Calculating Weekly Totals in SQLite Using Julian Day Manipulation

SQLite Weekly Aggregation of COVID-19 Cases and Deaths

When working with time-series data such as daily COVID-19 cases and deaths, a common requirement is to aggregate daily records into weekly totals. This is particularly useful for generating summary reports, identifying trends, or performing comparative analysis. In SQLite, achieving this requires a combination of date manipulation and aggregation functions. The core challenge lies in accurately grouping daily records into weeks, especially when the week boundary (e.g., Sunday to Saturday) must align with specific business or reporting requirements.

The primary table in this scenario, often named covid, contains three columns: date, cases, and deaths. Each row represents a single day’s data. The goal is to produce a result set that includes the end date of each week (e.g., Sunday), the total number of cases for that week (TotalCases), and the total number of deaths (TotalDeaths). This requires transforming the date column into a weekly grouping key and then performing aggregation.

Interrupted Write Operations Leading to Index Corruption

One of the most critical aspects of this task is ensuring that the date manipulation logic correctly aligns with the desired week boundaries. SQLite’s date and time functions, while powerful, require careful handling to avoid miscalculations. The Julian Day system, which represents dates as a continuous count of days since a fixed point in time, is particularly useful for this purpose. However, improper use of Julian Day calculations can lead to incorrect week groupings, especially when dealing with edge cases such as the start or end of a year.

The constant 1721061.5 used in the solution corresponds to the Julian Day value for ‘0000-01-03’, which is the first Monday in the range of dates supported by SQLite’s datetime functions. This constant serves as a reference point for calculating week numbers. Subtracting this value from the Julian Day of a given date and dividing by 7 yields the week number. Multiplying the week number by 7 and adding another constant (1721067.5, the Julian Day for ‘0000-01-09’) converts the week number back into a date, specifically the end date of the week (Sunday).

This approach ensures that the week boundaries are consistently calculated, even across year boundaries. However, it assumes that the week starts on Monday and ends on Sunday. If the week boundary requirements differ (e.g., starting on Sunday), the constants and logic must be adjusted accordingly.

Implementing Julian Day Calculations and Aggregation Queries

To implement the solution, the following steps are necessary:

1. Convert the date column to Julian Day values: This transformation allows for precise arithmetic operations on dates. The julianday() function in SQLite converts a date string into a Julian Day value.

2. Calculate the week number: Subtract the Julian Day value of the reference date (1721061.5) from the Julian Day value of the current date, divide by 7, and cast the result to an integer. This yields the week number as a count of weeks since the reference date.

3. Convert the week number back to a date: Multiply the week number by 7 to convert it back into days, add the Julian Day value of the end date of the first week (1721067.5), and convert the result back into a Gregorian date using the date() function.

4. Group by the calculated end date and aggregate: Use the calculated end date as the grouping key and apply the SUM() function to aggregate the cases and deaths columns.

The final query looks like this:

SELECT enddate,
       SUM(cases) AS TotalCases,
       SUM(deaths) AS TotalDeaths
FROM (
    SELECT date(CAST(julianday(date) - 1721061.5 AS INTEGER) / 7 * 7 + 1721067.5) AS enddate,
           cases,
           deaths
    FROM covid
)
GROUP BY enddate
ORDER BY enddate;

Explanation of the Query

• Subquery: The inner query calculates the end date of the week for each row in the covid table. The julianday(date) - 1721061.5 expression computes the number of days since the reference date. Dividing by 7 and casting to an integer yields the week number. Multiplying by 7 converts the week number back into days, and adding 1721067.5 gives the Julian Day value for the end of the week. The date() function converts this back into a Gregorian date.

• Outer Query: The outer query groups the results by the calculated enddate and sums the cases and deaths columns to produce the weekly totals.

Handling Edge Cases

• Year Boundaries: The query handles year boundaries seamlessly because the Julian Day system is continuous and does not reset at year boundaries.
• Leap Years: The Julian Day system accounts for leap years, ensuring accurate calculations.
• Time Zones: If the date column includes time zone information, ensure that all dates are normalized to the same time zone before performing calculations.

Performance Considerations

• Indexing: Ensure that the date column is indexed to speed up the julianday() calculations.
• Large Datasets: For very large datasets, consider materializing the intermediate results (e.g., the calculated end dates) in a temporary table to improve performance.

Alternative Approaches

If the week boundary requirements differ (e.g., starting on Sunday), adjust the constants in the query. For example, to start the week on Sunday, use 1721060.5 (the Julian Day for ‘0000-01-02’) as the reference date and 1721066.5 (the Julian Day for ‘0000-01-08’) as the end date.

Example with Different Week Boundaries

SELECT enddate,
       SUM(cases) AS TotalCases,
       SUM(deaths) AS TotalDeaths
FROM (
    SELECT date(CAST(julianday(date) - 1721060.5 AS INTEGER) / 7 * 7 + 1721066.5) AS enddate,
           cases,
           deaths
    FROM covid
)
GROUP BY enddate
ORDER BY enddate;

This query calculates weekly totals for weeks starting on Sunday and ending on Saturday.

Conclusion

Calculating weekly totals in SQLite requires a deep understanding of date manipulation functions, particularly the Julian Day system. By leveraging these functions, you can accurately group daily records into weeks and perform the necessary aggregations. The provided solution is robust, handling edge cases such as year boundaries and leap years, and can be adapted to different week boundary requirements. With proper indexing and performance considerations, this approach is suitable for both small and large datasets.