recuperando script de victor, renombrando el script de analisis de harmonizacion en local

valid_variables_script.R

 rm(list=ls())
 
-dir_name <- readline("Introduce the name of the directory please: ")
-
-setwd(dir_name)
+setwd("C:/Users/Victor/Documents/TFG/r-analytics-master")
 
+source("required_folder_checker.R")
+source("argument_hasher.R")
 source("dependency_installer.R")
 
+# install.packages("https://cran.r-project.org/src/contrib/Archive/DSI/DSI_1.2.0.tar.gz", repos=NULL, type="source")
+# install.packages("https://cran.r-project.org/src/contrib/Archive/DSOpal/DSOpal_1.2.0.tar.gz", repos=NULL, type="source")
+# install.packages("https://cran.r-project.org/src/contrib/Archive/DSLite/DSLite_1.2.0.tar.gz", repos=NULL, type="source")
+# install.packages("https://cran.r-project.org/src/contrib/Archive/opalr/opalr_2.1.0.tar.gz", repos=NULL, type="source")
+
 
-dep_list = c("jsonlite", "stringr","DSI","DSOpal","DSLite", "fields", "metafor", "ggplot2", "gridExtra", "data.table", "dsBaseClient", "openxlsx")
+dep_list = c("jsonlite", "stringr","DSI","DSOpal","DSLite", "fields", "metafor", "ggplot2", "gridExtra", "data.table", "dsBaseClient")
 install_dependencies(dep_list)
 
-#source("connection_parameters.R")
-#source("necessary_functions_connection.R")
+#,"DSI","DSOpal","DSLite"
 
-codebook <- read.csv("new_harmon.csv" , sep = ",")
+setwd("C:/Users/victor/Documents/TFG/r-analytics-master")
+source("connection_parameters.R")
+source("necessary_functions_connection.R")
 
-codebook_col_names <- as.data.frame(codebook$Harmonised.variable.name)
 
-names(codebook_col_names) <- c("col_names")
+setwd("C:/Users/Victor/Documents/TFG/r-analytics-master/harmonised_data")
 
-setwd(paste(dir_name ,"/harmonized_data", sep=""))
 
-file_name <- readline("Introduce the name of the file to check the values: ")
-harmonized_data <- ""
+ComAndRF <- data.frame(read.csv("Com&RF.csv", sep=","))[1:64,1:5]
+Complications <- data.frame(read.csv("Complications.csv", sep=";"))[1:20,1:5]
+Dates <-  data.frame(read.csv("Dates.csv", sep=";"))[1:12,1:5]
+Demographics <-  data.frame(read.csv("Demographics.csv", sep=";"))[1:9,1:5]
+Home_med <-  data.frame(read.csv("Home_med.csv", sep=";"))[1:13,1:5]
+Imaging_data <-  data.frame(read.csv("Imaging_data.csv", sep=";"))[1:11,1:5]
+Labo <-  data.frame(read.csv("Labo.csv", sep=";"))[1:143,1:5]
+SiAndSympt <-  data.frame(read.csv("Si&Sympt.csv", sep=";"))[1:50,1:5]
+Treatment <-  data.frame(read.csv("Treatment.csv", sep=";"))[1:32,1:5]
+LifestyleAndDiet <-  data.frame(read.csv("Lifestyle&Diet.csv", sep=";"))[1:165,1:5]
+
 
-if (grepl(".csv" , file_name , fixed = TRUE)){
-  harmonized_data <- read.csv(file_name)
-}else if (grepl(".xlsx" , file_name , fixed = TRUE)){
-  harmonized_data <- read.xlsx(file_name)
-}
 
+harmonised_data <- rbind(SiAndSympt,ComAndRF)
+harmonised_data <- rbind(harmonised_data,Treatment)
+harmonised_data <- rbind(harmonised_data,Dates)
+harmonised_data <- rbind(harmonised_data,Demographics)
+harmonised_data <- rbind(harmonised_data,Home_med)
+harmonised_data <- rbind(harmonised_data,Imaging_data)
+harmonised_data <- rbind(harmonised_data,Complications)
+harmonised_data <- rbind(harmonised_data,Labo)
+harmonised_data <- rbind(harmonised_data,LifestyleAndDiet)
+
+rm(list=c("SiAndSympt",
+          "Complications",
+          "ComAndRF",
+          "Dates",
+          "Demographics",
+          "Home_med",
+          "Imaging_data",
+          "Complications",
+          "Labo",
+          "LifestyleAndDiet"))
 
