Hello,
I have a challenge!
I have a large dataset with three columns, "date","temp", "location".
"date" is in the format %m/%d/%y %H:%M, with a "temp" recorded every 10
minutes. These temperatures of surface temperatures and so fluctuate during
the day, heating up and then cooling down, so the data is a series of peaks
and troughs. I would like to develop a function that would go through a
dataset consisting of many sequential dates and determine for each day the
maximum hourly slope of temp~date for each site (the fastest hourly rate of
heating). The output would be the date, the maximum hourly slope for that
date, and the location. It would also be great if I could extract when
during the day the maximum hourly slope occurred.
I have been playing around with using the package lubridate to identify
each hour of the day using something like this to create a separate column
grouping the data into hours
library(lubridate)
data$date2 <- floor_date(data$date, "hour")
I was then imagining something like this though this code doesn't work as
written.
ddply(data, .(location, date2), function(d)
max(rollapply(slope(d$temp~d$date, data=d)))
Essentially what I'm imagining is calculating the slope (though I'd have to
write a quick slope function) of the date/temp relationship, use rollapply
to apply this function across the dataset, and determine the maximum slope,
grouped by location and hour (using date2). Hmm... and per day!
This seems complicated. Can others think of a simpler, more elegant means
of extracting this type of data? I struggled to put together a working
example with a set of data, but if this doesn't make sense let me know and
I'll see what I can do.
Thanks,
Nate
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