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Paolo Rossi

2010-Nov-17 19:27 UTC

[R] Parameterising apply To Compute Rolling Average of Columns in a matrix

I sent a post to find a clever way to compute a Rolling Average of columns
in a matrix  and I was given the solution below which I am very pleased
with.


RollingAverage <- function(x, RollingObs) {
 cx <- cumsum(x);
 N <- length(x);
 Temp <- (cx[RollingObs:N] - c(0, cx[1:(N-RollingObs)]))/RollingObs
 Output <- array(NA, N)
 Output[RollingObs:N] <- Temp;
 Output
 }


The only drawback is that in order to use apply to iterate over columns one
needs to write a fucntion for any RollingObs unless one is able to pass a
parameter through an apply call.

 Example:
a= array(1:100, dim = c(17,2))
RollingAverage(a[, 1], 7)        # works fine
apply(a, 2, RollingAverage)    # this doesnt work as it doesnt see
RollingObs

Is there a way to pass an additional parameter to apply?

Thanks

Paolo






On 16 November 2010 20:05, Ray Brownrigg
<Ray.Brownrigg@ecs.vuw.ac.nz>wrote:
>  On Wed, 17 Nov 2010, Paolo Rossi wrote:
> > Hi,
> > Can anyone suggest a  clever way to compute a rolling weekly average
of
> the
> > columns in a matrix?  The column bit is straightforward – use apply
given
> a
> > function which does what you want on a column. With regard to a
> particular
> > column, the obvious way is to run a for loop indexing the last 7 days
and
> > computing the average . I simply would like to know if there is a 
better
> /
> > quicker way.
> >
> >
> >
> > Code:
> > Given a,
> >
> > > a= array(1:100, dim = c(17,2))
> > > a
> >
> >       [,1] [,2]
> >  [1,]    1   18
> >  [2,]    2   19
> >  [3,]    3   20
> >  [4,]    4   21
> >  [5,]    5   22
> >  [6,]    6   23
> >  [7,]    7   24
> >  [8,]    8   25
> >  [9,]    9   26
> > [10,]   10   27
> > [11,]   11   28
> > [12,]   12   29
> > [13,]   13   30
> > [14,]   14   31
> > [15,]   15   32
> > [16,]   16   33
> > [17,]   17   34
> > one needs to start computing the average from obs 7 s (at obs 7 you
have
> a
> > full week to compute the average) and compute the rolling weekly
average
> > from day 7 onwards
> > Results will look like b
> >       [,1] [,2]
> >  [1,]    4   14
> >  [2,]    5   21
> >  [3,]    6   22
> >  [4,]    7   23
> >  [5,]    8   24
> >  [6,]    9   25
> >  [7,]   10   26
> >  [8,]   11   27
> >  [9,]   12   28
> > [10,]   13   29
> > Thanks in advance,
> >
> I don't see how an average of 7 numbers all 18 or greater can be 14, as
in
> your
> result[1, 2], unless you have mis-stated the question.
>
> Anyway, try:
> apply(a, 2, function(x) {cx <- cumsum(x); N <- length(x); (cx[7:N] -
c(0,
> cx[1:(N-7)]))/7}
>
> HTH
> Ray Brownrigg
>
> > Paolo
>
	[[alternative HTML version deleted]]

Henrique Dallazuanna

2010-Nov-17 19:32 UTC

head link

[R] Parameterising apply To Compute Rolling Average of Columns in a matrix

You've tried?

apply(a, 2, RollingAverage, 7)

