search for: 1983

Hi all, Today I got problem below and my domU become unresponsive and I should restart the pc to make it running properly again. [ 240.172092] INFO: task kworker/u:0:5 blocked for more than 120 seconds. [ 240.172110] "echo 0 > /proc/sys/kernel/hung_task_timeout_secs" disables this message. [ 240.172376] INFO: task jbd2/xvda1-8:153 blocked for more than 120 seconds. [ 240.172388]

Example: Will ts objects be obsolete or modified? > a [,1] 1983 Q1 2.747365190 1983 Q2 2.791594762 1983 Q3 -0.009953715 1983 Q4 -0.015059485 1984 Q1 -1.190061246 1984 Q2 -0.553031799 1984 Q3 0.686874720 1984 Q4 0.953911035> lag(a,4) [,1] 1983 Q1 NA 1983 Q2 NA 1983 Q3 NA 1983 Q4 NA 1984 Q1 2.74736519...

Hi, I try to calculate the correlation between macroeconomic data from FRED vs Market Data However, since the timeseries are not in synch, the correlation fails. require(quantmod) USPBS =get(getSymbols("USPBS", src="FRED" )) USPBS = USPBS['1983-1-1::'] monDMANEMP = Cl(to.monthly(USPBS)) > length(monDMANEMP) [1] 312 > head(monDMANEMP) USPBS.Close Oct 1983 8192 Nov 1983 8231 Dec 1983 8265 Jan 1984 8309 Feb 1984 8344 Mar 1984 8382 symbol = getSymbols("^DJA",from = &...

...w I can do it with a loop but I'd like to figure out the simple way to vectorize it. Here's my solution (it works but it's sure complicated looking) : rtn <-c(0,log(x[2:length(x[,1]),2]/x[1:length(x[,1])-1,3])) Here's what x looks like: head(x) Date Open Close 1 03/30/1983 29.96 30.35 2 03/31/1983 30.35 30.24 3 04/04/1983 30.25 30.39 4 04/05/1983 30.45 30.66 5 04/06/1983 30.85 30.85 6 04/07/1983 30.85 31.12 -- View this message in context: http://r.789695.n4.nabble.com/There-must-be-a-smarter-way-tp3243651p3243651.html Sent from the R help mailing list archive at Na...

...s an example, suppose I create: A <- data.frame(year=rep(c(1980:1984),3), person= factor(sort(rep(1:3,5))), wage=c(rnorm(15))) > A year person wage 1 1980 1 0.17923212 2 1981 1 0.25610292 3 1982 1 0.50833655 4 1983 1 -0.42448395 5 1984 1 0.49233532 6 1980 2 -0.49928025 7 1981 2 0.06842660 8 1982 2 0.65677575 9 1983 2 0.15947390 10 1984 2 -0.46585116 11 1980 3 -0.29052635 12 1981 3 -0.27109203 13 1982 3 -0.76168164 14 1983 3 0...

https://bugzilla.mindrot.org/show_bug.cgi?id=1983 Bug #: 1983 Summary: add an option for disabling buffering Classification: Unclassified Product: Portable OpenSSH Version: 5.9p1 Platform: All OS/Version: All Status: NEW Severity: normal Priority:...

Greetings all, I have two questions. I have a data set that is arranged in the example below. I wish to obtain a plot of the performance of each ID over Year on v1. It's not clear how I set this up? ID Year V1 1 1980 1 1 1981 2 1 1982 6 1 1983 4 2 1980 5 2 1981 5 2 1982 5 2 1983 6 Also,I would like to transpose the data to have the variable across the columns such as ID v1980 v1981 v1982 v1983 1 1 2 6 4 2 5 5 5 6 Is there a straightforward way to do this in R? Thanks...

...1978 8 9 a 1979 9 10 a 1980 10 11 b 1971 11 12 b 1972 12 13 b 1973 13 14 b 1974 14 15 b 1975 15 16 b 1976 16 17 b 1977 17 18 b 1978 18 19 b 1979 19 20 b 1980 20 21 b 1981 21 22 b 1982 22 23 b 1983 23 24 b 1984 24 25 b 1985 25 and I'd like to reshape it so it is like this: X.1971 X.1972 X.1973 X.1974 X.1975 X.1976 X.1977 X.1978 X.1979 X.1980 X.1981 a 1 2 3 4 5 6 7 8 9 10 NA b 11 12 13 14 15 16...

CentOS Errata and Security Advisory 2014:1983 Important Upstream details at : https://rhn.redhat.com/errata/RHSA-2014-1983.html The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 25c346880f3748436057b98c8abdb4983348d929488d2e92f2d1a4df4702ea87 xorg-x11-server-common-1.1...

CentOS Errata and Security Advisory 2014:1983 Important Upstream details at : https://rhn.redhat.com/errata/RHSA-2014-1983.html The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) i386: 1cb2224fc013295b5042d713fdaae6349496ca3d2f5de1e7ce42681b916285be xorg-x11-server-common-1.15....

CentOS Errata and Bugfix Advisory 2018:1983 Upstream details at : https://access.redhat.com/errata/RHBA-2018:1983 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: ed36a5c2c321de40d9c21953da6a111217d814be9c7708eb987059028722640c kmod-kvdo-6.1.0.171-17.el7_5.x86_64.r...

