sql data muniplication help

  • hi guys

    hope everyone is well,

    I need some help

    I need to manipulate some data so I can produce a performance report

    I have the data below
    data:image/png;base64,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

    I need to rearrange this data to look like the below
    data:image/png;base64,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

    how can I do this? my sql knowledge isn't very good.

    the table this data is coming from is called Data_Table

    thank you for any help provides

  • Welcome to SSC. When posting data questions, it best to provide DDL and consumable sample data for other users. We don't have access to your server, or data, thus you need to supply it in a format that can be quickly and easily used by others. I've done this for you for this one:

    USE Sandbox;
    GO
    --CREATE TABLE statement (DDL)
    CREATE TABLE #Sample
      (ID tinyint,
      MainDate date,
      [System] varchar(5),
      Comment bit, --These are all NULL so I have no idea what the datatype is
      [Type] tinyint,
      LCount tinyint,
      NLCount tinyint,
      FCount tinyint,
      DepID tinyint);
    GO
    --INSERT sample data into table
    INSERT INTO #Sample
    VALUES
      (11,'20171105','red',NULL,1,5,10,3,5),
      (22,'20171105','red',NULL,2,4,1,4,7);
    GO
    --Display Data
    SELECT *
    FROM #Sample;
    GO
    --Clean up
    DROP TABLE #Sample;
    GO

    I'll start looking at a solution for you now, but this'll help others answer as well (who may get to the end result faster than myself).

    Thom~

    Excuse my typos and sometimes awful grammar. My fingers work faster than my brain does.
    Larnu.uk

  • Ok, for a solution, this should get you what you're after. The method is called Cross Pivoting (because you use a CROSS APPLY to Pivot your data). If you have any questions on how it works, please do ask.

    SELECT S.ID, S.MainDate,
       S.[System],
       S.Comment,
       S.[Type],
       C.F AS Fields,
       CASE C.F WHEN 'L Count' THEN S.LCount
           WHEN 'F Count' THEN S.FCount
          WHEN 'N L Count' THEN S.NLCount END AS [Values],
       S.DepID
    FROM #Sample S
      CROSS APPLY (VALUES ('L Count'),('N L Count'),('F Count')) C(F);

    Thom~

    Excuse my typos and sometimes awful grammar. My fingers work faster than my brain does.
    Larnu.uk

  • Hah.  I was just working through this myself.

    This seems to work, but using a table variable rather than your temporary table, Thom A.

    DECLARE @DataTable TABLE
    (
      id INTEGER,
      maindate DATE,
      system VARCHAR(10),
      comment VARCHAR(10),
      type INTEGER,
      lcount INTEGER,
      nlcount INTEGER,
      fcount INTEGER,
      depid INTEGER
    );
    INSERT INTO @DataTable
    VALUES
    (11, '2017-11-05', 'red', NULL, 1, 5, 20, 3, 5),
    (22, '2017-11-05', 'red', NULL, 2, 4, 1, 4, 7);

    SELECT id,
       maindate,
       system,
       comment,
       x.Fields,
       x.[Values],
       depid
    FROM @DataTable
      CROSS APPLY
    (
      VALUES
       ('lcount', lcount),
       ('nlcount', nlcount),
       ('fcount', fcount)
    ) x (Fields, [Values]);

    --edit: tweaked cross apply field names to match your requirement.  Note use of square brackets around keyword

    Technique lifted shamelessly from Kenneth Fisher's blog post "unpivot a table using cross apply"

    Thomas Rushton
    blog: https://thelonedba.wordpress.com

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