Is this what you are looking for:

> input <- readLines("C:\\Users\\Owner\\Downloads\\WAT_DEP.DAT")
> start <- grep("N:SNAPSHOT", input)  # find start of the data
> # add index of what would have been the last block
> start <- c(start, tail(start, 1) + 53L)
> # now read in the data using 'text' parameter of 'scan'
> columns <- lapply(seq(length(start) - 1L)
+                 , function(indx){
+                     scan(text = input[(start[indx] + 3L):(start[indx +
1L] - 1L)]
+                         , what = 0  # read in numeric
+                         , quiet = TRUE
+                         )
+                     }
+                 )
> # create the columns
> columns <- do.call(cbind, columns)
> # add column names based on the time
> cNames <- vapply(seq(length(start) - 1L)
+                 , function(indx){
+                     scan(text = input[start[indx] + 2L]
+                         , what = 0
+                         , quiet = TRUE
+                         )[2L]  # only the second value
+                     }
+                 , 1.0  # numeric
+                 )
> colnames(columns) <- cNames
> # sample data
> columns[1:10, 1:10]
      0.00208 0.00417 0.00625 0.00833 0.01042 0.0125 0.01458 0.01667
0.01875 0.02083
 [1,]   3.224   4.124   4.502   4.649   4.705  4.726   4.734   4.737
4.738   4.738
 [2,]   3.221   4.123   4.501   4.649   4.704  4.725   4.733   4.736
4.737   4.738
 [3,]   3.220   4.123   4.502   4.649   4.705  4.726   4.734   4.737
4.738   4.738
 [4,]   3.217   4.122   4.501   4.648   4.704  4.725   4.733   4.736
4.737   4.738
 [5,]   3.216   4.122   4.501   4.649   4.705  4.726   4.734   4.737
4.738   4.738
 [6,]   3.214   4.121   4.500   4.648   4.704  4.725   4.733   4.736
4.737   4.738
 [7,]   3.212   4.121   4.501   4.649   4.705  4.726   4.734   4.737
4.738   4.738
 [8,]   3.210   4.120   4.500   4.648   4.704  4.725   4.733   4.736
4.737   4.738
 [9,]   3.209   4.120   4.500   4.649   4.705  4.726   4.734   4.737
4.738   4.738
[10,]   3.207   4.119   4.500   4.648   4.704  4.725   4.733   4.736
4.737   4.738
>



On Thu, Apr 11, 2013 at 5:55 PM, Janesh Devkota <janesh.devkota@gmail.com>wrote:

> I have a data set for different time intervals. The data has three comment
> lines before data for each time interval. For each time interval there are
> 500 data points. I want to change the dataset such that I have the
> following
> format:
>
>
>
>     t1        t2            t3   ................
>
>     0.00208             0.00417 0.00625 .................
>
>     a1       a2           a3 ...................
>
>     b1       b2           b3 ...................
>
>     c1        c2            c3 .................
>
>     ...............................
>
>     ................................
>
>
>
> The link to the file is as follows:
> https://www.dropbox.com/s/hc8n3qcai1mlxca/WAT_DEP.DAT
>
>
>
> As you will see on the file, time for each interval is the second data of
> the third line before the data starts. For the first time, t= 0.00208. I
> need to change the data in several rows into one column. At last I need to
> create a dataframe with the format shown above. In the sample above, a1,
> b1,
> c1 are the data for time t1, and so on.