...666667 256 Apr 1982 70.0000000 257 May 1982 0.0000000 258 Jun 1982 1.0000000 259 Jul 1982 0.0000000 260 Aug 1982 0.0000000 261 Sep 1982 0.0000000 262 Oct 1982 0.5000000 263 Nov 1982 4.0000000 264 Dec 1982 7.6666667 265 Jan 1983 23.6666667 266 Feb 1983 40.0000000 267 Mar 1983 29.0000000 268 Apr 1983 6.0000000 269 May 1983 0.0000000 270 Jun 1983 0.0000000 271 Jul 1983 0.0000000 272 Aug 1983 0.0000000 273 Sep 1983 1.0000000 274 Oct 1983 0.3333333 275 No...

https://bugzilla.mindrot.org/show_bug.cgi?id=1983 Damien Miller <djm at mindrot.org> changed: What |Removed |Added ---------------------------------------------------------------------------- Status|RESOLVED |CLOSED --- Comment #4 from Damien Miller <djm at mindrot.org&g...

What am I doing wrong here ? And what's the right way to calculate the log differences in a column in a df ? # first 3 rows of 5000 rows y[1:3,] Date Open High Low Close 1 1983-03-30 29.96 30.51 29.96 30.35 2 1983-03-31 30.35 30.55 30.20 30.24 3 1983-04-04 30.25 30.65 30.24 30.39 #equation in question ...why is this giving zeros ? y1 <- 100*log(y[,5]/(lag(y[,5],1))) # first 10 values from the equation...all zeros head(y1,10) [1] 0 0 0 0 0 0 0 0 0 0 -- View this mes...

...yXts <- xts(df,order.by=dates) head(myXts) #The last command "head(myXts)" shows you the first few rows of the xts object year cnsm incm wlth 1980-01-01 1980 173.6527 53.3635 60.3013 1981-01-01 1981 175.4613 53.6929 60.4980 1982-01-01 1982 174.5724 53.4890 60.2358 1983-01-01 1983 171.5070 53.2223 60.1047 1984-01-01 1984 173.3462 53.2851 60.6946 1985-01-01 1985 171.7075 53.1596 60.7598 On Sat, Sep 16, 2017 at 9:55 AM, Berend Hasselman <bhh at xs4all.nl> wrote: > > > On 15 Sep 2017, at 11:38, yadav neog <yadavneog at gmail.com> wrote: > &...

...NA NA 243 244 NA NA NA NA 225 NA 231 NA 11264402000 1 1981 NA 251 NA 248 241 NA NA NA 235 NA NA 245 11264402000 0 1982 236 237 242 240 242 205 199 NA NA NA NA NA 11264402000 1 1982 236 NA NA 240 242 NA NA NA NA NA NA NA 11264402000 0 1983 NA 247 NA NA NA NA NA 205 NA NA NA NA 11264402000 1 1983 NA 247 NA NA NA NA NA NA NA 225 NA NA 11264402000 0 1986 NA NA NA 240 NA NA NA 213 NA NA NA NA 11264402000 0 1987 241 NA NA NA NA 218 NA NA 235 243 240 NA 11264402000 1...

....862500000 1958 0.750000000 1959 0.300000000 1960 0.287500000 1963 0.675000000 1964 0.937500000 1965 0.025000000 1966 0.387500000 1969 0.087500000 1970 0.275000000 1973 0.500000000 1974 0.362500000 1976 0.925000000 1978 0.712500000 1979 0.337500000 1980 0.700000000 1981 0.425000000 1982 0.212500000 1983 0.312500000 1986 0.237500000 1958 0.643564356 1963 0.250000000 1968 0.211538462 1976 0.317307692 1981 0.673076923 1985 0.730769231 1986 0.057692308 1957 0.073394495 1966 0.742574257 1961 0.082568807 1964 0.165137615 1965 0.137614679 1959 0.128712871 1968 0.587155963 1969 0.660550459 1970 0.47706422...

...I am working on macroeconomic data series of India, which in a yearly basis. I am unable to convert my data frame into time series. kindly help me. also using zoo and xts packages. but they take only monthly observations. 'data.frame': 30 obs. of 4 variables: $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... $ cnsm: num 174 175 175 172 173 ... $ incm: num 53.4 53.7 53.5 53.2 53.3 ... $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... -- Yadawananda Neog Research Scholar Department of Economics Banaras Hindu University Mob. 9838545073 [[alternative HTML version deleted]]

...ly biased). Best regards, baptiste statistics <- list(list(values=read.table(textConnection(" date count 2007 600 2006 588 2008 555 2005 430 2004 418 2003 334 2002 277 2001 239 2000 226 1997 184 1999 184 1998 182 1996 129 1995 108 1994 92 1993 67 1992 53 1991 47 1990 37 1989 14 1988 11 1983 10 1987 7 1985 6 1986 6 1981 5 1984 5 1979 4 1982 4 2009 3 1971 2 1933 1 1973 1 1974 1 1977 1 1978 1 1980 1"), head=T),type=1, cumSum=4833, search="photonics"), list(values=read.table(textConnection(" date count 2008 129 2007 92 2006 50 2005 26 2004 15 2003 4 1972 1 2001 1 2002...

.... Here is a snippet to show the problem. ## Strange plot results link <- "http://processtrends.com/files/RClimate_CTS_latest.csv" cts <- read.csv(link, header=T) ## Simple line plot - gap for no reason plot(cts$yr_frac, cts$GISS, type="l", xlim=c(1982, 1983),xaxs="i", yaxs="i") ## May, 1982 observation missing ## Add points to plot in red, to see if May shows up points(cts$yr_frac, cts$GISS, type="p", col="red") ## yes, May shows up in points ## Look at cts data.frame. No obvious problems to me??...