Dear all,
I have a time serie dataset such as the following with data acquired
every 15 minutes:
Date Heure Profondeur Temp?rature Salinit? Turbidit? Chloration
1 2012-07-06 08:47:22 -0.144 22.469 0.011 0.000 0
2 2012-07-06 09:02:21 -0.147 22.476 0.011 0.000 0
3 2012-07-06 09:17:21 -0.139 22.498 0.011 19.323 0
4 2012-07-06 09:32:21 -0.136 22.540 0.011 19.343 0
5 2012-07-06 09:47:21 -0.141 22.510 0.011 19.321 0
6 2012-07-06 10:02:21 -0.139 22.372 0.011 19.280 0
I wonder what is the best class to use to manage such time series
Dear all,
I have a time serie dataset such as the following with data acquired every 15
minutes:
Date Heure Profondeur Temp?rature Salinit? Turbidit? Chloration
1 2012-07-06 08:47:22 -0.144 22.469 0.011 0.000 0
2 2012-07-06 09:02:21 -0.147 22.476 0.011 0.000 0
3 2012-07-06 09:17:21 -0.139 22.498 0.011 19.323 0
4 2012-07-06 09:32:21 -0.136 22.540 0.011 19.343 0
5 2012-07-06 09:47:21 -0.141 22.510 0.011 19.321 0
6 2012-07-06 10:02:21 -0.139 22.372 0.011 19.280 0
I wonder what is the best class to use to manage such time series
On Thu, Oct 4, 2012 at 3:07 AM, Hasan Diwan <hasan.diwan at gmail.com> wrote:
Mr. Emmanuel,
On 4 October 2012 02:43, Poizot Emmanuel <emmanuel.poizot at cnam.fr> wrote:
Dear all,
I have a time serie dataset such as the following with data acquired every
15 minutes:
Date Heure Profondeur Temp?rature Salinit? Turbidit? Chloration
1 2012-07-06 08:47:22 -0.144 22.469 0.011 0.000 0
2 2012-07-06 09:02:21 -0.147 22.476 0.011 0.000 0
3 2012-07-06 09:17:21 -0.139 22.498 0.011 19.323 0
4 2012-07-06 09:32:21 -0.136 22.540 0.011 19.343 0
5 2012-07-06 09:47:21 -0.141 22.510 0.011 19.321 0
6 2012-07-06 10:02:21 -0.139 22.372 0.011 19.280 0
I wonder what is the best class to use to manage such time series
Use xts whenever dealing with timeseries, to construct:
xts(data.in[,-1:2], order.by=as.POSIXct(paste(data.in[,1:2])))
If you are using xts and reading in the data from an external file
then note that xts loads the zoo package and read.zoo can be used to
do the actually reading:
Lines <- "Date Heure Profondeur Temp?rature Salinit? Turbidit? Chloration
1 2012-07-06 08:47:22 -0.144 22.469 0.011 0.000 0
2 2012-07-06 09:02:21 -0.147 22.476 0.011 0.000 0
3 2012-07-06 09:17:21 -0.139 22.498 0.011 19.323 0
4 2012-07-06 09:32:21 -0.136 22.540 0.011 19.343 0
5 2012-07-06 09:47:21 -0.141 22.510 0.011 19.321 0
6 2012-07-06 10:02:21 -0.139 22.372 0.011 19.280 0"
library(xts) # also pulls in zoo
# z <- read.zoo("myfile.dat", header = TRUE, index = 1:2, tz = "")
z <- read.zoo(text = Lines, header = TRUE, index = 1:2, tz = "")
x <- as.xts(z)
Here index = 1:2 says that the date/time index is in the first two
columns and tz = "" says to interpret it as POSIXct with the indicated
time zone. tz = "GMT" is another possibility.
For more info on read.zoo see ?read.zoo . Also there is a document
entirely devoted to read.zoo examples obtained by issuing:
vignette("zoo-read") .
Statistics & Software Consulting
GKX Group, GKX Associates Inc.
tel: 1-877-GKX-GROUP
email: ggrothendieck at gmail.com
Hi,
library(xts)
xts(dat1[,-1:2],order.by=as.POSIXct(paste(dat1[,1],dat1[,2],sep=" "),format="%Y-%m-%d %H:%M:%S"))
#Error in .subset(x, j) : only 0's may be mixed with negative subscripts
xts(dat1[,3:7],order.by=as.POSIXct(paste(dat1[,1],dat1[,2],sep=" "),format="%Y-%m-%d %H:%M:%S"))
#??????????????????? Profondeur Temp?rature Salinit? Turbidit? Chloration
#2012-07-06 00:00:22???? -0.145????? 22.468??? 0.011???? 0.000????????? 0
#2012-07-06 00:02:21???? -0.143????? 22.475??? 0.011???? 0.000????????? 0
#2012-07-06 01:17:21???? -0.132????? 22.456??? 0.011???? 0.323????????? 0
#2012-07-06 08:47:22???? -0.144????? 22.469??? 0.011???? 0.000????????? 0
#2012-07-06 09:02:21???? -0.147????? 22.476??? 0.011???? 0.000????????? 0
#2012-07-06 09:17:21???? -0.139????? 22.498??? 0.011??? 19.323????????? 0
#2012-07-06 09:32:21???? -0.136????? 22.540??? 0.011??? 19.343????????? 0
#2012-07-06 09:47:21???? -0.141????? 22.510??? 0.011??? 19.321????????? 0
#2012-07-06 10:02:21???? -0.139????? 22.372??? 0.011??? 19.280????????? 0
A.K.
----- Original Message -----
From: Hasan Diwan <hasan.diwan at gmail.com>
To: Poizot Emmanuel <emmanuel.poizot at cnam.fr>
Cc: r-help at r-project.org
Sent: Thursday, October 4, 2012 3:07 AM
Subject: Re: [R] Class for time series
Mr. Emmanuel,
On 4 October 2012 02:43, Poizot Emmanuel <emmanuel.poizot at cnam.fr> wrote:
Dear all,
I have a time serie dataset such as the following with data acquired every
15 minutes:
Date? ? Heure Profondeur Temp?rature Salinit? Turbidit? Chloration
1 2012-07-06 08:47:22? ? -0.144? ? ? 22.469? ? 0.011 0.000? ? ? ? ? 0
2 2012-07-06 09:02:21? ? -0.147? ? ? 22.476? ? 0.011 0.000? ? ? ? ? 0
3 2012-07-06 09:17:21? ? -0.139? ? ? 22.498? ? 0.011 19.323? ? ? ? ? 0
4 2012-07-06 09:32:21? ? -0.136? ? ? 22.540? ? 0.011 19.343? ? ? ? ? 0
5 2012-07-06 09:47:21? ? -0.141? ? ? 22.510? ? 0.011 19.321? ? ? ? ? 0
6 2012-07-06 10:02:21? ? -0.139? ? ? 22.372? ? 0.011 19.280? ? ? ? ? 0
I wonder what is the best class to use to manage such time series
Use xts whenever dealing with timeseries, to construct:
xts(data.in[,-1:2], order.by=as.POSIXct(paste(data.in[,1:2])))
Also, when you post help requests, use dput on your data set. I'm assuming
it's a data.frame, but I'd be able to actually test my code if there were
dput output in your question. -- H
Sent from my mobile device
Envoyait de mon portable
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