R/PackageResultsForSharing.R

In Sigfried/hivTestStudy: hivTestStudy Phenotype Characerization

# Copyright 2015 Observational Health Data Sciences and Informatics
#
# This file is part of CelecoxibVsNsNSAIDs
#
# 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.

#' Package the results for sharing with OHDSI researchers
#'
#' @details
#' This function packages the results.
#'
#' @param outputFolder   Name of local folder to place results; make sure to use forward slashes (/)
#'
#' @export
packageResults <- function(outputFolder) {
  exportFolder <- file.path(outputFolder, "export")

  if (!file.exists(exportFolder))
    dir.create(exportFolder)

  ### Add all to zip file ###
  zipName <- file.path(exportFolder, "StudyResults.zip")
  OhdsiSharing::compressFolder(exportFolder, zipName)
  writeLines(paste("\nStudy results are ready for sharing at:", zipName))
}
#
#   outcomeReference <- readRDS(file.path(outputFolder, "outcomeModelReference.rds"))
#   analysisSummary <- read.csv(file.path(outputFolder, "Results.csv"))
#
#   ### Write main results table ###
#   write.csv(analysisSummary, file.path(exportFolder, "Results.csv"), row.names = FALSE)
#
#   ### Main propensity score plot ###
#   psFileName <- outcomeReference$sharedPsFile[outcomeReference$sharedPsFile != ""][1]
#   ps <- readRDS(psFileName)
#   CohortMethod::plotPs(ps, fileName = file.path(exportFolder, "PS_pref_scale.png"))
#   CohortMethod::plotPs(ps, scale = "propensity", fileName = file.path(exportFolder, "PS.png"))
#
#   ### Covariate balance table ###
#   balFileName <- outcomeReference$covariateBalanceFile[outcomeReference$covariateBalanceFile != ""][1]
#   balance <- readRDS(balFileName)
#
#   write.csv(balance, file.path(exportFolder, "Balance.csv"), row.names = FALSE)
#
#   ### Two covariate balance plots ###
#   CohortMethod::plotCovariateBalanceScatterPlot(balance, fileName = file.path(exportFolder,
#                                                                               "Balance_scatterplot.png"))
#   CohortMethod::plotCovariateBalanceOfTopVariables(balance, fileName = file.path(exportFolder,
#                                                                                  "Balance_topVars.png"))
#
#   ### Empiricial calibration plots ###
#   negControlCohortIds <- unique(analysisSummary$outcomeId[analysisSummary$outcomeId > 100])
#   for (analysisId in unique(analysisSummary$analysisId)) {
#     negControlSubset <- analysisSummary[analysisSummary$analysisId == analysisId & analysisSummary$outcomeId %in%
#       negControlCohortIds, ]
#     negControlSubset <- negControlSubset[!is.na(negControlSubset$logRr) & negControlSubset$logRr !=
#       0, ]
#     posControlSubset <- analysisSummary[analysisSummary$analysisId == analysisId & !(analysisSummary$outcomeId %in%
#       negControlCohortIds), ]
#     posControlSubset <- posControlSubset[!is.na(posControlSubset$logRr) & posControlSubset$logRr !=
#       0, ]
#
#     if (nrow(negControlSubset) > 10) {
#       EmpiricalCalibration::plotCalibrationEffect(negControlSubset$logRr,
#                                                   negControlSubset$seLogRr,
#                                                   posControlSubset$logRr,
#                                                   posControlSubset$seLogRr,
#                                                   fileName = file.path(exportFolder, paste("CalEffect_a",
#                                                                                            analysisId,
#                                                                                            ".png",
#                                                                                            sep = "")))
#       EmpiricalCalibration::plotCalibration(negControlSubset$logRr,
#                                             negControlSubset$seLogRr,
#                                             useMcmc = TRUE,
#                                             fileName = file.path(exportFolder, paste("Calibration_a",
#                                                                                      analysisId,
#                                                                                      ".png",
#                                                                                      sep = "")))
#     }
#   }
#