[R] Summarizing data containing data/time information (as factor)
arun
smartpink111 at yahoo.com
Tue Sep 18 14:19:33 CEST 2012
HI,
Try this:
source("Data20120918.txt")
dat2<-within(dat1,{V2<-as.POSIXct(V2,format="%d/%m/%Y %H:%M")})
res<-subset(dat3,V2>=as.POSIXct("2012-04-29 12:00:00")& V2<=as.POSIXct("2012-05-01 12:00:00"))
head(res)
# V1 V2 V3
#18 532703 2012-04-29 12:00:00 0.04
#19 532703 2012-04-29 12:10:00 0.04
#20 532703 2012-04-29 12:20:00 0.04
#21 532703 2012-04-29 12:30:00 0.04
#22 532703 2012-04-29 12:40:00 0.04
#23 532703 2012-04-29 12:50:00 0.04
nrow(res)
#[1] 642
nrow(dat1)
#[1] 912
A.K.
________________________________
From: HJ YAN <yhj204 at googlemail.com>
To: arun <smartpink111 at yahoo.com>; David Winsemius <dwinsemius at comcast.net>
Cc: r-help at r-project.org
Sent: Tuesday, September 18, 2012 7:21 AM
Subject: Re: [R] Summarizing data containing data/time information (as factor)
Dear Arun and David
For the same dataset I sent in my previous email (I am attaching my data 'Data20120918.tex' as txt file this time as required), how could I create a subset only contains data collected between 29/04/2012 12:00 -- 01/05/2012 12:00, where the time format in the date/time column is 'dd/mm/yyyy hh:mm'?
Many thanks again in advance!
HJ
p.s. in this case I'm not too sure whether I should open a new question or just do a reply like this one, as I was thinking this question needs relevant knowledge to the question in my previous email...?
On Tue, Sep 11, 2012 at 12:52 PM, arun <smartpink111 at yahoo.com> wrote:
>
>Hi HJ,
>No problem.
>Arun
>
>
>________________________________
>From: HJ YAN <yhj204 at googlemail.com>
>To: arun <smartpink111 at yahoo.com>
>Sent: Tuesday, September 11, 2012 4:58 AM
>
>Subject: Re: [R] Summarizing data containing data/time information (as factor)
>
>
>Hi Arun
>
>I've been out for a conference these days so sorry for getting back late.
>
>Just wanted to say THANK YOU SO MUCH for your patient explanation. I'll have a good look and learn those conventions before sending next question to R help.
>
>Also thanks VERY much for the R code hints! Really helpful!
>
>Have a good day!
>Cheers
>HJ
>
>On Thu, Sep 6, 2012 at 4:41 PM, arun <smartpink111 at yahoo.com> wrote:
>
>HI HJ,
>>No problem.
>>
>>
>>The gsub() was used to just format your date column to make almost the way you wanted the results:
>>dat2$date
>># [1] "2012-04-29" "2012-04-30" "2012-05-01" "2012-04-28" "2012-04-29"
>> #[6] "2012-04-30" "2012-05-01" "2012-05-02" "2012-04-30" "2012-05-01"
>>
>>
>>
>>
>>gsub(".*-(.*)-(.*)","\\2/\\1",dat2$date)
>> #[1] "29/04" "30/04" "01/05" "28/04" "29/04" "30/04" "01/05" "02/05" "30/04"
>>#[10] "01/05"
>>
>>
>>#Suppose I just reverse the contents inside the second " ".
>>gsub(".*-(.*)-(.*)","\\1/\\2",dat2$date)
>> #[1] "04/29" "04/30" "05/01" "04/28" "04/29" "04/30" "05/01" "05/02" "04/30"
>>#[10] "05/01"
>>#From the p.s., I guess you understand the difference.
>>
>>#The contents inside the first " " are the pattern we match, and the second " " are the replacement.
>>
>>#Now, the ".*-(.*)-(.*)" -first .* will match all the contents before "-" in dat2$date. I used brackets (.*) for the second and third so that it could be select those specifically and use it #as replacement \\1 and \\2 inside the second " ".
>>
>>
>>#If you want to learn regular expressions, there are a lot of resources on the net.
