{
  "format": "dataset-manifest/1",
  "slug": "fitness-tracker-data",
  "version": 2,
  "seed": "fitness-tracker-data:1",
  "spec": {
    "format": "dataset/1",
    "slug": "fitness-tracker-data",
    "name": "Fitness Tracker Data",
    "tagline": "User profiles, workouts, diet plans, equipment details, and progress logs from a fitness tracking application.",
    "description": "Users engage with a fitness tracker to manage their workout routines and nutritional intake, striving towards their specific fitness goals. Each user profile contains personal information and preferences, allowing for a tailored experience. Users log their workouts, detailing types of exercises, durations, and intensities, while also tracking their dietary plans that align with their fitness aspirations. The equipment table catalogs various tools available for workouts, enabling users to record the gear they utilize during their sessions. Over time, progress data reflects users' advancements towards their fitness objectives, showcasing metrics such as weight loss, muscle gain, or improved stamina.\n\nIn this dataset, each row in the users table represents an individual user, linked to multiple workout entries in the workouts table through a user ID. The workouts table connects to the equipment table, allowing for analysis of which equipment is most commonly used in different types of workouts. Each workout session also correlates with a specific diet plan from the diet_plans table, illustrating the relationship between dietary choices and fitness activities. Progress entries, associated with user IDs, allow for the evaluation of how effectively users are reaching their goals over time by analyzing trends and changes.\n\nThe data answers questions such as which workout routines yield the best results for various body types, how specific dietary adjustments impact overall progress, and which pieces of equipment contribute most to achieving fitness targets. It is particularly useful for practicing data analysis, exploring user behavior, or developing predictive models for fitness trends. Note that the data is synthetic, designed to provide a realistic but artificial representation of a fitness tracking environment.",
    "domain": "saas",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "fitness",
      "workout",
      "diet",
      "nutrition",
      "exercise",
      "tracker",
      "users",
      "progress",
      "equipment"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "users": {
        "description": "Each row represents a user of the fitness tracking application, capturing personal details and preferences.",
        "rows": 100,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each user."
          },
          "full_name": {
            "kind": "fullName",
            "description": "The full name of the user."
          },
          "email": {
            "kind": "email",
            "from": "full_name",
            "description": "The user's email address."
          },
          "age": {
            "kind": "int",
            "min": 18,
            "max": 65,
            "description": "The age of the user."
          },
          "gender": {
            "kind": "enum",
            "values": [
              "male",
              "female",
              "other"
            ],
            "description": "The gender of the user."
          }
        }
      },
      "workouts": {
        "description": "This table tracks individual workout sessions, including their type, duration, and the user performing them.",
        "rows": 300,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each workout session."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Reference to the user who performed the workout."
          },
          "workout_type": {
            "kind": "enum",
            "values": [
              "cardio",
              "strength",
              "flexibility"
            ],
            "description": "The type of workout performed."
          },
          "duration_minutes": {
            "kind": "int",
            "min": 10,
            "max": 120,
            "description": "Duration of the workout in minutes.",
            "distribution": "normal"
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date when the workout was performed."
          }
        }
      },
      "diet_plans": {
        "description": "This table outlines various dietary plans available for users, detailing meal structure and caloric intake.",
        "rows": 50,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each diet plan."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Reference to the user on this diet plan."
          },
          "meal_type": {
            "kind": "enum",
            "values": [
              "breakfast",
              "lunch",
              "dinner",
              "snack"
            ],
            "description": "The type of meal."
          },
          "calories": {
            "kind": "int",
            "min": 100,
            "max": 1500,
            "description": "Caloric value of the meal.",
            "distribution": "long-tail"
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date of the meal entry."
          }
        }
      },
      "equipment": {
        "description": "This table lists various exercise equipment available for workouts, linked to the user using it.",
        "rows": 20,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each piece of equipment."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Reference to the user who used the equipment."
