I have time-series data looking like this:
> dataIn[sample(c(1:nrow(dataIn)), 25),]
accelerometer_y id data_block_epoch_time
782 0.8424 201300 1331797330000
1868 0.3432 202386 1331797384000
1828 0.3510 202346 1331797382000
1026 0.2184 201544 1331797342000
1569 0.3432 202087 1331797369000
1453 0.3588 201971 1331797363000
1204 0.3666 201722 1331797351000
1821 0.3588 202339 1331797382000
860 0.8658 201378 1331797333000
910 0.8580 201428 1331797336000
1488 0.3432 202006 1331797365000
578 0.9126 201096 1331797319000
1478 0.3666 201996 1331797364000
1183 0.3588 201701 1331797350000
29 -0.1716 200547 1331797292000
1540 0.3588 202058 1331797367000
392 -0.1560 200910 1331797310000
1533 0.3744 202051 1331797367000
1016 0.6318 201534 1331797341000
314 -0.1560 200832 1331797306000
410 -0.1638 200928 1331797311000
769 0.8580 201287 1331797329000
1101 0.3588 201619 1331797346000
403 -0.1638 200921 1331797311000
1794 0.3666 202312 1331797380000
The id field represents the subsecond value in the timestamp (n
ids/second). What I'd like to do is combine the fields, so that I'm left
with an equally-spaced timeseries with readings. What's the most efficient
way to do this using R? There are an arbitrary number of timestamps per
second. Many thanks! -- H
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