Explore JCQ demand and control.

exploration/expl_esm_labels.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 # ---
 # jupyter:
 #   jupytext:
@@ -24,6 +25,7 @@ if nb_dir not in sys.path:
     sys.path.append(nb_dir)
 import participants.query_db
 from features.esm import *
+from features.esm_JCQ import *
 
 # %%
 participants_inactive_usernames = participants.query_db.get_usernames(
@@ -213,3 +215,53 @@ sns.displot(data=df_esm_SAM_period_summary_participant, x="mean", binwidth=0.2)
 
 # %%
 sns.displot(data=df_esm_SAM_period_summary_participant, x="std", binwidth=0.1)
+
+# %% [markdown]
+# # Job demand and control
+
+# %%
+df_esm_JCQ_demand_control = df_esm_preprocessed[
+    (df_esm_preprocessed["questionnaire_id"] >= 10)
+    & (df_esm_preprocessed["questionnaire_id"] <= 11)
+]
+df_esm_JCQ_demand_control_clean = clean_up_esm(df_esm_JCQ_demand_control)
+
+# %%
+df_esm_JCQ_demand_control_reversed = reverse_jcq_demand_control_scoring(
+    df_esm_JCQ_demand_control_clean
+)
+
+# %%
+df_esm_JCQ_daily = (
+    df_esm_JCQ_demand_control_reversed.groupby(
+        ["participant_id", "date_lj", "questionnaire_id"]
+    )
+    .esm_user_score.agg("mean")
+    .reset_index()
+    .rename(columns={"esm_user_score": "esm_score_mean"})
+)
+df_esm_JCQ_summary_participant = (
+    df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"])
+    .agg(["mean", "median", "std"])
+    .reset_index(col_level=1)
+)
+df_esm_JCQ_summary_participant.columns = df_esm_JCQ_summary_participant.columns.get_level_values(
+    1
+)
+df_esm_JCQ_summary_participant[
+    "JCQ_subscale"
+] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
+    "category"
+).cat.rename_categories(
+    {10: "job demand", 11: "job control"}
+)
+
+# %%
+sns.displot(
+    data=df_esm_JCQ_summary_participant, x="mean", hue="JCQ_subscale", binwidth=0.1,
+)
+
+# %%
+sns.displot(
+    data=df_esm_JCQ_summary_participant, x="std", hue="JCQ_subscale", binwidth=0.05,
+)

features/esm_JCQ.py

@@ -101,7 +101,7 @@ def reverse_jcq_demand_control_scoring(
             - x.esm_user_score
         )
         # Reverse the items that require it.
-    except KeyError as e:
+    except AttributeError as e:
         print("Please, clean the dataframe first using features.esm.clean_up_esm.")
         print(e)