nfdi4health · sofiasiamp · Jan 29, 2025 · Jan 27, 2025 · Jan 27, 2025 · Jan 27, 2025
diff --git a/Franzi/rmonize/data_dictionary/DD_DEGS1_FRANZI.xlsx b/Franzi/rmonize/data_dictionary/DD_DEGS1_FRANZI.xlsx
diff --git a/Franzi/DD_KARMEN_FRANZI.xlsx → ...ize/data_dictionary/DD_KARMEN_FRANZI.xlsx b/Franzi/DD_KARMEN_FRANZI.xlsx → ...ize/data_dictionary/DD_KARMEN_FRANZI.xlsx
diff --git a/Franzi/rmonize/data_proc_elem/DPE_DEGS1_FRANZI.xlsx b/Franzi/rmonize/data_proc_elem/DPE_DEGS1_FRANZI.xlsx
diff --git a/Franzi/DPE_KARMEN_FRANZI.xlsx → ...ize/data_proc_elem/DPE_KARMEN_FRANZI.xlsx b/Franzi/DPE_KARMEN_FRANZI.xlsx → ...ize/data_proc_elem/DPE_KARMEN_FRANZI.xlsx
diff --git a/Franzi/Dataschema_FRANZI.xlsx → ...monize/data_schema/Dataschema_FRANZI.xlsx b/Franzi/Dataschema_FRANZI.xlsx → ...monize/data_schema/Dataschema_FRANZI.xlsx
diff --git a/Franzi/scripts/DEGS1_FRANZI.R b/Franzi/scripts/DEGS1_FRANZI.R
@@ -0,0 +1,70 @@
+#### Script for harmonizing data for NFDI4Health
+
+####Installation of Rmonize and its dependent packages (necessary R Version > 3.4)
+# install.packages("Rmonize")
+# install.packages("readxl")
+# install.packages("tidyverse")
+# install.packages("here")
+# install.packages("car")
+# install.packages("writexl")
+
+#### Load the package in order to conduct
+library(Rmonize)
+library(readxl)
+library(tidyverse)
+library(here)
+library(car)
+library(writexl)
+
+
+#### Step 0: Name of the study and creation of mock data
+dataset_name <- 'DEGS1_FRANZI'
+folder_name <- 'Franzi'
+
+source(here::here("create_mock_data", "mock_data_function.R"))
+create_mock_data(studyname = dataset_name, folder_name = folder_name)
+
+#### Step 1: Import overall DataSchema
+dataschema_1 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 1))
+dataschema_2 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 2))
+
+dataschema <- list(Variables = dataschema_1,
+                   Categories = dataschema_2)
+
+#### Step 2: Import Datasets 
+#### Import check provided in case the csv file is in German style (delim = ";", dec.point = ",")
+
+input_dataset <- readr::read_csv(here::here(paste0(folder_name, "/data/", paste0("DATA_", dataset_name, ".csv"))))
+
+if(dim(input_dataset)[2] == 1){
+  input_dataset <- read.csv(here::here(paste0(folder_name, "/data", paste0("DATA_", dataset_name, ".csv"))), sep = ";", dec = ",")
+}
+
+#### Step 3: Import Data Dictionaries of the study
+dd_var <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 1))
+dd_cat <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name,"/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 2))
+
+dd <- list(Variables = dd_var,
+           Categories = dd_cat)
+
+#### Step 4: Import prepared Data Processing Elements (DPE)
+data_proc_elem <- readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_proc_elem"), paste0("DPE_",dataset_name, ".xlsx")), sheet = 1)
+
+#### Step 5: Combine input datasets and data dictionaries into a dossier
+dataset <- madshapR::data_dict_apply(
+  dataset = input_dataset,
+  data_dict = dd)
+
+dossier <- madshapR::dossier_create(dataset_list = list(
+  dataset))
+
+#### Step 6: Create the harmonized dossier using the dossier, overall dataschema and DPE's
+harmonized_dossier <- Rmonize::harmo_process(
+  dossier, 
+  dataschema, 
+  data_proc_elem)
+
+#### Step 9: Extract and save harmonized data into a pre-set folder
+harmonized_dataset <- Rmonize::pooled_harmonized_dataset_create(harmonized_dossier)
+
+
diff --git a/Franzi/scripts/KARMEN_FRANZI.R b/Franzi/scripts/KARMEN_FRANZI.R
@@ -0,0 +1,70 @@
+#### Script for harmonizing data for NFDI4Health
+
+####Installation of Rmonize and its dependent packages (necessary R Version > 3.4)
+# install.packages("Rmonize")
+# install.packages("readxl")
+# install.packages("tidyverse")
+# install.packages("here")
+# install.packages("car")
+# install.packages("writexl")