 categoric_vars = c("DMRGENDR", "DMRBORN", "DMRRETH1", "DMROCCU", "DMRHREDU", "DSXOS", "DSXHO", "DSXIC", "TRXAV","TRXRIB","TRXLR","TRXRM","TRXIA","TRXIB","TRXCH","TRXAB","TRXCS","TRXHEP","TRXAF","TRXCP","TRXOT","TRXECM","TRXIV","TRXNIV","TRXNO","TRXOX","TRXRR","TRXTR","TRXVA","TRXPE","TRXPV","TRXIT","TRXNMB","TRXAC","TRXINA","TRXIS","TRXIM","TRXVC","TRXVD","TRXZN",                         "CSXCOT","CSXCTR","SMXASAH","SMXFEA","SMXCOA","SMXSTA","SMXSBA","SMXRNA","SMXMYA","SMXARA","SMXCPA","SMXAPA","SMXINA","SMXNAA","SMXDIA","SMXFAA","SMXHEA","SMXCNA","SMXACA","SMXSLA","SMXTLA","SMXSYA","SMXWHA","SMXLYA","SMXANA","SMXIWA","SMXSRA","SMXBLA","CMXPRG","CMXCVD","CMXCMP","CMXHT","CMXDI","CMXCKD","CMXCLD","CMXCPD","CMXASM","CMXCND","CMXRHE","CMXCCI","CMXCBD","CMXDE","CMXPU","CMXST","CMXLY","CMXAP","RFXSM","RFXFSM","RFXOB","RFXTB","RFXIMD","RFXHIV","RFXAIDS","RFXUI","RFXHC","RFXONC","RFXMN",                         "HMRACI","HMRARB","HMRAHO","HMRNS","HMROS","HMRCS","HMRIS","HMRAV","HMRAB","HMRCOV","IMDXCT","IMDXCTCR","IMDXCTTE","IMDXCTAB","IMDXXR","IMDXPN",                         "COXRD","COXAR","COXPM","COXMOD","COXPT","COXEC","COXSH","COXIO","COXPE","COXST","COXDIC","COXRIO","COXKF","COXHF","COXBC")
 
@@ -37,45 +65,50 @@ categoric_vars = c("DMRGENDR", "DMRBORN", "DMRRETH1", "DMROCCU", "DMRHREDU", "DS
 #----------------------------------------------------------------------------
 
 #Test if column names are valid
-check_column_names <- function(col_names){
+check_column_names <- function(x){
   
   str_res <- "The column names:"
-  valid_colnames <- c()
-  repeated_colnames <- c()
-  for(i in 1:(nrow(col_names))){
-    col_name <- col_names[i,1]
-    number_of_column <- check_valid_name(col_name) 
-    if( number_of_column == 0){
-      str_res<- paste(str_res, col_name, sep=" ")
-    }else if (number_of_column == 1){
-      valid_colnames = c(valid_colnames, col_name)
-    }else{
-      repeated_colnames = c(repeated_colnames , col_name)
+  
+  for(i in 1:(nrow(data_colnames))){
+    if(!check_valid_name(data_colnames[i,1])){
+      str_res<- paste(str_res, data_colnames[i,1], sep=" ")
     }
   }
   
   str_res<- paste(str_res,"are not registered in the harmonized data codebook \n", sep=" ")
   
-  new_list <- list("not_colnames" = str_res , "colnames" = valid_colnames , "repeated_colnames" = repeated_colnames)
-  return (new_list)
+  return (str_res)
   
 }
 
 #Test if a single variable name is valid
-check_valid_name <- function(col_name){
+check_valid_name <- function(x){
   
-  valid <- 0
+  valid <- FALSE
   
-  if(col_name %in% codebook_col_names$col_names){
-    
-    valid <- length(grep(col_name, names(harmonized_data)))
+  aux <- as.data.frame(strsplit(x , split = "_"))
   
-  }
+  if(aux[1,1] %in% harmonised_data$Harmonised.variable.name)
+    valid <- TRUE
   
   return (valid)
   