On Wed, Nov 17, 2010 at 5:27 PM, Paolo Rossi <
statmailinglists@googlemail.com> wrote:
> I sent a post to find a clever way to compute a Rolling Average of columns
> in a matrix  and I was given the solution below which I am very pleased
> with.
>
>
> RollingAverage <- function(x, RollingObs) {
>  cx <- cumsum(x);
>  N <- length(x);
>  Temp <- (cx[RollingObs:N] - c(0, cx[1:(N-RollingObs)]))/RollingObs
>  Output <- array(NA, N)
>  Output[RollingObs:N] <- Temp;
>  Output
>  }
>
>
> The only drawback is that in order to use apply to iterate over columns one
> needs to write a fucntion for any RollingObs unless one is able to pass a
> parameter through an apply call.
>
>  Example:
> a= array(1:100, dim = c(17,2))
> RollingAverage(a[, 1], 7)        # works fine
> apply(a, 2, RollingAverage)    # this doesnt work as it doesnt see
> RollingObs
>
> Is there a way to pass an additional parameter to apply?
>
> Thanks
>
> Paolo
>
>
>
>
>
>
> On 16 November 2010 20:05, Ray Brownrigg <Ray.Brownrigg@ecs.vuw.ac.nz
> >wrote:
>
> >  On Wed, 17 Nov 2010, Paolo Rossi wrote:
> > > Hi,
> > > Can anyone suggest a  clever way to compute a rolling weekly
average of
> > the
> > > columns in a matrix?  The column bit is straightforward – use
apply
> given
> > a
> > > function which does what you want on a column. With regard to a
> > particular
> > > column, the obvious way is to run a for loop indexing the last 7
days
> and
> > > computing the average . I simply would like to know if there is a
>  better
> > /
> > > quicker way.
> > >
> > >
> > >
> > > Code:
> > > Given a,
> > >
> > > > a= array(1:100, dim = c(17,2))
> > > > a
> > >
> > >       [,1] [,2]
> > >  [1,]    1   18
> > >  [2,]    2   19
> > >  [3,]    3   20
> > >  [4,]    4   21
> > >  [5,]    5   22
> > >  [6,]    6   23
> > >  [7,]    7   24
> > >  [8,]    8   25
> > >  [9,]    9   26
> > > [10,]   10   27
> > > [11,]   11   28
> > > [12,]   12   29
> > > [13,]   13   30
> > > [14,]   14   31
> > > [15,]   15   32
> > > [16,]   16   33
> > > [17,]   17   34
> > > one needs to start computing the average from obs 7 s (at obs 7
you
> have
> > a
> > > full week to compute the average) and compute the rolling weekly
> average
> > > from day 7 onwards
> > > Results will look like b
> > >       [,1] [,2]
> > >  [1,]    4   14
> > >  [2,]    5   21
> > >  [3,]    6   22
> > >  [4,]    7   23
> > >  [5,]    8   24
> > >  [6,]    9   25
> > >  [7,]   10   26
> > >  [8,]   11   27
> > >  [9,]   12   28
> > > [10,]   13   29
> > > Thanks in advance,
> > >
> > I don't see how an average of 7 numbers all 18 or greater can be
14, as
> in
> > your
> > result[1, 2], unless you have mis-stated the question.
> >
> > Anyway, try:
> > apply(a, 2, function(x) {cx <- cumsum(x); N <- length(x);
(cx[7:N] - c(0,
> > cx[1:(N-7)]))/7}
> >
> > HTH
> > Ray Brownrigg
> >
> > > Paolo
> >
>
>        [[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>

-- 
Henrique Dallazuanna
Curitiba-Paraná-Brasil
25° 25' 40" S 49° 16' 22" O

	[[alternative HTML version deleted]]

Ray Brownrigg

2010-Nov-17 19:44 UTC

head link

[R] Parameterising apply To Compute Rolling Average of Columns in a matrix

On Thu, 18 Nov 2010, Paolo Rossi wrote:> I sent a post to find a clever way to compute a Rolling Average of columns
> in a matrix  and I was given the solution below which I am very pleased
> with.
>
>
> RollingAverage <- function(x, RollingObs) {
>  cx <- cumsum(x);
>  N <- length(x);
>  Temp <- (cx[RollingObs:N] - c(0, cx[1:(N-RollingObs)]))/RollingObs
>  Output <- array(NA, N)
>  Output[RollingObs:N] <- Temp;
>  Output
>  }
>
>
> The only drawback is that in order to use apply to iterate over columns one
> needs to write a fucntion for any RollingObs unless one is able to pass a
> parameter through an apply call.
>
>  Example:
> a= array(1:100, dim = c(17,2))
> RollingAverage(a[, 1], 7)        # works fine
> apply(a, 2, RollingAverage)    # this doesnt work as it doesnt see
> RollingObs
>
> Is there a way to pass an additional parameter to apply?
>RTFM.  Thats what the ... option of apply() is for.

