Determine the length of a season with conditions (ex. winter snow season)

Question

I want to determine the length of the snow season in the following data frame:

DATE    SNOW
1998-11-01  0
1998-11-02  0
1998-11-03  0.9
1998-11-04  1
1998-11-05  0
1998-11-06  1
1998-11-07  0.6
1998-11-08  1
1998-11-09  2
1998-11-10  2
1998-11-11  2.5
1998-11-12  3
1998-11-13  6.5
1999-01-01  15
1999-01-02  15
1999-01-03  19
1999-01-04  18
1999-01-05  17
1999-01-06  17
1999-01-07  17
1999-01-08  17
1999-01-09  16
1999-03-01  6
1999-03-02  5
1999-03-03  5
1999-03-04  5
1999-03-05  5
1999-03-06  2
1999-03-07  2
1999-03-08  1.6
1999-03-09  1.2
1999-03-10  1
1999-03-11  0.6
1999-03-12  0
1999-03-13  1

Snow season is defined by a snow depth (SNOW) of more than 1 cm for at least 10 consecutive days (so if there is snow one day in November but after it melts and depth is < 1 cm we consider the season not started).

My idea would be to determine:

1) the date of snowpack establishement (in my example 1998-11-08)

2) the date of "disappearing" (here 1999-03-11)

3) calculate the length of the period (nb of days between 1998-11-05 and 1999-03-11)

For the 3rd step I can easily get the number between 2 dates using this method.

But how to define the dates with conditions?

what are the rules to find 'snowpack establishment' and 'disappearing'. Is it something trivial I'm not seeing? There are other zeros and why 0.6? — Paulo E. Cardoso, Mar 07 '14 at 13:42
I'm looking for the first date where there is >= 1 cm for at least 10 consecutive days (beginning of snow season) and then the first date where snow < 1 cm (end of snow season). — user2165907, Mar 07 '14 at 13:49
Ahhh! Thanks! It should be there with your question. Add it to your text. — Paulo E. Cardoso, Mar 07 '14 at 13:57

Matthew Plourde · Accepted Answer · 2014-03-07T14:22:37.083

0

This is one way:

# copy data from clipboard
d <- read.table(text=readClipboard(), header=TRUE)
# coerce DATE to Date type, add event grouping variable that numbers the groups
# sequentially and has NA for values not in events.
d <- transform(d, DATE=as.Date(DATE),
                  event=with(rle(d$SNOW >= 1), rep(replace(ave(values, values, FUN=seq), !values, NA), lengths)))
# aggregate event lengths in days
event.days <- aggregate(DATE ~ event, data=d, function(x) as.numeric(max(x) - min(x), units='days'))
# get those events greater than 10 days
subset(event.days, DATE > 10)
#   event DATE
# 3     3  122

You can also use the event grouping variable to find the start dates:

starts <- aggregate(DATE ~ event, data=d, FUN=head, 1)
# 1     1 1998-11-04
# 2     2 1998-11-06
# 3     3 1998-11-08
# 4     4 1999-03-13

And then merge this with event.days:

merge(event.days, starts, by='event')
#   event DATE.x     DATE.y
# 1     1      0 1998-11-04
# 2     2      0 1998-11-06
# 3     3    122 1998-11-08
# 4     4      0 1999-03-13

edited Mar 07 '14 at 14:22

answered Mar 07 '14 at 13:54

Matthew Plourde

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Ok thanks, not very intuitive (for me) but apparently it's working... Any idea to get the dates (beginning and end of season)? – user2165907 Mar 07 '14 at 14:21
And to get the date of the end of the season: DATE.y + DATE.x (in fact DAT.x is the length of the snow period meeting conditions of >= 10 cm of snow for at least 10 consecutive days). – user2165907 Mar 07 '14 at 15:33
by the way, what is the "1" in the aggregate of starts? – user2165907 Mar 07 '14 at 16:12
it's the argument to `head`, saying give me the first element. – Matthew Plourde Mar 07 '14 at 16:13

Determine the length of a season with conditions (ex. winter snow season)

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