Take device ID into consideration for grouping sessions.
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@ -13,8 +13,6 @@
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# name: straw2analysis
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# name: straw2analysis
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# ---
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# ---
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import datetime
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# %%
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# %%
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import os
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import os
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import sys
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import sys
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@ -129,12 +127,16 @@ df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_tri
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df_session_counts[(df_session_counts["esm_session_count"] > 40)]
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df_session_counts[(df_session_counts["esm_session_count"] > 40)]
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# %%
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# %%
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df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username"]]
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df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username", "device_id"]]
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# %% [markdown]
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# %% [markdown]
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# Both, session ID and \_ID (and others) reset on application reinstall. Here, it can be seen that the application was reinstalled on 2 April (actually, the phone was replaced as reported by the participant).
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# Both, session ID and \_ID (and others) reset on application reinstall. Here, it can be seen that the application was reinstalled on 2 April (actually, the phone was replaced as reported by the participant).
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#
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#
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# Session IDs should therefore be grouped while taking the timestamp into account (e.g. by sorting first).
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# Session IDs should therefore be grouped while taking the device ID into account.
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# %%
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session_counts_device = df_esm_preprocessed.groupby(["participant_id", "device_id", "esm_session"]).count()["id"]
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sns.displot(session_counts_device.to_numpy(), binwidth=1, height=8)
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# %% [markdown]
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# %% [markdown]
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# ## Other possibilities
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# ## Other possibilities
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