Package 'smwrBase'

Description

This package has specialized functions for importing, managing, or manipulating hydrologic data.

Details

This package contains functions that import, manage, or manipulate hydrologic data and functions that apply specialized transformations used in hydrologic analyses amd modeling. A listing of the functions and their description is in the following table.

Author(s)

Dave Lorenz

References

Lorenz, D.L., 2015, smwrBase—an R package for managing hydrologic data, version 1.1.1: U.S. Geological Survey Open-File Report 2015–1202, 7 p.

See Also

smwrData

Description

Extract elements of a time-of-day object.

Usage

## S3 method for class 'timeDay'
x[i]

Arguments

Value

The subset of x indicated by i.

See Also

Extract

Description

Matches partial values, such as substrings.

Usage

x %cn% pattern

Arguments

Value

A vector the same length as x of logical values indicating whether pattern is found in the element of x or not.

See Also

%in%, regexpr

Examples

## A simple example
c("abc", "def") %cn% "c"

Description

Decompose a series of observations into deviations (anomalies) from the mean for selected periods and the remainder (HFV or high frequency variation) using the method described in Appendix A of Vecchia (2000).

Usage

anomalies(x, ...)

Arguments

Details

The intent of computing anomalies is to give flexibility in fitting the relation between flux, or concentration, and flow for time periods longer than a couple of years. Taking a very simple regression model:

C = B0 + B1 * Q + e,

where C is the concentration, B0 and B1 are the regression coefficients, Q is the flow, and e is the error. This can be re-expressed in terms of flow anomalies (for this example, 5- and 1-year anomalies are used, many others are possible):

C = B0 + B1 * Qbar + B1 * A5 + B1 * A1 + B1 * HFV + e,

where C, B0, B1, and e are the same as the simple regression, and Qbar is the mean flow, A5 is the 5-year anomaly, A1 is the 1-year anomaly, and HFV is the high-frequency variation. The simple regression model assumes that the regression coefficient (B1) is constant for all anomalies. Computing anomalies removes that constraint and is represented by this model:

C = B0 + B1 * A5 + B2 * A1 + B3 * HFV + e,

where C, A5, A1, HFV, and e are the same as the re-expressed model, and B0, B1, B2, and B3 are regression coefficients (numerically different from the simple coefficients). Qbar is a constant and is not needed for the regression.

Anomalies are computed sequentially. First, the mean of x is computed and subtracted from the data. Then for each anomaly, the running mean of the specified period is computed (the anomaly) and is subtracted from the data. The remainder is the HFV. This procedure ensures that the sum of the anomalies plus the mean is equal to the original data.

Value

A matrix of the specified anomalies and HFV. The mean of x is included as an attribute.

Note

The output matrix contains missing values in the beginning, corresponding to the length of the longest anomaly.

A long time-frame anomaly that is often of interest, is the 5-year anomaly, which is 1,826 days.

References

Vecchia, A.V., 2000, Water-quality trend analysis and sampling design for the Souris River, Saskatchewan, North Dakota, and Manitoba: U.S. Geological Survey Water-Resources Investigations Report 00-4019, 77 p.

Examples

## Not run: 
library(smwrData)
data(Q05078770)
anomalies(log(Q05078770$FLOW), A3mo=90)

## End(Not run)

Description

Addition of time-of-day data to either "Date" or "POSIXt" classes. This is useful when dates and times are recorded in separate columns in a dataset.

Usage

## S4 method for signature 'timeDay,POSIXt'
Arith(e1, e2)

## S4 method for signature 'POSIXt,timeDay'
Arith(e1, e2)

## S4 method for signature 'timeDay,Date'
Arith(e1, e2)

## S4 method for signature 'Date,timeDay'
Arith(e1, e2)

Arguments

Value

A vector of class "POSIXct."

Examples

as.Date("2001-03-04") + as.timeDay("10:00")
## Not run: 
library(smwrData)
data(QW05078470)
# Note that the result is reported in the local time zone!
QW05078470$DATES + as.timeDay(QW05078470$TIMES)

## End(Not run)

Description

Convert the time-of-day object to a character string.

Usage

## S3 method for class 'timeDay'
as.character(x, ...)

Arguments

Value

The values in x converted to a character representation.