+
+#### Load the package in order to conduct
+library(Rmonize)
+library(readxl)
+library(tidyverse)
+library(here)
+library(car)
+library(writexl)
+
+
+#### Step 0: Name of the study and creation of mock data
+dataset_name <- 'KARMEN_FRANZI'
+folder_name <- 'Franzi'
+
+source(here::here("create_mock_data", "mock_data_function.R"))
+create_mock_data(studyname = dataset_name, folder_name = folder_name)
+
+#### Step 1: Import overall DataSchema
+dataschema_1 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 1))
+dataschema_2 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 2))
+
+dataschema <- list(Variables = dataschema_1,
+                   Categories = dataschema_2)
+
+#### Step 2: Import Datasets 
+#### Import check provided in case the csv file is in German style (delim = ";", dec.point = ",")
+
+input_dataset <- readr::read_csv(here::here(paste0(folder_name, "/data/", paste0("DATA_", dataset_name, ".csv"))))
+
+if(dim(input_dataset)[2] == 1){
+  input_dataset <- read.csv(here::here(paste0(folder_name, "/data", paste0("DATA_", dataset_name, ".csv"))), sep = ";", dec = ",")
+}
+
+#### Step 3: Import Data Dictionaries of the study
+dd_var <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 1))
+dd_cat <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name,"/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 2))
+
+dd <- list(Variables = dd_var,
+           Categories = dd_cat)
+
+#### Step 4: Import prepared Data Processing Elements (DPE)
+data_proc_elem <- readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_proc_elem"), paste0("DPE_",dataset_name, ".xlsx")), sheet = 1)
+
+#### Step 5: Combine input datasets and data dictionaries into a dossier
+dataset <- madshapR::data_dict_apply(
+  dataset = input_dataset,
+  data_dict = dd)
+
+dossier <- madshapR::dossier_create(dataset_list = list(
+  dataset))
+
+#### Step 6: Create the harmonized dossier using the dossier, overall dataschema and DPE's
+harmonized_dossier <- Rmonize::harmo_process(
+  dossier, 
+  dataschema, 
+  data_proc_elem)
+
+#### Step 9: Extract and save harmonized data into a pre-set folder
+harmonized_dataset <- Rmonize::pooled_harmonized_dataset_create(harmonized_dossier)
+
+
diff --git a/Ines/rmonize/data_dictionary/DD_DEGS1_INES.xlsx b/Ines/rmonize/data_dictionary/DD_DEGS1_INES.xlsx
diff --git a/Ines/DD_KARMEN_INES.xlsx → ...onize/data_dictionary/DD_KARMEN_INES.xlsx b/Ines/DD_KARMEN_INES.xlsx → ...onize/data_dictionary/DD_KARMEN_INES.xlsx
diff --git a/Ines/rmonize/data_proc_elem/DPE_DEGS1_INES.xlsx b/Ines/rmonize/data_proc_elem/DPE_DEGS1_INES.xlsx
diff --git a/Ines/DPE_KARMEN_INES.xlsx → ...onize/data_proc_elem/DPE_KARMEN_INES.xlsx b/Ines/DPE_KARMEN_INES.xlsx → ...onize/data_proc_elem/DPE_KARMEN_INES.xlsx
diff --git a/Ines/Dataschema_INES.xlsx → .../rmonize/data_schema/Dataschema_INES.xlsx b/Ines/Dataschema_INES.xlsx → .../rmonize/data_schema/Dataschema_INES.xlsx
diff --git a/Ines/scripts/DEGS1_INES.R b/Ines/scripts/DEGS1_INES.R
@@ -0,0 +1,70 @@
+#### Script for harmonizing data for NFDI4Health
+
+####Installation of Rmonize and its dependent packages (necessary R Version > 3.4)
+# install.packages("Rmonize")
+# install.packages("readxl")
+# install.packages("tidyverse")
+# install.packages("here")
+# install.packages("car")
+# install.packages("writexl")
+
+#### Load the package in order to conduct
+library(Rmonize)
+library(readxl)
+library(tidyverse)
+library(here)
+library(car)
+library(writexl)
+
+
+#### Step 0: Name of the study and creation of mock data
+dataset_name <- 'DEGS1_INES'
+folder_name <- 'Ines'
+
+source(here::here("create_mock_data", "mock_data_function.R"))
+create_mock_data(studyname = dataset_name, folder_name = folder_name)
+
+#### Step 1: Import overall DataSchema
+dataschema_1 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 1))
