diff --git a/.bandit.yml b/.bandit.yml
index 95b3ccc2..b28435b1 100644
--- a/.bandit.yml
+++ b/.bandit.yml
@@ -1,5 +1,5 @@
 skips:
   - "B405"
   - "B314"
-exclude_dirs:
-  - "*_test.py"
+assert_used:
+  skips: ["**/test_*.py", "**/*_test.py"]
diff --git a/.gitignore b/.gitignore
index 9ae32231..8a2b6197 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,3 +17,5 @@ bundles-delta/
 *.csv
 
 .vscode
+
+.venv/
diff --git a/.mega-linter.yml b/.mega-linter.yml
index 1844ddd7..4318d3fe 100644
--- a/.mega-linter.yml
+++ b/.mega-linter.yml
@@ -11,6 +11,7 @@ DISABLE_LINTERS:
   #  due to import-error
   - PYTHON_PYLINT
   - SPELL_LYCHEE
+  - R_LINTR
 
 SHOW_ELAPSED_TIME: true
 
diff --git a/docker-compose/compose.obds-fhir-to-opal.yaml b/docker-compose/compose.obds-fhir-to-opal.yaml
index 838a34c0..75394a00 100644
--- a/docker-compose/compose.obds-fhir-to-opal.yaml
+++ b/docker-compose/compose.obds-fhir-to-opal.yaml
@@ -19,4 +19,4 @@ services:
       KAFKA_PROCEDURE_TOPIC: "fhir.obds.Procedure"
       KAFKA_MEDICATIONSTATEMENT_TOPIC: "fhir.obds.MedicationStatement"
     volumes:
-      - ${PWD}/opal-output:/opt/bitnami/spark/opal-output
+      - ${PWD}/opal-output:/home/spark/opal-output
diff --git a/src/decompose_xmls/decompose_xmls.py b/src/decompose_xmls/decompose_xmls.py
index f3e1dde7..6475750a 100644
--- a/src/decompose_xmls/decompose_xmls.py
+++ b/src/decompose_xmls/decompose_xmls.py
@@ -1,10 +1,10 @@
 import json
 import os
 import time
-import regex
 import xml.etree.ElementTree as ET
 from io import BytesIO
 
+import regex
 from confluent_kafka import Producer
 from pydantic import BaseSettings
 
@@ -48,8 +48,8 @@ def kafka_delivery_report(err, msg):
 
 
 def remove_leading_zeros(patient_id: str) -> str:
-    if os.environ.get('REMOVE_LEADING_PATIENTID_ZEROS') == 'true':
-        return regex.sub(r'^0+', '', patient_id)
+    if os.environ.get("REMOVE_LEADING_PATIENTID_ZEROS") == "true":
+        return regex.sub(r"^0+", "", patient_id)
     else:
         return patient_id
 
@@ -86,7 +86,7 @@ def decompose_sammelmeldung(root: ET.Element, filename: str):
             continue
 
         # remove all Menge_Meldung
-        menge_meldung = patient.find('./{http://www.gekid.de/namespace}Menge_Meldung')
+        menge_meldung = patient.find("./{http://www.gekid.de/namespace}Menge_Meldung")
         if menge_meldung is not None:
             patient.remove(menge_meldung)
 
@@ -122,9 +122,7 @@ def decompose_sammelmeldung(root: ET.Element, filename: str):
                 f = BytesIO()
                 tree = ET.ElementTree(meldung_root)
                 ET.indent(tree, "  ")
-                tree.write(
-                    f, encoding="utf-8", xml_declaration=True
-                )
+                tree.write(f, encoding="utf-8", xml_declaration=True)
 
                 xml_str = f.getvalue().decode()
                 # prepare json files for kafka bridge
@@ -150,10 +148,10 @@ def decompose_sammelmeldung(root: ET.Element, filename: str):
                         json.dump(result_data, output_file, indent=4)
 
                 save_xml_files(
-                        meldung_root,
-                        patient_id,
-                        meldung_id,
-                    )
+                    meldung_root,
+                    patient_id,
+                    meldung_id,
+                )
 
                 if kafka_producer is not None:
                     kafka_producer.poll(0)
diff --git a/src/decompose_xmls/decompose_xmls_test.py b/src/decompose_xmls/decompose_xmls_test.py
index dfc6321e..82f75da4 100644
--- a/src/decompose_xmls/decompose_xmls_test.py
+++ b/src/decompose_xmls/decompose_xmls_test.py
@@ -10,7 +10,7 @@
     "obds_input_file_path", [("input-obds-reports/test-2patients.xml")]
 )
 def test_decompose_sammelmeldung(snapshot, obds_input_file_path):
-    os.environ['REMOVE_LEADING_PATIENTID_ZEROS'] = 'false'
+    os.environ["REMOVE_LEADING_PATIENTID_ZEROS"] = "false"
     tree = ET.parse(obds_input_file_path)
     root = tree.getroot()
 
@@ -22,7 +22,7 @@ def test_decompose_sammelmeldung(snapshot, obds_input_file_path):
     "obds_input_file_path", [("input-obds-reports/test-patientid-with-zeros.xml")]
 )
 def test_decompose_with_pathient_id_starting_with_zero(snapshot, obds_input_file_path):
-    os.environ['REMOVE_LEADING_PATIENTID_ZEROS'] = 'true'
+    os.environ["REMOVE_LEADING_PATIENTID_ZEROS"] = "true"
     tree = ET.parse(obds_input_file_path)
     root = tree.getroot()
 
@@ -34,7 +34,7 @@ def test_decompose_with_pathient_id_starting_with_zero(snapshot, obds_input_file
     "obds_input_file_path", [("input-obds-reports/test-patientid-with-zeros.xml")]
 )
 def test_decompose_keep_pathient_id_starting_with_zero(snapshot, obds_input_file_path):
-    os.environ['REMOVE_LEADING_PATIENTID_ZEROS'] = 'false'
+    os.environ["REMOVE_LEADING_PATIENTID_ZEROS"] = "false"
 