Description

Convert an object to class "data.frame."

Usage

## S3 method for class 'timeDay'
as.data.frame(x, row.names = NULL, optional = FALSE, ...,
  nm = deparse(substitute(x)))

Arguments

Value

A data frame is created containing the data in x.

See Also

as.data.frame

Description

Valid conversions for function as.timeDay.

Usage

as.timeDay(time, format)

## S4 method for signature 'timeDay,missing'
as.timeDay(time, format)

## S4 method for signature 'numeric,missing'
as.timeDay(time, format)

## S4 method for signature 'character,character'
as.timeDay(time, format)

## S4 method for signature 'character,missing'
as.timeDay(time, format)

Arguments

Details

Inconsistent formats for time will result in an error. Missing values or empty strings in time will result in missing values in the output.

Examples

as.timeDay("10:00")
as.timeDay("3 PM", format="%I %p")

Description

Computes the base day of the year, a reference value that can be used to group days for the computation of summary statistics.

Usage

baseDay(x, numeric = TRUE, year = c("calendar", "water", "climate"))

Arguments

Details

The base day is computed such that all dates have the same reference value regardless of whether the year is a leap year or not. If year is "calendar," then the factor levels or day number begin on January 1; if year is "water," then the factor levels or day number begin on October 1; and if year is "climate," then the factor levels or day number begin on April 1.

Value

An integer value representing the base day number if numeric is TRUE. Otherwise a factor with levels for every day of the year.

Examples

# The default numeric result
baseDay(c("2000-02-29", "2000-03-01", "2001-03-01"))
# The result as a factor
baseDay(c("2000-02-29", "2000-03-01", "2001-03-01"), numeric=FALSE)

Description

Computes the decimal time representation of the base day of the year.

Usage

baseDay2decimal(x)

Arguments

Value

A numeric value representing the base day.

See Also

baseDay

Examples

# The baseDay ordered by calendar year
bd.tmp <- baseDay(c("2000-02-29", "2000-03-01", "2001-03-01"), 
  numeric=FALSE)
baseDay2decimal(bd.tmp)
# ordered by water year, result should agree
bd.tmp <- baseDay(c("2000-02-29", "2000-03-01", "2001-03-01"), 
  numeric=FALSE, year="water")
baseDay2decimal(bd.tmp)

Description

Functions for transforming and back-transforming data using the Box-Cox power transform, with options to preserve the measurement units.

Usage

boxCox(x, lambda = 1, GM, alpha = 0)

IboxCox(x, lambda = 1, GM, alpha = 0)

Arguments

Details

If x contains missing values, then GM is computed after omitting the missing values and the output vector has a missing value wherever x has a missing value.

The function boxCox computes the forward transform and the function IboxCox computes the inverse [boxCox] transform, or back-transform.

Value

A numeric vector of the transformed or back-transformed values in x with an attribute "GM" of the geometric mean.

Note

The original power transform described by Box and Cox (1964) is adjusted by a power transform of the geometric mean to retain the correct dimensional units of the original data as described in section 13.2 by Draper and Smith (1998).

References

Box, G.E.P., and Cox, D.R., 1964, An analysis of transformations: Journal of the Royal Statistical Society, v. 26, Series B, p. 211–243.
Draper, N.R., and Smith, H., 1998, Applied regression analysis: New York, John Wiley and Sons, 706 p.

See Also

hyperbolic

Examples

X.test <- c(1,4,9,16,25,36,49)
boxCox(X.test)
boxCox(X.test, lambda=0)
IboxCox(boxCox(1:3, lambda=0), lambda=0) # verify the back-transform
## Should return
# [1] 1 2 3
# attr(,"GM")
# [1] 1.817121

Description

Combine time-of-day data into a single vector.

Usage

## S3 method for class 'timeDay'
c(..., recursive = FALSE)

Arguments

Value

A single vector of class "timeDay."

Examples

c(as.timeDay("10:00"), as.timeDay("3 PM", format="%I %p"))

Description

Construct a vector with as few missing values as possible from a selected sequence of vectors.

Usage

coalesce(mat, ...)

index.coalesce(mat, ...)