>
>
>
> The sample data is as follows:
>
>
>
>
>
>     ** N:SNAPSHOT    TIME      DELT[S]
>
>     ** WATER DEPTH [M]: (HP(L),L=2,LA)
>
>           1800        0.00208   0.10000
>
>          3.224     3.221     3.220     3.217     3.216     3.214     3.212
> 3.210     3.209     3.207
>
>          3.205     3.203     3.202     3.200     3.199     3.197     3.196
> 3.193     3.192     3.190
>
>          3.189     3.187     3.186     3.184     3.184     3.182     3.181
> 3.179     3.178     3.176
>
>          3.175     3.174     3.173     3.171     3.170     3.169     3.168
> 3.167     3.166     3.164
>
>          3.164     3.162     3.162     3.160     3.160     3.158     3.158
> 3.156     3.156     3.155
>
>          3.154     3.153     3.152     3.151     3.150     3.150     3.149
> 3.149     3.147     3.147
>
>          3.146     3.146     3.145     3.145     3.144     3.144     3.143
> 3.143     3.142     3.142
>
>          3.141     3.142     3.141     3.141     3.140     3.141     3.140
> 3.140     3.139     3.140
>
>          3.139     3.140     3.139     3.140     3.139     3.140     3.139
> 3.140     3.139     3.140
>
>          3.139     3.140     3.140     3.140     3.140     3.141     3.141
> 3.142     3.141     3.142
>
>          3.142     3.142     3.143     3.143     3.144     3.144     3.145
> 3.145     3.146     3.146
>
>          3.147     3.148     3.149     3.149     3.150     3.150     3.152
> 3.152     3.153     3.154
>
>          3.155     3.156     3.157     3.158     3.159     3.160     3.161
> 3.162     3.163     3.164
>
>          3.165     3.166     3.168     3.169     3.170     3.171     3.173
> 3.174     3.176     3.176
>
>          3.178     3.179     3.181     3.182     3.184     3.185     3.187
> 3.188     3.190     3.191
>
>          3.194     3.195     3.196     3.198     3.199     3.202     3.203
> 3.205     3.207     3.209
>
>          3.210     3.213     3.214     3.217     3.218     3.221     3.222
> 3.225     3.226     3.229
>
>          3.231     3.233     3.235     3.238     3.239     3.242     3.244
> 3.247     3.248     3.251
>
>          3.253     3.256     3.258     3.261     3.263     3.266     3.268
> 3.271     3.273     3.276
>
>          3.278     3.281     3.283     3.286     3.289     3.292     3.294
> 3.297     3.299     3.303
>
>          3.305     3.307     3.311     3.313     3.317     3.319     3.322
> 3.325     3.328     3.331
>
>          3.334     3.337     3.340     3.343     3.347     3.349     3.353
> 3.356     3.359     3.362
>
>          3.366     3.369     3.372     3.375     3.379     3.382     3.386
> 3.388     3.392     3.395
>
>          3.399     3.402     3.406     3.409     3.413     3.416     3.420
> 3.423     3.427     3.430
>
>          3.435     3.438     3.442     3.445     3.449     3.453     3.457
> 3.460     3.464     3.468
>
>          3.472     3.475     3.479     3.483     3.486     3.491     3.494
> 3.498     3.502     3.506
>
>          3.510     3.514     3.518     3.522     3.526     3.531     3.534
> 3.539     3.542     3.547
>
>          3.551     3.555     3.559     3.564     3.567     3.572     3.576
> 3.581     3.584     3.589
>
>          3.593     3.598     3.602     3.606     3.610     3.615     3.619
> 3.624     3.628     3.633
>
>          3.637     3.642     3.646     3.651     3.655     3.660     3.664
> 3.669     3.673     3.678
>
>          3.682     3.686     3.691     3.695     3.700     3.704     3.710
> 3.714     3.719     3.723
>
>          3.728     3.733     3.738     3.742     3.747     3.752     3.757
> 3.761     3.766     3.771
>
>          3.776     3.780     3.786     3.790     3.795     3.800     3.805
> 3.810     3.815     3.819
>
>          3.825     3.829     3.835     3.839     3.845     3.849     3.855
> 3.859     3.865     3.869
>
>          3.875     3.879     3.885     3.889     3.895     3.900     3.905