     tree = ET.parse(obds_input_file_path)
     root = tree.getroot()
diff --git a/src/obds_fhir_to_opal/DataSHIELD_R-Script/entities_gender.R b/src/obds_fhir_to_opal/DataSHIELD_R-Script/entities_gender.R
new file mode 100644
index 00000000..6c330397
--- /dev/null
+++ b/src/obds_fhir_to_opal/DataSHIELD_R-Script/entities_gender.R
@@ -0,0 +1,134 @@
+install.packages("DSI")
+install.packages("DSOpal", dependencies = TRUE)
+install.packages(
+  "dsBaseClient",
+  repos = c(getOption("repos"),
+            "http://cran.datashield.org"),
+  dependencies = TRUE
+)
+install.packages("languageserver")
+
+library(dsBaseClient)
+library(DSOpal)
+library(DSI)
+library(sys)
+
+install.packages("formatR", repos = "http://cran.rstudio.com")
+library(formatR)
+
+
+# load utils.R file
+source("utils_entities_gender.R")
+
+# Login
+result <- ds_login()
+logindata <- result$logindata
+connections <- result$connections
+
+
+#### investigate size of df, colnames of df
+ds.dim("D")
+ds.colnames("D")
+
+
+#### check gender_mapped values where "other"
+#### create subsets - 1 = female, 2 = male, 3 = other/diverse
+
+for (loc in 1:seq_along(connections)) {
+  tryCatch(
+    ds.dataFrameSubset(
+      "D",
+      "D$gender_mapped",
+      "3",
+      "==",
+      newobj = "D1",
+      datasources = connections[loc]
+    ),
+    error = function(cond) {
+      warning(
+        "subset gendermapped=
+   other/diverse could not be generated in ",
+        names(connections[loc]),
+        ". Not enough cases?"
+      )
+    }
+  )
+}
+ds.dim("D1", datasources = connections[1])
+
+
+#### optional: additional filter D date_diagnosis_year == 2022 here to assure
+# only data from 2022
+ds.dataFrameSubset(
+  df.name = "D",
+  V1.name = "D$date_diagnosis_year",
+  V2.name = "2022",
+  Boolean.operator = "==",
+  newobj = "D",
+  datasources = connections
+)
+ds.dim("D")
+
+
+#### STEP 1: generate subsets for relevant diagnoses
+# generate_subsets: detailed description in utils_entities_gender.R
+colname <- "icd10_grouped_entities"
+start_subset <- 0
+end_subset <- 23
+connections <- connections
+
+generate_subsets(colname, start_subset, end_subset, connections)
+
+# useful: list all Dataframes on Server side - check which subsets you created
+all_loc_dfs <- get_all_loc_dfs()
+all_loc_dfs
+
+#### STEP 2: create a list of all relevant subset names
+icd_group_list <-
+  paste0("subset_", colname, "_", start_subset:end_subset)
+icd_group_list
+
+#### STEP 3:
+# subset_prevalence_by_gender: detailed description in utils_entities_gender.R
+subset_by_gender_result <-
+  subset_prevalence_by_gender(icd_group_list, all_loc_dfs, connections)
+
+#### change row names
+new_rownames_eng <- c(
+  "Lip, Oral Cavity, Pharynx (C00-C14)",
+  "Oesophagus (C15)",
+  "Stomach (C16)",
+  "Colon and Rectum (C18-C21)",
+  "Liver (C22)",
+  "Gallbladder and Biliary Tract (C23-C24)",
+  "Pancreas (C25)",
+  "Larynx (C32)",
+  "Trachea, Bronchus and Lungs (C33-C34)",
+  "Malignant Melanoma of Skin (C43)",
+  "Breast (C50, D05)",
+  "Cervis Uteri (C53, D06)",
+  "Corpus Uteri (C54-C55)",
+  "Ovary (C56, D39.1)",
+  "Prostate (C61)",
+  "Testis (C62)",
+  "Kidney (C64)",
+  "Bladder (C67, D09.0, D41.4)",
+  "Brain and Central Nervous System (C70-C72)",
+  "Thyroid (C73)",
+  "Hodgkin Lymphoma (C81)",
+  "Non-Hodgkin Lymphoma (C82-C88, C96)",
+  "Plasmacytoma (C90)",
+  "Leukaemia (C91-C95)"
+)
+
+rownames(subset_by_gender_result) <- new_rownames_eng
+subset_by_gender_result
+
+
+# write to csv file
+write.csv(subset_by_gender_result,
+          file = "subset_by_gender_result_matrix.csv",
+          row.names = TRUE)
+
+# clear DataSHIELD R-Session and logout
+datashield.logout(connections)
diff --git a/src/obds_fhir_to_opal/DataSHIELD_R-Script/utils_entities_gender.R b/src/obds_fhir_to_opal/DataSHIELD_R-Script/utils_entities_gender.R
new file mode 100644
index 00000000..d4f126e3
--- /dev/null
+++ b/src/obds_fhir_to_opal/DataSHIELD_R-Script/utils_entities_gender.R
@@ -0,0 +1,283 @@
+# utils for entities_gender.R script
+
+# Login
+ds_login <- function() {
+  builder <- DSI::newDSLoginBuilder()
+
+  builder$append(
+    server = "Erlangen",
+    url = "<enter-url-local-opal-server>",
+    user = Sys.getenv("user_uker"),
+    password = Sys.getenv("password_uker"),
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  builder$append(
+    server = "Augsburg",
+    url = "<enter-url-local-opal-server>",
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  builder$append(
+    server = "Würzburg",
+    url = "<enter-url-local-opal-server>",
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  builder$append(
+    server = "Regensburg",
+    url = "<enter-url-local-opal-server>",
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  builder$append(
+    server = "München TUM",
+    url = "<enter-url-local-opal-server>",
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  builder$append(
+    server = "München LMU",
+    url = "<enter-url-local-opal-server>",
+    user = "",
+    password = "",
+    table = "<projectname.tablename>",
+    driver = "OpalDriver"
+  )
+
+  logindata <- builder$build()
+  connections <-
+    DSI::datashield.login(logins = logindata,
+                          assign = TRUE,
+                          symbol = "D")
+
+  return(list(logindata = logindata, connections = connections))
+}
+
+
+#' Create a list of all server-side dataframes with the corresponding
+#' location names for all participating sites
+#'
+#' This function creates a list containing all dataframes present on the
+#' servers, along with the names of the respective locations. The function
+#' is used to iterate over these dataframes later.
+#'
+#' @return
+#' The function returns a list where each element contains the dataframes
+#' available on a server. The names of the list correspond to the names of
+#' the locations.
+get_all_loc_dfs <- function() {
+  all_loc_dfs <- list()
+  all_dfs <- ds.ls()
+  for (i in seq_along(connections)) {
+    all_loc_dfs <- c(all_loc_dfs, list(all_dfs[[i]][[2]]))
+  }
+  names(all_loc_dfs) <- names(connections)
+  return(all_loc_dfs)
+}
+
+
+#' Create subsets of data based on a specified column
+#'
+#' This function creates subsets of the data based on the specified column
+#' and the specified range of subset groups. It iterates over the specified
+#' range of subset groups and attempts to create a subset for each group
+#' within each location from the connections (from start_subset to
+#' end_subset). If a subset cannot be created (e.g., due to insufficient
+#' cases), a warning is issued.
+#'
+#' @param colname The column name used to create the subsets.
+#' @param start_subset The starting group of the subset range.
+#' @param end_subset The ending group of the subset range.
+#' @param connections A list of DataSHIELD connections to the data sources
+#' of the participating locations.
+#'
+#' @details
+#' The function uses `ds.dataFrameSubset` to create subsets for the groups
+#' between start_subset and end_subset. The subsets are dynamically named
+#' based on the column name and the subset group.
+#'
+#' @return
+#' The function does not return a value. Instead, new subsets are created
+#' on the server side within each specified connection, with names in the
+#' format `subset_<colname>_<subset_group>`.
+#'
+#' @examples
+#' generate_subsets("icd10_grouped", 1, 3, connections)
+generate_subsets <-
+  function(colname,
+           start_subset,
+           end_subset,
+           connections) {
+    for (subset_group in start_subset:end_subset) {
+      for (loc in seq_along(connections)) {
+        tryCatch({
+          ds.dataFrameSubset(
+            "D",
+            paste0("D$", colname),
+            as.character(subset_group),
+            "==",
+            newobj = paste0("subset_", colname, "_", subset_group),
+            datasources = connections[loc]
+          )
+        },
+        error = function(cond) {
+          warning(
+            paste0(
+              "Subset ",
+              paste0("subset_", colname, "_", subset_group),
+              " could not be generated in ",
+              names(connections[loc]),
+              ". Not enough cases?"
+            )
+          )
+        })
+      }
+    }
+  }
+
+
+#' Generate a results table for subset frequencies (e.g., diagnoses) by gender
+#'
+#' This function creates a results table that contains the subset frequencies
+#' by gender (male, female, and other) for each specified subset and each
+#' location. The function iterates over a list of subsets and checks for each
+#' subset and each location whether the subset is available. If available,
+#' the total count and the counts split by gender are calculated and stored
+#' in a vector. This vector is then converted into a matrix, which is returned
+#' as a tabular summary of the results.
+#'
+#' @param subset_list A list of subsets to be analyzed.
+#' @param all_loc_dfs A list of data sources containing the available dataframes
+#'     for each connection, e.g., obtained via get_all_loc_dfs().
+#' @param connections A list of DataSHIELD connections to the data sources of
+#'     the participating locations.
+#'
+#' @details The function uses the `ds.dataFrameSubset` function to create subsets
+#'     based on gender (`gender_mapped`). For each subset and each connection,
+#'     the total count as well as the counts for the genders "male" (2), "female"
+#'     (1), and "other" (3) are calculated. The results are stored in a vector and
+#'     then converted into a matrix.
+#'
+#' @return The function returns a matrix that contains the total count and the
+#'     counts split by gender for each subset and each connection.
+#'
+#' @examples
+#' generate_gender_subset_output(c("subset_1", "subset_2"), all_loc_dfs,
+#'     connections)
+subset_prevalence_by_gender <-
+  function(subset_list, all_loc_dfs, connections) {
+    # Generate the output as a vector
+    output_vector <- NULL
+
+    for (subs in subset_list) {
+      for (loc in seq_along(connections)) {
+        # Check if the subset is available
+        if (subs %in% all_loc_dfs[[loc]]) {
+          male <- NA
+          female <- NA
+          other <- NA
+          print(paste0(subs, " at site ", names(connections[loc])))
+
+          # total count
+          total <-
+            ds.dim(subs, datasources = connections[loc])[[1]][1]
+
+          # male
+          male <- tryCatch({
+            ds.dataFrameSubset(
+              subs,
+              paste0(subs, "$gender_mapped"),
+              "2",
+              "==",
+              newobj = paste0(subs, "_male"),
+              datasources = connections[loc]
+            )
+            ds.dim(paste0(subs, "_male"), datasources = connections[loc])[[1]][1]
+          },
+          error = function(cond) {
+            warning(paste0(
+              subs,
+              "_male could not be generated in ",
+              names(connections[loc])
+            ))
+            return(NA)
+          })
+
+          # female
+          female <- tryCatch({
+            ds.dataFrameSubset(
+              subs,
+              paste0(subs, "$gender_mapped"),
+              "1",
+              "==",
+              newobj = paste0(subs, "_female"),
+              datasources = connections[loc]
+            )
+            ds.dim(paste0(subs, "_female"), datasources = connections[loc])[[1]][1]
+          },
+          error = function(cond) {
+            warning(paste0(
+              subs,
+              "_female could not be generated in ",
+              names(connections[loc])
+            ))
+            return(NA)
+          })
+
+          # other
+          other <- tryCatch({
+            ds.dataFrameSubset(
+              subs,
+              paste0(subs, "$gender_mapped"),
+              "3",
+              "==",
+              newobj = paste0(subs, "_other"),
+              datasources = connections[loc]
+            )
+            ds.dim(paste0(subs, "_other"), datasources = connections[loc])[[1]][1]
+          },
+          error = function(cond) {
+            warning(paste0(
+              subs,
+              "_other could not be generated in ",
+              names(connections[loc])
+            ))
+            return(NA)
+          })
+
+          output_vector <-
+            c(output_vector, total, male, female, other)
+        } else {
+          output_vector <- c(output_vector, NA, NA, NA, NA)
+        }
+      }
+    }
+
+    # format output
+    output <-
+      matrix(output_vector,
+             ncol = length(connections) * 4,
+             byrow = TRUE)
+    colnames(output) <-
+      paste0(rep(names(connections), each = 4),
+             c("_total", "_male", "_female", "_other"))
+    rownames(output) <- subset_list
+
+    return(output)
+  }
diff --git a/src/obds_fhir_to_opal/Dockerfile b/src/obds_fhir_to_opal/Dockerfile
index f870083a..93b19dc4 100644
--- a/src/obds_fhir_to_opal/Dockerfile
+++ b/src/obds_fhir_to_opal/Dockerfile
@@ -13,6 +13,9 @@ USER 1001:1001
 RUN spark-shell -v --conf spark.jars.ivy=${SPARK_JARS_IVY}\
     --packages "org.apache.spark:spark-sql-kafka-0-10_2.12:3.3.2,au.csiro.pathling:library-api:6.2.1,ch.cern.sparkmeasure:spark-measure_2.13:0.21,io.delta:delta-core_2.12:2.3.0"
 
+WORKDIR /home/spark
+
 COPY obds_fhir_to_opal.py obds_fhir_to_opal.py
+COPY utils_onco_analytics.py utils_onco_analytics.py
 
