Implement a method to recode JCQ answers.

features/esm.py

@@ -228,7 +228,8 @@ def classify_sessions_by_completion_time(
 def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
     """
     This function eliminates invalid ESM responses.
-    It removes unanswered ESMs.
+    It removes unanswered ESMs and those that indicate end of work and similar.
+    It also extracts a numeric answer from strings such as "4 - I strongly agree".
 
     Parameters
     ----------

features/esm_JCQ.py

@@ -1,3 +1,8 @@
+import pandas as pd
+
+JCQ_ORIGINAL_MAX = 4
+JCQ_ORIGINAL_MIN = 1
+
 dict_JCQ_demand_control_reverse = {
     75: (
         "I was NOT asked",
@@ -29,3 +34,75 @@ dict_JCQ_demand_control_reverse = {
         "Pri svojem delu sem imela zelo malo svobode",
     ),
 }
+
+
+def reverse_jcq_demand_control_scoring(
+    df_esm_jcq_demand_control: pd.DataFrame,
+) -> pd.DataFrame:
+    """
+    This function recodes answers in Job content questionnaire by first incrementing them by 1,
+    to be in line with original (1-4) scoring.
+    Then, some answers are reversed (i.e. 1 becomes 4 etc.), because the questions are negatively phrased.
+    These answers are listed in dict_JCQ_demand_control_reverse and identified by their question ID.
+    However, the existing data is checked against literal phrasing of these questions
+        to protect against wrong numbering of questions (differing question IDs).
+
+    Parameters
+    ----------
+    df_esm_jcq_demand_control: pd.DataFrame
+        A cleaned up dataframe, which must also include esm_user_answer_numeric.
+
+    Returns
+    -------
+    df_esm_jcq_demand_control: pd.DataFrame
+        The same dataframe with a column esm_user_score containing answers recoded and reversed.
+    """
+    df_esm_jcq_demand_control_unique_answers = (
+        df_esm_jcq_demand_control.groupby("question_id")
+        .esm_instructions.value_counts()
+        .rename()
+        .reset_index()
+    )
+    # Tabulate all possible answers to each question (group by question ID).
+    for q_id in dict_JCQ_demand_control_reverse.keys():
+        # Look through all answers that need to be reversed.
+        possible_answers = df_esm_jcq_demand_control_unique_answers.loc[
+            df_esm_jcq_demand_control_unique_answers["question_id"] == q_id,
+            "esm_instructions",
+        ]
+        # These are all answers to a given question (by q_id).
+        answers_matches = possible_answers.str.startswith(
+            dict_JCQ_demand_control_reverse.get(q_id)
+        )
+        # See if they are expected, i.e. included in the dictionary.
+        if ~answers_matches.all():
+            print("One of the answers that occur in the data should not be reversed.")
+            print("This was the answer found in the data: ")
+            raise KeyError(possible_answers[~answers_matches])
+            # In case there is an unexpected answer, raise an exception.
+
+    try:
+        df_esm_jcq_demand_control = df_esm_jcq_demand_control.assign(
+            esm_user_score=lambda x: x.esm_user_answer_numeric + 1
+        )
+        # Increment the original answer by 1
+        # to keep in line with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
+        df_esm_jcq_demand_control[
+            df_esm_jcq_demand_control["question_id"].isin(
+                dict_JCQ_demand_control_reverse.keys()
+            )
+        ] = df_esm_jcq_demand_control[
+            df_esm_jcq_demand_control["question_id"].isin(
+                dict_JCQ_demand_control_reverse.keys()
+            )
+        ].assign(
+            esm_user_score=lambda x: JCQ_ORIGINAL_MAX
+            + JCQ_ORIGINAL_MIN
+            - x.esm_user_score
+        )
+        # Reverse the items that require it.
+    except KeyError as e:
+        print("Please, clean the dataframe first using features.esm.clean_up_esm.")
+        print(e)
+
+    return df_esm_jcq_demand_control