🎯 Enhanced uHabits Analytics

• Category field present:
• Field name used:
• Sample value:

Raw API Data

Detected Mapping Issues

AWS Storage Analysis

Use this section to understand how categories are stored in AWS and fix them at the source.

# List DynamoDB tables
aws dynamodb list-tables --region eu-central-1

# Scan a table (replace TABLE_NAME)
aws dynamodb scan --table-name TABLE_NAME --region eu-central-1

# Query for habits (replace TABLE_NAME and USER_ID)
aws dynamodb query --table-name TABLE_NAME \
  --key-condition-expression "userId = :uid" \
  --expression-attribute-values '{":uid":{"S":"USER_ID"}}' \
  --region eu-central-1

# Export S3 bucket data (replace BUCKET_NAME)
aws s3 sync s3://BUCKET_NAME ./exported-data --region eu-central-1

AWS Storage Analysis

Use this section to understand how categories are stored in AWS and fix them at the source.

# List DynamoDB tables
aws dynamodb list-tables --region eu-central-1

# Scan a table (replace TABLE_NAME)
aws dynamodb scan --table-name TABLE_NAME --region eu-central-1

# Query for habits (replace TABLE_NAME and USER_ID)
aws dynamodb query --table-name TABLE_NAME \
  --key-condition-expression "userId = :uid" \
  --expression-attribute-values '{":uid":{"S":"USER_ID"}}' \
  --region eu-central-1

# Export S3 bucket data (replace BUCKET_NAME)
aws s3 sync s3://BUCKET_NAME ./exported-data --region eu-central-1

Android App Fix

1. Check the Category Field: In your Android app code, look for how categories are defined.

   // Possible locations in Android code:
   // 1. Habit model class
   public class Habit {
       private String category;  // Check how this is defined
   }
   
   // 2. When saving habits
   habit.setCategory("Personal");  // Check what values are used
   

2. Standardize Categories: Update your Android code to use consistent category names:

   // Define constants for categories
   public static final String CATEGORY_RELIGIOUS = "Religious";
   public static final String CATEGORY_CAREER = "Career & Work";
   public static final String CATEGORY_SOCIAL = "Social & Family";
   public static final String CATEGORY_PERSONAL = "Personal Improvement";
   
   // Use these constants when saving habits
   habit.setCategory(CATEGORY_RELIGIOUS);
   

3. Update UI: Ensure your habit creation/editing UI presents these standard categories.
4. Migrate Existing Data: Add code to migrate existing habits to the new category scheme.

AWS Database Fix

1. Locate your data: Determine if you're using DynamoDB, S3, or another storage service.
2. Export current data: Make a backup before making changes.

   # For DynamoDB
   aws dynamodb export-table-to-point-in-time \
     --table-arn arn:aws:dynamodb:eu-central-1:ACCOUNT_ID:table/TABLE_NAME \
     --s3-bucket BACKUP_BUCKET \
     --s3-prefix "backups/habits" \
     --export-format DYNAMODB_JSON

3. Create update script: Write a script to standardize categories in your database:

   import boto3
   import json
   
   dynamodb = boto3.resource('dynamodb')
   table = dynamodb.Table('YOUR_TABLE_NAME')
   
   # Category mapping
   category_map = {
       # Map existing categories to standard ones
       'Personal': 'Personal Improvement',
       'personal': 'Personal Improvement',
       'PERSONAL': 'Personal Improvement',
       # Add more mappings based on your data
   }
   
   # Scan the table for habits
   response = table.scan()
   items = response['Items']
   
   # Update each item with standardized category
   for item in items:
       if 'category' in item:
           old_category = item['category']
           if old_category in category_map:
               new_category = category_map[old_category]
               
               # Update the item
               table.update_item(
                   Key={'id': item['id']},
                   UpdateExpression='SET category = :cat',
                   ExpressionAttributeValues={':cat': new_category}
               )
               print(f"Updated {item['name']} from {old_category} to {new_category}")

4. Run and verify: Run your script and verify the data is updated correctly.

Advanced Database Inspection

If you need to analyze the data structure in more detail:

import boto3
import json
from collections import defaultdict

# Connect to DynamoDB
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('YOUR_TABLE_NAME')

# Analyze category field
response = table.scan()
items = response['Items']

# Analyze all fields that might contain category information
field_values = defaultdict(set)
field_types = {}

print(f"Analyzing {len(items)} records...")

