data parser

data-parser/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
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+
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+#.idea/
+
+data

data-parser/main.py

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+import polars as pl
+import json 
+from collections.abc import Iterable
+from collections import ChainMap
+from datetime import datetime
+import os
+from os import listdir
+from os.path import isfile, join
+
+# Function for flattening an array of arbitrary nested arrays or objects
+def flatten(xs):
+    result = []
+    for x in xs:
+        if isinstance(x, Iterable) and not isinstance(x, (str, bytes, type({}))):
+            result.extend(flatten(x))
+        else:
+            result.append(x)
+    return result
+
+# Check that the ./data folder exsists
+try:
+    dir = os.listdir("./data")
+except OSError as error:
+    print(error)
+
+# Read the needed files from the ./data repository
+submission_files = []
+userdetail_files = []
+exercisetasks_files = []
+datafiles = [f for f in listdir('./data/') if isfile(join('./data/', f))]
+
+for f in datafiles:
+    [course_name, file_name] = f.split(' - ', 1)
+    if 'Submissions' in file_name:
+        submission_files.append(f)
+    elif 'Exercise tasks' in file_name:
+        exercisetasks_files.append(f)
+    elif 'User Details' in file_name:
+        userdetail_files.append(f)
+
+# Sort the files by date, most recent first
+submission_files = sorted(submission_files, key=lambda x: (x.split(' ')[-1]), reverse=True)
+exercisetasks_files = sorted(exercisetasks_files, key=lambda x: (x.split(' ')[-1]), reverse=True)
+userdetail_files = sorted(userdetail_files, key=lambda x: (x.split(' ')[-1]), reverse=True)
+
+try:
+    exercise_tasks = pl.read_csv(join('./data/', exercisetasks_files[0]))
+except OSError as error:
+    print(error)
+
+
+try:
+    submissions = (pl.read_csv(join('./data/', submission_files[0]))
+            # remove outdated format
+            .filter(pl.col('created_at') >= '2023-03-03'))  
+except OSError as error:
+    print(error)
+
+try:
+    user_details = (pl.read_csv(join('./data/', userdetail_files[0]))
+                .select(['user_id', pl.concat_str(pl.col('^.*_name$').fill_null('').alias('name'), separator=' '), 'email']))
+except OSError as error:
+    print(error)
+
+cleaned_subs = (submissions
+            .join(user_details.select(pl.exclude('created_at')), on='user_id', how='left')
+            .join(exercise_tasks.select(['id', 'exercise_type']), left_on='exercise_task_id', right_on='id', how='left')
+            .filter(pl.col('exercise_type') == 'dogs-factorial-analysis-survey')
+            .drop(['course_instance_id', 'score_given','exercise_type'])
+            .sort('created_at', descending=True)
+            .unique(subset=['exercise_task_id', 'user_id'], keep='first')
+        )
+
+# The map of private-specs: { exercise_task_id : { private_spec } }
+exercise_tasks_map = dict([(x[0], json.loads(x[4])) for x in exercise_tasks.rows() if 'factorial' in x[3]])
+
+# Formatting the private_specs to needed fields for exstracting submission info
+keys_to_delete = []
+for k,v in exercise_tasks_map.items():
+
+# factorial survey type: {
+#   id, 
+#   options: [{id, name, value}],
+#   questions: [{id, questionLabel}],
+#   type: 'factorial'
+# }
+    if v['type'] == 'factorial':
+        exercise_tasks_map[k] = dict([(key, val) for key, val in dict(v).items() if key in ['id', 'type', 'questions', 'options']])
+        exercise_tasks_map[k]['questions'] = ([
+            dict([(key, val) for key,val in dict(quest).items() if key not in ['question', 'mandatory']]) 
+            for quest in exercise_tasks_map[k]['questions'] if quest['questionLabel'] not in 'info'
+            ])
+# non-factorial survey type: {
+#   id,
+#   content: [{surveyItemId, options, questionLabel, answer-type}],
+#   type: 'non-factorial'
+# }
+    else:
+        exercise_tasks_map[k] = dict([(un, val) for un, val in dict(v).items() if un in ['id', 'type', 'content']])
+        content = exercise_tasks_map[k]['content']
+
+        content = ([