>>#For example:
>>http://www.regular-expressions.info/
>>http://gnosis.cx/publish/programming/regular_expressions.html
>>
>>
>>A.K.
>>
>>________________________________
>>From: HJ YAN <yhj204 at googlemail.com>
>>To: arun <smartpink111 at yahoo.com>
>>Sent: Thursday, September 6, 2012 10:49 AM
>>
>>Subject: Re: [R] Summarizing data containing data/time information (as factor)
>>
>>
>>Wow! Thanks so much Arun!!
>>
>>I like this piece of code very much, especially using 'aggregate()' to solve my problem. Marvellous!
>>
>>The code does what I wanted perfectly. May I just ask how the gsub() bit works, e.g. now I understand it replace the pattern in the first part, and `(.*)` groups everything between the two '-' in my date/time data, but I did not get how the second part works (e.g. '\\2/\\1')?? I think this gsub() and grep() are very useful for coding, so if there is a good source (better than using help(grub)) to learn them that can be recommended that would be very much grateful.
>>
>>Massive thanks again!
>>
>>p.s. I just learned using as.Date and mended the following code to make the table in Chronological order as I originally wanted,
>>e.g. ' 28/04 29/04 30/04 01/05 02/05' rather than '01/05 02/05 28/04 29/04 30/04'
>>
>>dat2$date1<-as.Date(gsub(".*-(.*)-(.*)","\\2/\\1",dat2$date),format="%d/%m")
>>
>>HJ
>>
>>
>>
>>On Thu, Sep 6, 2012 at 1:40 PM, arun <smartpink111 at yahoo.com> wrote:
>>
>>Hi,
>>>May be this is what you wanted:
>>>I named your dput() data as dat1.
>>> source("HuYan.txt")
>>>head(dat1)
>>>dat1$date<- as.Date(dat1$V2,format="%d/%m/%Y %H:%M")
>>>dat2<-aggregate(V3~date+V1,data=dat1,mean)
>>>dat2$date1<-gsub(".*-(.*)-(.*)","\\2/\\1",dat2$date)
>>> dat2$V3<-1
>>> xtabs(V3~V1+date1,data=dat2)
>>> date1
>>>V1 01/05 02/05 28/04 29/04 30/04
>>>
>>> 532703 1 0 0 1 1
>>>
>>> 532704 1 1 1 1 1
>>> 532705 1 0 0 0 1
>>>A.K.
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>________________________________
>>>From: HJ YAN <yhj204 at googlemail.com>
>>>To: arun <smartpink111 at yahoo.com>
>>>Cc: r-help at r-project.org; dwinsemius at comcast.net
>>>Sent: Thursday, September 6, 2012 5:02 AM
>>>Subject: Re: [R] Summarizing data containing data/time information (as factor)
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>Hi Arun and David
>>>
>>>
>>>Thanks a lot for your reply and sorry for sending the csv file. (p.s. I can download my csv file from the email I sent, so I'm not sure why this doesn`t work for other users...)