 }
 
+valid_data_colnames <- function(x){
+  
+  valid_colnames = c()
+  
+  for(i in  1:(nrow(data_colnames))){
+    if(check_valid_name(data_colnames[i,1])){
+      valid_colnames = c(valid_colnames,data_colnames[i,1])
+    }
+  }
+  
+  
+  return(valid_colnames)
+}
+
 remove_space <- function(x){
   searchString <- ' '
   replacementString <- ''
@@ -99,7 +132,7 @@ is_number <- function(x){
     x <- str_replace(x,",",".")
     
     aux <- as.numeric(x)
-    
+
     
     if(!is.na(aux))
       res <- TRUE
@@ -130,7 +163,7 @@ check_values_not_categoric <- function(values, colname){
     if(is.null(value)){
       res <- TRUE
     }
-    
+      
     else if( value == "NA" | value == "nan" | value == ".")
       res <- TRUE
     else{
@@ -186,7 +219,7 @@ possible_values <- function(x){
   
   else{
     
-    possible_value <- subset(harmonized_data,harmonized_data$harmonized.variable.name==x)[1,5]
+    possible_value <- subset(harmonised_data,harmonised_data$Harmonised.variable.name==x)[1,5]
     res <- strsplit(x=possible_value,split="/")
   }
   
@@ -195,7 +228,7 @@ possible_values <- function(x){
 
 possible_values_categoric <- function(x){
   
-  possible_value <- subset(harmonized_data,harmonized_data$harmonized.variable.name==x)[1,4]
+  possible_value <- subset(harmonised_data,harmonised_data$Harmonised.variable.name==x)[1,4]
   res <- strsplit(x=possible_value,split="/")
   
   return(as.data.frame(res))
@@ -221,7 +254,7 @@ check_values_categoric <- function(values, colname){
 
 get_values_from_quantiles <- function(x){
   
-  data_summary <- summary(x)
+  data_summary <- ds.summary(x)
   
   low_quantile <- data_summary[[1]][3][[1]][[1]]
   
@@ -324,7 +357,7 @@ error_message <- function(colname, invalid_values){
   else
     new_colname <- colname
   
-  range <- subset(harmonized_data, harmonized_data$harmonized.variable.name == new_colname)
+  range <- subset(harmonised_data, harmonised_data$Harmonised.variable.name == new_colname)
   range <- range[5]
   range <- as.data.frame(strsplit(range[1,1], '/'))
   
@@ -377,7 +410,7 @@ error_message <- function(colname, invalid_values){
   
 }
 
-check_valid_values <- function(valid_colnames){
+check_valid_values <- function(){
   
   invalid_name_list <- c()
   cannot_analyse_list <- c()
@@ -389,59 +422,85 @@ check_valid_values <- function(valid_colnames){
   k <- 1
   
   for(i in  1:(nrow(valid_colnames))){
-    name <- names(valid_colnames_with_data)[i]
-    if("DMRBORN" == name | grepl("DAT",colname, fixed=TRUE) | "ISO" == name | "BEF" == name){
-      next
-    }
-    
-    column <- valid_colnames[,i]
-    
-    data_table <- as.data.frame(table(column))
-    
-    values <- row.names(data_table)
     
-    numeric_col<- paste(valid_colnames[,i],"_numeric", sep="")
+    data_table ="empty"
     
-    if( name %in% categoric_vars ){
+    if(!grepl("DMRBORN",valid_colnames[i,1], fixed=TRUE) & (!grepl("DAT",valid_colnames[i,1], fixed=TRUE)) & (!grepl("ISO",valid_colnames[i,1], fixed=TRUE))& (!grepl("BEF",valid_colnames[i,1], fixed=TRUE))){
       
-      #is_numeric <- grepl("numeric",valid_colnames[i,1], fixed=TRUE)
-      has_numeric <-  numeric_col %in% valid_colnames$`valid_data_colnames(data_colnames)`
+      column <- "data$"
+      column <- paste(column, valid_colnames[i,1], sep="")
       
-      if(!has_numeric)
-        missing_numeric <- c(missing_numeric, valid_colnames[i,1])
+      tryCatch(
+        error = function(cnd) {
+          print("Unable to analyse data")
+          res <- FALSE
+        },
+        data_table <- as.data.frame(ds.table(column))
+      )
       
-      
-      if(!check_values_categoric(values,valid_colnames[i,1])){
+      if(data_table == "empty"){
         
-        print("Wrong categoric value:")
-        print(valid_colnames[i,1])
+        cannot_analyse_list <- c(cannot_analyse_list,valid_colnames[i,1])
         