 ENTRYPOINT [ "python", "obds_fhir_to_opal.py" ]
diff --git a/src/obds_fhir_to_opal/README.md b/src/obds_fhir_to_opal/README.md
new file mode 100644
index 00000000..010dcf4d
--- /dev/null
+++ b/src/obds_fhir_to_opal/README.md
@@ -0,0 +1,32 @@
+# obds_fhir_to_opal
+
+`obds_fhir_to_opal` is a tool designed to convert FHIR data from Apache Kafka topics into a CSV file format which is then used in a Federated Analysis with DataSHIELD.
+
+## Features
+
+- **Convert FHIR resources from Kafka topics** into a structured CSV file.
+- **Generate a data dictionary** for the Federated Analysis with DataSHIELD.
+- **Result CSV File Columns**:
+  - `ID`: Unique identifier for the record.
+  - `condition_id`: Identifier for the medical condition.
+  - `date_diagnosis`: Date when the diagnosis was made.
+  - `icd10_code`: ICD-10 code for the diagnosis.
+  - `gender`: Gender of the patient.
+  - `date_diagnosis_year`: Year when the diagnosis was made.
+  - `date_diagnosis_month`: Month when the diagnosis was made.
+  - `date_diagnosis_day`: Day when the diagnosis was made.
+  - `icd10_mapped`: Mapped ICD-10 code.
+  - `icd10_grouped_entities`: Grouped ICD-10 entities.
+  - `gender_mapped`: Mapped gender information.
+
+  For a more detailed description of the data elements, please check the data_dictionary_df.xlsx.
+
+## DataSHIELD R Scripts
+
+In addition to the main tool, this repository contains a subfolder `DataSHIELD_R-Script`. This folder includes R scripts for a Federated Analysis querying the result CSV file with help of DataSHIELD.
+
+Results from this analysis will be published soon. Check back for updates.
+
+## Contributing
+
+Contributions are welcome! If you have any suggestions or improvements, please open an issue or submit a pull request.
diff --git a/src/obds_fhir_to_opal/compose.yaml b/src/obds_fhir_to_opal/compose.yaml
index 90bfa3b1..8b386cf4 100644
--- a/src/obds_fhir_to_opal/compose.yaml
+++ b/src/obds_fhir_to_opal/compose.yaml
@@ -5,7 +5,7 @@ services:
       dockerfile: Dockerfile
     environment:
       OUTPUT_FOLDER: "/opt/bitnami/spark/opal-output/"
-      OUTPUT_FILENAME: "bzkf_q4_22.csv"
+      OUTPUT_FILENAME: "df.csv"
       KAFKA_TOPIC_YEAR_SUFFIX: ".2022"
       KAFKA_BOOTSTRAP_SERVER: "kafka:9092"
       KAFKA_PATIENT_TOPIC: "fhir.obds.Patient"
@@ -14,4 +14,4 @@ services:
       KAFKA_PROCEDURE_TOPIC: "fhir.obds.Procedure"
       KAFKA_MEDICATIONSTATEMENT_TOPIC: "fhir.obds.MedicationStatement"
     volumes:
-      - ${PWD}/output:/opt/bitnami/spark/opal-output
+      - ${PWD}/output:/home/spark/opal-output
diff --git a/src/obds_fhir_to_opal/data_dictionary_df.xlsx b/src/obds_fhir_to_opal/data_dictionary_df.xlsx
new file mode 100644
index 00000000..868cd697
Binary files /dev/null and b/src/obds_fhir_to_opal/data_dictionary_df.xlsx differ
diff --git a/src/obds_fhir_to_opal/datadictionary_bzkf_q4_22.xlsx b/src/obds_fhir_to_opal/datadictionary_bzkf_q4_22.xlsx
deleted file mode 100644
index 852301b8..00000000
Binary files a/src/obds_fhir_to_opal/datadictionary_bzkf_q4_22.xlsx and /dev/null differ
diff --git a/src/obds_fhir_to_opal/obds_fhir_to_opal.py b/src/obds_fhir_to_opal/obds_fhir_to_opal.py
index ffc5fe1e..351aa393 100644
--- a/src/obds_fhir_to_opal/obds_fhir_to_opal.py
+++ b/src/obds_fhir_to_opal/obds_fhir_to_opal.py
@@ -1,24 +1,27 @@
-import datetime
-import math
 import os
 import shutil
 import time
 
-import pyspark
+import pathling as ptl
+from pathling import Expression as exp
 from pathling import PathlingContext
 from pathling.etc import find_jar
-from pydantic import BaseSettings
+from pydantic import BaseSettings  # pydantic_settings ?
 from pyspark.sql import SparkSession
-from pyspark.sql.functions import col, explode, first, regexp_replace, to_date, udf, \
-    substring
-from pyspark.sql.types import StringType
-
-from dateutil.relativedelta import relativedelta
+from pyspark.sql.functions import monotonically_increasing_id, udf
+from pyspark.sql.types import DoubleType, IntegerType, StringType
+from utils_onco_analytics import (
+    deconstruct_date,
+    generate_datadictionary,
+    group_entities,
+    map_gender,
+    map_icd10,
+)
 
 
 class Settings(BaseSettings):
     output_folder: str = "~/opal-output"
-    output_filename: str = "bzkf_q4_22.csv"
+    output_filename: str = "df.csv"
     kafka_topic_year_suffix: str = ".2022"
     kafka_patient_topic: str = "fhir.obds.Patient"
     kafka_condition_topic: str = "fhir.obds.Condition"
@@ -36,10 +39,7 @@ class Settings(BaseSettings):
     master: str = "local[*]"
     kafka_bootstrap_server: str = "kafka:9092"
 
-    spark_worker_memory: str = "10g"
-    spark_executor_memory: str = "8g"
-    spark_driver_memory: str = "8g"
-    spark_executor_cores: str = "4"
+    spark_driver_memory: str = "18g"
 
     spark_jars_ivy: str = "/home/spark/.ivy2"
 
@@ -53,10 +53,7 @@ def setup_spark_session(appName: str, master: str):
         .master(master)
         .config("spark.ui.port", "4040")
         .config("spark.rpc.message.maxSize", "1000")
-        .config("spark.worker.memory", settings.spark_worker_memory)
-        .config("spark.executor.memory", settings.spark_executor_memory)
         .config("spark.driver.memory", settings.spark_driver_memory)
-        .config("spark.executor.cores", settings.spark_executor_cores)
         .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")
         .config("spark.jars.packages", ",".join(settings.jar_list))
         .config("spark.jars.ivy", settings.spark_jars_ivy)
@@ -74,6 +71,7 @@ def setup_spark_session(appName: str, master: str):
             + "-XX:InitiatingHeapOccupancyPercent=35 -verbose:gc -XX:+PrintGCDetails "
             + "-XX:+PrintGCDateStamps -XX:OnOutOfMemoryError='kill -9 %p'",
         )
+        .config("spark.task.maxDirectResultSize", "256MB")
         .getOrCreate()
     )
     spark.sparkContext.addFile(find_jar())
@@ -99,7 +97,9 @@ def create_list_of_kafka_topics():
     )
 
 
-def read_data_from_kafka_save_delta(spark: SparkSession, kafka_topics: str):
+def read_data_from_kafka_save_delta(
+    spark: SparkSession, kafka_topics: str, pc: PathlingContext
+):
     # https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html
     df = (
         spark.readStream.format("kafka")
@@ -119,7 +119,58 @@ def read_data_from_kafka_save_delta(spark: SparkSession, kafka_topics: str):
     kafka_data = spark.sql("select * from gettable")
     kafka_data = kafka_data.select("value")
     bundle_folder = os.path.join(settings.output_folder, "bundles-delta")
-    kafka_data.write.format("delta").mode("overwrite").save(bundle_folder)
+
+    if kafka_data is None:
+        raise ValueError("kafka_data is None")
+
+    # PATIENTS
+    patients = pc.encode_bundle(kafka_data.select("value"), "Patient")
+    if patients is not None:
+        patients_dataset = pc.read.datasets({"Patient": patients})
+        patients_dataset.write.delta(bundle_folder)
+    else:
+        print("Warning: 'patients' is None, skipping Patients dataset processing.")
+
+    # CONDITIONS
+    conditions = pc.encode_bundle(kafka_data.select("value"), "Condition")
+    if conditions is not None:
+        conditions_dataset = pc.read.datasets({"Condition": conditions})
+        conditions_dataset.write.delta(bundle_folder)
+    else:
+        print("Warning: 'conditions' is None, skipping Conditions dataset processing.")
+
+    # PROCEDURES
+    procedures = pc.encode_bundle(kafka_data.select("value"), "Procedure")
+    if procedures is not None:
+        procedures_dataset = pc.read.datasets({"Procedure": procedures})
+        procedures_dataset.write.delta(bundle_folder)
+    else:
+        print("Warning: 'procedures' is None, skipping Procedures dataset processing.")
+
+    # OBSERVATIONS
+    observations = pc.encode_bundle(kafka_data.select("value"), "Observation")
+    if observations is not None:
+        observations_dataset = pc.read.datasets({"Observation": observations})
+        observations_dataset.write.delta(bundle_folder)
+    else:
+        print(
+            "Warning: 'observations' is None, skipping Observations dataset processing."
+        )
+
+    # MEDICATION STATEMENTS
+    medicationstatements = pc.encode_bundle(
+        kafka_data.select("value"), "MedicationStatement"
+    )
+    if medicationstatements is not None:
+        medicationstatements_dataset = pc.read.datasets(
+            {"MedicationStatement": medicationstatements}
+        )
+        medicationstatements_dataset.write.delta(bundle_folder)
+    else:
+        print(
+            "Warning: 'medicationstatements' is None, skipping MedicationStatements"
+            "dataset processing."
+        )
 
 
 def lookup_gender(gender_string):
@@ -134,98 +185,6 @@ def lookup_gender(gender_string):
     return gender_mapped
 
 
-def calculate_age(birthdate):
-    year = birthdate[0:4]
-    today = datetime.date.today()
-    current_year = today.year
-    age = current_year - int(year)
-    return age
-
-
-def calculate_age_at_conditiondate(birthdate, conditiondate):
-    if conditiondate is None:
-        # todo: change this definitely
-        return birthdate - datetime.date(2022, 6, 15)
-    else:
-        return relativedelta(conditiondate, birthdate).years
-
-
-# 0-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59...80-84, 85+
-def diagnosis_age_group_small(age_at_diagnosis):
-    age_ranges = [
-        14,
-        19,
-        24,
-        29,
-        34,
-        39,
-        44,
-        49,
-        54,
-        59,
-        64,
-        69,
-        74,
-        79,
-        84,
-        math.inf,
-    ]
-
-    for index, upper_limit in enumerate(age_ranges):
-        if age_at_diagnosis <= upper_limit:
-            return index
-
-
-# 0-10, 11-20, 21-30... 71-80, 81-90, 90+
-def diagnosis_age_group_large(age_at_diagnosis):
-    age_ranges = [
-        10,
-        20,
-        30,
-        40,
-        50,
-        60,
-        70,
-        80,
-        90,
-        math.inf,
-    ]
-
-    for index, upper_limit in enumerate(age_ranges):
-        if age_at_diagnosis <= upper_limit:
-            return index
-
-
-def add_age_at_condition_and_groups(df_pat_cond_joined):
-    calculate_age_at_conditiondateUDF = udf(
-        lambda x, y: calculate_age_at_conditiondate(x, y), StringType()
-    )
-    df_pat_cond_joined = df_pat_cond_joined.withColumn(
-        "age_at_diagnosis",
-        calculate_age_at_conditiondateUDF(
-            to_date(df_pat_cond_joined.birthDate),
-            df_pat_cond_joined.conditiondate
-        ),
-    )
-
-    diagnosis_age_group_small_UDF = udf(
-        lambda x: diagnosis_age_group_small(x), StringType()
-    )
-    df_pat_cond_joined = df_pat_cond_joined.withColumn(
-        "age_group_small",
-        diagnosis_age_group_small_UDF(df_pat_cond_joined.age_at_diagnosis),
-    )
-
-    diagnosis_age_group_large_UDF = udf(
-        lambda x: diagnosis_age_group_large(x), StringType()
-    )
-    df_pat_cond_joined = df_pat_cond_joined.withColumn(
-        "age_group_large",
-        diagnosis_age_group_large_UDF(df_pat_cond_joined.age_at_diagnosis),
-    )
-    return df_pat_cond_joined
-
-
 def return_year(deceasedDateTime):
     if deceasedDateTime is not None:
         year = deceasedDateTime[0:4]
@@ -234,476 +193,88 @@ def return_year(deceasedDateTime):
         return deceasedDateTime
 