>>>
>>>
>>>Anyway, below I used dput() and I am attaching the output from R:
>>>
>>>
>>>structure(list(V1 = c(532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532703L,
>>>532703L, 532703L, 532703L, 532703L, 532703L, 532703L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532704L,
>>>532704L, 532704L, 532704L, 532704L, 532704L, 532704L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L, 532705L, 532705L, 532705L,
>>>532705L, 532705L, 532705L, 532705L), V2 = structure(c(258L, 259L,
>>>260L, 261L, 262L, 263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L,
>>>271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L,
>>>282L, 283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L,
>>>293L, 294L, 295L, 296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L,
>>>304L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L, 313L, 314L,
>>>315L, 316L, 317L, 318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L,
>>>326L, 327L, 328L, 329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L,
>>>337L, 338L, 339L, 340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L,
>>>348L, 349L, 350L, 351L, 352L, 353L, 354L, 355L, 356L, 357L, 358L,
>>>359L, 360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L,
>>>370L, 371L, 372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 380L,
>>>381L, 382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L,
>>>392L, 393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L,
>>>403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L, 412L, 413L,
>>>414L, 415L, 416L, 417L, 418L, 419L, 420L, 421L, 422L, 423L, 424L,
>>>425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L,
>>>436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L, 445L, 446L,
>>>447L, 448L, 449L, 450L, 451L, 452L, 453L, 454L, 455L, 456L, 457L,
>>>458L, 459L, 460L, 461L, 462L, 463L, 464L, 465L, 466L, 467L, 468L,
>>>469L, 470L, 471L, 472L, 473L, 474L, 475L, 476L, 477L, 478L, 479L,
>>>480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L, 488L, 489L, 490L,
>>>1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
>>>15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
>>>28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
>>>41L, 42L, 43L, 44L, 194L, 195L, 196L, 197L, 198L, 199L, 200L,
>>>201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L,
>>>212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L,
>>>223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L,
>>>234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L,
>>>245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L,
>>>256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L,
>>>267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L,
>>>278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L,
>>>289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L,
>>>300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L,
>>>311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L,
>>>322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L,
>>>333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
>>>344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
>>>355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L,
>>>366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L,
>>>377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L,
>>>388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L,
>>>399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L,
>>>410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L,
>>>421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L,
>>>432L, 433L, 434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L,
>>>443L, 444L, 445L, 446L, 447L, 448L, 449L, 450L, 451L, 452L, 453L,
>>>454L, 455L, 456L, 457L, 458L, 459L, 460L, 461L, 462L, 463L, 464L,
>>>465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L,
>>>476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L,
>>>487L, 488L, 489L, 490L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
>>>11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
>>>24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
>>>37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
>>>50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
>>>63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L,
>>>76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L,
>>>89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
>>>101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
>>>112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L,
>>>123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L,