-        wrong_categoric <- c(wrong_categoric, valid_colnames[i,1])
-        wrong_categoric_values[[k]] <- values
-        k <- k+1
-      }
-      
-    }else{
-      
-      if(grepl("numeric", valid_colnames[i,1],fixed=TRUE))
-        new_colname <- strsplit(x=valid_colnames[i,1],split="_")[[1]][1]
-      else
-        new_colname <- valid_colnames[i,1]
-      
-      valid <- check_values_not_categoric(values, new_colname)
-      
-      if (FALSE %in% valid){
-        invalid_name_list <- c(invalid_name_list,valid_colnames[i,1])
-        invalid_values_list[[j]] <- values
-        j <- j+1
+      }else{
+        
+        
+        if (data_table[[1]] == "All studies failed for reasons identified below")
+          values <- get_values_from_quantiles(column)
+        else
+          values <- row.names(data_table)
+        
+        numeric_col<- paste(valid_colnames[i,1],"_numeric", sep="")
+        
+        if( valid_colnames[i,1] %in% categoric_vars ){
+          
+          #is_numeric <- grepl("numeric",valid_colnames[i,1], fixed=TRUE)
+          has_numeric <-  numeric_col %in% valid_colnames$`valid_data_colnames(data_colnames)`
+          
+          if(!has_numeric)
+            missing_numeric <- c(missing_numeric, valid_colnames[i,1])
+          
+          
+          if (data_table[[1]] == "All studies failed for reasons identified below"){
+            
+            cannot_analyse_list <- c(cannot_analyse_list,valid_colnames[i,1])
+            
+          }else  if(!check_values_categoric(values,valid_colnames[i,1])){
+            
+            print("Wrong categoric value:")
+            print(valid_colnames[i,1])
+            
+            wrong_categoric <- c(wrong_categoric, valid_colnames[i,1])
+            wrong_categoric_values[[k]] <- values
+            k <- k+1
+          }
+          
+          # if((!is_numeric & !has_numeric) | is_numeric)
+        }else{
+          
+          if(grepl("numeric", valid_colnames[i,1],fixed=TRUE))
+            new_colname <- strsplit(x=valid_colnames[i,1],split="_")[[1]][1]
+          else
+            new_colname <- valid_colnames[i,1]
+          
+          valid <- check_values_not_categoric(values, new_colname)
+          
+          if (FALSE %in% valid){
+            invalid_name_list <- c(invalid_name_list,valid_colnames[i,1])
+            invalid_values_list[[j]] <- values
+            j <- j+1
+          }
+          
+          
+          #print(valid_colnames[i,1])
+          #print(values)
+          
+        }#else
+        # print("This variable has a numeric version")
       }
       
       
     }
-    
-    
-    
-    
   }
   
   missing_numeric
@@ -456,7 +515,7 @@ check_valid_values <- function(valid_colnames){
     
     res <- paste(res, notify_unable_analyse(cannot_analyse_list), sep="\n" )
   }
-  
+    
   
   
   
@@ -477,33 +536,57 @@ notify_unable_analyse <- function(x){
   
 }
 
-data_colnames <- as.data.frame(colnames(harmonized_data))
+auxConnections <- connect()
+connections <- auxConnections[[1]]
+inp <- auxConnections[[2]]
+
+#Conexión a la base de datos
+
+ds.dim("data", datasources = connections)
+ds.colnames("data")
+
+#----------------------------------------------------------------------------
+
+#Check valid column names
+datastructure_name <- "data"
+data_colnames <- ds.colnames(x=datastructure_name, datasources= connections)
+
+data_colnames <- as.data.frame(data_colnames)
 
 check_valid_columns <- check_column_names(data_colnames)
 
-columns_not_valid <- check_valid_columns$not_colnames
+valid_colnames <- as.data.frame(valid_data_colnames(data_colnames))
 
-valid_colnames <- as.data.frame(check_valid_columns$colnames)
-names(valid_colnames) = c("valid_colnames")
-valid_colnames_with_data <- subset(harmonized_data , select = valid_colnames$valid_colnames)
 
 
+#possible_values("CSXCTR")
 result <- ""
-result<-check_valid_values(valid_colnames_with_data)
+result<-check_valid_values()
 print(check_valid_columns)
-#datashield.logout(connections) 
+datashield.logout(connections) 
 cat(result)
 
