Thank you Jim and Bob. This is really big help for me.

Jim, this is your second time to help me out.
Best

Alemu


On Tue, Jun 16, 2015 at 1:50 PM, boB Rudis <b...@rudis.net> wrote:

> This look similar to snow data I used last year:
> https://github.com/hrbrmstr/snowfirst/blob/master/R/snowfirst.R
>
> All the data worked pretty well.
>
> On Tue, Jun 16, 2015 at 3:21 PM, jim holtman <jholt...@gmail.com> wrote:
> > Here is an example of reading in the data.  After that it is a data frame
> > and should be able to process it with dplyr/data.table without much
> trouble:
> >
> >> x <- readLines("
> >
> http://www1.ncdc.noaa.gov/pub/data/snowmonitoring/fema/06-2015-dlysndpth.txt
> > ")
> >> writeLines(x, '/temp/snow.txt')  # save for testing
> >> head(x)
> > [1]
> > ""
> >
> > [2] "State:
> > AL"
> >
> > [3] "   Lat     Lon  COOP# StnID State City/Station Name
> > County                     Elev      Jun 1      Jun 2      Jun 3      Jun
> > 4      Jun 5      Jun 6      Jun 7      Jun 8      Jun 9      Jun10
> > Jun11      Jun12      Jun13      Jun14      Jun15      Jun16"
> > [4] " 33.59  -85.86 010272          AL ANNISTON ARPT ASOS
> > CALHOUN                      594      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> > [5] " 33.83  -85.78 014209          AL JACKSONVILLE
> > CALHOUN                      608  -9999.000  -9999.000  -9999.000
> > -9999.000  -9999.000      0.000      0.000  -9999.000  -9999.000
> > -9999.000  -9999.000  -9999.000  -9999.000  -9999.000  -9999.000
> -9999.000"
> > [6] " 34.74  -87.60 015749          AL MUSCLE SHOALS AP
> > COLBERT                      540      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> >> z <- grepl("(^$)|(^State)|(^   Lat)", x)  # get lines to discard
> >> xm <- x[!z]  # remove info lines
> >> head(xm)
> > [1] " 33.59  -85.86 010272          AL ANNISTON ARPT ASOS
> > CALHOUN                      594      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> > [2] " 33.83  -85.78 014209          AL JACKSONVILLE
> > CALHOUN                      608  -9999.000  -9999.000  -9999.000
> > -9999.000  -9999.000      0.000      0.000  -9999.000  -9999.000
> > -9999.000  -9999.000  -9999.000  -9999.000  -9999.000  -9999.000
> -9999.000"
> > [3] " 34.74  -87.60 015749          AL MUSCLE SHOALS AP
> > COLBERT                      540      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> > [4] " 31.32  -85.45 012372          AL DOTHAN FAA AIRPORT
> > DALE                         374      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> > [5] " 32.70  -87.58 013511          AL GREENSBORO
> > HALE                         220      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> > [6] " 33.57  -86.74 010831          AL BIRMINGHAM AP ASOS
> > JEFFERSON                    615      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000
> > 0.000      0.000      0.000      0.000      0.000      0.000  -9999.000"
> >>
> >> # read in the data
> >> xf <- textConnection(xm)
> >> snow <- read.fwf(xf
> > +         , width = c(6,8,7,10,3,32,26,6,rep(11,16))
> > +         , comment.char = ''
> > +         , as.is = TRUE
> > +         )
> >> str(snow)
> > 'data.frame':   3067 obs. of  24 variables:
> >  $ V1 : num  33.6 33.8 34.7 31.3 32.7 ...
> >  $ V2 : num  -85.9 -85.8 -87.6 -85.5 -87.6 ...
> >  $ V3 : int  10272 14209 15749 12372 13511 10831 11225 14064 12245 15478
> ...
> >  $ V4 : chr  "          " "          " "          " "          " ...
> >  $ V5 : chr  "AL " "AL " "AL " "AL " ...
> >  $ V6 : chr  "ANNISTON ARPT ASOS              "
> > "JACKSONVILLE                    " "MUSCLE SHOALS AP                "
> > "DOTHAN FAA AIRPORT              " ...
> >  $ V7 : chr  "CALHOUN                   " "CALHOUN                   "
> > "COLBERT                   " "DALE                      " ...
> >  $ V8 : int  594 608 540 374 220 615 461 624 100 215 ...
> >  $ V9 : num  0 -9999 0 0 0 ...
> >  $ V10: num  0 -9999 0 0 0 ...
> >  $ V11: num  0 -9999 0 0 0 ...
> >  $ V12: num  0 -9999 0 0 0 ...
> >  $ V13: num  0 -9999 0 0 0 ...
> >  $ V14: num  0 0 0 0 0 ...
> >  $ V15: num  0 0 0 0 0 ...
> >  $ V16: num  0 -9999 0 0 0 ...
> >  $ V17: num  0 -9999 0 0 0 ...
> >  $ V18: num  0 -9999 0 0 0 ...
> >  $ V19: num  0 -9999 0 0 0 ...
> >  $ V20: num  0 -9999 0 0 0 ...
> >  $ V21: num  0 -9999 0 0 0 ...
> >  $ V22: num  0 -9999 0 0 0 ...
> >  $ V23: num  0 -9999 0 0 0 ...
> >  $ V24: num  -9999 -9999 -9999 -9999 -9999 ...
> >> table(snow$V5)  # tally up the states
> > AK  AL  AR  AZ  CA  CO  CT  DE  FL  GA  HI  IA  ID  IL  IN  KS  KY  LA
> MA
> > MD  ME  MI  MN  MO  MS  MT
> >  72  18  65  55  99 128  10   1  30  33   6 112  57 103  85  90  49  29
> > 35  14  40  86  90 124  27 113
> > NC  ND  NE  NH  NJ  NM  NV  NY  OH  OK  OR  PA  RI  SC  SD  TN  TX  UT
> VA
> > VT  WA  WI  WV  WY
> >  45  19 136  22  13  53  65  76  31 106  51  84   2  30  79  64 185  68
> > 70  18  56 103  36  84
> >>
> >
> >
> > Jim Holtman
> > Data Munger Guru
> >
> > What is the problem that you are trying to solve?
> > Tell me what you want to do, not how you want to do it.
> >
> > On Tue, Jun 16, 2015 at 11:38 AM, Alemu Tadesse <alemu.tade...@gmail.com
> >
> > wrote:
> >
> >> Dear All,
> >>
> >> I was going to read daily snow data  for each state and station/city
> from
> >> the following link. I was not able to separate a given state's data from
> >> the rest of the contents of the file, read the data to a data frame and
> >> save it to file.
> >>
> >>
> >>
> http://www1.ncdc.noaa.gov/pub/data/snowmonitoring/fema/06-2015-dlysndpth.txt
> >>
> >> I really appreciate your time and help, and also appreciate any
> information
> >>  for an alternative source.
> >>
> >> Best,
> >>
> >> Alemu
> >>
> >>         [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
> >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >> 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.
> >>
> >
> >         [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > 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.
>

        [[alternative HTML version deleted]]

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