Back compatibility with other time series best I can tell, but to be honest, I'm not even sure how it plays into that. Perhaps it's just an artifact in the signature.
It doesn't seem to have a role in the xts constructor. E.g., identical(xts(1:5, Sys.Date()+1:5, frequency = 1), xts(1:5, Sys.Date()+1:5, frequency = 3)) Michael On Sun, Nov 27, 2011 at 4:51 PM, Kevin Burton <rkevinbur...@charter.net> wrote: > I was just trying to be complete. Why is the frequency argument and > attribute available? > > -----Original Message----- > From: R. Michael Weylandt [mailto:michael.weyla...@gmail.com] > Sent: Saturday, November 26, 2011 2:40 PM > To: Kevin Burton > Cc: r-help@r-project.org > Subject: Re: [R] Missing data? > > Why do you need to use a frequency attribute for these data? The point of > the zoo/xts line of time series implementations is that the time stamps are > carried through for each observation (unlike ts) and can be irregular. Both > classes exist precisely to avoid being forced into a frequency attribute. > > As far as setting up the time elements, wouldn't this work? Change the start > date to get weeks on any desired day > > d <- seq.Date(from = as.Date("2011-11-26"), by = -7, length.out = 100) > xts(rep(NA, length(d)), d) > > You can avoid the OHLC formatting of to.weekly if you want with the OHLC = > FALSE parameter. And if you want to index it by the first of the week rather > htan the last, just try this: > > time(x) <- time(x) - 6 > > Michael > > On Tue, Nov 22, 2011 at 6:50 PM, Kevin Burton <rkevinbur...@charter.net> > wrote: >> Void of any other suggestions this approach makes sense but for my >> case I think I need to use zoo objects rather than xts. If I sequence >> the data generally I don't know if there will be 365 days in the year >> or 366. So I have to sequence the dates as: >> >> seq(from=as.Date("2011-01-01"), to=as.Date("2011-12-31"), by="day") >> >> If I use this sequence with xts I get: >> >>> ds <- xts(NA, seq(from=as.Date("2011-01-01"), >>> to=as.Date("2011-12-31"), >> by="day")) >> Error in xts(NA, seq(from = as.Date("2011-01-01"), to = >> as.Date("2011-12-31"), : >> NROW(x) must match length(order.by) >> >> If I leave the 'data' empty I don't get the error but if I try to >> assign an individual item (fill as appropriate) >> >>> ds <- xts(, seq(from=as.Date("2011-01-01"), to=as.Date("2011-12-31"), >> by="day")) >>> ds["2011-12-24"] <- 10 >>> ds >> Error in structure(coredata(x), names = x.attr$dimnames[[1]]) : >> 'names' attribute [365] must be the same length as the vector [358] >> >> So now I need to remember that I have not filled in all of the data. >> Also simple dereferencing gives: >> >>> ds[1] >> Error in `[.xts`(ds, 1) : subscript out of bounds >> >> With zoo I am able to create a time-series where all of the data is >> initially NA: >> >>> ds <- zoo(NA, seq(from=as.Date("2011-01-01"), >>> to=as.Date("2011-12-31"), >> by="day")) >> >> So I can fill the data as appropriate and the remaining slots will have > NA. >> I may be new with xts but I cannot see a way of creating a useable 'blank' >> time-series. >> >> Also with xts it seems like the frequency is ignored. >> >>> ds <- xts(1:365, seq(from=as.Date("2011-01-01"), >>> to=as.Date("2011-12-31"), >> by="day"), frequency=52) >>> frequency(ds) >> [1] 1 >> >> Whereas zoo remembers the frequency setting >> >>> ds <- zoo(1:365, seq(from=as.Date("2011-01-01"), >>> to=as.Date("2011-12-31"), >> by="day"), frequency=52) >>> frequency(ds) >> [1] 52 >> >> But since the ultimate goal is to get the time-series in a 'ts' format >> (as many functions require 'ts') it seems like even zoo has problems: >> >>> as.ts(ds) >> >> Time Series: >> Start = c(14975, 1) >> End = c(15339, 1) >> Frequency = 52 >> [1] 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA >> [42] NA NA NA NA NA NA NA NA NA NA NA 2 NA NA NA NA >> NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA >> [83] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA 3 NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA >> [124] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 4 >> NA NA NA NA NA NA NA >> [165] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA >> NA NA NA NA NA NA NA >> [206] . . . . . . >> So the conversion from zoo to ts maintained the frequency but I am >> not sure where it decided on the start and end values. Also the >> conversion seemed to changed the data also. Notice that every period >> (52 entries) the original data is maintained. In other words if ds is >> the original zoo time series then ds[1] is 1 and ds[2] is 2 etc. The >> converted time-series keeps ds[1] but inserts 51 NA's then adds ds[2] >> etc