Janesh Devkota
2013-Apr-11 21:55 UTC
[R] Read the data from a text file and reshape the data
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]]
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]]
Hi,
May be this helps:
?lines1<- readLines("WAT_DEP.DAT.part")
indx<- which(grepl("[*]",lines1))
indx2<-indx[seq(from=indx[2],length(indx),by=2)]+1
lines2<-str_trim(lines1[indx2],side="left")
dat1<-read.table(text=lines2,sep="",header=FALSE)
library(stringr)
lst1<- lapply(split(indx,((seq_along(indx)-1)%/%2)+1), function(x)
{x1<-seq(max(x)+2,max(x)+2+49,by=1); x2<-
str_trim(lines1[x1][!is.na(lines1[x1])],side="left");
x3<-as.vector(as.matrix(read.table(text=x2[x2!=""],header=FALSE,sep="")));
x4<- if(length(x3)< 500) c(x3,rep(NA,500-length(x3))) else x3;x4 })
dat2<- as.data.frame(do.call(cbind,lst1))
colnames(dat2)<-paste("t",colnames(dat2),"_",dat1[,2],sep="")
?dim(dat2)
#[1] 500 622
dat2[1:3,1:3]
#? t1_0.00208 t2_0.00417 t3_0.00625
#1????? 3.224????? 4.124????? 4.502
#2????? 3.205????? 4.118????? 4.500
#3????? 3.189????? 4.114????? 4.498
A.K.
----- Original Message -----
From: Janesh Devkota <janesh.devkota at gmail.com>
To: r-help at r-project.org
Cc:
Sent: Thursday, April 11, 2013 5:55 PM
Subject: [R] Read the data from a text file and reshape the data
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
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