          },
          "equipment_name": {
            "kind": "sentence",
            "description": "The name of the exercise equipment."
          },
          "purchase_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "The date when the equipment was purchased.",
            "trend": "growing"
          }
        }
      },
      "progress": {
        "description": "This table tracks user progress towards fitness goals, summarizing achievements over time.",
        "rows": 150,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each progress entry."
          },
          "user_id": {
            "kind": "fk",
            "ref": "users.id",
            "description": "Reference to the user whose progress is being tracked."
          },
          "goal_type": {
            "kind": "enum",
            "values": [
              "weight_loss",
              "muscle_gain",
              "endurance"
            ],
            "description": "Type of fitness goal."
          },
          "progress_value": {
            "kind": "decimal",
            "min": 0,
            "max": 100,
            "scale": 2,
            "description": "Percentage of the goal achieved.",
            "distribution": "high"
          },
          "date": {
            "kind": "date",
            "from": "2023-01-01",
            "to": "2023-12-31",
            "description": "The date of the progress entry."
          }
        }
      }
    },
    "structure": "multi-table",
    "traits": [
      "categorical",
      "timestamped",
      "nullable"
    ],
    "analysisQuestions": [
      "What is the average workout duration per user?",
      "How many users are following each type of diet plan?",
      "Which workout types are the most popular among users?",
      "What percentage of users meet their fitness goals over time?",
      "How does equipment usage correlate with progress towards fitness goals?"
    ],
    "suggestedQueries": [
      {
        "title": "Join Users with Workouts",
        "sql": "SELECT users.full_name, workouts.workout_type, workouts.duration_minutes FROM users JOIN workouts ON users.id = workouts.user_id;",
        "dialect": "postgres"
      },
      {
        "title": "Average Duration by Workout Type",
        "sql": "SELECT workout_type, AVG(duration_minutes) FROM workouts GROUP BY workout_type;",
        "dialect": "postgres"
      },
      {
        "title": "Diet Plans per User",
        "sql": "SELECT users.full_name, COUNT(diet_plans.id) AS diet_plan_count FROM users LEFT JOIN diet_plans ON users.id = diet_plans.user_id GROUP BY users.id;",
        "dialect": "postgres"
      },
      {
        "title": "Progress Tracking Over Time",
        "sql": "SELECT date, AVG(progress_value) AS average_progress FROM progress GROUP BY date ORDER BY date;",
        "dialect": "postgres"
      },
      {
        "title": "Equipment Usage and Goal Achievement",
        "sql": "SELECT equipment.equipment_name, AVG(progress.progress_value) FROM equipment JOIN progress ON equipment.user_id = progress.user_id GROUP BY equipment.equipment_name;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Personal Trainer Analytics",
        "body": "Personal trainers can leverage this dataset to analyze their clients' workout habits and dietary choices, helping them to personalize fitness programs. By understanding which workouts yield the best results, trainers can refine their approaches and improve client outcomes."
      },
      {
        "title": "User Engagement Monitoring",
        "body": "Fitness app developers can utilize this data to measure user engagement and track the effectiveness of their features. By evaluating workout frequency and dietary adherence, they can identify areas for improvement and enhance user experience."
      },
      {
        "title": "Nutritional Research",
        "body": "Nutritionists can use this dataset to study the dietary patterns of individuals engaged in fitness activities. This can help in understanding how different meal plans affect workout performance and overall fitness goals."
      },
      {
        "title": "Equipment Popularity Studies",
        "body": "Gym managers can analyze which equipment is most frequently used by their members, allowing them to make informed decisions on equipment purchases and layout design to optimize gym usage."
      }
    ]
  },
  "counts": {
    "users": 100,
    "diet_plans": 50,
    "equipment": 20,
    "progress": 150,
    "workouts": 300
  },
  "stats": {
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      "columns": {
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          "distinct": 100,
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          "max": 100
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          "min": "Alice Brown",
          "max": "Zoe Flores"
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