Às 12:15 de 31/08/2024, Stefano Sofia escreveu:
Dear R-list users,
I deal with semi-hourly data from automatic meteorological stations.
They have to pass a manual validation; suppose that status = "C" stands for correct and
status = "D" for discarded.
Here a simple example with "Snow height" (HS):
mydf <- data.frame(data_POSIX=seq(as.POSIXct("2024-01-01 00:00:00", format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT-1"),
as.POSIXct("2024-01-02 23:30:00", format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT-1"), by="30 min"))
mydf$hs <- round(runif(96, 0, 100))
mydf$status <- c(rep("C", 50), "S", rep("C", 45))
Evaluating the daily mean indipendently from the status is very easy:
aggregate(mydf$hs, by=list(format(mydf$data_POSIX, "%Y"), format(mydf$data_POSIX, "%m"),
format(mydf$data_POSIX, "%d")), my.mean)
Things become more complicated when I need to export also the status: this should be "C" when all 48 data
have status equal to "C", and status "D" when at least one value has status ="D".
I have no clue on how to do that in an efficient way.
Could some of you give me some clues on how to do that?
Thank you for your usual support
Stefano Sofia
(oo)
--oOO--( )--OOo--------------------------------------
Stefano Sofia PhD
Civil Protection - Marche Region - Italy
Meteo Section
Snow Section
Via del Colle Ameno 5
60126 Torrette di Ancona, Ancona (AN)
Uff: +39 071 806 7743
E-mail: stefano.so...@regione.marche.it
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Hello,
The aggregate.formula method has a subset argument that you can use to
extract only the rows matching a condition. The condition below tells if
there is any "D" and aggregates based on it.
I create a variable subset_condition in order to make the code more
readable.
First data with no "D"
set.seed(2024)
mydf <- data.frame(data_POSIX = seq(as.POSIXct("2024-01-01 00:00:00",
format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT-1"),
as.POSIXct("2024-01-02 23:30:00",
format = "%Y-%m-%d %H:%M:%S", tz="Etc/GMT-1"), by="30 min"))
mydf$hs <- round(runif(96, 0, 100))
mydf$status <- c(rep("C", 50), "S", rep("C", 45))
my.mean <- function(x, na.rm = TRUE) mean(x, na.rm = na.rm)
aggregate(hs ~ format(mydf$data_POSIX, "%Y-%m-%d"), mydf, my.mean)
#> format(mydf$data_POSIX, "%Y-%m-%d") hs
#> 1 2024-01-01 52.37500
#> 2 2024-01-02 45.64583
subset_condition <- if(any(mydf$status == "D")) mydf$status == "D" else TRUE
aggregate(hs ~ format(mydf$data_POSIX, "%Y-%m-%d") + status, mydf,
my.mean, subset = subset_condition)
#> format(mydf$data_POSIX, "%Y-%m-%d") status hs
#> 1 2024-01-01 C 52.37500
#> 2 2024-01-02 C 46.48936
#> 3 2024-01-02 S 6.00000
Now data with "D"'s.
my.mean <- function(x, na.rm = TRUE) mean(x, na.rm = na.rm)
status_with_D <- sample(c('C', 'D'), 45, TRUE, c(.9, .1))
mydf$status <- c(rep("C", 50), "S", status_with_D)
subset_condition <- if(any(mydf$status == "D")) mydf$status == "D" else TRUE
aggregate(hs ~ format(data_POSIX, "%Y-%m-%d") + status, mydf, my.mean,
subset = subset_condition)
#> format(data_POSIX, "%Y-%m-%d") status hs
#> 1 2024-01-02 D 51.2
# the formats in the OP but extracted from the date/time and used in the
formula that follows.
year <- format(mydf$data_POSIX, "%Y")
month <- format(mydf$data_POSIX, "%m")
day <- format(mydf$data_POSIX, "%d")
aggregate(hs ~ year + month + day, mydf, my.mean)
#> year month day hs
#> 1 2024 01 01 52.37500
#> 2 2024 01 02 45.64583
aggregate(hs ~ year + month + day + status, mydf, my.mean, subset =
subset_condition)
#> year month day status hs
#> 1 2024 01 02 D 51.2
Hope this helps,
Rui Barradas
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