 
-def encode_patients(ptl: PathlingContext, df_bundles: pyspark.sql.dataframe.DataFrame):
-    df_patients = ptl.encode_bundle(df_bundles, "Patient")
-    lookup_genderUDF = udf(lambda x: lookup_gender(x), StringType())
-    calculate_ageUDF = udf(lambda x: calculate_age(x), StringType())
-    return_yearUDF = udf(lambda x: return_year(x), StringType())
-
-    patients = df_patients.selectExpr(
-        "id as pat_id",
-        # todo: remove this later or change opal datadictionary
-        "EXPLODE_OUTER(identifier.value) as patID",
-        "gender", "birthDate",
-        "deceasedBoolean", "deceasedDateTime"
-    )
-
-    patients = patients.withColumns(
-        {
-            "gender_mapped": lookup_genderUDF(patients.gender),
-            "age_in_years": calculate_ageUDF(patients.birthDate),
-            "deceased_year": return_yearUDF(patients.deceasedDateTime),
-        }
-    )
-
-    patients = patients.select(
-        patients.pat_id,
-        patients.patID,
-        patients.gender,
-        patients.gender_mapped,
-        patients.birthDate,
-        patients.age_in_years,
-        patients.deceasedDateTime,
-        patients.deceased_year,
-    )
-
-    patients_cached = patients.cache()
-    return patients_cached
-
-
-def lookup_condcodingcode_mapping(icd10_code):
-    if icd10_code[0] == "C":
-        icd10_code = icd10_code.replace("C", "3")
-    elif icd10_code[0] == "D":
-        icd10_code = icd10_code.replace("D", "4")
-    return icd10_code
-
-
-def group_entities(condcodingcode_mapped):
-    condcodingcode_mapped = float(condcodingcode_mapped)
-    ranges = [
-        (300, 315, 0),  # Lippe, Mundhöhle und Rachen (C00-C14)
-        (315, 316, 1),  # Speiseröhre (C15)
-        (316, 317, 2),  # Magen (C16)
-        (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
-        (322, 323, 4),  # Leber (C22)
-        (323, 325, 5),  # Gallenblase und Gallenwege (C23-C24)
-        (325, 326, 6),  # Bauchspeicheldrüse (C25)
-        (332, 333, 7),  # Kehlkopf (C32)
-        (333, 335, 8),  # Trachea, Bronchien und Lunge (C33-C34)
-        (343, 344, 9),  # Malignes Melanom der Haut (C43)
-        (350, 351, 10),  # Brust (C50, D05)
-        (405, 406, 10),
-        (353, 354, 11),  # Gebärmutterhals (C53, D06)
-        (406, 407, 11),
-        (354, 356, 12),  # Gebärmutterkörper (C54-C55)
-        (356, 357, 13),  # Eierstöcke (C56, D39.1)
-        (439.1, 439.2, 13),
-        (361, 362, 14),  # Prostata (C61)
-        (362, 363, 15),  # Hoden (C62)
-        (364, 365, 16),  # Niere (C64)
-        (367, 368, 17),  # Harnblase (C67, D09.0, D41.4)
-        (409.0, 409.1, 17),
-        (441.4, 441.5, 17),
-        (370, 373, 18),  # Gehirn und zentrales Nervensystem (C70-C72)
-        (373, 374, 19),  # Schilddrüse (C73)
-        (381, 382, 20),  # Morbus Hodgkin (C81)
-        (382, 389, 21),  # Non-Hodgkin-Lymphome (C82-C88, C96)
-        (396, 397, 21),
-        (390, 391, 22),  # Plasmozytom (C90)
-        (391, 396, 23),  # Leukämien (C91-C95)
-    ]
-
-    for start, end, group in ranges:
-        if start <= condcodingcode_mapped < end:
-            return group
-
-    return 100
-
-
-def encode_conditions(ptl: PathlingContext, df_bundles):
-    lookup_condcodingcode_mappingUDF = udf(
-        lambda x: lookup_condcodingcode_mapping(x), StringType()
-    )
-    group_entities_UDF = udf(lambda x: group_entities(x), StringType())
-    df_conditions = ptl.encode_bundle(df_bundles, "Condition")
-    conditions = df_conditions.selectExpr(
-        "id as cond_id",
-        "subject.reference as subjectreference",
-        "onsetDateTime as conditiondate",
-        "code.coding.code[0] as condcodingcode",
-        "stage.assessment as stageassessment",
-        "evidence.detail as evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "EXPLODE_OUTER(stageassessment) as stageassessmentTNM",
-        "evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "EXPLODE_OUTER(stageassessmentTNM) as stageassessmentTNM",
-        "evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "stageassessmentTNM.reference as stagereference",
-        "evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "stagereference",
-        "EXPLODE_OUTER(evidencedetail) as evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "stagereference",
-        "EXPLODE_OUTER(evidencedetail) as evidencedetail",
-    )
-    conditions = conditions.selectExpr(
-        "cond_id",
-        "subjectreference",
-        "conditiondate",
-        "condcodingcode",
-        "stagereference",
-        "evidencedetail.reference as evidencereference",
-    ).withColumns(
-        {
-            "subjectreference": regexp_replace("subjectreference", "Patient/", ""),
-            "evidencereference": regexp_replace(
-                "evidencereference", "Observation/", ""
+def extract_df(pc: PathlingContext, data: ptl.datasource.DataSource):
+    map_icd10UDF = udf(lambda x: map_icd10(x), StringType())
+    group_entitiesUDF = udf(lambda x: group_entities(x), StringType())
+    map_genderUDF = udf(lambda x: map_gender(x), StringType())
+
+    df = data.extract(
+        "Condition",
+        columns=[
+            exp("id", "condition_id"),
+            exp("onsetDateTime", "date_diagnosis"),
+            exp(
+                """code.coding
+                .where(system='http://fhir.de/CodeSystem/bfarm/icd-10-gm').code""",
+                "icd10_code",
             ),
-            "conditiondate_year": substring("conditiondate", 1, 4),
-            "stagereference": regexp_replace("stagereference", "Observation/", ""),
-            "conditiondate": regexp_replace("conditiondate", "T", " "),
-        }
-    )
-    conditions = conditions.withColumn(
-        "condcodingcode_mapped",
-        lookup_condcodingcode_mappingUDF(conditions.condcodingcode),
-    )
-    conditions = conditions.withColumn(
-        "entity_group",
-        group_entities_UDF(conditions.condcodingcode_mapped),
-    )
-    conditions = conditions.select(
-        "cond_id",
-        "conditiondate",
-        "conditiondate_year",
-        "subjectreference",
-        "condcodingcode",
-        "condcodingcode_mapped",
-        "entity_group",
-        "stagereference",
-        "evidencereference",
-    ).withColumn("conditiondate", to_date(conditions.conditiondate))
-
-    conditions_cached = conditions.cache()
-
-    return conditions_cached
-
-
-def join_dataframes(df_one, partition_col_one: str, df_two, partition_col_two: str):
-    # repartition before joining
-    df_one_repartitioned = df_one.repartition(20, col(partition_col_one))
-    df_two_repartitioned = df_two.repartition(20, col(partition_col_two))
-
-    df_one_two_joined = df_one_repartitioned.join(
-        df_two_repartitioned,
-        df_one_repartitioned[partition_col_one]
-        == df_two_repartitioned[partition_col_two],
-        "left_outer",
-    )
-
-    df_one_two_joined_cached = df_one_two_joined.cache()
-    return df_one_two_joined_cached
-
-
-def lookup_obs_value_codingcode_tnm_uicc_mapping(obs_value_codingcode_tnm_UICC):
-    obs_value_codingcode_tnm_uicc_mapping_dict = {
-        "0": "0",
-        "0a": "1",
-        "0is": "2",
-        "I": "3",
-        "IA": "4",
-        "IA1": "5",
-        "IA2": "6",
-        "IA3": "7",
-        "IB": "8",
-        "IB1": "9",
-        "IB2": "10",
-        "IC": "11",
-        "IS": "12",
-        "II": "13",
-        "IIA": "14",
-        "IIA1": "15",
-        "IIA2": "16",
-        "IIB": "17",
-        "IIC": "18",
-        "III": "19",
-        "IIIA": "20",
-        "IIIB": "21",
-        "IIIC": "22",
-        "IIIC1": "23",
-        "IIIC2": "24",
-        "IV": "25",
-        "IVA": "26",
-        "IVB": "27",
-        "IVC": "28",
-    }
-    if obs_value_codingcode_tnm_UICC in obs_value_codingcode_tnm_uicc_mapping_dict:
-        return obs_value_codingcode_tnm_uicc_mapping_dict[obs_value_codingcode_tnm_UICC]
-    else:
-        return obs_value_codingcode_tnm_UICC
-
-
-def lookup_grouped_uicc(obs_value_codingcode_tnm_UICC):
-    grouped_uicc_dict = {
-        "0": "0",
-        "0a": "0",
-        "0is": "0",
-        "I": "1",
-        "IA": "1",
-        "IA1": "1",
-        "IA2": "1",
-        "IA3": "1",
-        "IB": "1",
-        "IB1": "1",
-        "IB2": "1",
-        "IC": "1",
-        "IS": "1",
-        "II": "2",
-        "IIA": "2",
-        "IIA1": "2",
-        "IIA2": "2",
-        "IIB": "2",
-        "IIC": "2",
-        "III": "3",
-        "IIIA": "3",
-        "IIIB": "3",
-        "IIIC": "3",
-        "IIIC1": "3",
-        "IIIC2": "3",
-        "IV": "4",
-        "IVA": "4",
-        "IVB": "4",
-        "IVC": "4",
+            exp("Condition.subject.resolve().ofType(Patient).gender", "gender"),
+        ],
+    )
+    # deconstruct date in YYYY, MM, DD
+    df = deconstruct_date(df, "date_diagnosis")
+
+    # map ICD10 code to numeric value for DataSHIELD, C = 3, D = 4
+    df = df.withColumn(
+        "icd10_mapped",
+        map_icd10UDF(df.icd10_code).cast(DoubleType()),
+    )
+    # groupd icd10 to entities, inspired from "Jahresbericht 2023 des Bayerischen
+    # Krebsregisters - Krebs in Bayern in den Jahren 2015 bis 2019 - Band 5"
+    df = df.withColumn(
+        "icd10_grouped_entities",
+        group_entitiesUDF(df.icd10_mapped).cast(IntegerType()),
+    )
+    # map gender to numeric value for DataSHIELD, None = 0, "female" = 1, "male" = 2,
+    # "other/diverse" = 3
+    df = df.withColumn(
+        "gender_mapped",
+        map_genderUDF(df.gender).cast(IntegerType()),
+    )
+    df = df.dropDuplicates()
+
+    return df
+
+
+def save_final_df(df):
+    df_with_id = df.withColumn("ID", monotonically_increasing_id())
+
+    # rearrange columns to have ID as the first column
+    df_with_id = df_with_id.select("ID", *df.columns)
+    # to have only one single csv
+    df_with_id = df_with_id.coalesce(1)
+    # write DataFrame to CSV, rename it
+    output_dir = "output_dir"
+    df_with_id.write.mode("overwrite").csv(output_dir, header=True)
+    output_file = "df.csv"
+    part_file = [file for file in os.listdir(output_dir) if file.startswith("part-")][0]
+    shutil.move(os.path.join(output_dir, part_file), output_file)
+    shutil.rmtree(output_dir)
+
+
+def prepare_datadictionary(df):
+    # generate data dictionary
+    dtypes_list_df = [str(dtype[1]) for dtype in df.dtypes]
+
+    description_list_df_dictionary = {
+        "condition_id": "Condition ID, unique for each condition",
+        "date_diagnosis": "date of diagnosis",
+        "icd10_code": "ICD10 GM diagnosis code",
+        "icd10_mapped": """ICD10 GM diagnosis code mapped A = 1, B = 2, C = 3, D = 4,
+        e.g.: A01.9 = 101.9, C50.1 = 350.1 or D41.9 = 441.9""",
+        "icd10_grouped_entities": """ICD10 GM diagnosis code grouped to entity groups
+        from 0-23 according to LGL Report Cancer in Bavaria 2019, mapping see
+        github.com/bzkf/onco-analytics-on-fhir/src/obds_fhir_to_opal
+        /utils_onco_analytics.py""",
+        "date_diagnosis_year": "Year of Diagnosis",
+        "date_diagnosis_month": "Month of Diagnosis",
+        "date_diagnosis_day": "Day of Diagnosis",
+        "gender": "Gender - male, female, other/diverse",
+        "gender_mapped": """Gender mapped: 0 = None, 1 = female, 2 = male,
+        3 = other/diverse""",
     }
-    if obs_value_codingcode_tnm_UICC in grouped_uicc_dict:
-        return grouped_uicc_dict[obs_value_codingcode_tnm_UICC]
-    else:
-        return obs_value_codingcode_tnm_UICC
 
