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><H1
><A
NAME="PLR-WINDOW-FUNCS"
></A
>Chapter 9. Window Functions</H1
><P
> Starting with version 8.4, PostgreSQL supports <TT
CLASS="LITERAL"
>WINDOW</TT
>
functions which provide the ability to perform calculations across sets
of rows that are related to the current query row. This is comparable to
the type of calculation that can be done with an aggregate function. But
unlike regular aggregate functions, use of a window function does not
cause rows to become grouped into a single output row; the rows retain
their separate identities. Behind the scenes, the window function is able
to access more than just the current row of the query result. See the
PostgreSQL documentation for more general information related to the use
of this capability.
</P
><P
> PL/R functions may be defined as <TT
CLASS="LITERAL"
>WINDOW</TT
>. For example:
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE OR REPLACE
FUNCTION r_regr_slope(float8, float8)
RETURNS float8 AS
$BODY$
slope <- NA
y <- farg1
x <- farg2
if (fnumrows==9) try (slope <- lm(y ~ x)$coefficients[2])
return(slope)
$BODY$
LANGUAGE plr WINDOW;
</PRE
><P>
</P
><P
> A number of variables are automatically provided by PL/R to the R
interpreter:
</P
><P
></P
><DIV
CLASS="VARIABLELIST"
><DL
><DT
><TT
CLASS="LITERAL"
>farg</TT
><TT
CLASS="REPLACEABLE"
><I
>N</I
></TT
></DT
><DD
><P
> <TT
CLASS="LITERAL"
>farg1</TT
> and <TT
CLASS="LITERAL"
>farg2</TT
> are R vectors containing
the current row's data plus that of the related rows.
</P
></DD
><DT
><TT
CLASS="LITERAL"
>fnumrows</TT
></DT
><DD
><P
> The number of rows in the current <TT
CLASS="LITERAL"
>WINDOW</TT
> frame.
</P
></DD
><DT
><TT
CLASS="LITERAL"
>prownum</TT
> (not shown)</DT
><DD
><P
> Provides the 1-based row offset of the current row in the current
<TT
CLASS="LITERAL"
>PARTITION</TT
>.
</P
></DD
></DL
></DIV
><P
> A more complete example follows:
</P><PRE
CLASS="PROGRAMLISTING"
>-- create test table
CREATE TABLE test_data (
fyear integer,
firm float8,
eps float8
);
-- insert randomly pertubated data for test
INSERT INTO test_data
SELECT (b.f + 1) % 10 + 2000 AS fyear,
floor((b.f+1)/10) + 50 AS firm,
f::float8/100 + random()/10 AS eps
FROM generate_series(-500,499,1) b(f);
CREATE OR REPLACE
FUNCTION r_regr_slope(float8, float8)
RETURNS float8 AS
$BODY$
slope <- NA
y <- farg1
x <- farg2
if (fnumrows==9) try (slope <- lm(y ~ x)$coefficients[2])
return(slope)
$BODY$
LANGUAGE plr WINDOW;
SELECT *, r_regr_slope(eps, lag_eps) OVER w AS slope_R
FROM (SELECT firm, fyear, eps,
lag(eps) OVER (ORDER BY firm, fyear) AS lag_eps
FROM test_data) AS a
WHERE eps IS NOT NULL
WINDOW w AS (ORDER BY firm, fyear ROWS 8 PRECEDING);
</PRE
><P>
</P
><P
> In this example, the variables <TT
CLASS="LITERAL"
>farg1</TT
> and <TT
CLASS="LITERAL"
>farg2</TT
>
contain the current row value for eps and lag_eps, as well as the
preceding 8 rows which are also in the same <TT
CLASS="LITERAL"
>WINDOW</TT
> frame
within the same <TT
CLASS="LITERAL"
>PARTITION</TT
>. In this case since no
<TT
CLASS="LITERAL"
>PARTITION</TT
> is explicitly defined, the <TT
CLASS="LITERAL"
>PARTITION</TT
>
is the entire set of rows returned from the inner sub-select.
</P
><P
> Another interesting example follows. The idea of
<SPAN
CLASS="QUOTE"
>"Winsorizing"</SPAN
> is to return either the original value
or, if that value is outside certain bounds, a trimmed value. So for
example winsor(eps, 0.1) would return the value at the 10th percentile
for values of eps less that that, the value of the 90th percentile for
eps greater than that value, and the unmodified value of eps otherwise.
</P><PRE
CLASS="PROGRAMLISTING"
>CREATE OR REPLACE FUNCTION winsorize(float8, float8)
RETURNS float8 AS
$BODY$
library(psych)
return(winsor(as.vector(farg1), arg2)[prownum])
$BODY$ LANGUAGE plr VOLATILE WINDOW;
SELECT fyear, eps,
winsorize(eps, 0.1) OVER (PARTITION BY fyear) AS w_eps
FROM test_data ORDER BY fyear, eps;
</PRE
><P>
</P
><P
> In this example, use of the variable <TT
CLASS="LITERAL"
>prownum</TT
>
is illustrated.
</P
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