+dataschema_2 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 2))
+
+dataschema <- list(Variables = dataschema_1,
+                   Categories = dataschema_2)
+
+#### Step 2: Import Datasets 
+#### Import check provided in case the csv file is in German style (delim = ";", dec.point = ",")
+
+input_dataset <- readr::read_csv(here::here(paste0(folder_name, "/data/", paste0("DATA_", dataset_name, ".csv"))))
+
+if(dim(input_dataset)[2] == 1){
+  input_dataset <- read.csv(here::here(paste0(folder_name, "/data", paste0("DATA_", dataset_name, ".csv"))), sep = ";", dec = ",")
+}
+
+#### Step 3: Import Data Dictionaries of the study
+dd_var <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 1))
+dd_cat <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name,"/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 2))
+
+dd <- list(Variables = dd_var,
+           Categories = dd_cat)
+
+#### Step 4: Import prepared Data Processing Elements (DPE)
+data_proc_elem <- readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_proc_elem"), paste0("DPE_",dataset_name, ".xlsx")), sheet = 1)
+
+#### Step 5: Combine input datasets and data dictionaries into a dossier
+dataset <- madshapR::data_dict_apply(
+  dataset = input_dataset,
+  data_dict = dd)
+
+dossier <- madshapR::dossier_create(dataset_list = list(
+  dataset))
+
+#### Step 6: Create the harmonized dossier using the dossier, overall dataschema and DPE's
+harmonized_dossier <- Rmonize::harmo_process(
+  dossier, 
+  dataschema, 
+  data_proc_elem)
+
+#### Step 9: Extract and save harmonized data into a pre-set folder
+harmonized_dataset <- Rmonize::pooled_harmonized_dataset_create(harmonized_dossier)
+
+
diff --git a/Ines/scripts/KARMEN_INES.R b/Ines/scripts/KARMEN_INES.R
@@ -0,0 +1,70 @@
+#### Script for harmonizing data for NFDI4Health
+
+####Installation of Rmonize and its dependent packages (necessary R Version > 3.4)
+# install.packages("Rmonize")
+# install.packages("readxl")
+# install.packages("tidyverse")
+# install.packages("here")
+# install.packages("car")
+# install.packages("writexl")
+
+#### Load the package in order to conduct
+library(Rmonize)
+library(readxl)
+library(tidyverse)
+library(here)
+library(car)
+library(writexl)
+
+
+#### Step 0: Name of the study and creation of mock data
+dataset_name <- 'KARMEN_INES'
+folder_name <- 'Ines'
+
+source(here::here("create_mock_data", "mock_data_function.R"))
+create_mock_data(studyname = dataset_name, folder_name = folder_name)
+
+#### Step 1: Import overall DataSchema
+dataschema_1 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 1))
+dataschema_2 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 2))
+
+dataschema <- list(Variables = dataschema_1,
+                   Categories = dataschema_2)
+
+#### Step 2: Import Datasets 
+#### Import check provided in case the csv file is in German style (delim = ";", dec.point = ",")
+
+input_dataset <- readr::read_csv(here::here(paste0(folder_name, "/data/", paste0("DATA_", dataset_name, ".csv"))))
+
+if(dim(input_dataset)[2] == 1){
+  input_dataset <- read.csv(here::here(paste0(folder_name, "/data", paste0("DATA_", dataset_name, ".csv"))), sep = ";", dec = ",")
+}
+
+#### Step 3: Import Data Dictionaries of the study
+dd_var <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 1))
+dd_cat <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name,"/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 2))
+
+dd <- list(Variables = dd_var,
+           Categories = dd_cat)
+
+#### Step 4: Import prepared Data Processing Elements (DPE)
+data_proc_elem <- readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_proc_elem"), paste0("DPE_",dataset_name, ".xlsx")), sheet = 1)
+
+#### Step 5: Combine input datasets and data dictionaries into a dossier
+dataset <- madshapR::data_dict_apply(
+  dataset = input_dataset,
+  data_dict = dd)
+