 
 
+# ds.dataFrameSubset(df.name = "data", V1.name = "data$DMXWT", "400" , Boolean.operator = '>', newobj = "columna")
+# # 
+# ds.summary("columna$DMXWT")
+# ds.dim("columna$DMXWT")
+# ds.table("columna$DMXWT")
+
 file_name<- paste(hospital_name,"_invalid_values.txt", sep="")
 
+#ds.heatmapPlot("data$LBDSALSIA", "data$RFXHC_numeric",type="combine", datasources = connections)
 
+
+#setwd("C:/Users/victor/Desktop/TFG/r-analytics-master/invalid_values")
 dir.create("../invalid_values", showWarnings = FALSE)
 setwd("../invalid_values")
 
+
 cat(check_valid_columns,file=file_name,sep="\n")
 cat(result,file=file_name,append=TRUE)
 
-#datashield.logout(connections) 
+datashield.logout(connections) 
+  
 

valid_variables_script_local.R

+rm(list=ls())
+
+dir_name <- readline("Introduce the name of the directory please: ")
+
+setwd(dir_name)
+
+source("dependency_installer.R")
+
+
+dep_list = c("jsonlite", "stringr","DSI","DSOpal","DSLite", "fields", "metafor", "ggplot2", "gridExtra", "data.table", "dsBaseClient", "openxlsx")
+install_dependencies(dep_list)
+
+#source("connection_parameters.R")
+#source("necessary_functions_connection.R")
+
+codebook <- read.csv("new_harmon.csv" , sep = ",")
+
+codebook_col_names <- as.data.frame(codebook$Harmonised.variable.name)
+
+names(codebook_col_names) <- c("col_names")
+
+setwd(paste(dir_name ,"/harmonized_data", sep=""))
+
+file_name <- readline("Introduce the name of the file to check the values: ")
+harmonized_data <- ""
+
+if (grepl(".csv" , file_name , fixed = TRUE)){
+  harmonized_data <- read.csv(file_name)
+}else if (grepl(".xlsx" , file_name , fixed = TRUE)){
+  harmonized_data <- read.xlsx(file_name)
+}
+
+#----------------------------------------------------------------------------
+
+#Test if column names are valid
+check_column_names <- function(col_names){
+  
+  str_res <- "The column names:"
+  valid_colnames <- c()
+  repeated_colnames <- c()
+  for(i in 1:(nrow(col_names))){
+    col_name <- col_names[i,1]
+    number_of_column <- check_valid_name(col_name) 
+    if( number_of_column == 0){
+      str_res<- paste(str_res, col_name, sep=" ")
+    }else if (number_of_column == 1){
+      valid_colnames = c(valid_colnames, col_name)
+    }else{
+      repeated_colnames = c(repeated_colnames , col_name)
+    }
+  }
+  
+  str_res<- paste(str_res,"are not registered in the harmonized data codebook \n", sep=" ")
+  
+  new_list <- list("not_colnames" = str_res , "colnames" = valid_colnames , "repeated_colnames" = repeated_colnames)
+  return (new_list)
+  
+}
+
+
+#Test if a single variable name is valid
+check_valid_name <- function(col_name){
+  
+  valid <- 0
+  
+  if(col_name %in% codebook_col_names$col_names){
+    
+    valid <- length(grep(col_name, names(harmonized_data)))
+    
+  }
+  
+  return (valid)
+  
+}
+
+check_valid_values_continuous <- function(colname , codebook_param , column){
+  column <- column[column != "."]
+  possible_values_format <- codebook_param$Possible.values.format[codebook_param$Harmonised.variable.name == colname]
+  possible_values_list =  str_split(possible_values_format , "/")[[1]]
+  
+  # Fallará cuando el codebook no tenga min-max / .
+  range_as_str <- str_trim(possible_values_list[1])
+  missing_value_format <- str_trim(str_trim(possible_values_list[2]))
+  
+  separate_range <- str_split(range_as_str , "-")[[1]]
+  min_value  <- strtoi(separate_range[1]) 
+  max_value  <- strtoi(separate_range[2])
+  
+  failing_values <- column[column < min_value | column > max_value]
+  number_of_failing_values <- length(failing_values[!is.na(failing_values)])
+  
+  str_res <- ""
+  if (number_of_failing_values == 0)
+    str_res <- "No failing values"
+  else{
+    failing_values <- failing_values[!is.na(failing_values)]