apply(a, 2, RollingAverage, RollingObs=7)

Ray
> Thanks
>
> Paolo
>
> On 16 November 2010 20:05, Ray Brownrigg <Ray.Brownrigg at
ecs.vuw.ac.nz>wrote:
> >  On Wed, 17 Nov 2010, Paolo Rossi wrote:
> > > Hi,
> > > Can anyone suggest a  clever way to compute a rolling weekly
average of
> >
> > the
> >
> > > columns in a matrix?  The column bit is straightforward ? use
apply
> > > given
> >
> > a
> >
> > > function which does what you want on a column. With regard to a
> >
> > particular
> >
> > > column, the obvious way is to run a for loop indexing the last 7
days
> > > and computing the average . I simply would like to know if there
is a
> > > better
> >
> > /
> >
> > > quicker way.
> > >
> > >
> > >
> > > Code:
> > > Given a,
> > >
> > > > a= array(1:100, dim = c(17,2))
> > > > a
> > >
> > >       [,1] [,2]
> > >  [1,]    1   18
> > >  [2,]    2   19
> > >  [3,]    3   20
> > >  [4,]    4   21
> > >  [5,]    5   22
> > >  [6,]    6   23
> > >  [7,]    7   24
> > >  [8,]    8   25
> > >  [9,]    9   26
> > > [10,]   10   27
> > > [11,]   11   28
> > > [12,]   12   29
> > > [13,]   13   30
> > > [14,]   14   31
> > > [15,]   15   32
> > > [16,]   16   33
> > > [17,]   17   34
> > > one needs to start computing the average from obs 7 s (at obs 7
you
> > > have
> >
> > a
> >
> > > full week to compute the average) and compute the rolling weekly
> > > average from day 7 onwards
> > > Results will look like b
> > >       [,1] [,2]
> > >  [1,]    4   14
> > >  [2,]    5   21
> > >  [3,]    6   22
> > >  [4,]    7   23
> > >  [5,]    8   24
> > >  [6,]    9   25
> > >  [7,]   10   26
> > >  [8,]   11   27
> > >  [9,]   12   28
> > > [10,]   13   29
> > > Thanks in advance,
> >
> > I don't see how an average of 7 numbers all 18 or greater can be
14, as
> > in your
> > result[1, 2], unless you have mis-stated the question.
> >
> > Anyway, try:
> > apply(a, 2, function(x) {cx <- cumsum(x); N <- length(x);
(cx[7:N] - c(0,
> > cx[1:(N-7)]))/7}
> >
> > HTH
> > Ray Brownrigg
> >
> > > Paolo

Gabor Grothendieck

2010-Nov-28 00:39 UTC

head link

[R] Parameterising apply To Compute Rolling Average of Columns in a matrix

On Wed, Nov 17, 2010 at 2:27 PM, Paolo Rossi
<statmailinglists at googlemail.com> wrote:> I sent a post to find a clever way to compute a Rolling Average of columns
> in a matrix ?and I was given the solution below which I am very pleased
> with.
>
There is a zoo function that does this.  The following converts a to
zoo, performs the rolling average on columns and then converts back to
a matrix.  (A zoo object is just a matrix with a time index.)

library(zoo)
coredata(rollmean(as.zoo(a), 7))

Depending on what this is part of, you might be better off to keep it
as a zoo object since then you have access to all the other facilities
as well.

-- 
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com

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