>>>134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L,
>>>145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L,
>>>156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L,
>>>167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L,
>>>178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L,
>>>189L, 190L, 191L, 192L, 193L, 471L, 472L, 473L, 474L, 475L, 476L,
>>>477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L, 487L,
>>>488L, 489L, 490L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
>>>12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
>>>25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
>>>38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
>>>51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L,
>>>64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
>>>77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
>>>90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L,
>>>102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
>>>113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L,
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>>>"28/04/2012 22:40", "28/04/2012 22:50", "28/04/2012 23:00", "28/04/2012 23:10",
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>>>"29/04/2012 12:40", "29/04/2012 12:50", "29/04/2012 13:00", "29/04/2012 13:10",
>>>"29/04/2012 13:20", "29/04/2012 13:30", "29/04/2012 13:40", "29/04/2012 13:50",
>>>"29/04/2012 14:00", "29/04/2012 14:10", "29/04/2012 14:20", "29/04/2012 14:30",
>>>"29/04/2012 14:40", "29/04/2012 14:50", "29/04/2012 15:00", "29/04/2012 15:10",
>>>"29/04/2012 15:20", "29/04/2012 15:30", "29/04/2012 15:40", "29/04/2012 15:50",
>>>"29/04/2012 16:00", "29/04/2012 16:10", "29/04/2012 16:20", "29/04/2012 16:30",
>>>"29/04/2012 16:40", "29/04/2012 16:50", "29/04/2012 17:00", "29/04/2012 17:10",
>>>"29/04/2012 17:20", "29/04/2012 17:30", "29/04/2012 17:40", "29/04/2012 17:50",
>>>"29/04/2012 18:00", "29/04/2012 18:10", "29/04/2012 18:20", "29/04/2012 18:30",
>>>"29/04/2012 18:40", "29/04/2012 18:50", "29/04/2012 19:00", "29/04/2012 19:10",
>>>"29/04/2012 19:20", "29/04/2012 19:30", "29/04/2012 19:40", "29/04/2012 19:50",
>>>"29/04/2012 20:00", "29/04/2012 20:10", "29/04/2012 20:20", "29/04/2012 20:30",
>>>"29/04/2012 20:40", "29/04/2012 20:50", "29/04/2012 21:00", "29/04/2012 21:10",
>>>"29/04/2012 21:20", "29/04/2012 21:30", "29/04/2012 21:40", "29/04/2012 21:50",
>>>"29/04/2012 22:00", "29/04/2012 22:10", "29/04/2012 22:20", "29/04/2012 22:30",
>>>"29/04/2012 22:40", "29/04/2012 22:50", "29/04/2012 23:00", "29/04/2012 23:10",
>>>"29/04/2012 23:20", "29/04/2012 23:30", "29/04/2012 23:40", "29/04/2012 23:50",
>>>"30/04/2012 00:00", "30/04/2012 00:10", "30/04/2012 00:20", "30/04/2012 00:30",
>>>"30/04/2012 00:40", "30/04/2012 00:50", "30/04/2012 01:00", "30/04/2012 01:10",
>>>"30/04/2012 01:20", "30/04/2012 01:30", "30/04/2012 01:40", "30/04/2012 01:50",
>>>"30/04/2012 02:00", "30/04/2012 02:10", "30/04/2012 02:20", "30/04/2012 02:30",
>>>"30/04/2012 02:40", "30/04/2012 02:50", "30/04/2012 03:00", "30/04/2012 03:10",
>>>"30/04/2012 03:20", "30/04/2012 03:30", "30/04/2012 03:40", "30/04/2012 03:50",
>>>"30/04/2012 04:00", "30/04/2012 04:10", "30/04/2012 04:20", "30/04/2012 04:30",
>>>"30/04/2012 04:40", "30/04/2012 04:50", "30/04/2012 05:00", "30/04/2012 05:10",
>>>"30/04/2012 05:20", "30/04/2012 05:30", "30/04/2012 05:40", "30/04/2012 05:50",
>>>"30/04/2012 06:00", "30/04/2012 06:10", "30/04/2012 06:20", "30/04/2012 06:30",
>>>"30/04/2012 06:40", "30/04/2012 06:50", "30/04/2012 07:00", "30/04/2012 07:10",
>>>"30/04/2012 07:20", "30/04/2012 07:30", "30/04/2012 07:40", "30/04/2012 07:50",
>>>"30/04/2012 08:00", "30/04/2012 08:10", "30/04/2012 08:20", "30/04/2012 08:30",
>>>"30/04/2012 08:40", "30/04/2012 08:50", "30/04/2012 09:00", "30/04/2012 09:10",
>>>"30/04/2012 09:20", "30/04/2012 09:30", "30/04/2012 09:40", "30/04/2012 09:50",
>>>"30/04/2012 10:00", "30/04/2012 10:10", "30/04/2012 10:20", "30/04/2012 10:30",
>>>"30/04/2012 10:40", "30/04/2012 10:50", "30/04/2012 11:00", "30/04/2012 11:10",
>>>"30/04/2012 11:20", "30/04/2012 11:30", "30/04/2012 11:40", "30/04/2012 11:50",
>>>"30/04/2012 12:00", "30/04/2012 12:10", "30/04/2012 12:20", "30/04/2012 12:30",
>>>"30/04/2012 12:40", "30/04/2012 12:50", "30/04/2012 13:00", "30/04/2012 13:10",
>>>"30/04/2012 13:20", "30/04/2012 13:30", "30/04/2012 13:40", "30/04/2012 13:50",
>>>"30/04/2012 14:00", "30/04/2012 14:10", "30/04/2012 14:20", "30/04/2012 14:30",
>>>"30/04/2012 14:40", "30/04/2012 14:50", "30/04/2012 15:00", "30/04/2012 15:10",