-
-def transform_tnmp(observations_tnmp):
-    lookup_obs_value_codingcode_tnm_UICC_mappingUDF = udf(
-        lambda x: lookup_obs_value_codingcode_tnm_uicc_mapping(x), StringType()
-    )
-    observations_tnmp = observations_tnmp.withColumn(
-        "tnmp_UICC_mapped",
-        lookup_obs_value_codingcode_tnm_UICC_mappingUDF(observations_tnmp.obsvaluecode),
-    )
-    lookup_lookup_grouped_uiccUDF = udf(lambda x: lookup_grouped_uicc(x), StringType())
-    observations_tnmp = observations_tnmp.withColumn(
-        "tnmp_UICC_grouped",
-        lookup_lookup_grouped_uiccUDF(observations_tnmp.obsvaluecode),
-    )
-    observations_tnmp = observations_tnmp.selectExpr(
-        "obssubjreference as tnm_obssubjreference",
-        "obsid as tnmp_obsid",
-        "obscodingcode as tnmp_codingcode",
-        "obscodingdisplay as tnmp_obscodingdisplay",
-        "obsvaluecode as tnmp_obsvalue_UICC",
-        "obsdate as tnmp_obsdate",
-        "tnmp_UICC_mapped",
-        "tnmp_UICC_grouped",
-    )
-
-    observations_tnmp_cached = observations_tnmp.cache()
-
-    return observations_tnmp_cached
-
-
-def replace_element(obs_value_coding_code_hist):
-    if obs_value_coding_code_hist is None:
-        pass
-    else:
-        obs_value_coding_code_hist = obs_value_coding_code_hist.replace("/", "")
-
-    return obs_value_coding_code_hist
-
-
-def transform_histology(observations_histology):
-    replace_elementUDF = udf(lambda x: replace_element(x), StringType())
-
-    observations_histology = observations_histology.select(
-        col("obssubjreference"),
-        col("obsid").alias("obs_id_hist"),
-        col("obscodingcode").alias("obs_coding_code_hist"),
-        col("obscodingdisplay").alias("obs_coding_display_hist"),
-        col("obsvaluecode").alias("obs_value_coding_code_hist"),
-        col("obsdate").alias("obsdate_hist"),
-    ).withColumn("obsdate_hist", regexp_replace("obsdate_hist", "T", " "))
-
-    observations_histology = observations_histology.withColumns(
-        {
-            "obs_value_hist_mapped": replace_elementUDF(
-                observations_histology.obs_value_coding_code_hist
-            ),
-            "obs_value_hist_mapped_0_4": replace_elementUDF(
-                observations_histology.obs_value_coding_code_hist
-            )[0:4],
-            "obs_value_hist_mapped_5": replace_elementUDF(
-                observations_histology.obs_value_coding_code_hist
-            )[5:5],
-        }
-    )
-    observations_histology_cached = observations_histology.cache()
-    return observations_histology_cached
-
-
-def encode_observations(ptl: PathlingContext, df_bundles):
-    df_observations = ptl.encode_bundle(df_bundles, "Observation")
-    observations = df_observations.selectExpr(
-        "id as obsid",
-        "subject.reference as obssubjreference",
-        "code.coding[0].code as obscodingcode",
-        "code.coding[0].display as obscodingdisplay",
-        "valueCodeableConcept as obs_value_coding",
-        "effectiveDateTime as obsdate",
-    )
-    observations = observations.selectExpr(
-        "obsid",
-        "obssubjreference",
-        "obscodingcode",
-        "obscodingdisplay",
-        "obs_value_coding.*",
-        "obsdate",
-    )
-    observations = observations.select(
-        col("obsid"),
-        col("obssubjreference"),
-        col("obscodingcode"),
-        col("obscodingdisplay"),
-        explode("coding").alias("obs_value_coding"),
-        col("obsdate"),
-    )
-    observations = observations.selectExpr(
-        "obsid",
-        "obssubjreference",
-        "obscodingcode",
-        "obscodingdisplay",
-        "obs_value_coding.code as obsvaluecode",
-        "obs_value_coding.display as obs_value_codingdisplay",
-        "obsdate",
-    ).withColumns(
-        {
-            "obssubjreference": regexp_replace("obssubjreference", "Patient/", ""),
-            "obsdate": regexp_replace("obsdate", "T", " "),
-        }
-    )
-    observations = observations.withColumn("obsdate", to_date(observations.obsdate))
-    observations_cached = observations.cache()
-
-    # tnmp
-    observations_tnmp = observations_cached.where(
-        (observations_cached.obscodingcode == "21902-2")
-    )
-    df_observations_tnmp = transform_tnmp(observations_tnmp)
-
-    # histology
-    observations_histology = observations_cached.where(
-        (observations_cached.obscodingcode == "59847-4")
-    )
-    df_observations_histology = transform_histology(observations_histology)
-
-    return df_observations_tnmp, df_observations_histology
-
-
-def group_df(joined_dataframe):
-    joined_dataframe_grouped = joined_dataframe.groupBy("cond_id").agg(
-        first("pat_id").alias("pat_id"),
-        first("patID").alias("patID"),
-        first("gender_mapped").alias("gender_mapped"),
-        first("conditiondate").alias("conditiondate"),
-        first("conditiondate_year").alias("conditiondate_year"),
-        first("condcodingcode").alias("condcodingcode"),
-        first("condcodingcode_mapped").alias("condcodingcode_mapped"),
-        first("entity_group").alias("entity_group"),
-        first("deceased_year").alias("deceased_year"),
-        first("age_in_years").alias("age_in_years"),
-        first("age_at_diagnosis").alias("age_at_diagnosis"),
-        first("age_group_small").alias("age_group_small"),
-        first("age_group_large").alias("age_group_large"),
-        first("tnmp_obsid").alias("tnmp_obsid"),
-        first("tnmp_obsvalue_UICC").alias("tnmp_obsvalue_UICC"),
-        first("tnmp_UICC_mapped").alias("tnmp_UICC_mapped"),
-        first("tnmp_UICC_grouped").alias("tnmp_UICC_grouped"),
-        first("tnmp_obsdate").alias("tnmp_obsdate"),
-        first("evidencereference").alias("evidencereference"),
-        first("obsdate_hist").alias("obsdate_hist"),
-        first("obs_value_hist_mapped_0_4").alias("obs_value_hist_mapped_0_4"),
-        first("obs_value_hist_mapped_5").alias("obs_value_hist_mapped_5"),
-        first("obs_id_hist").alias("obs_id_hist"),
-    )
-    joined_dataframe_grouped_repartitioned = joined_dataframe_grouped.repartition(
-        20, col("pat_id")
-    )
-    # fix order
-    joined_dataframe_grouped_repartitioned = (
-        joined_dataframe_grouped_repartitioned.select(
-            "pat_id",
-            "patID",
-            "cond_id",
-            "gender_mapped",
-            "conditiondate",
-            "conditiondate_year",
-            "condcodingcode",
-            "condcodingcode_mapped",
-            "entity_group",
-            "age_in_years",
-            "age_at_diagnosis",
-            "age_group_small",
-            "age_group_large",
-            "deceased_year",
-            "tnmp_obsvalue_UICC",
-            "tnmp_UICC_mapped",
-            "tnmp_UICC_grouped",
-            "tnmp_obsdate",
-            "obs_value_hist_mapped_0_4",
-            "obs_value_hist_mapped_5",
-            "obsdate_hist",
-        )
-    )
-    return joined_dataframe_grouped_repartitioned
-
-
-def save_final_df(final_df):
-    final_df_pandas = final_df.toPandas()
-    final_df_pandas = final_df_pandas.rename_axis("ID")  # required for opal import
-
-    output_path_filename = os.path.join(
-        settings.output_folder, settings.output_filename
-    )
-    print("###### current dir: ", os.getcwd())
-    print("###### output_path_filename : ", output_path_filename)
-
-    final_df_pandas.to_csv(output_path_filename)
+    return dtypes_list_df, description_list_df_dictionary
 
 
 def main():
@@ -713,44 +284,27 @@ def main():
     print("kafka_topics: ", kafka_topics)
 
     spark = setup_spark_session(settings.spark_app_name, settings.master)
-    ptl = PathlingContext.create(spark=spark, enable_extensions=True)
+    pc = PathlingContext.create(spark=spark, enable_extensions=True)
 
