How to build a chart with multiple dimensions when the same field changes over time?

Consider the following sample table:

  day  |  payment_id  |  status
---------------------------------
 Jan 1 |      1       | processing
 Jan 1 |      2       | processing
 Jan 2 |      1       | completed
 Jan 2 |      2       | processing

As you can see, it consists of the day, payment_id, and status. Each payment can appear on multiple days with different statuses (e.g., payment 1 in the table below has different statuses on two different days).

What I need to do is to build a table in Looker Studio (Google Data Studio), which would be a simple stacked bar chart displaying the number of payments with different status over time. It's easy to do this for each day, or for each week, but the problem arises when I need to create a chart where you would be able to switch between different time periods (days, weeks, months, etc.)

For example, for a daily chart, Jan 1 would show 2 payments with status processing, while Jan 2 would show 1 payment with status processing and 1 payment with status completed.

For a weekly chart, on the other hand, it would just show 1 payment with status processing and 1 payments with status completed for this whole week.

So it looks like for a weekly chart, I would need to take the latest status for each payment. Something like max_by function, but it doesn't exist in looker studio.

Is it possible to create such a chart with multiple dimensions (days, weeks, months)? If so, how do I correctly aggregate the data?

Based on the sample table in OP:

You can create a parameter for user to select date interval

parameter: date_interval_param {
    allowed_value: {
      label: "Day"
      value: "day"
    }
    allowed_value: {
      label: "Week"
      value: "week"
    }
    allowed_value: {
      label: "Month"
      value: "month"
    }
    allowed_value: {
      label: "Quarter"
      value: "quarter"
    }
    allowed_value: {
      label: "Year"
      value: "year"
    }
  }

Then have a dimension that updates based on that parameter value and use the existing Looker timeframe options

dimension_group: day {
   type: time
   sql: cast(${TABLE}.day as timestamp) ;;  ## field based on sample table in OP
}

dimension: date_interval_field_dash {
    sql:

    {% if date_interval_param._is_filtered %}
      {% if date_interval_param._parameter_value == "'year'" %}
        ${day_year}
      {% elsif date_interval_param._parameter_value == "'quarter'" %}
        ${day_quarter}
      {% elsif date_interval_param._parameter_value == "'month'" %}
        ${day_month}
      {% elsif date_interval_param._parameter_value == "'week'" %}
        ${day_week}
      {% elsif date_interval_param._parameter_value == "'day'" %}
        ${day_date}
      {% endif %}
    {% else %}
    YOU HAVE TO SELECT "DATE INTERVAL OPTIONS" FILTER
    {% endif %}
    ;;
  }

And if you reference date_interval_field_dash as the dimension in your Explore viz, it'll update based on the interval selected by user.

This is less redundant and more flexible than trying to maintain a table that contains the pre-aggregated interval options