+dossier <- madshapR::dossier_create(dataset_list = list(
+  dataset))
+
+#### Step 6: Create the harmonized dossier using the dossier, overall dataschema and DPE's
+harmonized_dossier <- Rmonize::harmo_process(
+  dossier, 
+  dataschema, 
+  data_proc_elem)
+
+#### Step 9: Extract and save harmonized data into a pre-set folder
+harmonized_dataset <- Rmonize::pooled_harmonized_dataset_create(harmonized_dossier)
+
+
diff --git a/Tracy/rmonize/data_dictionary/DD_DEGS1_TRACY.xlsx b/Tracy/rmonize/data_dictionary/DD_DEGS1_TRACY.xlsx
diff --git a/Tracy/DD_KARMEN_TRACY.xlsx → ...nize/data_dictionary/DD_KARMEN_TRACY.xlsx b/Tracy/DD_KARMEN_TRACY.xlsx → ...nize/data_dictionary/DD_KARMEN_TRACY.xlsx
diff --git a/Tracy/rmonize/data_proc_elem/DPE_DEGS1_TRACY.xlsx b/Tracy/rmonize/data_proc_elem/DPE_DEGS1_TRACY.xlsx
diff --git a/Tracy/DPE_KARMEN_TRACY.xlsx → ...nize/data_proc_elem/DPE_KARMEN_TRACY.xlsx b/Tracy/DPE_KARMEN_TRACY.xlsx → ...nize/data_proc_elem/DPE_KARMEN_TRACY.xlsx
diff --git a/Tracy/Dataschema_TRACY.xlsx → ...rmonize/data_schema/Dataschema_TRACY.xlsx b/Tracy/Dataschema_TRACY.xlsx → ...rmonize/data_schema/Dataschema_TRACY.xlsx
diff --git a/Tracy/scripts/DEGS1_TRACY.R b/Tracy/scripts/DEGS1_TRACY.R
@@ -0,0 +1,70 @@
+#### Script for harmonizing data for NFDI4Health
+
+####Installation of Rmonize and its dependent packages (necessary R Version > 3.4)
+# install.packages("Rmonize")
+# install.packages("readxl")
+# install.packages("tidyverse")
+# install.packages("here")
+# install.packages("car")
+# install.packages("writexl")
+
+#### Load the package in order to conduct
+library(Rmonize)
+library(readxl)
+library(tidyverse)
+library(here)
+library(car)
+library(writexl)
+
+
+#### Step 0: Name of the study and creation of mock data
+dataset_name <- 'DEGS1_TRACY'
+folder_name <- 'Tracy'
+
+source(here::here("create_mock_data", "mock_data_function.R"))
+create_mock_data(studyname = dataset_name, folder_name = folder_name)
+
+#### Step 1: Import overall DataSchema
+dataschema_1 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 1))
+dataschema_2 <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_schema/", paste0("Dataschema_", toupper(folder_name), ".xlsx"))), sheet = 2))
+
+dataschema <- list(Variables = dataschema_1,
+                   Categories = dataschema_2)
+
+#### Step 2: Import Datasets 
+#### Import check provided in case the csv file is in German style (delim = ";", dec.point = ",")
+
+input_dataset <- readr::read_csv(here::here(paste0(folder_name, "/data/", paste0("DATA_", dataset_name, ".csv"))))
+
+if(dim(input_dataset)[2] == 1){
+  input_dataset <- read.csv(here::here(paste0(folder_name, "/data", paste0("DATA_", dataset_name, ".csv"))), sep = ";", dec = ",")
+}
+
+#### Step 3: Import Data Dictionaries of the study
+dd_var <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 1))
+dd_cat <- tibble::tibble(readxl::read_excel(here::here(paste0(folder_name,"/rmonize/data_dictionary/"), paste0("DD_",dataset_name, ".xlsx")), sheet = 2))
+
+dd <- list(Variables = dd_var,
+           Categories = dd_cat)
+
+#### Step 4: Import prepared Data Processing Elements (DPE)
+data_proc_elem <- readxl::read_excel(here::here(paste0(folder_name, "/rmonize/data_proc_elem"), paste0("DPE_",dataset_name, ".xlsx")), sheet = 1)
+
+#### Step 5: Combine input datasets and data dictionaries into a dossier
+dataset <- madshapR::data_dict_apply(
+  dataset = input_dataset,
+  data_dict = dd)
+
+dossier <- madshapR::dossier_create(dataset_list = list(
+  dataset))
+
+#### Step 6: Create the harmonized dossier using the dossier, overall dataschema and DPE's
+harmonized_dossier <- Rmonize::harmo_process(
+  dossier, 
+  dataschema, 
+  data_proc_elem)
+
+#### Step 9: Extract and save harmonized data into a pre-set folder
+harmonized_dataset <- Rmonize::pooled_harmonized_dataset_create(harmonized_dossier)
+
+