Arguments

Value

For coalesce, a vector in which each element is determined by selecting the first non-missing value in the order in which they are specified in the argument list. The first step is to construct a matrix from all arguments. The output is initially set to column 1; for any missing values in the column, the data from column 2 are used and so on until all columns have been searched or all missing values replaced.

For index.coalesce, an integer vector indicating which column from mat or from the vectors or constant specified for ... produced the result in coalesce.

Note

This function is most useful for creating a column in a dataset from related columns that represent different methods. For example, a single column of alkalinity may be desired when there are multiple columns of alkalinity determined by various methods.

Examples

coalesce(c(1,NA,NA,3), c(2,2,NA,2))
# should be: [1]  1  2 NA  3
coalesce(c(1,NA,NA,3), c(2,2,NA,2), 0)
# should be: [1] 1 2 0 3

Description

Support function to supply data for functions within smwrBase.

Usage

conc.meq()

Value

A list containing necessary information for the function conc2meq.

Note

The user may choose to make local copies of the data with the same name (conc.meq) to be able to change or add to the list.

See Also

conc2meq

Description

Convert concentrations in milligrams per liter (mg/L) to milli-equivalents per liter.

Usage

conc2meq(conc, constituent)

Arguments

Value

Vector containing the milli-equivalent values. Missing values (NAs) are returned if the constituent name is invalid.

Note

The user must verify that the units of concentration are milligrams per liter. Only those constituents that are typically reported in milligrams per liter (rather than micrograms per liter) are provided in this function. Aluminum, iron, and manganese (and possibly sulfide) are sometimes reported in micrograms per liter. Such values should be divided by 1000.0 before using this function.

The conversion for iron assumes that the dissolved iron is iron II.

The conversion for phosphorus assumes that most of the phosphorus is divalent. The actual conversion for phosphorus is very dependent on pH.

The conversion factors are taken from table 9 (page 56) of Hem (1985). The available conversion factors are stored in the list created by conc.meq in smwrBase.

References

Hem, J.D., 1985, Study and interpretation of the chemical characteristics of natural water: U.S. Geological Survey Water-Supply Paper 2254, 263 p.

Examples

conc2meq(c(1,2,3), "Nitrate")
# should be: [1] 0.07139 0.14278 0.21417

Description

Computes the number of days in a month or the total number of days in the year to the end of the month.

Usage

daysInMonth(month, year, cum = FALSE)

Arguments

Value

A vector matching month of the requested number of days. Missing values are returned wherever either month or year is missing.

Examples

# Check February on a leap year and regular year.
# Should return 29, 28
daysInMonth(c(2,2), c(2000, 2001))

Description

Convert date/time data to be expressed as year and fractional part of year. This can be useful for plotting or representing time in a regression model.

Usage

dectime(dates, times, time.format, date.format, Date.noon = TRUE,
  year.type = c("calendar", "water", "climate"))

Arguments

Details

The format for times must be one of "hm," "hms," or "ms." Note that this is actually a conversion function, see See Also. If times is missing, dates is class "Date," and Date.noon is TRUE, then set the time to 12:00, so that the decimal time represents the center of the day.

Value

A vector representation of the data in decimal format–year and decimal fraction.

See Also

hm, strptime

Examples

dectime("11/11/1918", date.format="%m/%d/%Y")
dectime(1988:1990)

Description

Convert time data expressed as year and fractional part of year to class "Date."

Usage

dectime2Date(x, Date.noon = TRUE)

Arguments

Value

A vector of class "Date" corresponding to each value in x.

Note

A small value, representing about 1 minute, is added to each value in x to prevent truncation errors in the conversion. This can cause some errors if the data were converted from date and time data.

See Also

dectime, as.Date

Examples

dectime("02/07/2013", date.format="%m/%d/%Y")
# Convert back the printed result:
dectime2Date(2013.103)

Description

Density, cumulative probability, quantiles, and random generation for the log-Pearson Type III distribution.

Usage

dlpearsonIII(x, meanlog = 0, sdlog = 1, skew = 0)

plpearsonIII(q, meanlog = 0, sdlog = 1, skew = 0)

qlpearsonIII(p, meanlog = 0, sdlog = 1, skew = 0)

rlpearsonIII(n, meanlog = 0, sdlog = 1, skew = 0)

Arguments

Details

Elements of x, q, or p that are missing will result in missing values in the returned data.

