Here is a continuation to turn DF into a zoo series: It depends on
the fact that all NAs are structural, i.e. they indicate dates which
cannot exist such as Feb 31 as opposed to missing data. dd is the
data as one long series with component names being the dates in the
indicated format. That is
On Feb 27, 2010, at 6:17 PM, Phil Spector wrote:
Tim -
I don't understand what you mean about interleaving rows. I'm
guessing
that you want a single large data frame with all the data, and not a
list with each year separately. If that's the case:
x = read.table('http://climate.arm.ac.
Tim -
I don't understand what you mean about interleaving rows. I'm guessing
that you want a single large data frame with all the data, and not a
list with each year separately. If that's the case:
x =
read.table('http://climate.arm.ac.uk/calibrated/soil/dsoil100_cal_1910-1919.dat',
On Feb 27, 2010, at 4:33 PM, Gabor Grothendieck wrote:
No one else posted so the other post you are referring to must have
been an email to you, not a post. We did not see it.
By one off I think you are referring to the row names, which are
meaningless, rather than the day numbers. The data
Sorry, I forgot to cc the group:
Tim -
Here's a way to read the data into a list, with one entry per year:
x =
read.table('http://climate.arm.ac.uk/calibrated/soil/dsoil100_cal_1910-1919.dat',
header=FALSE,fill=TRUE,skip=13)
cts = apply(x,1,function(x)sum(is.na(x)))
wh = whic
No one else posted so the other post you are referring to must have
been an email to you, not a post. We did not see it.
By one off I think you are referring to the row names, which are
meaningless, rather than the day numbers. The data for day 1 is
present, not missing. The example code did re
Thanks, Gabor. My take away from this and Phil's post is that I'm
going to have to construct some code to do the parsing, rather than
use a standard function. I'm afraid that neither approach works, yet:
Gabor's gets has an off-by-one error (days start on the 2nd, not the
first), and the ye
Mark Leeds pointed out to me that the code wrapped around in the post
so it may not be obvious that the regular expression in the grep is
(i.e. it contains a space):
"[^ 0-9.]"
On Sat, Feb 27, 2010 at 7:15 AM, Gabor Grothendieck
wrote:
> Try this. First we read the raw lines into R using grep t
Try this. First we read the raw lines into R using grep to remove any
lines containing a character that is not a number or space. Then we
look for the year lines and repeat them down V1 using cumsum. Finally
we omit the year lines.
myURL <- "http://climate.arm.ac.uk/calibrated/soil/dsoil100_cal
Hullo
I'm trying to read some time series data of meteorological records
that are available on the web (eg http://climate.arm.ac.uk/calibrated/soil/dsoil100_cal_1910-1919.dat)
. I'd like to be able to read in the digital data directly into R.
However, I cannot work out the right function and
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