seas.sum {seas} | R Documentation |
Create a seasonal sum object used for analysis of precipitation data (among other things, such as recharge rates).
# minimum usage seas.sum(x) # all options seas.sum(x, start, end, width = 11, var, prime, a.cut = 0.3, na.cut = 0.2, unit = "mm", id, name)
x |
time-varying data.frame with variables which are
summed, such as precipitation |
start |
start year; if omitted minimum year will be used |
end |
end year; if omitted will use same as start , and if
start is omitted, will use maximum year |
width |
a number specifying the width of the bin (factor) in
days, or "mon" for Months (see mkfact for
others) |
var |
a character array of the
names from x that are to be summed, such as
c("rain","snow","precip") , if available |
prime |
a single variable from var which is the prime
variable of interest, such as "precip" ; this is the variable
used for comparison with a.cut and na.cut in the
resulting active and na dimensions |
a.cut |
cut-off value for the day to be considered an
active or ‘wet day’ (based on the prime
variable); a trace amount of 0.3 mm is suggested; if a.cut
is NA or zero, the active variable and analysis will be ignored |
na.cut |
cut-off fraction of missing values; can be single value
or a vector for c(annual,seasonal) ; details given below |
unit |
unit for seas.sum object; useful for future plotting |
id |
unique station identifier used to extract a subset of data
from dat |
name |
provide a name for the seasonal sum object; otherwise
generated from id (if available) |
This function is used to discretize and sum time-varying data in a
data.frame
for analysis in seasonal and
annual parts. This is particularly useful for calculating
normals of rates, such as precipitation and recharge. This function
simply sums up each variable in each bin for each year, and provides
the results in several arrays.
Sums are not normalized, and represent a sum for the number of
days in the bin (seasonal data) or year (for annual data). Seasonal
data can be normalized by the number of days (for a rate per day) or
by the number of active days where prime > a.cut
.
For annual sums, years with many missing values are ignored
(receiving a value of NA
) since it has insufficient data for a
complete sum. The amount of allowable NA
values is controlled
by na.cut
, which is a fraction of NA
values for the
whole year (default is 0.2).
The seasonal sums are calculated independently from the annual
sums. Individual bins from each year with many missing values
are ignored, where the amount of allowable NA
values is
controlled by na.cut
(fraction of NA
s in each bin of
each individual year; default is 0.2).
Returns a seas.sum
object, which is a list
with
the following elements:
ann |
data.frame of annual data; the columns are:
year active a.cut (if used)days na |
seas |
array of seasonal data; the dimensions are:
[[1]] [[2]] mkfact [[3]] days item |
active |
the number of ‘active’ days in the bin where the
prime variable is above a.cut (if used) |
days |
number of days in the bin; this array is useful for
normalizing the numbers in seas to comparable units of mm/day |
na |
number of missing days in each bin |
call |
function call |
years |
years (same as ann[[1]] and seas[[1]] ) |
var |
variables which the sums represent (part of
ann[[2]] and seas[[3]] ) |
prime |
prime variable |
unit |
unit of variable |
width |
width argument passed to mkfact
mkfact (same as
seas[[2]] ) |
precip.only |
value used in argument (modified if insufficient
data found in dat ) |
na.cut |
value used in argument |
a.cut |
value used in argument; if it is zero or NA , this
will be FALSE |
id |
value used in argument; or found in dat$id if present
and not given as argument |
name |
either given by name argument or generated from
id or from dat object name |
M.W. Toews
image.seas.sum
to view the result result,
seas.norm
to view a “normal”
loc <- Sys.getlocale() on.exit(Sys.setlocale(locale=loc)) Sys.setlocale(loc="C") data(mscstn) data(mscdata) dat.ss <- seas.sum(mscdata,id=1108447,width="mon") # Annual data dat.ss$ann # Demonstrate how to slice through a cubic array dat.ss$seas["1990",,] dat.ss$seas[,"Feb",] # month names are locale specific dat.ss$seas[,,"precip"] # Simple calculation on an array (monthly.mean <- apply(dat.ss$seas[,,"precip"],2,mean,na.rm=TRUE)) barplot(monthly.mean,ylab="Mean monthly total (mm/month)", main="Un-normalized mean precipitation in Vancouver, BC") text(6.5,150,paste("Un-normalized rates given 'per month' should be", "avoided since ~3-9% error is introduced", "to the analysis between months",sep="\n")) # Normalized precip norm.monthly <- dat.ss$seas[,,"precip"]/dat.ss$days norm.monthly.mean <- apply(norm.monthly,2,mean,na.rm=TRUE) print(round(norm.monthly,2)) print(round(norm.monthly.mean,2)) barplot(norm.monthly.mean,ylab="Normalized mean monthly total (mm/day)", main="Normalized mean precipitation in Vancouver, BC") # Better graphics of data dat.ss <- seas.sum(mscdata,id=1108447) image(dat.ss)