How to calculate range in SQL

SQL query for range, run on a downloadable 30-day sample table, with the expected result and which databases support it.

In SQL, range over the sample table is calculated with the query below. On the sample data it returns 39.

Range = Largest value − Smallest value

Step by step

Load daily.csv into a table called daily (day, ad_spend, website_orders, app_orders, avg_basket).
Run the query and compare with the expected result.

Table

CREATE TABLE daily (
  day INTEGER,
  ad_spend INTEGER,
  website_orders INTEGER,
  app_orders INTEGER,
  avg_basket INTEGER
);

The query

Query

SELECT MAX(website_orders) - MIN(website_orders) AS range_orders
FROM daily;

Expected result on the sample data: 39.

What the result means

Because it depends only on the two most extreme values, the range is mostly a pointer to them. Here the maximum is the one-day sale on day 17. Leave that day out and the range shrinks to less than half, which tells you the rest of the month was fairly steady. Reporting the range with and without the extreme day is a quick, honest summary.

Notes for SQL

Tested on SQLite and DuckDB; it uses only standard SQL, so it also runs on PostgreSQL, MySQL and BigQuery.
Statistical aggregates ignore NULL rows. Decide whether a missing day should be NULL (left out) or 0 (counted as a zero day).

Where range misleads

One outlier sets the whole range. Use the interquartile range for a robust view of spread.
The range grows with the number of rows: more days give more chances of an extreme one.
Check that the extremes are real and not data-entry errors before reporting it.

The plain-language explanation is on the Range page.

The data

daily.csv (made up): 30 days from one store
day	website_orders
1	24
2	27
3	22
4	30
5	28
6	25
7	31
8	29
9	26
10	33
11	30
12	28
13	35
14	32
15	29
16	34
17	61
18	31
19	36
20	33
21	30
22	37
23	35
24	32
25	38
26	36
27	34
28	40
29	37
30	39

Same method, other tools

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About the data on this page

The figures come from a small made-up dataset, printed above in full so you can check every number by hand. Download it as daily.csv. The results are computed from those rows when the page is built, and the Excel formulas, SQL and Python on these pages are run against the same rows before publishing, so the code and the answers agree.

To get these numbers from your own data without writing formulas, try AnalyzeIt: ask in plain words and it shows the working behind every figure.

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