+            dict([(key, value ) for key, value in dict(item).items() if key in ['id', 'answer', 'question']]) 
+            for item in content if item['question']['questionLabel'] not in ['info']])
+
+        for idx,item in enumerate(content):
+            newItem = {'surveyItemId': item['id'],
+                       'options': item['answer']['options'],
+                       'answer-type': item['answer']['type'],
+                       'questionLabel': item['question']['questionLabel']}
+            content[idx] = newItem
+        if content:
+            exercise_tasks_map[k]['content'] = content
+        else:
+            keys_to_delete.append(k)
+
+for key in keys_to_delete:
+    del exercise_tasks_map[key]
+
+# Building additional columns to final dataframe exercise tasks at a time
+for k,v in exercise_tasks_map.items():
+    submissions_data = cleaned_subs.filter(pl.col('exercise_task_id') == k).select('user_id','data_json').rows()
+
+    user_submissions, col_labels, typed_col_labels = [],[],{}
+
+    if v['type'] == 'factorial':
+        col_labels = [lab['questionLabel'] for lab in v['questions']]
+        col_labels.append('user_id')
+
+        typed_col_labels = dict(ChainMap(*[{lab['questionLabel']: pl.Int8} for lab in v['questions']]))
+        typed_col_labels['user_id'] = pl.Utf8
+
+        options = dict([(option['id'], option['value']) for option in v['options']])
+
+        for row in submissions_data:
+            row = [row[0], json.loads(row[1])]
+            user_answers = dict(row[1])
+
+            submission = dict(ChainMap(*[{item['questionLabel']: options.get(item.get('chosenOptionId'))} for item  in user_answers.get('answeredQuestions')]))
+
+            submission['user_id'] = row[0]
+            user_submissions.append(submission)
+
+    else: 
+        # Columns are "questionLabel", unless it is mulptiple-choice question,
+        # then columns are "questionLabel option" per option. Some options
+        # end with empty space, we strip it away.
+        col_labels = [[' '.join([item['questionLabel'], option.strip()]) for option in item['options']] if item['answer-type'] == 'multiple-choice' else item['questionLabel'] for item in v['content']]
+
+        col_labels = flatten(col_labels)
+        col_labels.append('user_id')
+
+        typed_col_labels = dict(ChainMap(*[{col: pl.Utf8} for col in col_labels]))
+
+        for row in submissions_data:
+            row = [row[0], json.loads(row[1])]
+
+            user_answer = dict(row[1])
+            user_answer = dict([(answeredQ.get('questionLabel'), answeredQ.get('answer')) for answeredQ in user_answer.get('answeredQuestions')])
+
+            submission = []
+            for col in col_labels:
+                questionAnswer = user_answer.get(col.split(' ', 1)[0], None)
+                # The question is not multipple-choice
+                if len(col.split(' ', 1)) == 1:     
+                    submission.append({col: questionAnswer if questionAnswer else None})
+                # The question is multipple-choice
+                else:
+                    # If user has not answered this question, all cloumns belonging to this question are NaN, 
+                    # otherwise "1" indicates option is checked else "0".
+                    # Have to consider that the column names contain the stripped option
+                    submission.append({col: (1 if col.split(' ', 1)[1] in list(map(str.strip, questionAnswer)) else 0) if questionAnswer else None})
+
+            submission = dict(ChainMap(*submission))
+
+            submission['user_id']= row[0]
+
+            user_submissions.append(submission)
+
+    data = user_submissions if user_submissions else [[None for _ in col_labels]]
+
+    additional_cols = pl.DataFrame(data, schema=typed_col_labels).select(col_labels)
+
+    user_details = user_details.join(additional_cols, how='left', on='user_id')
+
+try:
+    os.mkdir("./parsed-outputs")
+except OSError as error:
+    if error.errno == 17:
+        pass
+    else: print(error)
+
+dt = datetime.now().strftime('%d-%m-%Y %H:%M:%S')
+filename = f'./parsed-outputs/Submissions {dt}.csv'
+user_details.write_csv(filename, has_header=True, quote='"', null_value='', separator=';')