> 3.910     3.915     3.920
>
>          3.926     3.930     3.935     3.941     3.945     3.951     3.956
> 3.961     3.966     3.972
>
>          3.976     3.982     3.987     3.993     3.997     4.003     4.008
> 4.014     4.018     4.024
>
>          4.029     4.035     4.039     4.045     4.050     4.056     4.061
> 4.066     4.071     4.077
>
>          4.082     4.088     4.093     4.099     4.103     4.109     4.114
> 4.120     4.125     4.131
>
>          4.136     4.142     4.147     4.153     4.157     4.163     4.168
> 4.174     4.179     4.185
>
>          4.190     4.195     4.201     4.206     4.212     4.217     4.223
> 4.228     4.234     4.239
>
>          4.245     4.250     4.256     4.261     4.267     4.272     4.278
> 4.283     4.289     4.294
>
>          4.300     4.305     4.311     4.316     4.322     4.327     4.333
> 4.339     4.345     4.350
>
>          4.356     4.361     4.367     4.372     4.378     4.383     4.389
> 4.394     4.400     4.405
>
>          4.411     4.417     4.423     4.428     4.434     4.439     4.445
> 4.450     4.456     4.461
>
>          4.467     4.473     4.478     4.484     4.489     4.495     4.500
> 4.506     4.511     4.517
>
>          4.523     4.529     4.534     4.540     4.545     4.551     4.556
> 4.562     4.568     4.574
>
>          4.579     4.585     4.590     4.596     4.601     4.607     4.613
> 4.619     4.624     4.630
>
>          4.635     4.641     4.646     4.652     4.658     4.664     4.669
> 4.675     4.680     4.686
>
>          4.691     4.697     4.703     4.709     4.714     4.720     4.725
> 4.731     4.736     4.741
>
>     ** N:SNAPSHOT    TIME      DELT[S]
>
>     ** WATER DEPTH [M]: (HP(L),L=2,LA)
>
>           3600        0.00417   0.10000
>
>          4.124     4.123     4.123     4.122     4.122     4.121     4.121
> 4.120     4.120     4.119
>
>          4.118     4.117     4.117     4.116     4.116     4.115     4.115
> 4.114     4.114     4.114
>
>          4.114     4.113     4.113     4.112     4.112     4.111     4.111
> 4.110     4.110     4.109
>
>          4.109     4.109     4.109     4.108     4.108     4.107     4.107
> 4.106     4.107     4.106
>
>          4.106     4.105     4.105     4.105     4.105     4.104     4.104
> 4.104     4.104     4.103
>
>          4.103     4.103     4.102     4.102     4.102     4.102     4.101
> 4.102     4.101     4.101
>
>          4.101     4.101     4.100     4.101     4.100     4.101     4.100
> 4.100     4.100     4.100
>
>          4.100     4.100     4.100     4.100     4.100     4.100     4.100
> 4.100     4.100     4.100
>
>          4.100     4.100     4.100     4.100     4.100     4.100     4.100
> 4.100     4.100     4.101
>
>          4.100     4.101     4.100     4.101     4.101     4.101     4.101
> 4.102     4.101     4.102
>
>          4.102     4.101     4.102     4.102     4.103     4.102     4.103
> 4.103     4.104     4.103
>
>          4.104     4.104     4.105     4.104     4.105     4.105     4.106
> 4.106     4.107     4.106
>
>          4.107     4.107     4.108     4.108     4.109     4.109     4.110
> 4.110     4.110     4.110
>
>          4.111     4.111     4.112     4.112     4.113     4.113     4.114
> 4.114     4.115     4.115
>
>          4.116     4.116     4.117     4.117     4.118     4.118     4.120
> 4.120     4.121     4.121
>
>          4.122     4.122     4.122     4.123     4.123     4.125     4.125
> 4.126     4.126     4.127
>
>          4.128     4.129     4.129     4.130     4.130     4.132     4.132
> 4.133     4.133     4.135
>
>          4.135     4.136     4.137     4.138     4.138     4.139     4.140
> 4.141     4.141     4.143
>
>          4.143     4.145     4.145     4.146     4.147     4.148     4.149
> 4.150     4.150     4.152
>
>          4.152     4.154     4.154     4.156     4.156     4.158     4.158
