Rows to columns with no specific column headings - tsql

Question

I have a query that returns data which is in rows and I need to change the results so the results are in columns and not rows. I have done research and found this article which has a dynamic query that can be used but it doesn't seem my situation will be able to use that solution. That solution seemed to rely on each row having a unique name that could be used as a column name, I don't have that.
My data contains customer records of the visits a customer has had to our facility. Some customers will only see us once and some will see us many times in the same time span, so we have no way of predicting how many visits per customer we will have in a given time period. Note ID 10219 has only one visit, 5180 has 3 and there are many for 5199.
ID Task Visit Date RF Score PF Score
10219 Follow Up Visit 12/26/2013 1 6
5180 Initial Visit 6/9/2011 3 9
5180 Follow Up Visit 7/8/2011 3 10
5180 Follow Up Visit 9/2/2011 1 10
5199 Follow Up Visit 9/15/2011 2 7
5199 Follow Up Visit 9/8/2011 5 6
5199 Follow Up Visit 10/27/2011 4 7
5199 Follow Up Visit 10/20/2011 2 4
5199 Follow Up Visit 10/13/2011 4 8
5199 Follow Up Visit 11/17/2011 3 4
5199 Follow Up Visit 11/10/2011 2 5
5199 Follow Up Visit 11/3/2011 3 3
With data that is structured like this does anyone know how to convert these rows to columns dynamically even though I don't know how many columns will be needed?
EDIT: the final result should look like this:
ID Task1 Visit Date1 RF Score1 PF Score1 Task2 Visit Date2 RF Score2 PF Score2 Task3 Visit Date3 RF Score3 PF Score3
5180 Initial Visit 6/9/2011 3 9 Follow Up Visit 7/8/2011 3 10 Follow Up Visit 9/2/2011 1 10