# First pass - identify fields and their types
for item in items:
    for field, value in item.items():
        value_type = type(value).__name__
        field_values[field].add(str(value))
        field_types[field] = value_type

# Look for fields that might be categories
potential_category_fields = []
for field, values in field_values.items():
    # If field has a small number of distinct values, it might be categorical
    if 1 < len(values) < 10:
        potential_category_fields.append(field)
    # Or if the field name suggests it's a category
    elif any(kw in field.lower() for kw in ['category', 'group', 'type']):
        potential_category_fields.append(field)

print("\nPotential category fields:")
for field in potential_category_fields:
    print(f"- {field} ({field_types.get(field, 'unknown')}): {', '.join(list(field_values[field])[:5])}")

# If you find the correct field, print detailed distribution
category_field = potential_category_fields[0] if potential_category_fields else None
if category_field:
    category_counts = defaultdict(int)
    for item in items:
        if category_field in item:
            category_counts[str(item[category_field])] += 1
    
    print(f"\nCategory distribution for field '{category_field}':")
    for category, count in sorted(category_counts.items(), key=lambda x: -x[1]):
        print(f"- {category}: {count} items")

Android App Fix

1. Check the Category Field: In your Android app code, look for how categories are defined.

   // Possible locations in Android code:
   // 1. Habit model class
   public class Habit {
       private String category;  // Check how this is defined
   }
   
   // 2. When saving habits
   habit.setCategory("Personal");  // Check what values are used
   

2. Standardize Categories: Update your Android code to use consistent category names:

   // Define constants for categories
   public static final String CATEGORY_RELIGIOUS = "Religious";
   public static final String CATEGORY_CAREER = "Career & Work";
   public static final String CATEGORY_SOCIAL = "Social & Family";
   public static final String CATEGORY_PERSONAL = "Personal Improvement";
   
   // Use these constants when saving habits
   habit.setCategory(CATEGORY_RELIGIOUS);
   

3. Update UI: Ensure your habit creation/editing UI presents these standard categories.
4. Migrate Existing Data: Add code to migrate existing habits to the new category scheme.

AWS Database Fix

1. Locate your data: Determine if you're using DynamoDB, S3, or another storage service.
2. Export current data: Make a backup before making changes.

   # For DynamoDB
   aws dynamodb export-table-to-point-in-time \
     --table-arn arn:aws:dynamodb:eu-central-1:ACCOUNT_ID:table/TABLE_NAME \
     --s3-bucket BACKUP_BUCKET \
     --s3-prefix "backups/habits" \
     --export-format DYNAMODB_JSON

3. Create update script: Write a script to standardize categories in your database:

   import boto3
   import json
   
   dynamodb = boto3.resource('dynamodb')
   table = dynamodb.Table('YOUR_TABLE_NAME')
   
   # Category mapping
   category_map = {
       # Map existing categories to standard ones
       'Personal': 'Personal Improvement',
       'personal': 'Personal Improvement',
       'PERSONAL': 'Personal Improvement',
       # Add more mappings based on your data
   }
   
   # Scan the table for habits
   response = table.scan()
   items = response['Items']
   
   # Update each item with standardized category
   for item in items:
       if 'category' in item:
           old_category = item['category']
           if old_category in category_map:
               new_category = category_map[old_category]
               
               # Update the item
               table.update_item(
                   Key={'id': item['id']},
                   UpdateExpression='SET category = :cat',
                   ExpressionAttributeValues={':cat': new_category}
               )
               print(f"Updated {item['name']} from {old_category} to {new_category}")

4. Run and verify: Run your script and verify the data is updated correctly.