diff --git a/exploration/expl_esm_labels.py b/exploration/expl_esm_labels.py
index 01107c3..e3298d7 100644
--- a/exploration/expl_esm_labels.py
+++ b/exploration/expl_esm_labels.py
@@ -46,7 +46,43 @@ df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
 
 # %%
 df_esm_PANAS_grouped = df_esm_PANAS_clean.groupby(
-    ["participant_id", "questionnaire_id"]
+    ["participant_id", "date_lj", "questionnaire_id"]
 )
 
 # %%
+df_esm_PANAS_daily_sums = (
+    df_esm_PANAS_grouped.esm_user_answer_numeric.agg("sum")
+    .reset_index()
+    .rename(columns={"esm_user_answer_numeric": "esm_numeric_sum"})
+)
+
+# %% [markdown]
+# Group by participants, date, and subscale and calculate daily sums.
+
+# %%
+df_esm_PANAS_summary_participant = (
+    df_esm_PANAS_daily_sums.groupby(["participant_id", "questionnaire_id"])
+    .agg(["mean", "median", "std"])
+    .reset_index(col_level=1)
+)
+df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
+    1
+)
+df_esm_PANAS_summary_participant[
+    "PANAS_subscale"
+] = df_esm_PANAS_daily_sums.questionnaire_id.astype("category").cat.rename_categories(
+    {8.0: "PA", 9.0: "NA"}
+)
+
+# %% [markdown]
+# Next, calculate mean and standard deviation across all days for each participant.
+
+# %%
+sns.displot(
+    data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=2
+)
+
+# %%
+sns.displot(
+    data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=1
+)