Daily -> Weekly

Generic function for transforming a DAILY (or sub-daily) regular time series into a WEEKLY one

Usage

daily2weekly(x, ...)

# S3 method for default
daily2weekly(x, FUN, na.rm=TRUE, na.rm.max=0, ...)

# S3 method for zoo
daily2weekly(x, FUN, na.rm=TRUE, na.rm.max=0, ...)

# S3 method for data.frame
daily2weekly(x, FUN, na.rm=TRUE, na.rm.max=0, dates=1, 
        date.fmt = "%Y-%m-%d", out.type = "data.frame", out.fmt="numeric", 
        verbose=TRUE, ...)

# S3 method for matrix
daily2weekly(x, FUN, na.rm=TRUE, na.rm.max=0, dates=1, 
        date.fmt = "%Y-%m-%d", out.type = "data.frame", out.fmt="numeric", 
        verbose=TRUE, ...)

Arguments

x

zoo, data.frame or matrix object, with (sub)daily time series.
Measurements at several gauging stations can be stored in a data.frame or matrix object, and in that case, each column of x represents the time series measured in each gauging station, and the column names of x have to correspond to the ID of each station (starting by a letter).

FUN

Function that have to be applied for transforming from daily to weekly time step (e.g., for precipitation FUN=sum and for temperature and streamflow ts FUN=mean).

FUN MUST accept the na.rm argument, because na.rm is passed to FUN.

na.rm

Logical. Should missing values be removed?
-) TRUE : the weekly values are computed only for weeks with a percentage of missing values less than na.rm.max
-) FALSE: if there is AT LEAST one NA within a month, the corresponing weekly values in the output object will be NA.

na.rm.max

Numeric in [0, 1]. It is used to define the maximum percentage of missing values allowed in each month to keep the weekly aggregated value in the output object of this function. In other words, if the percentage of missing values in a given month is larger or equal than na.rm.max the corresponding weekly value will be NA.

dates

numeric, factor or Date object indicating how to obtain the dates for each gauging station
If dates is a number (default), it indicates the index of the column in x that stores the dates
If dates is a factor, it is converted into Date class, using the date format specified by date.fmt
If dates is already of Date class, the code verifies that the number of days on it be equal to the number of elements in x

date.fmt

character indicating the format in which the dates are stored in dates, e.g. %Y-%m-%d. See format in as.Date.
ONLY required when class(dates)=="factor" or class(dates)=="numeric".

out.type

Character that defines the desired type of output. Valid values are:
-) data.frame: a data.frame, with as many columns as stations are included in x, and row names indicating the month and year for each value.
-) db : a data.frame, with 4 columns will be produced.
The first column (StationID) stores the ID of the station,
The second column (Year) stores the year
The third column (Month) stores the Month
The fourth column (Value) stores the numerical values corresponding to the values specified in the three previous columns.

out.fmt

OPTIONAL. Only used when x is a matrix or data.frame object /cr character, for selecting if the result will be a matrix/data.frame or a zoo object. Valid values are: numeric, zoo.

verbose

logical; if TRUE, progress messages are printed

...

arguments additional to na.rm passed to FUN.

Value

a zoo object with weekly time frequency

Author

Mauricio Zambrano-Bigiarini, mzb.devel@gmail

Examples

######################
## Ex1: Computation of weekly values, removing any missing value in 'x'

# Loading the DAILY precipitation data at SanMartino
data(SanMartinoPPts)
x <- SanMartinoPPts

# Subsetting 'x' to its first three weeks (Jan/1921 - Mar/1921)
x <- window(x, end="1921-03-31")

## Transforming into NA the 10% of values in 'x'
set.seed(10) # for reproducible results
n           <- length(x)
n.nas       <- round(0.1*n, 0)
na.index    <- sample(1:n, n.nas)
x[na.index] <- NA

## Agreggating from Daily to Weekly, removing any missing value in 'x'
w <- daily2weekly(x, FUN=sum, na.rm=TRUE)

######################
## Ex2: Computation of Weekly values only when the percentage of NAs in each
#       week is lower than a user-defined percentage (10% in this example).

# Loading the DAILY precipitation data at SanMartino
data(SanMartinoPPts)
x <- SanMartinoPPts

# Subsetting 'x' to its first three weeks (Jan/1921 - Mar/1921)
x <- window(x, end="1921-03-31")

## Transforming into NA the 10% of values in 'x'
set.seed(10) # for reproducible results
n           <- length(x)
n.nas       <- round(0.1*n, 0)
na.index    <- sample(1:n, n.nas)
x[na.index] <- NA

## Daily to Weekly, only for weeks with less than 10% of missing values
w2 <- daily2weekly(x, FUN=sum, na.rm=TRUE, na.rm.max=0.1)

# Verifying that the weeks 01, 02, 06, 08, 10, 11, 12 of 'x' had 10% or more of missing values
cmv(x, tscale="weekly")

######################
## Ex3: Computation of Weekly values in a two-column zoo object, 
##      only when the percentage of NAs in each week is lower than a user-defined 
##      percentage (10% in this example).

# Loading the DAILY precipitation data at SanMartino
data(SanMartinoPPts)
x <- SanMartinoPPts

# Subsetting 'x' to its first three weeks (Jan/1921 - Mar/1921)
x <- window(x, end="1921-03-31")

## Transforming into NA the 10% of values in 'x'
set.seed(10) # for reproducible results
n           <- length(x)
n.nas       <- round(0.1*n, 0)
na.index    <- sample(1:n, n.nas)
x[na.index] <- NA

## Creating a two-column zoo object
X <- cbind(x, y=x)

## Daily to Weekly, only for weeks with less than 10% of missing values
w2 <- daily2weekly(X, FUN=sum, na.rm=TRUE, na.rm.max=0.1)

# Verifying that the weeks 01, 02, 06, 08, 10, 11, 12 of 'x' had 10% or more of missing values
cmv(X, tscale="weekly")

Usage

Arguments

Value

Author

See also

Examples