我有一张要显示的数据透视表。该表具有一个 URL whatismyip = new URL("http://checkip.amazonaws.com");
BufferedReader in = new BufferedReader(new
InputStreamReader(whatismyip.openStream()));
String ip = in.readLine();
try {
SSLServerSocketFactory sslFactory = (SSLServerSocketFactory)SSLServerSocketFactory.getDefault();
SSLServerSocket ss = (SSLServerSocket) sslFactory.createServerSocket(PORT);
int idSession = 0;
while (true) {
SSLSocket socket = (SSLSocket)ss.accept();
if(socket.getInetAddress().getHostAddress().equals(ip)){
if (socket != null && !socket.isClosed()) {
socket.close();
}
}
((ServidorThread) new ServidorThread(socket, idSession)).start();
idSession++;
}
} catch (IOException ex) {
ex.printStackTrace(System.out);
Logger.getLogger(Servidor.class.getName()).log(Level.SEVERE, null, ex);
}
}
和category列。我想为每个sub-category添加一个总计行，并在运行计数中添加一个初始值，该初始值来自另一个表，并添加来自特定category的值。
这里有一个完整的工作示例来帮助说明。
设置：
sub-category我当前的CREATE TABLE dbo.rawdata
(
MyMonth [NVARCHAR](7) NOT NULL
, MyCategory [NVARCHAR](50) NOT NULL
, MySubCategory [NVARCHAR](50) NOT NULL
, MyCount [INT] NOT NULL
) ON [DEFAULT];
CREATE TABLE dbo.initial
(
MyCategory [NVARCHAR](50) NOT NULL
, MyStart [INT] NOT NULL
) ON [DEFAULT];
INSERT INTO dbo.rawdata (MyMonth, MyCategory, MySubCategory, MyCount)
VALUES
('2018-12', 'three', 'bravo', 7),
('2018-10', 'three', 'echo', 10),
('2018-07', 'four', 'echo', 17),
('2018-12', 'five', 'bravo', 35),
('2018-03', 'three', 'delta', 11),
('2018-03', 'six', 'charlie', 1),
('2018-09', 'five', 'echo', 11),
('2018-12', 'one', 'charlie', 23),
('2018-02', 'five', 'charlie', 36),
('2018-02', 'three', 'delta', 46),
('2018-01', 'two', 'delta', 29),
('2018-02', 'four', 'charlie', 15),
('2018-11', 'one', 'charlie', 25),
('2018-10', 'two', 'bravo', 27),
('2018-05', 'four', 'bravo', 17),
('2018-12', 'five', 'echo', 12),
('2018-05', 'four', 'charlie', 21),
('2018-12', 'one', 'delta', 43),
('2018-12', 'three', 'bravo', 33),
('2018-07', 'two', 'alpha', 32),
('2018-11', 'five', 'delta', 44),
('2018-01', 'six', 'echo', 38),
('2018-08', 'one', 'charlie', 9),
('2018-06', 'three', 'echo', 15),
('2018-08', 'four', 'bravo', 44),
('2018-07', 'six', 'alpha', 50),
('2018-12', 'two', 'echo', 4),
('2018-04', 'six', 'bravo', 40),
('2018-03', 'six', 'delta', 33),
('2018-05', 'five', 'alpha', 11),
('2018-01', 'three', 'echo', 24),
('2018-09', 'five', 'charlie', 10),
('2018-09', 'four', 'delta', 36),
('2018-04', 'two', 'echo', 13),
('2018-02', 'one', 'alpha', 24),
('2018-07', 'one', 'bravo', 2),
('2018-06', 'five', 'echo', 33),
('2018-07', 'five', 'charlie', 46),
('2018-12', 'six', 'bravo', 28),
('2018-10', 'two', 'echo', 10),
('2018-01', 'four', 'delta', 1),
('2018-06', 'three', 'bravo', 25),
('2018-05', 'four', 'charlie', 27),
('2018-04', 'three', 'alpha', 48),
('2018-10', 'three', 'alpha', 8),
('2018-04', 'two', 'delta', 17),
('2018-07', 'five', 'charlie', 2),
('2018-03', 'five', 'alpha', 45),
('2018-08', 'two', 'charlie', 21),
('2018-11', 'three', 'bravo', 32),
('2018-07', 'one', 'echo', 34),
('2018-12', 'one', 'echo', 21),
('2018-08', 'two', 'delta', 8),
('2018-04', 'three', 'delta', 32),
('2018-11', 'five', 'alpha', 23),
('2018-03', 'two', 'echo', 16),
('2018-02', 'six', 'echo', 35),
('2018-11', 'three', 'alpha', 16),
('2018-08', 'four', 'alpha', 40),
('2018-03', 'one', 'echo', 39),
('2018-09', 'one', 'charlie', 22),
('2018-06', 'three', 'bravo', 38),
('2018-02', 'one', 'bravo', 18),
('2018-11', 'four', 'echo', 41),
('2018-12', 'three', 'alpha', 49),
('2018-02', 'six', 'delta', 24),
('2018-09', 'five', 'alpha', 41),
('2018-09', 'six', 'delta', 12),
('2018-04', 'three', 'delta', 15),
('2018-12', 'three', 'delta', 36),
('2018-05', 'five', 'delta', 26),
('2018-01', 'three', 'echo', 22),
('2018-03', 'four', 'delta', 26),
('2018-05', 'three', 'echo', 33),
('2018-07', 'three', 'bravo', 1),
('2018-10', 'four', 'echo', 37),
('2018-01', 'three', 'alpha', 32),
('2018-04', 'two', 'bravo', 2),
('2018-08', 'one', 'bravo', 41),
('2018-03', 'three', 'bravo', 40),
('2018-07', 'three', 'alpha', 38),
('2018-02', 'three', 'bravo', 2),
('2018-11', 'six', 'charlie', 17),
('2018-08', 'three', 'echo', 5),
('2018-02', 'six', 'bravo', 49),
('2018-02', 'one', 'alpha', 9),
('2018-07', 'five', 'charlie', 26),
('2018-05', 'five', 'echo', 7),
('2018-11', 'six', 'bravo', 31),