-    read_data_from_kafka_save_delta(spark, kafka_topics)
+    read_data_from_kafka_save_delta(spark, kafka_topics, pc)
 
-    df_bundles = spark.read.format("delta").load(
-        os.path.join(settings.output_folder, "bundles-delta")
-    )
+    data = pc.read.delta(os.path.join(settings.output_folder, "bundles-delta"))
 
-    df_patients = encode_patients(ptl, df_bundles)
-    df_conditions = encode_conditions(ptl, df_bundles)
+    df = extract_df(ptl, data)
 
-    df_pat_cond_joined = join_dataframes(
-        df_patients, "pat_id", df_conditions, "subjectreference"
-    )
-    df_pat_cond_joined = add_age_at_condition_and_groups(df_pat_cond_joined)
-
-    df_observations_tnmp, df_observations_histology = encode_observations(
-        ptl, df_bundles
-    )
-    df_pat_cond_obstnmp_joined = join_dataframes(
-        df_pat_cond_joined, "pat_id", df_observations_tnmp, "tnm_obssubjreference"
-    )
-    df_pat_cond_obstnmp_obshist_joined = join_dataframes(
-        df_pat_cond_obstnmp_joined,
-        "pat_id",
-        df_observations_histology,
-        "obssubjreference",
-    )
-    df_pat_cond_obstnmp_obshist_joined_grouped = group_df(
-        df_pat_cond_obstnmp_obshist_joined
-    )
-
-    save_final_df(df_pat_cond_obstnmp_obshist_joined_grouped)
+    save_final_df(df)
 
     shutil.rmtree(os.path.join(settings.output_folder, "bundles-delta"))
-    print("###### DELETED bundles-delta folder and files")
 
-    # df_pat_cond_obstnmp_obshist_joined_grouped.show()
+    dtypes_list_df, description_list_df_dictionary = prepare_datadictionary(df)
+
+    generate_datadictionary(
+        file_path="data_dictionary_df.xlsx",
+        table_name="df",
+        colnames_list=df.columns,
+        value_type_list=dtypes_list_df,
+        description=description_list_df_dictionary,
+    )
 
