Read the relevant files.

Snakefile

@@ -403,6 +403,9 @@ for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
         files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv"))
 
+# Demographic features
+files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"]))
+
 rule all:
     input:
         files_to_compute

config.yaml

@@ -629,7 +629,7 @@ ALL_CLEANING_OVERALL:
 
 PARAMS_FOR_ANALYSIS:
   DEMOGRAPHIC:
-    FOLDER: E:/STRAWbaseline
+    FOLDER: data/external/baseline
     CONTAINER: [results-survey637813_final.csv,  # Slovenia
                 results-survey358134_final.csv,  # Belgium 1
                 results-survey413767_final.csv  # Belgium 2

rules/models.smk

@@ -1,7 +1,7 @@
 rule download_demographic_data:
     input:
         participant_file = "data/external/participant_files/{pid}.yaml",
-        data = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FOLDER"] + "/" + config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CONTAINER"]
+        data = expand(config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FOLDER"] + "/{container}", container=config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CONTAINER"])
     output:
         "data/raw/{pid}/participant_baseline_raw.csv"
     script:

src/data/download_demographic_data.py

@@ -0,0 +1,14 @@
+import pandas as pd
+
+filenames = snakemake.input["data"]
+
+baseline_dfs = []
+
+for fn in filenames:
+    baseline_dfs.append(pd.read_csv(fn))
+
+baseline = (
+    pd.concat(baseline_dfs, join="inner")
+    .reset_index()
+    .drop(columns="index")
+)