> For the complete documentation index, see [llms.txt](https://docs.collibra.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.collibra.com/data-quality-self-hosted/core-concepts/reports/rules-passing-fraction-roll-up-report.md).
# Rules Passing Fraction Roll-Up Report
## What is a Rules Passing Fraction Roll-Up Report?
The Rules Passing Fraction Roll-Up report provides the passing and total rows scanned for user-defined rules over the past 30 days, which are aggregated by dimensions.
## Steps
{% stepper %}
{% step %}
Sign in to the Collibra DQ instance and click the 
Reports icon in the left navigation pane.\ :chevrons-right: The Reports page opens.
{% endstep %}
{% step %}
From the Reports page, click the Rules Passing Fraction Roll-Up link.
{% endstep %}
{% step %}
To configure the type of chart configuration and data you want to see in the chart, click the ellipsis icon.
{% endstep %}
{% step %}
Click 
**Chart Configuration**.The Chart Configuration window displays.
{% hint style="info" %}
As a best practice, keep the Chart Configuration as the default.
{% endhint %}
{% endstep %}
{% step %}
For x-axis, select one of the following options from the X-Axis Column dropdown list:
* avg\_percent\_rows\_passing
* dimension
{% endstep %}
{% step %}
For y-axis, select one or more of the following options from the Y-Axis Columns dropdown list:
* dimension
* avg\_observability\_score
{% endstep %}
{% step %}
For the type of chart you want to display, select one of the following options from the Chart Type dropdown list:
* Line
* Area
* Column
* Scatter
{% endstep %}
{% step %}
Click **Save**.
{% endstep %}
{% step %}
Navigate the pages of your report by clicking the **Previous** and **Next** pagination buttons, located bottom-right of the columns.
{% endstep %}
{% endstepper %}
## Sample SQL query
You can use the following sample SQL query for a Rules Passing Fraction Roll-Up report.
```
WITH a AS (
SELECT * FROM rule_output
WHERE updt_ts BETWEEN (NOW() - INTERVAL '30 DAY') AND NOW()
),
b AS (
SELECT * FROM dataset_scan WHERE rc > 1
),
c AS (
SELECT * FROM owl_rule
),
e AS (
SELECT * FROM dq_dimension
),
g AS (
SELECT * FROM owl_catalog
),
h AS (
SELECT * FROM business_unit_to_dataset
),
i AS (
SELECT * FROM business_units
),
j AS (
SELECT DISTINCT dataset, col_nm, col_semantic FROM dataset_schema
),
f AS (
SELECT
a.dataset,
a.rule_nm,
CASE
WHEN c.column_name = '' OR c.column_name IS NULL THEN 'global'
ELSE c.column_name
END AS column_name,
COALESCE(e.dim_name, 'UNSPECIFIED') AS dim_name,
c.dim_id,
ROUND(a.perc) AS perc,
ROUND(a.perc * b.rc) AS breaking_rows,
100 - ROUND((a.perc * b.rc) / CAST(b.rc AS DECIMAL)) AS score,
b.rc AS row_count,
a.run_id,
g.alias,
g.catalog_rank,
g.db_nm,
g.run_mode,
g.source_name,
g.table_nm,
i.name,
j.col_nm,
j.col_semantic
FROM a
LEFT JOIN b ON a.dataset = b.dataset AND a.run_id = b.run_id
INNER JOIN c ON a.dataset = c.dataset AND a.rule_nm = c.rule_nm
LEFT JOIN e ON e.dim_id = c.dim_id
INNER JOIN g ON g.dataset = a.dataset
LEFT JOIN h ON h.dataset = g.dataset
LEFT JOIN i ON i.id = h.id
LEFT JOIN j ON a.dataset = j.dataset AND c.column_name = j.col_nm
)
SELECT
CASE
WHEN AVG(score) < 0 THEN 0
ELSE AVG(score)
END AS avg_percent_rows_passing,
dim_name AS dimension
FROM f
GROUP BY dim_name;
```