('2018-08', 'four', 'alpha', 19),
('2018-05', 'one', 'charlie', 30),
('2018-05', 'one', 'echo', 31),
('2018-01', 'four', 'bravo', 31),
('2018-06', 'four', 'alpha', 29),
('2018-10', 'one', 'alpha', 45),
('2018-04', 'two', 'charlie', 41),
('2018-08', 'one', 'delta', 24),
('2018-01', 'five', 'bravo', 27),
('2018-08', 'two', 'charlie', 43),
('2018-02', 'four', 'delta', 19);
INSERT INTO dbo.initial (MyCategory, MyStart)
VALUES
('five', 9),
('four', 4),
('one', 6),
('six', 6),
('three', 3),
('two', 9);
SQL查询：
pivotPIVOT输出：
SELECT
MyCategory
, MySubCategory
, ISNULL("2018-01", 0) AS "2018-01"
, ISNULL("2018-02", 0) AS "2018-02"
, ISNULL("2018-03", 0) AS "2018-03"
, ISNULL("2018-04", 0) AS "2018-04"
, ISNULL("2018-05", 0) AS "2018-05"
, ISNULL("2018-06", 0) AS "2018-06"
, ISNULL("2018-07", 0) AS "2018-07"
, ISNULL("2018-08", 0) AS "2018-08"
, ISNULL("2018-09", 0) AS "2018-09"
, ISNULL("2018-10", 0) AS "2018-10"
, ISNULL("2018-11", 0) AS "2018-11"
, ISNULL("2018-12", 0) AS "2018-12"
FROM
(SELECT
MyCategory
, MySubCategory
, MyMonth
, MyCount
FROM
dbo.rawdata) SourceTable
PIVOT
(SUM(MyCount)
FOR MyMonth IN ("2018-01", "2018-02", "2018-03", "2018-04", "2018-05", "2018-06", "2018-07", "2018-08", "2018-09", "2018-10", "2018-11", "2018-12")
) PivotTable
ORDER BY
MyCategory, MySubCategory;
所需的输出：
为每个 +------------+---------------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+
| MYCATEGORY | MYSUBCATEGORY | JAN-18 | FEB-18 | MAR-18 | APR-18 | MAY-18 | JUN-18 | JUL-18 | AUG-18 | SEP-18 | OCT-18 | NOV-18 | DEC-18 |
+------------+---------------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+
| five | alpha | 0 | 0 | 45 | 0 | 11 | 0 | 0 | 0 | 41 | 0 | 23 | 0 |
| five | bravo | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 |
| five | charlie | 0 | 36 | 0 | 0 | 0 | 0 | 74 | 0 | 10 | 0 | 0 | 0 |
| five | delta | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 44 | 0 |
| five | echo | 0 | 0 | 0 | 0 | 7 | 33 | 0 | 0 | 11 | 0 | 0 | 12 |
| four | alpha | 0 | 0 | 0 | 0 | 0 | 29 | 0 | 59 | 0 | 0 | 0 | 0 |
| four | bravo | 31 | 0 | 0 | 0 | 17 | 0 | 0 | 44 | 0 | 0 | 0 | 0 |
| four | charlie | 0 | 15 | 0 | 0 | 48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| four | delta | 1 | 19 | 26 | 0 | 0 | 0 | 0 | 0 | 36 | 0 | 0 | 0 |
| four | echo | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 37 | 41 | 0 |
| one | alpha | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 0 | 0 |
| one | bravo | 0 | 18 | 0 | 0 | 0 | 0 | 2 | 41 | 0 | 0 | 0 | 0 |
| one | charlie | 0 | 0 | 0 | 0 | 30 | 0 | 0 | 9 | 22 | 0 | 25 | 23 |
| one | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 43 |
| one | echo | 0 | 0 | 39 | 0 | 31 | 0 | 34 | 0 | 0 | 0 | 0 | 21 |
| six | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 |
| six | bravo | 0 | 49 | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 28 |
| six | charlie | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 |
| six | delta | 0 | 24 | 33 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 |
| six | echo | 38 | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| three | alpha | 32 | 0 | 0 | 48 | 0 | 0 | 38 | 0 | 0 | 8 | 16 | 49 |
| three | bravo | 0 | 2 | 40 | 0 | 0 | 63 | 1 | 0 | 0 | 0 | 32 | 40 |
| three | delta | 0 | 46 | 11 | 47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 |
| three | echo | 46 | 0 | 0 | 0 | 33 | 15 | 0 | 5 | 0 | 10 | 0 | 0 |
| two | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 0 |
| two | bravo | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 |
| two | charlie | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 64 | 0 | 0 | 0 | 0 |
| two | delta | 29 | 0 | 0 | 17 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 |
| two | echo | 0 | 0 | 16 | 13 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 4 |
+------------+---------------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+--------+
添加一个总计行。 第一个月月的值将从category表中的初始值加上 dbo.initial alpha的值。下个月的值将取上个月的值加上sub-category alpha。如果sub-category没有category alpha，则使用0。
所需的输出示例：
为了简洁起见，我只显示了几行和几个月，并且出于示例目的，我仅显示了数学。