Value

Either the density (dlpearsonIII), cumulative probability (plpearsonIII), quantile (qlpearsonIII), or random sample (rlpearsonIII) for the described distribution.

Note

The log-Pearson Type III distribution is used extensively in flood-frequency analysis in the United States.

See Also

dpearsonIII, dlnorm

Examples

## Simple examples
dlpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qlnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
dlpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)

## Simple examples
plpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qlnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
plpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)

## Simple examples
qlpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qlnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
qlpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)

## Simple examples
rlpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qlnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
rlpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)

Description

Convert data in degrees, minutes, and seconds format to decimal degrees.

Usage

dms2dd(x, minutes = NULL, seconds = 0, split = "")

Arguments

Value

A numeric vector of decimal degrees the same length as x. Missing values are returned wherever x, minutes, or seconds has a missing value.

Examples

dms2dd(983206) # using a numeric value
# should be [1] 98.535
dms2dd("0983206") # using a character value
# should be [1] 98.535
dms2dd(98, 32, 6) # using numeric values for degrees, minutes and seconds
# should be [1] 98.535
dms2dd("98:32", split=":") # Note seconds not included in text
# should be [1] 98.53333

Description

Density, cumulative probability, quantiles, and random generation for the Pearson Type III distribution.

Usage

dpearsonIII(x, mean = 0, sd = 1, skew = 0)

ppearsonIII(q, mean = 0, sd = 1, skew = 0)

qpearsonIII(p, mean = 0, sd = 1, skew = 0)

rpearsonIII(n, mean = 0, sd = 1, skew = 0)

Arguments

Details

Elements of x, q, or p that are missing will result in missing values in the returned data.

Value

Either the density (dpearsonIII), cumulative probability (ppearsonIII), quantile (qpearsonIII), or random sample (rpearsonIII) for the described distribution.

Note

The log-Pearson Type III distribution is used extensively in flood-frequency analysis in the United States. The Pearson Type III forms the basis for that distribution.

See Also

dlpearsonIII, dnorm

Examples

## Simple examples
dpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
dpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)
## Simple examples
ppearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
ppearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)
## Simple examples
qpearsonIII(c(.5, .75, .9), 1.5, .25, 0)
## compare to normal
qnorm(c(.5, .75, .9), 1.5, .25)
## Make a skewed distribution
qpearsonIII(c(.5, .75, .9), 1.5, .25, 0.25)
# Simple examples
rpearsonIII(c(.5, .75, .9), 1.5, .25, 0)

Description

Computes the event number eventNum, the length of events eventLen, or the sequence number for individual observations within an event eventSeq.

Usage

eventNum(event, reset = FALSE, na.fix = FALSE)

eventSeq(eventno)

eventLen(eventno, summary = FALSE)

Arguments

Value

The function eventNum returns an integer vector the same length as event indicating the event sequence number.

The function eventLen returns an integer vector the same length as eventno indicating the sequence length of the event if summary is FALSE, or a named integer vector indicating the sequence length of each event if summary is TRUE.

The function eventSeq returns an integer vector the same length as eventno indicating the sequence number of each element in the event.

Examples

## Notice the difference caused by setting reset to TRUE
eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE))
eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE), reset=TRUE)

## Notice the difference caused by setting reset to TRUE
eventSeq(eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE)))
eventSeq(eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE), reset=TRUE))

## Notice the difference caused by setting reset to TRUE
eventLen(eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE), reset=TRUE))
## This is an example of the summary option
eventLen(eventNum(c(TRUE,TRUE,FALSE,FALSE,TRUE,FALSE), reset=TRUE), summary=TRUE)

Description

Some time-series analyses require data that are uniformly spaced in time. This function will construct a regular series from randomly spaced events.

Usage

eventSeries(times, period = "hour", which = "cumsum", begin, end,
  k.period = 1)

Arguments

Details

If there is no observation during a period, then that value is set to 0 if which is "sum" or the value for the previous period if which is "cumsum." The initial value of the series is always 0.

