x
Jan 2010 6.08
> zoo (aggregate (vv, as.yearmon(index(vv)), mean))
x
Feb 2010 6.08
On 8 Apr 2010, at 15:53, Tim Coote wrote:
On 8 Apr 2010, at 15:53, Tim Coote wrote:
Hullo
I may have missed something blindingly obvious here. I'm using xts
to handle some timeseries d
x
Jan 2010 6.08
> zoo (aggregate (vv, as.yearmon(index(vv)), mean))
x
Feb 2010 6.08
On 8 Apr 2010, at 15:53, Tim Coote wrote:
Hullo
I may have missed something blindingly obvious here. I'm using xts
to handle some timeseries data. I've got daily measurements for 100
y
Hullo
I may have missed something blindingly obvious here. I'm using xts to
handle some timeseries data. I've got daily measurements for 100
years. If I try to reduce the error rate by taking means of each
month, I'm getting what at first sight appears to be conflicting
information. Here's
self-contained, reproducible code.
Tim Coote
t...@coote.org
+44 (0)7866 479 760
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;- readLines(myURL)
DF <- read.table(textConnection(raw.lines[!grepl("[^
0-9.]",raw.lines)]), fill = TRUE)
DF$V1 <- DF[cumsum(is.na(DF[[2]])), 1]
DF <- na.omit(DF)
head(DF)
On Sat, Feb 27, 2010 at 6:32 AM, Tim Coote > wrote:
Hullo
I'm trying to read some time series data
o that
it is easier to consume by machines, while being easy to read for
humans.
The early records are a serious machine parsing challenge as they are
tiff images of old notebooks ;-)
tia
Tim
Tim Coote
t...@coote.org
vincit veritas
__
R-he
I've hunted around to try to work this out and cannot find anything
aposite, although there are exhortations to read News files and the fine
manual in response to similar queries. Hope I haven't missed anything
obvious.
I want to read in csv files that contain dates, or date times. If I read
the
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