function working for fake data

R/get_bw_score.R

@@ -4,6 +4,8 @@
 #'   Studyid number
 #' @param path_db Mandatory, character \cr
 #'   path of database
+#' @param fake_study optional, Boolean \cr
+#' whether study generated by SENDsanitizer package
 #' @return dataframe
 #'
 #' @examples
@@ -16,7 +18,7 @@
 #' @importFrom RSQLite dbConnect
 #' @importFrom RSQLite SQLite
 
-get_bw_score <- function(studyid, path_db) {
+get_bw_score <- function(studyid, path_db,fake_study=FALSE) {
 
 studyid <- as.character(studyid)
 
@@ -319,7 +321,7 @@ path <- path_db
     BW_df_selected_column <- joined_BW_df[, c("USUBJID", "STUDYID", "BWSTRESN", "BWSTRESN_Init")]
 
     # Add "ARMCD","SETCD","SEX" to "selected_df"
-    master_CompileData <- get_compile_data(studyid = studyid, path_db = path_db)
+    master_CompileData <- get_compile_data(studyid = studyid, path_db = path_db,fake_study = fake_study)
     STUDYID_less_master_CompileData <- master_CompileData[, c("USUBJID", "ARMCD","SETCD","SEX")]
     BW_df_merged_ARMCD <- merge(BW_df_selected_column, STUDYID_less_master_CompileData, by = "USUBJID")
 

R/get_compile_data.R

@@ -3,6 +3,8 @@
 #'   Studyid number
 #' @param path_db Mandatory, character \cr
 #'   path of database
+#' @param fake_study optional, Boolean \cr
+#' whether study generated by SENDsanitizer package
 #' @return dataframe
 #'
 #' @examples
@@ -11,7 +13,7 @@
 #' }
 #' @export
 
-get_compile_data <- function(studyid, path_db) {
+get_compile_data <- function(studyid, path_db,fake_study=FALSE) {
   studyid <- as.character(studyid)
   path <- path_db
   con <- DBI::dbConnect(DBI::dbDriver('SQLite'), dbname = path)
@@ -23,7 +25,28 @@ get_compile_data <- function(studyid, path_db) {
                               statement = stat,
                               params=list(x=studyid))
     domain
-}
+  }
+
+  if(fake_study){
+  dm <- con_db('dm')
+  data.table::setDT(dm)
+  ts <- con_db('ts')
+  data.table::setDT(ts)
+species <- ts$TSVAL[which(ts$TSPARMCD=='SPECIES')]
+dm <- dm[,c('STUDYID','USUBJID','SPECIES','SEX','ARMCD','ARM','SETCD')]
+dm[,`:=`(Species=species,SPECIES=NULL,ARMCD=ARM,ARM=NULL)]
+dm[ARMCD=='Control',`:=`(ARMCD='vehicle')]
+dm <- dm[ARMCD %in% c('vehicle','HD')]
+data.table::setDF(dm)
+  return(dm)
+
+
+  } else{
+
+
+
+
+
 
 #Pull relevant domain data for each domain
   bw <- con_db('bw')
@@ -301,4 +324,5 @@ get_compile_data <- function(studyid, path_db) {
       dplyr::rename(ARMCD = DOSE_RANKING)
 
   master_CompileData
+    }
 }

R/get_final_score.R

@@ -10,6 +10,8 @@
 #'   Studyid number
 #' @param path_db Mandatory, character \cr
 #'   path of database
+#' @param fake_study optional, Boolean \cr
+#' whether study generated by SENDsanitizer package
 #' @return dataframe
 #'
 #' @param domain Mandatory, character or vector \cr
@@ -20,7 +22,8 @@
 #' }
 #' @export
 