     end = time.monotonic()
     print(f"time elapsed: {end - start}s")
diff --git a/src/obds_fhir_to_opal/plots/README.md b/src/obds_fhir_to_opal/plots/README.md
new file mode 100644
index 00000000..284648da
--- /dev/null
+++ b/src/obds_fhir_to_opal/plots/README.md
@@ -0,0 +1,39 @@
+# README
+
+## Overview
+This folder contains two Jupyter notebooks designed for visualizing the results of a federated analysis using DataSHIELD. The analysis focuses on the distribution of tumor entities across various hospitals in Bavaria, based on cases diagnosed within a one-year time span. The results of the DataSHIELD analysis are provided in a CSV file with the following structure:
+
+
+| entity_ORDER | entity                         | Site1_total | Site1_male | Site1_female | Site2_total | Site2_male | Site2_female | Site3_total | Site3_male | Site3_female | Site4_total | Site4_male | Site4_female | Site5_total | Site5_male | Site5_female | Site6_total | Site6_male | Site6_female |
+|--------------|--------------------------------|-------------|------------|--------------|-------------|------------|--------------|-------------|------------|--------------|-------------|------------|--------------|-------------|------------|--------------|-------------|------------|--------------|
+| 0            | Lip, Oral Cavity, Pharynx (C00-C14) |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+| 1            | Esophagus (C15)                |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+| 2            | Stomach (C16)                  |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+| 3            | Colon and Rectum (C18-C21)     |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+| ...            | ...                    |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+| 23           | Leukemia (C91-C95)             |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |             |            |              |
+
+
+### Example Rows
+- **Entity:** Represents the cancer type or tumor entity (e.g., Lip, Oral Cavity, Pharynx (C00-C14), Esophagus (C15)).
+- **_total/male/female:** Columns represent the total number of cases, as well as the number of male and female cases, respectively, at each of the six sites.
+
+## Notebooks Description
+
+1. **Notebook 1: Tumor Entity Distribution**
+   - **Purpose:** The notebook visualizes the relative numbers of cases diagnosed within a one-year period, broken down by site and tumor type.
+
+2. **Notebook 2: Gender Distribution Among Tumor Entities**
+   - **Purpose:** Visualizes the aggregated frequencies of cancer diagnoses for each entity group across all six locations and highlights relative distribution for female and male patients.
+
+## How to Use
+1. Enter the path to the CSV file with the DataSHIELD results in the notebook..
+2. Open the notebooks in Jupyter and run the cells to generate the plots.
+
+## Additional Information
+- **Data Source:** The analysis is based on Real-World Data (RWD) collected from six university hospitals in Bavaria.
+- **Data Format:** The CSV file includes 24 tumor entities along with corresponding case counts for each site, segmented by gender.
+
+
+
+
diff --git a/src/obds_fhir_to_opal/plots/gender_distribution_among_tumor_entities_online_version.ipynb b/src/obds_fhir_to_opal/plots/gender_distribution_among_tumor_entities_online_version.ipynb
new file mode 100644
index 00000000..a3af16db
--- /dev/null
+++ b/src/obds_fhir_to_opal/plots/gender_distribution_among_tumor_entities_online_version.ipynb
@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T08:20:44.273658Z",
+     "start_time": "2024-04-12T08:20:44.242980Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_style('darkgrid')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fa699160",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in file\n",
+    "filename = \"\"\n",
+    "sheet_name = \"entity\"\n",
+    "\n",
+    "df = pd.read_excel(filename, sheet_name)\n",
+    "df = df.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c52b289bc4b40c04",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T08:20:44.300792Z",
+     "start_time": "2024-04-12T08:20:44.294035Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# sum up all male and female cases\n",
+    "df['total_male_abs'] = df.filter(like='_male').sum(axis=1)\n",
+    "df['total_female_abs'] = df.filter(like='_female').sum(axis=1)\n",
+    "\n",
+    "df_sums = df[['entity', 'total_male_abs', 'total_female_abs']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0854b12e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute totals and relative numbers for each entity\n",
+    "df_sums.loc[:, 'total'] = df_sums.loc[:, 'total_male_abs'].copy() + df_sums.loc[:, 'total_female_abs'].copy()\n",
+    "df_sums.loc[:, 'total_male_rel'] = df_sums.loc[:, 'total_male_abs'].copy() / df_sums.loc[:, 'total'].copy()\n",
+    "df_sums.loc[:, 'total_female_rel'] = df_sums.loc[:, 'total_female_abs'].copy() / df_sums.loc[:, 'total'].copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff89a6db795c6d70",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "### Butterfly bar chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7838594",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_butterfly(df, relative=True):\n",
+    "    # Plot\n",
+    "    plt.figure(figsize=(10, 8))\n",
+    "\n",
+    "    if relative == True:\n",
+    "        # Set the x-axis limits and ticks\n",
+    "        plt.xticks([-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1],\n",
+    "                   ['100%', '75%', '50%', '25%', '0%', '25%', '50%', '75%', '100%'])\n",
+    "\n",
+    "        df = df.sort_values(by='total_female_rel', ascending=False)\n",
+    "\n",
+    "        # Heights of bars\n",
+    "        bars1 = df['total_female_rel']\n",
+    "        bars2 = df['total_male_rel']\n",
+    "\n",
+    "    else:\n",
+    "        # Sort by total\n",
+    "        df = df.sort_values(by='total_female_abs', ascending=False)\n",
+    "\n",
+    "        # Heights of bars\n",
+    "        bars1 = df['total_female_abs']\n",
+    "        bars2 = df['total_male_abs']\n",
+    "\n",
+    "    # Positions of bars\n",
+    "    barWidth = 0.4\n",
+    "    r = np.arange(len(df))\n",
+    "\n",
+    "    # Create bars for female patients (to the left)\n",
+    "    plt.barh(r, bars1, color='#66c159', edgecolor='white', height=barWidth, label='Female')\n",
+    "    # Create bars for male patients (to the right)\n",
+    "    plt.barh(r, -bars2, color='#7187da', edgecolor='white', height=barWidth, label='Male')\n",
+    "\n",
+    "    plt.yticks(r, df['entity'])\n",
+    "\n",
+    "    plt.legend()\n",
+    "    plt.gca().invert_yaxis()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4017e751",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-12T08:20:45.081669Z",
+     "start_time": "2024-04-12T08:20:45.079762Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_butterfly(df_sums, relative=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfb07dc3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99fca434",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/obds_fhir_to_opal/plots/tumor_entity_distribution_online_version.ipynb b/src/obds_fhir_to_opal/plots/tumor_entity_distribution_online_version.ipynb
new file mode 100644
index 00000000..70dbdfa7
--- /dev/null
+++ b/src/obds_fhir_to_opal/plots/tumor_entity_distribution_online_version.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.355372Z",
+     "start_time": "2024-08-01T22:36:30.166807Z"
+    },
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib as plt\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from colorcet.plotting import swatch\n",
+    "from IPython.display import display\n",
+    "import matplotlib.cm as cm\n",
+    "import matplotlib.patches as mpatches\n",
+    "\n",
+    "sns.set_style(\"darkgrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "aeb52862dde9ed99",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.355934Z",
+     "start_time": "2024-08-01T22:36:30.167762Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "filename = \"\"\n",
+    "sheet_name = \"entity\"\n",
+    "\n",
+    "df = pd.read_excel(filename, sheet_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "7500786901268c48",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.356544Z",
+     "start_time": "2024-08-01T22:36:30.247357Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Drop columns ending with \"_male\" or \"_female\"\n",
+    "df = df.drop(df.filter(like=\"_male\", axis=1).columns, axis=1)\n",
+    "df = df.drop(df.filter(like=\"_female\", axis=1).columns, axis=1)\n",
+    "\n",
+    "# Remove \"_total\" from colnames\n",
+    "df = df.rename(\n",
+    "    columns={col: col.split(\"_\")[0] for col in df.columns if col.endswith(\"_total\")}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "53d6e3ee59f0dce0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.356692Z",
+     "start_time": "2024-08-01T22:36:30.258375Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# pseudonymize and rename locations if necessary\n",
+    "location_pseudonyms = {\n",
+    "    \"\": \"Site 1\",\n",
+    "    \"\": \"Site 2\",\n",
+    "    \"\": \"Site 3\",\n",
+    "    \"\": \"Site 4\",\n",
+    "    \"\": \"Site 5\",\n",
+    "    \"\": \"Site 6\",\n",
+    "}\n",
+    "desired_order = [\"entity\", \"Site 6\", \"Site 5\", \"Site 4\", \"Site 3\", \"Site 2\", \"Site 1\"]\n",
+    "\n",
+    "df = df.rename(columns=location_pseudonyms)[desired_order]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5d5f59b0863dcede",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.356897Z",
+     "start_time": "2024-08-01T22:36:30.278630Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# calculate relative tumor cases per location and entity in %\n",
+    "for location in [col for col in df.columns if col.startswith(\"Site\")]:\n",
+    "    new_col = location + \" [%]\"\n",
+    "    if new_col not in df.columns:\n",
+    "        df[new_col] = (df[location] / df[location].sum() * 100).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1a377ee093bca583",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.357722Z",
+     "start_time": "2024-08-01T22:36:30.288513Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# melt down \"site 1, 2, 3...\" to a \"site\" col with values as second column \"abs_cases\"\n",
+    "sites_abs = [col for col in df.columns if col.startswith(\"Site\") and col[-1].isdigit()]\n",
+    "\n",
+    "df_melt_abs = pd.melt(\n",
+    "    df,\n",
+    "    id_vars=[\"entity\"],\n",
+    "    value_vars=sites_abs,\n",
+    "    var_name=\"site\",\n",
+    "    value_name=\"abs_cases\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bc2bd040363c6d06",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.357997Z",
+     "start_time": "2024-08-01T22:36:30.304501Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# melt down \"site 1 [%], 2 [%]...\" to a \"site [%]\" col with values as another volumn \"%_cases\"\n",
+    "sites_rel = [\n",
+    "    col for col in df.columns if col.startswith(\"Site\") and not col[-1].isdigit()\n",
+    "]\n",
+    "\n",
+    "df_melt_rel = pd.melt(\n",
+    "    df,\n",
+    "    id_vars=[\"entity\"],\n",
+    "    value_vars=sites_rel,\n",
+    "    var_name=\"site %\",\n",
+    "    value_name=\"%_cases\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "d00c89c4e5581011",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.358252Z",
+     "start_time": "2024-08-01T22:36:30.315366Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# join both melted dfs\n",
+    "df_melt = pd.concat([df_melt_abs, df_melt_rel], axis=1)\n",
+    "df_melt = df_melt.drop([\"site %\"], axis=1)\n",
+    "df_melt = df_melt.loc[:, ~df_melt.columns.duplicated()].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9be4f7e68f89b663",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:30.358312Z",
+     "start_time": "2024-08-01T22:36:30.326575Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pivoted_dfs = []\n",
+    "\n",
+    "unique_sites = df_melt[\"site\"].unique()\n",
+    "\n",
+    "# Loop through each site, filter the dataframe, pivot and sort\n",
+    "for site in unique_sites:\n",
+    "    df_site = df_melt[df_melt[\"site\"] == site]\n",
+    "    df_pivot_site = df_site.pivot(index=\"site\", columns=\"entity\", values=\"%_cases\")\n",
+    "    df_pivot_site = df_pivot_site.reindex(\n",
+    "        df_pivot_site.mean().sort_values(ascending=False).index, axis=1\n",
+    "    )\n",
+    "    pivoted_dfs.append(df_pivot_site)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "aac8d925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "custom_colors = [\n",
+    "    \"#e6194b\",\n",
+    "    \"#FF8C00\",\n",
+    "    \"#3cb44b\",\n",
+    "    \"#0082c8\",\n",
+    "    \"#ffe119\",\n",
+    "    \"#6e3a7d\",\n",
+    "    \"#46f0f0\",\n",
+    "    \"#b722af\",\n",
+    "    \"#d2f53c\",\n",
+    "    \"#FF69B4\",\n",
+    "    \"#008080\",\n",
+    "    \"#e6beff\",\n",
+    "    \"#aa6e28\",\n",
+    "    \"#fffac8\",\n",
+    "    \"#800000\",\n",
+    "    \"#aaffc3\",\n",
+    "    \"#808000\",\n",
+    "    \"#FFA07A\",\n",
+    "    \"#3288bd\",\n",
+    "    \"#808080\",\n",
+    "    \"#FFFFFF\",\n",
+    "    \"#000000\",\n",
+    "    \"#e6194b\",\n",
+    "    \"#9e4242\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1b2e9265de3a71ba",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:37:02.563965Z",