Answer 1

The solution that you link to would work for your case but you have to adjust it slightly because you want to pivot on multiple columns of data. Since you need to pivot multiple columns, then you first will want to unpivot your Visit Date, Task, Rf Score and Pf Score columns into multiple rows, then apply the pivot function. Besides the process of unpivoting, I would also suggest using a windowing function like row_number to generate a unique sequence for each id with date.
You will start your query by using the following:
select id, task, [visit date], [rf score], [pf score],
row_number() over(partition by id
order by [visit date]) seq
from yourtable
See SQL Fiddle with Demo. This creates a number that will be used to associate each value in Visit Date, Task, Rf Score and Pf Score with the actual number of the visit.
Once you have this row number, then you will want to unpivot your multiple columns into multiple rows of data. There are several ways to do this including using the unpivot function. But since you are using SQL Server 2008R2, you can use CROSS APPLY with VALUES:
select id,
col = col + cast(seq as varchar(10)),
value
from
(
select id, task, [visit date], [rf score], [pf score],
row_number() over(partition by id
order by [visit date]) seq
from yourtable
) d
cross apply
(
values
('VisitDate', convert(varchar(10), [visit date], 120)),
('Task', [task]),
('RfScore', cast([rf score] as varchar(10))),
('PfScore', cast([pf score] as varchar(10)))
) c (col, value)
See SQL Fiddle with Demo. Your data is now in a format that can easily be pivoted:
| ID | COL | VALUE |
|-------|------------|-----------------|
| 5180 | VisitDate1 | 2011-06-09 |
| 5180 | Task1 | Initial Visit |
| 5180 | RfScore1 | 3 |
| 5180 | PfScore1 | 9 |
| 5180 | VisitDate2 | 2011-07-08 |
| 5180 | Task2 | Follow Up Visit |
| 5180 | RfScore2 | 3 |
| 5180 | PfScore2 | 10 |
The code when the PIVOT is added willbe:
select id,
VisitDate1, Task1, RfScore1, PfScore1,
VisitDate2, Task2, RfScore2, PfScore2,
VisitDate3, Task3, RfScore3, PfScore3,
VisitDate4, Task4, RfScore4, PfScore4,
VisitDate5, Task5, RfScore5, PfScore5,
VisitDate6, Task6, RfScore6, PfScore6
from
(
select id,
col = col + cast(seq as varchar(10)),
value
from
(
select id, task, [visit date], [rf score], [pf score],
row_number() over(partition by id
order by [visit date]) seq
from yourtable
) d
cross apply
(
values
('VisitDate', convert(varchar(10), [visit date], 120)),
('Task', [task]),
('RfScore', cast([rf score] as varchar(10))),
('PfScore', cast([pf score] as varchar(10)))
) c (col, value)
) d
pivot
(
max(value)
for col in (VisitDate1, Task1, RfScore1, PfScore1,
VisitDate2, Task2, RfScore2, PfScore2,
VisitDate3, Task3, RfScore3, PfScore3,
VisitDate4, Task4, RfScore4, PfScore4,
VisitDate5, Task5, RfScore5, PfScore5,
VisitDate6, Task6, RfScore6, PfScore6)
) piv;
See SQL Fiddle with Demo.
The above works great if you have a limited number of value, but if they are unknown then you will need to use dynamic SQL and the code above will be converted to:
DECLARE #cols AS NVARCHAR(MAX),
#query AS NVARCHAR(MAX)
select #cols = STUFF((SELECT ',' + QUOTENAME(col + cast(seq as varchar(10)))
from
(
select row_number() over(partition by id
order by [visit date]) seq
from yourtable
) d
cross apply
(
select 'VisitDate', 1 union all
select 'Task', 2 union all
select 'RfScore', 3 union all
select 'PfScore', 4
) c (col, so)
group by seq, col, so
order by seq, so
FOR XML PATH(''), TYPE
).value('.', 'NVARCHAR(MAX)')
,1,1,'')
set #query = 'select id, ' + #cols + '
from
(
select id,
col = col + cast(seq as varchar(10)),
value
from
(
select id, task, [visit date], [rf score], [pf score],
row_number() over(partition by id
order by [visit date]) seq
from yourtable
) d
cross apply
(
values
(''VisitDate'', convert(varchar(10), [visit date], 120)),
(''Task'', task),
(''RfScore'', cast([rf score] as varchar(10))),
(''PfScore'', cast([pf score] as varchar(10)))
) c (col, value)
) s
pivot
(
max(value)
for col in (' + #cols + ')
) p '
execute sp_executesql #query;
See SQL Fiddle with Demo. Both versions give a result:
| ID | VISITDATE1 | TASK1 | RFSCORE1 | PFSCORE1 | VISITDATE2 | TASK2 | RFSCORE2 | PFSCORE2 | VISITDATE3 | TASK3 | RFSCORE3 | PFSCORE3 | VISITDATE4 | TASK4 | RFSCORE4 | PFSCORE4 | VISITDATE5 | TASK5 | RFSCORE5 | PFSCORE5 | VISITDATE6 | TASK6 | RFSCORE6 | PFSCORE6 | VISITDATE7 | TASK7 | RFSCORE7 | PFSCORE7 | VISITDATE8 | TASK8 | RFSCORE8 | PFSCORE8 |
|-------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|------------|-----------------|----------|----------|
| 5180 | 2011-06-09 | Initial Visit | 3 | 9 | 2011-07-08 | Follow Up Visit | 3 | 10 | 2011-09-02 | Follow Up Visit | 1 | 10 | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) |
| 5199 | 2011-09-08 | Follow Up Visit | 5 | 6 | 2011-09-15 | Follow Up Visit | 2 | 7 | 2011-10-13 | Follow Up Visit | 4 | 8 | 2011-10-20 | Follow Up Visit | 2 | 4 | 2011-10-27 | Follow Up Visit | 4 | 7 | 2011-11-03 | Follow Up Visit | 3 | 3 | 2011-11-10 | Follow Up Visit | 2 | 5 | 2011-11-17 | Follow Up Visit | 3 | 4 |
| 10219 | 2013-12-26 | Follow Up Visit | 1 | 6 | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) | (null) |