+    str_res <- paste("The failing values of column ", colname , paste(unlist(failing_values) , collapse =" "))
+  }
+    
+  
+  return(str_res)
+}
+
+check_valid_values_binary <- function(colname , column){
+  column <- column[column != "."]
+  failing_values <- column[column < 0 | column > 1]
+  number_of_failing_values <- length(failing_values[!is.na(failing_values)])
+  
+  str_res <- ""
+  if (number_of_failing_values == 0)
+    str_res <- "No failing values"
+  else{
+    failing_values <- failing_values[!is.na(failing_values)]
+    str_res <- paste("The failing values of column ", colname , paste(unlist(failing_values) , collapse =" "))
+  }
+    
+  
+  return(str_res)
+}
+
+check_valid_values_categorical <- function(colname , codebook_param , column){
+  column <- column[column != "."]
+  possible_values_format <- codebook_param$Possible.values.format[codebook_param$Harmonised.variable.name == colname]
+  possible_values_list <-  str_split(possible_values_format , "/")[[1]]
+  
+  possible_values_list <- lapply(possible_values_list , str_trim)
+  
+  str_res <- ""
+  min_value <- 0
+  max_value <- 0 
+  if (length(possible_values_list[[1]]) == 2){
+    separate_range <-  str_split(possible_values_list[[1]][1], "-")[[1]]
+    min_value  <- strtoi(separate_range[1]) 
+    max_value  <- strtoi(separate_range[2])
+    
+  }else{
+    
+    possible_values_list <- lapply(possible_values_list , strtoi)[[1]]
+    min_value <- possible_values_list[1]
+    max_value <- possible_values_list[length(possible_values_list) - 1]
+  }
+  
+  failing_values <- column[column < min_value | column > max_value ]
+  number_of_failing_values <- length(failing_values[!is.na(failing_values)])
+  
+  if(number_of_failing_values == 0){
+    str_res <- "No failing values"
+  }else{
+    failing_values <- failing_values[!is.na(failing_values)]
+    str_res <- paste("The failing values of column ", colname , paste(unlist(failing_values) , collapse =" "))
+  }
+  
+}
+
+check_valid_values <- function(valid_colnames, codebook_param){
+  
+  res <- ""
+  
+  for(i in  1:(ncol(valid_colnames))){
+    name <- names(valid_colnames)[i]
+    #if("DMRBORN" == name | grepl("DAT", name, fixed=TRUE) | grepl("ISO", name , fixed=TRUE) | grepl("BEF", name, fixed=TRUE)){
+     # next
+    #}
+    
+    column <- valid_colnames[,i]
+    
+    # Esto falla si tu codebook no es mismo que new_harmon.csv
+    column_type <- codebook_param$Variable.type[codebook_param$Harmonised.variable.name == name]
+    
+    result = switch(  
+      column_type,  
+      "Continuous"= check_valid_values_continuous(name , codebook_param , column),
+      "Binary"= check_valid_values_binary(name , column),  
+      "Categorical"= check_valid_values_categorical(name, codebook_param , column),
+      "Calendar date" = paste("No failing values"),
+      "ISO country code"= paste("No failing values"),
+    )
+    
+    if (result != "No failing values"){
+      res <- paste(res , result, sep="\n")
+    }
+  }
+  
+  return(res)
+  
+}
+
+
+data_colnames <- as.data.frame(colnames(harmonized_data))
+
+check_valid_columns <- check_column_names(data_colnames)
+
+columns_not_valid <- check_valid_columns$not_colnames
+
+valid_colnames_column <- as.data.frame(check_valid_columns$colnames)
+names(valid_colnames_column) = c("valid_colnames")
+valid_colnames_with_data <- subset(harmonized_data , select = valid_colnames_column$valid_colnames)
+
+
+result <- ""
+result<-check_valid_values(valid_colnames_with_data, codebook)
+print(check_valid_columns)
+#datashield.logout(connections) 
+cat(result)
+
+
+
+file_name<- paste(hospital_name,"_invalid_values.txt", sep="")
+
+
+dir.create("../invalid_values", showWarnings = FALSE)
+setwd("../invalid_values")
+
+cat(check_valid_columns,file=file_name,sep="\n")
+cat(result,file=file_name,append=TRUE)
+
+#datashield.logout(connections) 
+