> 4.160     4.160     4.162
>
>          4.162     4.163     4.164     4.165     4.166     4.167     4.168
> 4.169     4.171     4.171
>
>          4.173     4.173     4.175     4.176     4.177     4.178     4.180
> 4.180     4.182     4.183
>
>          4.184     4.185     4.187     4.187     4.189     4.190     4.192
> 4.192     4.194     4.195
>
>          4.197     4.197     4.199     4.200     4.202     4.203     4.204
> 4.205     4.207     4.208
>
>          4.210     4.210     4.212     4.213     4.215     4.216     4.218
> 4.219     4.221     4.221
>
>          4.223     4.224     4.225     4.227     4.228     4.230     4.231
> 4.233     4.234     4.236
>
>          4.237     4.239     4.240     4.242     4.243     4.245     4.246
> 4.248     4.249     4.251
>
>          4.252     4.254     4.255     4.257     4.258     4.260     4.262
> 4.264     4.265     4.267
>
>          4.268     4.270     4.271     4.273     4.275     4.277     4.278
> 4.280     4.281     4.283
>
>          4.285     4.287     4.288     4.290     4.291     4.294     4.295
> 4.297     4.298     4.301
>
>          4.302     4.303     4.305     4.307     4.309     4.310     4.312
> 4.314     4.316     4.317
>
>          4.320     4.321     4.323     4.325     4.327     4.328     4.331
> 4.332     4.334     4.336
>
>          4.338     4.339     4.342     4.343     4.346     4.347     4.349
> 4.351     4.353     4.355
>
>          4.357     4.359     4.361     4.362     4.365     4.366     4.369
> 4.370     4.373     4.374
>
>          4.377     4.378     4.381     4.382     4.385     4.386     4.389
> 4.390     4.393     4.394
>
>          4.397     4.398     4.400     4.402     4.404     4.406     4.408
> 4.411     4.412     4.415
>
>          4.416     4.419     4.421     4.423     4.425     4.427     4.429
> 4.432     4.433     4.436
>
>          4.437     4.440     4.442     4.444     4.446     4.449     4.450
> 4.453     4.455     4.457
>
>          4.459     4.462     4.463     4.466     4.468     4.470     4.472
> 4.475     4.476     4.479
>
>          4.481     4.484     4.485     4.488     4.490     4.492     4.494
> 4.497     4.499     4.501
>
>          4.503     4.505     4.508     4.509     4.512     4.514     4.517
> 4.519     4.521     4.523
>
>          4.526     4.528     4.530     4.532     4.535     4.537     4.540
> 4.541     4.544     4.546
>
>          4.549     4.551     4.554     4.555     4.558     4.560     4.563
> 4.565     4.568     4.569
>
>          4.572     4.574     4.577     4.579     4.582     4.584     4.586
> 4.588     4.591     4.593
>
>          4.596     4.598     4.601     4.603     4.605     4.607     4.610
> 4.612     4.615     4.617
>
>          4.620     4.622     4.624     4.627     4.628     4.631     4.633
> 4.636     4.638     4.641
>
>          4.643     4.646     4.648     4.651     4.653     4.656     4.657
> 4.660     4.662     4.665
>
>          4.667     4.670     4.672     4.675     4.677     4.680     4.682
> 4.685     4.687     4.690
>
>          4.692     4.695     4.697     4.700     4.702     4.705     4.706
> 4.709     4.711     4.714
>
>          4.716     4.719     4.721     4.724     4.726     4.729     4.731
> 4.734     4.736     4.741
>
>
>
> Currently, I have around 10 columns of data for each time. I want to make a
> data frame such that all those data on different columns will be combined
> in
> 1 column of data. So, I want to arrange the data columns such that first
> the
> data on row 1 will be used and then data on second row and so on. This way,
> we will have one column for one time.
>
>
>
> Thank you for your help and suggestion.
>
>
>
> Best,
>
> Janesh
>
>
>         [[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.
>



-- 
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.

	[[alternative HTML version deleted]]