till the end of the series. That is not what the initial data was. > The conversion is inserting data of its own. >> >> The conversion to ts from xts seems better behaved: >> >> ds <- xts(1:365, seq(from=as.Date("2011-01-01"), >> to=as.Date("2011-12-31"), by="day"), frequency=52) >>> as.ts(ds) >> Time Series: >> Start = 1 >> End = 365 >> Frequency = 1 >> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 >> 17 >> 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 >> 36 >> 37 38 39 40 41 42 >> [43] 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 >> 59 >> 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 >> 78 >> 79 80 81 82 83 84 >> [85] 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 >> 101 >> 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 >> 119 120 >> 121 122 123 124 125 126 >> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 >> 143 >> 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 >> 161 162 >> 163 164 165 166 167 168 >> [169] 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 >> 185 >> 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 >> 203 204 >> 205 206 207 208 209 210 >> [211] 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 >> 227 >> 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 >> 245 246 >> 247 248 249 250 251 252 >> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 >> 269 >> 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 >> 287 288 >> 289 290 291 292 293 294 >> [295] 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 >> 311 >> 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 >> 329 330 >> 331 332 333 334 335 336 >> [337] 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 >> 353 >> 354 355 356 357 358 359 360 361 362 363 364 365 >> >> But alas the frequency is ignored. >> >> So this is what I have found out using these two packages. If I want >> to create a 'blank' data set it seems like zoo is 'better' since I can >> create a time-series initialized with NA irrespective of the length of the > series. >> However I must be unfamiliar with the conversion because zoo doesn't >> convert to a regular 'ts' very well. But zoo remembers the frequency >> setting whereas xts just ignores it. >> >> It seems like there is still considerable work to solve the original >> problem. If I create a time series and fill in the values that are >> appropriate I still could have NA in the series it seems to.weekly has >> a problem with NA in the time series: >>> ds <- xts(rep(NA,365), seq(from=as.Date("2011-01-01"), >> to=as.Date("2011-12-31"), by="day"), frequency=52) >>> to.weekly(ds, sum) >> Error in if (drop.time) x <- .drop.time(x) : >> argument is not interpretable as logical In addition: Warning >> message: >> In to.period(x, "weeks", name = name, ...) : >> missing values removed from data >> >> >> -----Original Message----- >> From: R. Michael Weylandt <michael.weyla...@gmail.com> >> [mailto:michael.weyla...@gmail.com] >> Sent: Tuesday, November 22, 2011 3:10 PM >> To: Kevin Burton >> Cc: <r-help@r-project.org> >> Subject: Re: [R] Missing data? >> >> Couldn't you use seq.Date() to set up the time index and then just >> fill as appropriate? >> >> Alternatively, to.weekly if you are starting with a daily series. >> >> Michael >> >> On Nov 22, 2011, at 4:00 PM, "Kevin Burton" <rkevinbur...@charter.net> >> wrote: >> >>> I was wondering what the best approach is for missing data in a time >> series. >>> I give an example using xts but I would like to know what seems to be >>> the "best" method. Say I have >>> >>> >>> >>> library(xts) >>> >>> xts.ts <- xts(1:4,as.Date(c("1970-01-01", "1970-1-3", "1980-10-10", >>> "2007-8-19")), frequency=52) >>> >>> >>> >>> I would like to turn this into a time series (still could be xts, or >>> converted to ts) that has values for every week starting with the >>> week that includes the start date and ending with the week that >>> includes the >> end date. >>> If there is data for the week then use it otherwise set it to NA or 0. >>> Remember some years have 52, 53, or rarely 54 full or partial weeks. >>> What to do with the partials at the beginning and ending of the year? >>> This seems to be a fairly common problem and doing it myself is very >>> cumbersome. Does a solution to this kind of problem exist? Once the >>> approach to a weekly period is found I am sure that adjustment to >>> daily, monthly, or quarterly would be relatively straightforward. >>> >>> >>> >>> Thank you. >>> >>> >>> >>> Kevin >>> >>> >>> >>> >>> [[alternative HTML version deleted]] >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >> >> > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
