Creating SQLite Views for Weight Tracking: Handling NULLs and Weekly/Monthly Calculations

Filtering NULLs and Invalid Values in Weight Tracking Views

When designing a database for weight tracking, one of the key challenges is ensuring that the data presented is both accurate and meaningful. In the context of SQLite, this often involves creating views that filter out invalid or incomplete data, such as rows where striveWeight is set to -1 or realWeight is NULL. These conditions are essential to avoid misleading calculations and to ensure that the data reflects only valid measurements.

The monthValues table, which stores monthly weight goals (striveWeight) and actual weights (realWeight), is a prime example of where such filtering is necessary. The goal is to create a view that calculates the difference between realWeight and striveWeight, but only for rows where striveWeight is not -1 and realWeight is not NULL. This ensures that the view only includes data points that are both meaningful and complete.

To achieve this, the WHERE clause in the view definition must include conditions that explicitly exclude rows where striveWeight is -1 and realWeight is NULL. The SQLite IS NOT NULL operator is used to filter out NULL values, while a simple inequality operator (<> or >) can be used to exclude rows where striveWeight is -1. This combination ensures that the view only includes rows that meet both criteria.

For example, the following SQL statement creates a view that calculates the difference between realWeight and striveWeight, but only for valid rows:

CREATE VIEW monthValuesExt AS
SELECT *,
       striveWeight - realWeight AS extraLoss
FROM monthValues
WHERE striveWeight > 0
  AND realWeight IS NOT NULL;

This view, monthValuesExt, will only include rows where striveWeight is greater than 0 and realWeight is not NULL. The extraLoss column in the view represents the difference between the target weight (striveWeight) and the actual weight (realWeight), providing a clear and accurate measure of progress.

Calculating Weekly Weight Loss with Self-Joins in SQLite

Another common requirement in weight tracking databases is calculating the weight loss between consecutive weeks. This involves comparing the weight recorded in the current week with the weight recorded in the previous week. In SQLite, this can be achieved using a self-join, where the weekValues table is joined with itself to create pairs of consecutive weeks.

The weekValues table, which stores weekly weight measurements, is structured such that each row represents a week and its corresponding weight. To calculate the weight loss between consecutive weeks, the table must be joined with itself, aligning each week with its previous week. This is done by joining the table on the condition that the week value of the previous week is one less than the week value of the current week.

The following SQL statement demonstrates how to create a view that calculates the weight loss between consecutive weeks:

CREATE VIEW weekValuesExt AS
SELECT curWeek.*,
       prevWeek.weight AS oldWeight,
       ROUND(prevWeek.weight - curWeek.weight, 1) AS lost
FROM weekValues AS curWeek
JOIN weekValues AS prevWeek ON prevWeek.week = curWeek.week - 1;

In this view, weekValuesExt, the curWeek alias represents the current week, while the prevWeek alias represents the previous week. The JOIN condition ensures that each row in curWeek is paired with the corresponding row in prevWeek where the week value is one less. The lost column calculates the difference between the weight in the previous week and the weight in the current week, rounded to one decimal place for readability.

This approach assumes that the week values are sequential and start from 1. If the week values are not sequential or do not start from 1, additional logic would be required to correctly align consecutive weeks. However, for most weight tracking applications, this assumption is reasonable and simplifies the calculation.

Schema Design Considerations for Accurate Weight Tracking

While the views described above provide useful insights into weight tracking data, the underlying schema design plays a crucial role in ensuring the accuracy and usability of the data. One of the key considerations is the use of appropriate data types and constraints to enforce data integrity.

In the monthValues table, the month column is defined as an INTEGER PRIMARY KEY, which ensures that each month has a unique identifier. The striveWeight and realWeight columns are defined as REAL, which is appropriate for storing decimal values. However, the schema does not include any constraints to enforce valid values for striveWeight and realWeight. For example, there is no constraint to ensure that striveWeight is greater than 0 or that realWeight is not NULL.

To improve the schema, consider adding CHECK constraints to enforce these conditions. For example:

CREATE TABLE monthValues (
  month INTEGER PRIMARY KEY,
  striveWeight REAL NOT NULL CHECK (striveWeight > 0),
  realWeight REAL CHECK (realWeight IS NOT NULL)
);

This ensures that striveWeight is always greater than 0 and that realWeight is never NULL. While these constraints can be enforced at the application level, including them in the schema provides an additional layer of data integrity.

Another consideration is the use of timestamps to track weeks and months. In the current schema, the week and month columns are defined as integers, which assumes that weeks and months are sequential and start from 1. However, this approach can lead to issues if the data spans multiple years or if there are gaps in the data. A more robust approach is to use timestamps to track the start date of each week and month. This allows for more accurate calculations and avoids issues with non-sequential or non-contiguous data.

For example, the weekValues table could be modified to include a startDate column:

CREATE TABLE weekValues (
  startDate TEXT PRIMARY KEY,
  weight REAL NOT NULL
);

This allows for more accurate tracking of weekly weight measurements and simplifies calculations involving time intervals. For example, calculating the weight loss between consecutive weeks can be done by comparing the startDate values rather than relying on sequential week values.

In conclusion, while the views described above provide useful insights into weight tracking data, the underlying schema design plays a crucial role in ensuring the accuracy and usability of the data. By using appropriate data types, constraints, and timestamps, you can create a more robust and reliable weight tracking database.