# @file ffHelperFunctions.R
#
# Copyright 2021 Observational Health Data Sciences and Informatics
#
# This file is part of PatientLevelPrediction
#
# 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.
limitCovariatesToPopulation <- function(covariateData, rowIds) {
ParallelLogger::logTrace(paste0('Starting to limit covariate data to population...'))
metaData <- attr(covariateData, 'metaData')
newCovariateData <- Andromeda::andromeda(covariateRef = covariateData$covariateRef,
analysisRef = covariateData$analysisRef)
covariateData$pop <- data.frame(rowId = rowIds)
Andromeda::createIndex(tbl = covariateData$pop, columnNames = 'rowId',
indexName = 'pop_rowIds')
on.exit(covariateData$pop <- NULL, add = T)
newCovariateData$covariates <- covariateData$covariates %>%
dplyr::inner_join(covariateData$pop, by = 'rowId')
Andromeda::createIndex(tbl = newCovariateData$covariates, columnNames = 'covariateId',
indexName = 'covariates_ncovariateIds')
metaData$populationSize <- length(rowIds)
attr(newCovariateData, 'metaData') <- metaData
class(newCovariateData) <- "CovariateData"
ParallelLogger::logTrace(paste0('Finished limiting covariate data to population...'))
return(newCovariateData)
}
batchRestrict <- function(covariateData, population, sizeN = 10000000){
ParallelLogger::logInfo('Due to data size using batchRestrict to limit covariate data to population')
start <- Sys.time()
metaData <- attr(covariateData, 'metaData')
newCovariateData <- Andromeda::andromeda(covariateRef = covariateData$covariateRef,
analysisRef = covariateData$analysisRef)
Andromeda::batchApply(covariateData$covariates, function(tempData) {
filtered <- dplyr::inner_join(tempData, population, by = 'rowId')
if ("covariates" %in% names(newCovariateData)) {
Andromeda::appendToTable(newCovariateData$covariates, data = filtered)
} else {
newCovariateData$covariates <- filtered
}
},
progressBar = TRUE,
batchSize = sizeN)
Andromeda::createIndex(tbl = newCovariateData$covariates,
columnNames = 'covariateId',
indexName = 'covariates_ncovariateIds')
Andromeda::createIndex(tbl = newCovariateData$covariates,
columnNames = 'rowId',
indexName = 'covariates_rowId')
Andromeda::createIndex(tbl = newCovariateData$covariates,
columnNames = c('covariateId', 'covariateValue'),
indexName = 'covariates_covariateId_value')
metaData$populationSize <- nrow(population)
attr(newCovariateData, 'metaData') <- metaData
class(newCovariateData) <- "CovariateData"
timeTaken <- as.numeric(Sys.time() - start, units = "mins")
ParallelLogger::logInfo(paste0('Limiting covariate data took: ', timeTaken, ' mins'))
return(newCovariateData)
}
# is this used?
# return prev of ffdf
calculatePrevs <- function(plpData, population){
#===========================
# outcome prevs
#===========================
# add population to sqllite
population <- dplyr::as_tibble(population)
plpData$covariateData$population <- population %>%
dplyr::select("rowId", "outcomeCount")
outCount <- nrow(plpData$covariateData$population %>% dplyr::filter(.data$outcomeCount == 1))
nonOutCount <- nrow(plpData$covariateData$population %>% dplyr::filter(.data$outcomeCount == 0))
# join covariate with label
prevs <- plpData$covariateData$covariates %>% dplyr::inner_join(plpData$covariateData$population) %>%
dplyr::group_by(.data$covariateId) %>%
dplyr::summarise(prev.out = 1.0*sum(.data$outcomeCount==1, na.rm = TRUE)/outCount,
prev.noout = 1.0*sum(.data$outcomeCount==0, na.rm = TRUE)/nonOutCount) %>%
dplyr::select("covariateId", "prev.out", "prev.noout")
#clear up data
##plpData$covariateData$population <- NULL
return(as.data.frame(prevs))
}
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