+------------+---------------+---------+---------+---------+---------+ | MYCATEGORY | MYSUBCATEGORY | INITIAL | JAN-18 | FEB-18 | MAR-18 | +------------+---------------+---------+---------+---------+---------+ | five | alpha | | 0 | 0 | 45 | | five | bravo | 27 | 0 | 0 | | | five | ...snippit... | ....... | ....... | ....... | ....... | | five | total | 9 | 9+0=9 | 9+0=9 | 9+45=54 | | three | alpha | | 32 | 0 | 0 | | three | delta | 0 | 46 | 11 | 47 | | five | ...snippit... | ....... | ....... | ....... | ....... | | three | total | 3 | 3+32=35 | 35+0=35 | 35+0=35 | +------------+---------------+---------+---------+---------+---------+
答案 0 :(得分：0)
您可以尝试一下。先写一个数据透视CTE，然后写UNION ALL CTE结果集和CTE totle结果集。
;WITH CTE AS(
SELECT
MyCategory
, MySubCategory
, IIF(MyMonth ='2018-01', MyCount,0) AS 'col01'
, IIF(MyMonth ='2018-02', MyCount,0) AS 'col02'
, IIF(MyMonth ='2018-03', MyCount,0) AS 'col03'
, IIF(MyMonth ='2018-04', MyCount,0) AS 'col04'
, IIF(MyMonth ='2018-05', MyCount,0) AS 'col05'
, IIF(MyMonth ='2018-06', MyCount,0) AS 'col06'
, IIF(MyMonth ='2018-07', MyCount,0) AS 'col07'
, IIF(MyMonth ='2018-08', MyCount,0) AS 'col08'
, IIF(MyMonth ='2018-09', MyCount,0) AS 'col09'
, IIF(MyMonth ='2018-10', MyCount,0) AS 'col10'
, IIF(MyMonth ='2018-11', MyCount,0) AS 'col11'
, IIF(MyMonth ='2018-12', MyCount,0) AS 'col12'
FROM dbo.rawdata
)
SELECT t.*,i.MyStart
FROM
(
select
MyCategory
,MySubCategory
,col01 AS '2018-01'
,col02 AS '2018-02'
,col03 AS '2018-03'
,col04 AS '2018-04'
,col05 AS '2018-05'
,col06 AS '2018-06'
,col07 AS '2018-07'
,col08 AS '2018-08'
,col09 AS '2018-09'
,col10 AS '2018-10'
,col11 AS '2018-11'
,col12 AS '2018-12'
from CTE t
union all
select MyCategory,
'totle' as MySubCategory,
SUM(col01),
SUM(col01 + col02),
SUM(col01 + col02+ col03),
SUM(col01 + col02+ col03 +col04),
SUM(col01 + col02+ col03 +col04 +col05),
SUM(col01 + col02+ col03 +col04 +col05 + col06),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07 +col08),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07 +col08 +col09),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07 +col08 +col09 +col10),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07 +col08 +col09 +col10 +col11),
SUM(col01 + col02+ col03 +col04 +col05 + col06 +col07 +col08 +col09 +col10 +col11+col12)
from CTE
GROUP BY MyCategory
) t inner join dbo.initial i on t.MyCategory = i.MyCategory
order by MyCategory,MySubCategory
结果：
| MyCategory | MySubCategory | 2018-01 | 2018-02 | 2018-03 | 2018-04 | 2018-05 | 2018-06 | 2018-07 | 2018-08 | 2018-09 | 2018-10 | 2018-11 | 2018-12 | MyStart |
|------------|---------------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|---------|
| five | alpha | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | alpha | 0 | 0 | 45 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 0 | 9 |
| five | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 9 |
| five | bravo | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35 | 9 |
| five | charlie | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 0 | 9 |
| five | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | delta | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 0 | 9 |
| five | echo | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | echo | 0 | 0 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| five | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 9 |
| five | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 | 0 | 0 | 0 | 9 |
| five | totle | 27 | 63 | 108 | 108 | 152 | 185 | 259 | 259 | 321 | 321 | 388 | 435 | 9 |
| four | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 40 | 0 | 0 | 0 | 0 | 4 |
| four | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19 | 0 | 0 | 0 | 0 | 4 |
| four | alpha | 0 | 0 | 0 | 0 | 0 | 29 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | bravo | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 44 | 0 | 0 | 0 | 0 | 4 |
| four | bravo | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | charlie | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | charlie | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | charlie | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 0 | 0 | 0 | 4 |
| four | delta | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | delta | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | delta | 0 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 37 | 0 | 0 | 4 |
| four | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 4 |
| four | echo | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 4 |
