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On 26 Jul 2008, at 8:00 PM, [EMAIL PROTECTED] wrote:
From: nmarti <[EMAIL PROTECTED]>
Date: 26 July 2008 1:42:09 AM
To: r-help@r-project.org
Subject: Re: [R] Dividing by 0
I'm well aware these are not errors, I guess I miss-wrote.
I understand your concern. Thanks
I would think that the result of your rolling
calculation should be NA if there are NAs
or NaNs in the window. Producing an error
given NAs seems like a broken function to me.
One of the main purposes of NA is so that you
can do operations like what you want to do
and get reasonable answers.
P
Hi,
what about:
mydata <- c(1,2,3,NA, Inf, -Inf, NaN, 5, 6, 7)
mydata2 <- ifelse(is.na(mydata) | is.infinite(mydata),
0, mydata)
mydata
mydata2
nmarti wrote:
I know I can use x <- na.omit(x), and other forms of this, to get rid of
some of these errors.
I know what y
I'm well aware these are not errors, I guess I miss-wrote.
I understand your concern. Thanks for passionately looking out for my well
being, you saved my life.
My variable has about 10,000 elements and sometime for the first 100 to 500
elements there is lots of 0's, so I end up with lots of NA/N
On Thu, 2008-07-24 at 06:57 -0700, nmarti wrote:
> I'm trying to calculate the percent change for a time-series variable.
> Basically the first several observations often look like this,
>
> x <- c(100, 0, 0, 150, 130, 0, 0, 200, 0)
>
> and then later in the life of the variable they're are gene
On 25/07/2008, at 5:24 AM, Robert Baer wrote:
I'm trying to calculate the percent change for a time-series
variable.
Basically the first several observations often look like this,
x <- c(100, 0, 0, 150, 130, 0, 0, 200, 0)
and then later in the life of the variable they're are generally
I'm trying to calculate the percent change for a time-series variable.
Basically the first several observations often look like this,
x <- c(100, 0, 0, 150, 130, 0, 0, 200, 0)
and then later in the life of the variable they're are generally no more
0's. So when I try to calculate the percent c
I'm trying to calculate the percent change for a time-series variable.
Basically the first several observations often look like this,
x <- c(100, 0, 0, 150, 130, 0, 0, 200, 0)
and then later in the life of the variable they're are generally no more
0's. So when I try to calculate the percent c
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