-get_all_score <- function(studyid, path_db, domain= c('lb','mi', 'bw')) {
+get_all_score <- function(studyid, path_db, domain= c('lb','mi', 'bw'),
+                          fake_study=FALSE) {
 
 studyid <- as.character(studyid)
   path_db <- fs::path(path_db)
@@ -29,15 +32,18 @@ result_return <- list(studyid_res = studyid)
   for(i in 1:domain_len){
     cur_dom <- domain[i]
     if(cur_dom=='lb'){
-      df <- get_lb_score(studyid, path_db)
+      df <- get_lb_score(studyid, path_db,
+                         fake_study = fake_study)
 
     }else if(cur_dom=='mi') {
 
-      df <- get_mi_score(studyid, path_db)
+      df <- get_mi_score(studyid, path_db,
+                         fake_study = fake_study)
 
     } else if (cur_dom=='bw'){
 
-      df <- get_bw_score(studyid, path_db)
+      df <- get_bw_score(studyid, path_db,
+                         fake_study = fake_study)
 
     } else{
       stop('check your domain')

R/get_lb_score.R

@@ -4,6 +4,8 @@
 #'   Studyid number
 #' @param path_db Mandatory, character \cr
 #'   path of database
+#' @param fake_study optional, Boolean \cr
+#' whether study generated by SENDsanitizer package
 #' @return score
 #'
 #' @examples
@@ -12,7 +14,7 @@
 #' }
 #' @export
 
-get_lb_score <- function(studyid, path_db) {
+get_lb_score <- function(studyid, path_db,fake_study=FALSE) {
 
 studyid <- as.character(studyid)
 path <- path_db
@@ -97,7 +99,7 @@ path <- path_db
       dplyr::ungroup()
 
   # get master compile data
-  master_CompileData <- get_compile_data(studyid, path_db)
+  master_CompileData <- get_compile_data(studyid, path_db,fake_study = fake_study)
     #<><><><><><><><><><><><><><><><>... Remove TK animals and Recovery animals......<><><><><><>.............
     #<><><><><><><><> master_CompileData is free of TK animals and Recovery animals<><><><><><><><><><><><><><>
     # Remove the TK animals and Recovery animals

R/get_mi_score.R

@@ -4,6 +4,8 @@
 #'   Studyid number
 #' @param path_db Mandatory, character \cr
 #'   path of database
+#' @param fake_study optional, Boolean \cr
+#' whether study generated by SENDsanitizer package
 #' @return score
 #'
 #' @examples
@@ -12,7 +14,7 @@
 #' }
 #' @export
 
-get_mi_score <- function(studyid, path_db) {
+get_mi_score <- function(studyid, path_db,fake_study=FALSE) {
 studyid <- as.character(studyid)
 path <- path_db
   con <- DBI::dbConnect(DBI::dbDriver('SQLite'), dbname = path)
@@ -101,7 +103,7 @@ path <- path_db
     #:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
 
 master_CompileData <- get_compile_data(studyid = studyid,
-                                       path_db = path_db)
+                                       path_db = path_db,fake_study = fake_study)
 
     # Filter the data frame
     tk_recovery_less_MIData <- MIData %>% dplyr::filter (USUBJID %in% master_CompileData$USUBJID)
@@ -347,7 +349,8 @@ master_CompileData <- get_compile_data(studyid = studyid,
       } else {
         # If number of columns is more than 7, get the max value from column 7 to the end
         #ScoredData_subset_HD$highest_score <- apply(ScoredData_subset_HD[, 7:ncol(ScoredData_subset_HD)], 1, max, na.rm = TRUE)
-        ScoredData_subset_HD$highest_score <- matrixStats::rowMaxs(as.matrix(ScoredData_subset_HD[, 7:ncol(ScoredData_subset_HD)]), na.rm = TRUE)
+        ScoredData_subset_HD$highest_score <- matrixStats::rowMaxs(as.matrix(ScoredData_subset_HD[, 7:ncol(ScoredData_subset_HD)]),
+                                                                   na.rm = TRUE)
       }
 
       # Move the highest_score column to be the third column