GROUP BY or COUNT Like Field Values - UNPIVOT?

Question

I have a table with test fields, Example

id         | test1    | test2    | test3    | test4    | test5
+----------+----------+----------+----------+----------+-         


        

Answer 1

I may have come up with a solution:

SELECT id
      ,l - length(replace(t, 'P', '')) AS nr_p
      ,l - length(replace(t, 'F', '')) AS nr_f
      ,l - length(replace(t, 'I', '')) AS nr_i
FROM   (SELECT id, test::text AS t, length(test::text) AS l  FROM test) t

The trick works like this:

• Transform the rowtype into its text representation.
• Measure character-length.
• Replace the character you want to count and measure the change in length.
• Compute the length of the original row in the subselect for repeated use.

This requires that P, F, I are present nowhere else in the row. Use a sub-select to exclude any other columns that might interfere.

Tested in 8.4 - 9.1. Nobody uses PostgreSQL 7.4 anymore nowadays, you'll have to test yourself. I only use basic functions, but I am not sure if casting the rowtype to text is feasible in 7.4. If that doesn't work, you'll have to concatenate all test-columns once by hand:

SELECT id
      ,length(t) - length(replace(t, 'P', '')) AS nr_p
      ,length(t) - length(replace(t, 'F', '')) AS nr_f
      ,length(t) - length(replace(t, 'I', '')) AS nr_i
FROM   (SELECT id, test1||test2||test3||test4 AS t FROM test) t

This requires all columns to be NOT NULL.

Answer 2

Edit: just saw the comment about 7.4, I don't think this will work with that ancient version (unnest() came a lot later). If anyone thinks this is not worth keeping, I'll delete it.

Taking Erwin's idea to use the "row representation" as a base for the solution a bit further and automatically "normalize" the table on-the-fly:

select id,
       sum(case when flag = 'F' then 1 else null end) as failed,
       sum(case when flag = 'P' then 1 else null end) as passed,
       sum(case when flag = 'I' then 1 else null end) as incomplete
from (
  select id, 
         unnest(string_to_array(trim(trailing ')' from substr(all_column_values,strpos(all_column_values, ',') + 1)), ',')) flag
  from (
    SELECT id,
           not_normalized::text AS all_column_values
    FROM not_normalized
  ) t1
) t2
group by id

The heart of the solution is Erwin's trick to make a single value out of the complete row using the cast not_normalized::text. The string functions are applied to strip of the leading id value and the brackets around it.

The result of that is transformed into an array and that array is transformed into a result set using the unnest() function.

To understand that part, simply run the inner selects step by step.

Then the result is grouped and the corresponding values are counted.

Answer 3

Essentially, you need to unpivot your data by test:

id         | test     | result   
+----------+----------+----------+
12345      | test1    | P        
12345      | test2    | P        
12345      | test3    | F        
12345      | test4    | I        
12345      | test5    | P       

...

- so that you can then group it by test result.

Unfortunately, PostgreSQL doesn't have pivot/unpivot functionality built in, so the simplest way to do this would be something like:

select id, 'test1' test, test1 result from mytable union all
select id, 'test2' test, test2 result from mytable union all
select id, 'test3' test, test3 result from mytable union all
select id, 'test4' test, test4 result from mytable union all
select id, 'test5' test, test5 result from mytable union all

...

There are other ways of approaching this, but with 40 columns of data this is going to get really ugly.

EDIT: an alternative approach -

select r.result, sum(char_length(replace(replace(test1||test2||test3||test4||test5,excl1,''),excl2,'')))
from   mytable m, 
       (select 'P' result, 'F' excl1, 'I' excl2 union all
        select 'F' result, 'P' excl1, 'I' excl2 union all
        select 'I' result, 'F' excl1, 'P' excl2) r
group by r.result

Answer 4

You could use an auxiliary on-the-fly table to turn columns into rows, then you would be able to apply aggregate functions, something like this:

SELECT
  SUM(fields = 'P') AS passed,
  SUM(fields = 'F') AS failed,
  SUM(fields = 'I') AS incomplete
FROM (
  SELECT
    t.id,
    CASE x.idx
      WHEN 1 THEN t.test1
      WHEN 2 THEN t.test2
      WHEN 3 THEN t.test3
      WHEN 4 THEN t.test4
      WHEN 5 THEN t.test5
    END AS fields
  FROM atable t
    CROSS JOIN (
      SELECT 1 AS idx
      UNION ALL SELECT 2
      UNION ALL SELECT 3
      UNION ALL SELECT 4
      UNION ALL SELECT 5
    ) x
  WHERE t.id = 12345
) s