSchema,
outputFolder) {
if (!file.exists(outputFolder))
dir.create(outputFolder, recursive = TRUE)
conn <- DatabaseConnector::connect(connectionDetails)
.createCohorts(connection = conn,
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
oracleTempSchema = oracleTempSchema,
outputFolder = outputFolder)
sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "SampleTarget.sql",
packageName = "DistributedRegressionEval",
dbms = conn@dbms,
oracleTempSchema = oracleTempSchema,
cohort_database_schema = cohortDatabaseSchema,
cohort_table = cohortTable,
cohort_id = 2,
sample_size = 100000,
new_cohort_id = 102)
DatabaseConnector::executeSql(conn, sql, progressBar = FALSE, reportOverallTime = FALSE)
sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id"
sql <- SqlRender::renderSql(sql,
cohort_database_schema = cohortDatabaseSchema,
cohort_table = cohortTable)$sql
sql <- SqlRender::translateSql(sql, targetDialect = conn@dbms)$sql
counts <- DatabaseConnector::querySql(conn, sql)
names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts))
write.csv(counts, file.path(outputFolder, "CohortCounts.csv"))
DatabaseConnector::disconnect(conn)
}
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