+     "start_time": "2024-08-01T22:37:01.942305Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot_all_sites(data_dfs):\n",
+    "    fig, ax = plt.subplots(figsize=(35, 12))\n",
+    "\n",
+    "    unique_entities = data_dfs[-1].columns.tolist()\n",
+    "\n",
+    "    color_dict = {\n",
+    "        entity: custom_colors[i % len(custom_colors)]\n",
+    "        for i, entity in enumerate(unique_entities)\n",
+    "    }\n",
+    "\n",
+    "    site_names = [f\"Site {i + 1}\" for i in range(len(data_dfs))]\n",
+    "    bar_positions = list(range(len(data_dfs)))\n",
+    "\n",
+    "    for i, data_df in enumerate(data_dfs):\n",
+    "        data_df.plot(\n",
+    "            kind=\"barh\",\n",
+    "            stacked=True,\n",
+    "            ax=ax,\n",
+    "            width=0.8,\n",
+    "            color=[color_dict[col] for col in data_df.columns],\n",
+    "            position=i,\n",
+    "        )\n",
+    "\n",
+    "    for c in ax.containers:\n",
+    "        labels = [f\"{v.get_width():.1f}\" if v.get_width() > 2.0 else \"\" for v in c]\n",
+    "        ax.bar_label(\n",
+    "            c,\n",
+    "            label_type=\"center\",\n",
+    "            labels=labels,\n",
+    "            size=28,\n",
+    "            fontweight=\"bold\",\n",
+    "            color=\"black\",\n",
+    "        )\n",
+    "\n",
+    "    ax.set_position([0.1, 0.1, 0.8, 0.8])\n",
+    "\n",
+    "    ax.set_xticks(range(0, 101, 20))\n",
+    "    ax.set_xticklabels([f\"{x}%\" for x in range(0, 101, 20)])\n",
+    "\n",
+    "    legend_elements = [\n",
+    "        mpatches.Patch(\n",
+    "            facecolor=color_dict[entity], edgecolor=\"black\", linewidth=1, label=entity\n",
+    "        )\n",
+    "        for entity in unique_entities\n",
+    "    ]\n",
+    "    ax.legend(\n",
+    "        handles=legend_elements,\n",
+    "        loc=\"upper center\",\n",
+    "        bbox_to_anchor=(0.5, -0.05),\n",
+    "        fancybox=True,\n",
+    "        shadow=True,\n",
+    "        ncol=4,\n",
+    "        fontsize=24,\n",
+    "    )\n",
+    "\n",
+    "    ax.tick_params(axis=\"x\", labelsize=33)\n",
+    "    ax.tick_params(axis=\"y\", labelsize=33)\n",
+    "\n",
+    "    ax.set_ylabel(\"\")\n",
+    "\n",
+    "    for label in ax.get_yticklabels():\n",
+    "        label.set_y(1.0)\n",
+    "\n",
+    "    # Set y-ticks and add site names\n",
+    "    bar_positions_adjusted = [p + 0.4 for p in bar_positions]\n",
+    "    ax.set_yticks(bar_positions_adjusted)\n",
+    "\n",
+    "    ax.set_ylim(-4, len(data_dfs) - 5.2)\n",
+    "    ax.set_xlim(0, 100)\n",
+    "\n",
+    "    ax.invert_yaxis()\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "902d5f776bbb4a33",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-08-01T22:36:33.958764Z",
+     "start_time": "2024-08-01T22:36:33.957370Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 3500x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_all_sites(pivoted_dfs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": ".venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/obds_fhir_to_opal/requirements-test.txt b/src/obds_fhir_to_opal/requirements-test.txt
new file mode 100644
index 00000000..9a6c85a4
--- /dev/null
+++ b/src/obds_fhir_to_opal/requirements-test.txt
@@ -0,0 +1,2 @@
+pytest==7.4.4
+pytest-cov==4.1.0
diff --git a/src/obds_fhir_to_opal/requirements.txt b/src/obds_fhir_to_opal/requirements.txt
index c311bf67..defdf1fa 100644
--- a/src/obds_fhir_to_opal/requirements.txt
+++ b/src/obds_fhir_to_opal/requirements.txt
@@ -3,3 +3,4 @@ pydantic==1.10.14
 pyspark==3.3.2
 pandas==2.2.0
 python-dateutil==2.9.0.post0
+xlsxwriter==3.2.0
diff --git a/src/obds_fhir_to_opal/test_utils.py b/src/obds_fhir_to_opal/test_utils.py
new file mode 100644
index 00000000..2dfb28c3
--- /dev/null
+++ b/src/obds_fhir_to_opal/test_utils.py
@@ -0,0 +1,102 @@
+import pytest
+
+from .utils_onco_analytics import group_entities, map_gender, map_icd10
+
+
+# test functions separately
+@pytest.mark.parametrize(
+    "icd10_code, expected",
+    [
+        ("C31.8", "3" + "31.8"),  # C -> 3
+        ("C50.9", "3" + "50.9"),  # C -> 3
+        ("D06.4", "4" + "06.4"),  # D -> 4
+        ("A00.0", "1" + "00.0"),  # A -> 1
+        ("Z99.9", "26" + "99.9"),  # Z -> 26
+        ("B12.5", "2" + "12.5"),  # B -> 2
+        ("F13.2", "6" + "13.2"),  # F -> 6
+        ("X99.0", "24" + "99.0"),  # X -> 24
+    ],
+)
+def test_map_icd10(icd10_code, expected):
+    result = map_icd10(icd10_code)
+    assert result == expected
+
+
+@pytest.mark.parametrize(
+    "gender, expected",
+    [
+        (None, 0),
+        ("", 0),
+        ("female", 1),
+        ("weiblich", 1),
+        ("male", 2),
+        ("männlich", 2),
+        ("other", 3),
+        ("diverse", 3),
+        ("non-binary", 3),
+    ],
+)
+def test_map_gender(gender, expected):
+    result = map_gender(gender)
+    assert result == expected
+
+
+""" icd10_mapped = float(icd10_mapped)
+    ranges = [
+        (300, 315, 0),  # Lippe, Mundhöhle und Rachen (C00-C14)
+        (315, 316, 1),  # Speiseröhre (C15)
+        (316, 317, 2),  # Magen (C16)
+        (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        (322, 323, 4),  # Leber (C22)
+        (323, 325, 5),  # Gallenblase und Gallenwege (C23-C24)
+        (325, 326, 6),  # Bauchspeicheldrüse (C25)
+        (332, 333, 7),  # Kehlkopf (C32)
+        (333, 335, 8),  # Trachea, Bronchien und Lunge (C33-C34)
+        (343, 344, 9),  # Malignes Melanom der Haut (C43)
+        (350, 351, 10),  # Brust (C50, D05)
+        (405, 406, 10),
+        (353, 354, 11),  # Gebärmutterhals (C53, D06)
+        (406, 407, 11),
+        (354, 356, 12),  # Gebärmutterkörper (C54-C55)
+        (356, 357, 13),  # Eierstöcke (C56, D39.1)
+        (439.1, 439.2, 13),
+        (361, 362, 14),  # Prostata (C61)
+        (362, 363, 15),  # Hoden (C62)
+        (364, 365, 16),  # Niere (C64)
+        (367, 368, 17),  # Harnblase (C67, D09.0, D41.4)
+        (409.0, 409.1, 17),
+        (441.4, 441.5, 17),
+        (370, 373, 18),  # Gehirn und zentrales Nervensystem (C70-C72)
+        (373, 374, 19),  # Schilddrüse (C73)
+        (381, 382, 20),  # Morbus Hodgkin (C81)
+        (382, 389, 21),  # Non-Hodgkin-Lymphome (C82-C88, C96)
+        (396, 397, 21),
+        (390, 391, 22),  # Plasmozytom (C90)
+        (391, 396, 23),  # Leukämien (C91-C95)
+    ]
+
+    for start, end, group in ranges:
+        if start <= icd10_code_mapped < end:
+            return group
+
+    return -100 """
+
+
+@pytest.mark.parametrize(
+    "icd10_mapped, expected",
+    [
+        ("309", 0),  # (300, 315, 0),  # Lippe, Mundhöhle und Rachen (C00-C14)
+        ("409.0", 17),  # (367, 368, 17),(409.0, 409.1, 17), (441.4, 441.5, 17),
+        ("441.4", 17),  # Harnblase (C67, D09.0, D41.4)
+        ("316", 2),  # (316, 317, 2),  # Magen (C16)
+        ("354", 12),  # (354, 356, 12),  # Gebärmutterkörper (C54-C55)
+        ("318", 3),  # (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        ("319", 3),  # (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        ("320", 3),  # (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        ("321", 3),  # (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        ("395", 23),  # (391, 396, 23),  # Leukämien (C91-C95)
+    ],
+)
+def test_group_entities(icd10_mapped, expected):
+    result = group_entities(icd10_mapped)
+    assert result == expected
diff --git a/src/obds_fhir_to_opal/utils_onco_analytics.py b/src/obds_fhir_to_opal/utils_onco_analytics.py
new file mode 100644
index 00000000..790c8b48
--- /dev/null
+++ b/src/obds_fhir_to_opal/utils_onco_analytics.py
@@ -0,0 +1,189 @@
+from typing import List, Optional
+
+import pandas as pd
+from pyspark.sql import DataFrame
+from pyspark.sql import functions as F
+
+
+def map_icd10(icd10_code):
+    letter_to_number = {
+        "A": "1",
+        "B": "2",
+        "C": "3",
+        "D": "4",
+        "E": "5",
+        "F": "6",
+        "G": "7",
+        "H": "8",
+        "I": "9",
+        "J": "10",
+        "K": "11",
+        "L": "12",
+        "M": "13",
+        "N": "14",
+        "O": "15",
+        "P": "16",
+        "Q": "17",
+        "R": "18",
+        "S": "19",
+        "T": "20",
+        "U": "21",
+        "V": "22",
+        "W": "23",
+        "X": "24",
+        "Y": "25",
+        "Z": "26",
+    }
+    icd10_code = str(icd10_code)
+
+    first_letter = icd10_code[0]
+
+    if first_letter in letter_to_number:
+        icd10_code_mapped = icd10_code.replace(
+            first_letter, letter_to_number[first_letter]
+        )
+    else:
+        icd10_code_mapped = -1
+
+    return icd10_code_mapped
+
+
+# to do: add case "unkown" here separately in the future
+def map_gender(gender_string):
+    match gender_string:
+        case None | "":
+            return 0
+        case "female" | "weiblich":
+            return 1
+        case "male" | "männlich":
+            return 2
+        case _:
+            return 3  # other / divers / unknown
+
+
+def group_entities(icd10_code_mapped):
+    icd10_code_mapped = float(icd10_code_mapped)
+    ranges = [
+        (300, 315, 0),  # Lippe, Mundhöhle und Rachen (C00-C14)
+        (315, 316, 1),  # Speiseröhre (C15)
+        (316, 317, 2),  # Magen (C16)
+        (318, 322, 3),  # Dickdarm und Rektum (C18-C21)
+        (322, 323, 4),  # Leber (C22)
+        (323, 325, 5),  # Gallenblase und Gallenwege (C23-C24)
+        (325, 326, 6),  # Bauchspeicheldrüse (C25)
+        (332, 333, 7),  # Kehlkopf (C32)
+        (333, 335, 8),  # Trachea, Bronchien und Lunge (C33-C34)
+        (343, 344, 9),  # Malignes Melanom der Haut (C43)
+        (350, 351, 10),  # Brust (C50, D05)
+        (405, 406, 10),
+        (353, 354, 11),  # Gebärmutterhals (C53, D06)
+        (406, 407, 11),
+        (354, 356, 12),  # Gebärmutterkörper (C54-C55)
+        (356, 357, 13),  # Eierstöcke (C56, D39.1)
+        (439.1, 439.2, 13),
+        (361, 362, 14),  # Prostata (C61)
+        (362, 363, 15),  # Hoden (C62)
+        (364, 365, 16),  # Niere (C64)
+        (367, 368, 17),  # Harnblase (C67, D09.0, D41.4)
+        (409.0, 409.1, 17),
+        (441.4, 441.5, 17),
+        (370, 373, 18),  # Gehirn und zentrales Nervensystem (C70-C72)
+        (373, 374, 19),  # Schilddrüse (C73)
+        (381, 382, 20),  # Morbus Hodgkin (C81)
+        (382, 389, 21),  # Non-Hodgkin-Lymphome (C82-C88, C96)
+        (396, 397, 21),
+        (390, 391, 22),  # Plasmozytom (C90)
+        (391, 396, 23),  # Leukämien (C91-C95)
+    ]
+
+    for start, end, group in ranges:
+        if start <= icd10_code_mapped < end:
+            return group
+
+    return -100
+
+
+def deconstruct_date(df: DataFrame, date_col: str):
+    # Extract year, month, and day from date_col
+    year_col = f"{date_col}_year"
+    month_col = f"{date_col}_month"
+    day_col = f"{date_col}_day"
+
+    df = df.withColumn(year_col, F.year(date_col))
+    df = df.withColumn(month_col, F.month(date_col))
+    df = df.withColumn(day_col, F.dayofmonth(date_col))
+
+    return df
+
+
+# optionally pass lists to fill the datadictionary
+def generate_datadictionary(
+    file_path: str = "./",
+    table_name: str = "",
+    colnames_list: List[str] = [],
+    value_type_list: List[str] = [],
+    entity_type: str = "Participant",
+    referenced_entity_type: str = "",
+    mime_type: str = "",
+    unit: str = "",
+    repeatable: int = 0,
+    occurence_group: str = "",
+    index: int = 1,
+    label: str = "",
+    description: Optional[dict] = None,
+    categories: str = "",
+) -> pd.DataFrame:
+
+    if not len(colnames_list) == len(value_type_list):
+        raise ValueError("All lists must have the same length as colnames_list")
+
+    # OPAL DataDictionary predefined colnames
+    columns = [
+        "table",
+        "name",
+        "valueType",
+        "entityType",
+        "referencedEntityType",
+        "mimeType",
+        "unit",
+        "repeatable",
+        "occurrenceGroup",
+        "index",
+        "label:en",
+        "description",
+        "categories",
+    ]
+    # exchange data type namings to fit OPAL requirements
+    updated_value_types = [
+        "integer" if dtype == "int" else "decimal" if dtype == "double" else dtype
+        for dtype in value_type_list
+    ]
+
+    # create datadictionary
+    df = pd.DataFrame(columns=columns)
+
+    df["name"] = colnames_list
+    df["table"] = table_name
+    df["valueType"] = updated_value_types
+    df["entityType"] = entity_type
+    df["referencedEntityType"] = referenced_entity_type
+    df["mimeType"] = mime_type
+    df["unit"] = unit
+    df["repeatable"] = repeatable
+    df["occurrenceGroup"] = occurence_group
+    df["index"] = index
+    df["label:en"] = label
+    df["description"] = (
+        [description[col] for col in colnames_list] if description else ""
+    )
+    df["categories"] = categories
+
+    # create second (empty) sheet for data dictionary "Categories"
+    categories_columns = ["table", "variable", "name", "code", "missing"]
+    df_categories = pd.DataFrame(columns=categories_columns)
+
+    with pd.ExcelWriter(file_path, engine="xlsxwriter") as writer:
+        df.to_excel(writer, index=False, sheet_name="Variables")
+        df_categories.to_excel(writer, index=False, sheet_name="Categories")
+
+    return df