| four | totle | 32 | 66 | 92 | 92 | 157 | 186 | 203 | 306 | 342 | 379 | 420 | 420 | 4 |
| one | alpha | 0 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 45 | 0 | 0 | 6 |
| one | alpha | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | bravo | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 6 |
| one | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | 0 | 0 | 0 | 6 |
| one | charlie | 0 | 0 | 0 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0 | 0 | 0 | 6 |
| one | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23 | 6 |
| one | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 | 0 | 6 |
| one | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 6 |
| one | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0 | 0 | 0 | 0 | 6 |
| one | echo | 0 | 0 | 0 | 0 | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | echo | 0 | 0 | 0 | 0 | 0 | 0 | 34 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 6 |
| one | echo | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| one | totle | 0 | 51 | 90 | 90 | 151 | 151 | 187 | 261 | 283 | 328 | 353 | 440 | 6 |
| six | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 50 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | bravo | 0 | 0 | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 6 |
| six | bravo | 0 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0 | 6 |
| six | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 6 |
| six | charlie | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | delta | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | delta | 0 | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 12 | 0 | 0 | 0 | 6 |
| six | echo | 0 | 35 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | echo | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| six | totle | 38 | 146 | 180 | 220 | 220 | 220 | 270 | 270 | 282 | 282 | 330 | 358 | 6 |
| three | alpha | 0 | 0 | 0 | 48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 3 |
| three | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 | 0 | 3 |
| three | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 3 |
| three | alpha | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 0 | 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 25 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 38 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 33 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 0 | 3 |
| three | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 3 |
| three | delta | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | delta | 0 | 0 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | delta | 0 | 46 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | delta | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 3 |
| three | echo | 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | echo | 0 | 0 | 0 | 0 | 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | echo | 0 | 0 | 0 | 0 | 0 | 15 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | echo | 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| three | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 3 |
| three | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 3 |
| three | totle | 78 | 126 | 177 | 272 | 305 | 383 | 422 | 427 | 427 | 445 | 493 | 618 | 3 |
| two | alpha | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | bravo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 | 0 | 0 | 9 |
| two | bravo | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | charlie | 0 | 0 | 0 | 41 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 43 | 0 | 0 | 0 | 0 | 9 |
| two | charlie | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 | 0 | 0 | 0 | 0 | 9 |
| two | delta | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 0 | 0 | 0 | 0 | 9 |
| two | delta | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | delta | 29 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | echo | 0 | 0 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 9 |
| two | echo | 0 | 0 | 0 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| two | echo | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 0 | 9 |
| two | totle | 29 | 29 | 45 | 118 | 118 | 118 | 150 | 222 | 222 | 259 | 259 | 263 | 9 |