Hi,
I hope this is an acceptable data.table problem (also posted to
StackOverflow
http://stackoverflow.com/questions/38890034/using-data-table-to-summarize-monthly-sequences-count-specific-events).
I have a 3-column table with:
- `id` geographic location IDs (303,453 locations)
- `month` month over 24 years 1990-2014
- `spei` a climatic index that varies between -7 and 7.
I need to count the occurrence of droughts at each location over the
entire 1990-2014 period. A drought event is defined as "a period in
which the SPEI is continuously negative and the SPEI reaches a value of
-1.0 or less. Drought starts when the SPEI first falls below zero and
ends with the first positive SPEI value following a value of -1.0 or less".
I know this should be feasible using shift() and rolling joins but would
very welcome some pointers!
# Sample table structure
> dt <- data.table(
id = rep(1:303453, each=24*12),
month = rep(seq(as.Date("1990-01-01"), as.Date("2014-12-31"),
"month"), 303453),
spei = runif(303453*24*12, -7, 7))
Hoping some of you are more used to working with time series.
Many thanks, --Mel.
--
Melanie BACOU
International Food Policy Research Institute
Snr. Program Manager, Spatial Data and Analytics
Work +1(202)862-5699
E-mail [email protected]
Visit www.harvestchoice.org
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