seas.sum {seas} | R Documentation |
Create a seasonal sum object used for analysis of precipitation data (among other things, such as recharge rates); this object has sums in each ‘bin’ of a season, as well as for each annum (or year).
# minimum usage seas.sum(x) # all options seas.sum(x, var, width = 11, start.day=1, prime, a.cut = 0.3, na.cut = 0.2)
x |
a data.frame with daily variables to be summed,
such as precipitation |
var |
the names of one or more variables in x , such as
c("rain","snow","precip") |
width |
a number specifying the width of the bin (factor) in
days, or "mon" for Months (see mkseas for
others) |
start.day |
the first day of the season, specified as either a
Date or as an integer day of the year; annual sums
start on this day, and end a day before start.day in the
following year |
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 |
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 annum (or 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 annum (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, annums 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 per annum is
controlled by na.cut[1]
, which is a fraction of NA
values for the whole annum (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[2]
(or na.cut[1]
, if the
length
of na.cut
is 1). The default fraction of
NA
s in each bin of each annum 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]]
:mkseas
[[3]]
:days
itemactive
:a.cut
(if used)days
:seas
to comparable
units of mm/dayna
:start.day
:years
:ann[[1]]
and seas[[1]]
); if
start.day
is not 1, this represents the starting and ending
years (i.e., 1991_1992
) of each annum; see mkann
var
:ann[[2]]
and seas[[3]]
)units
:list
of units for each
var
, such as “mm/day”; these are obtained from the
units
attribute (using attr
) found in
x$var
long.name
:list
of long names for each var
;
these are obtained from long.name
in x$var
; set to
be var
if NULL
prime
:prime
variable, such as "precip"
width
:width
argument passed to mkseas
bins
:mkseas
(same as
seas[[2]]
)bin.lengths
:bin
year.range
:x
precip.only
:x
)na.cut
:a.cut
:NA
, this
will be FALSE
id
:attr(x,"id")
(NULL
if not set)name
:attr(x,"name")
(NULL
if not set)M.W. Toews
To view the result try image.seas.sum
, or
alternatively, plot.seas.sum
To calculate and view a “normal”, use seas.norm
and plot.seas.norm
, or for precipitation use
precip.norm
and plot.precip.norm
data(mscdata) dat <- mksub(mscdata,id=1108447) dat.ss <- seas.sum(dat,width="mon") # Structure in R str(dat.ss) # Annual data dat.ss$ann # Demonstrate how to slice through a cubic array dat.ss$seas["1990",,] dat.ss$seas[,2,] # or "Feb", if using English locale dat.ss$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(dat,width=11) image(dat.ss)