>>>"30/04/2012 15:20", "30/04/2012 15:30", "30/04/2012 15:40", "30/04/2012 15:50",
>>>"30/04/2012 16:00", "30/04/2012 16:10", "30/04/2012 16:20", "30/04/2012 16:30",
>>>"30/04/2012 16:40", "30/04/2012 16:50", "30/04/2012 17:00", "30/04/2012 17:10",
>>>"30/04/2012 17:20", "30/04/2012 17:30", "30/04/2012 17:40", "30/04/2012 17:50",
>>>"30/04/2012 18:00", "30/04/2012 18:10", "30/04/2012 18:20", "30/04/2012 18:30",
>>>"30/04/2012 18:40", "30/04/2012 18:50", "30/04/2012 19:00", "30/04/2012 19:10",
>>>"30/04/2012 19:20", "30/04/2012 19:30", "30/04/2012 19:40", "30/04/2012 19:50",
>>>"30/04/2012 20:00", "30/04/2012 20:10", "30/04/2012 20:20", "30/04/2012 20:30",
>>>"30/04/2012 20:40", "30/04/2012 20:50", "30/04/2012 21:00", "30/04/2012 21:10",
>>>"30/04/2012 21:20", "30/04/2012 21:30", "30/04/2012 21:40", "30/04/2012 21:50",
>>>"30/04/2012 22:00", "30/04/2012 22:10", "30/04/2012 22:20", "30/04/2012 22:30",
>>>"30/04/2012 22:40", "30/04/2012 22:50", "30/04/2012 23:00", "30/04/2012 23:10",
>>>"30/04/2012 23:20", "30/04/2012 23:30", "30/04/2012 23:40", "30/04/2012 23:50"
>>>), class = "factor"), V3 = c(0.03, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03,
>>>0.04, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.03, 0.03,
>>>0.03, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.03, 0.03,
>>>0.03, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.04,
>>>0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.04, 0.03, 0.04, 0.03,
>>>0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04,
>>>0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
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>>>0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
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>>>0.04, 0.04, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.03, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.03, 0.03, 0.03, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04,
>>>0.04, 0.03, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.03, 0.03, 0.04, 0.04, 0.03, 0.04, 0.04, 0.03, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.03, 0.03,
>>>0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.03,
>>>0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.03, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04,
>>>0.04, 0.04, 0.04, 0.04)), .Names = c("V1", "V2", "V3"), class = "data.frame", row.names = c(NA,
>>>-912L))
>>>
>>>
>>>Let make my question clearer:
>>>
>>>I want to make summary of the data above into a table as below to show the days that there are any data available, e.g.value=1 if
>>>there are any data available for that day, otherwise value=0. There are three id in my data: 532703, 532704 and 532705. data are collected at 10 minutes interval each day (e.g. 144 observations for a day if no missing data).
>>>
>>>
>>> 28/04 29/04 30/04 01/05 02/05
>>>532703 0 1 1 1 0
>>>532704 1 1 1 1 1
>>>532705 0 0 1 1 0
>>>
>>>
>>>
>>>Sorry for the confusion to David, and hope this is clear now.
>>>
>>>Many thanks again.
>>>
>>>Best wishes,
>>>
>>>HJ
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>On Thu, Sep 6, 2012 at 2:08 AM, arun <smartpink111 at yahoo.com> wrote:
>>>
>>>Hi,
>>>>
>>>>I couldn't find any attached data. Could you dput() the data?
>>>>A.K.
>>>>
>>>>
>>>>
>>>>
>>>>----- Original Message -----
>>>>From: HJ YAN <yhj204 at googlemail.com>
>>>>To: r-help at r-project.org
>>>>Cc:
>>>>Sent: Wednesday, September 5, 2012 7:57 PM
>>>>Subject: [R] Summarizing data containing data/time information (as factor)
>>>>
>>>>Dear R user
>>>>
>>>>I want to create a table (as below) to summarize the attached data
>>>>(Test.csv, which can be read into R by using 'read.csv(Test.csv, header=F)'
>>>>), to indicate the day that there are any data available, e.g.value=1 if
>>>>there are any data available for that day, otherwise value=0.
>>>>
>>>>
>>>> 28/04 29/04 30/04 01/05 02/05
>>>>532703 0 1 1 1 0
>>>>532704 1 1 1 1 1
>>>>532705 0 0 1 1 0
>>>>
>>>>Only Column A (Names: automatically stored as integer if being read into R)
>>>>and Column B (date/time: automatically stored as factor if being read into
>>>>R) are useful for this task.
>>>>
>>>>Could anyone kindly provide me some hints/ideas about how to write some
>>>>code to to this job please?
>>>>
>>>>
>>>>Many thanks in advance!
>>>>
>>>>Best wishes
>>>>HJ
>>>>
>>>>______________________________________________
>>>>R-help at 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.
>>>>
>>>>
>>>
>>
>
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