Value

The function eventSeries returns a data frame with two columns:

Examples

## Not run: 
library(smwrData)
data(QW05078470)
# Count the number of samples per month
with(QW05078470, eventSeries(DATES, "month", which="sum"))

## End(Not run)

Description

Exports a data frame to a text-based file.

Usage

exportCSV(x, file.name = "")

Arguments

Value

The file name is returned.

Note

The function exportCSV also writes a meta file that contains information about column formatting.

See Also

write.table, importCSV

Description

Exports a data frame to a text-based file.

Usage

exportRDB(x, file.name = "data.rdb", col.names = NULL, meta = FALSE,
  code.rule = 10)

Arguments

Details

Setting the meta argument to TRUE generates a header that contains a template that can be edited by the user to describe the contents of the file.

Value

The file name is returned. write.table, importRDB

Description

Replace missing values in time-series data by interpolation.

Usage

fillMissing(x, span = 10, Dates = NULL, max.fill = 10)

Arguments

Details

Missing values at the beginning and end of x will not be replaced.

The argument span is used to help set the range of values used to construct the StructTS model. If span is set small, then the variance of epsilon dominates and the estimates are not smooth. If span is large, then the variance of level dominates and the estimates are linear interpolations. The variances of level and epsilon are components of the state-space model used to interpolate values, see StructTS for details. See Note for more information about the method.

If span is set larger than 99, then the entire time series is used to estimate all missing values. This approach may be useful if there are many periods of missing values. If span is set to any number less than 4, then simple linear interpolation will be used to replace missing values.

Value

The observations in x with missing values replaced by interpolation.

Note

The method used to interpolate missing values is based on tsSmooth constructed using StructTS on x with type set to "trend." The smoothing method basically uses the information (slope) from two values previous to missing values and the two values following missing values to smoothly interpolate values accounting for any change in slope. Beauchamp (1989) used time-series methods for synthesizing missing streamflow records. The group that is used to define the statistics that control the interpolation is very simply defined by span rather than the more in-depth measures described in Elshorbagy and others (2000).

If the data have gaps rather than missing values, then fillMissing will return a vector longer than x if Dates is given and the return data cannot be inserted into the original data frame. If Dates is not given, then the gap will be recognized and not be filled. The function insertMissing can be used to create a data frame with the complete sequence of dates.

References

Beauchamp, J.J., 1989, Comparison of regression and time-series methods for synthesizing missing streamflow records: Water Resources Bulletin, v. 25, no. 5, p. 961–975.

Elshorbagy, A.A., Panu, U.S., and Simonovic, S.P., 2000, Group-based estimation of missing hydrological data, I. Approach and general methodology: Hydrological Sciences Journal, v. 45, no. 6, p. 849–866.

See Also

tsSmooth, StructTS, insertMissing

Examples

## Not run: 
library(smwrData)
data(Q05078470)
# Create missing values in flow, the first sequence is a peak and the second is a recession
Q05078470$FlowMiss <- Q05078470$FLOW
Q05078470$FlowMiss[c(109:111, 198:201)] <- NA
# Interpolate the missing values
Q05078470$FlowFill <- fillMissing(Q05078470$FlowMiss)
# How did we do (line is actual, points are filled values)?
par(mfrow=c(2,1), mar=c(5.1, 4.1, 1.1, 1.1))
with(Q05078470[100:120, ], plot(DATES, FLOW, type="l"))
with(Q05078470[109:111, ], points(DATES, FlowFill))
with(Q05078470[190:210, ], plot(DATES, FLOW, type="l"))
with(Q05078470[198:201, ], points(DATES, FlowFill))

## End(Not run)

Description

Format an object of class "timeDay."

Usage

## S3 method for class 'timeDay'
format(x, format, ...)

Arguments

Value

A vector of character strings representing the time of day values in x.

See Also

strptime

Description

Compute sine and cosine terms for describing annual or daily variations.

Usage

fourier(x, k.max = 1)

Arguments

Details

The argument x can be expressed as decimal time, either annual or diel; or it can be an object of class "Date," "POSIXct," or "POSIXlt" in which case it will be converted to annual decimal time using the dectime function.

Value

A matrix of the sine and cosine terms corresponding to the value—two terms are computed for each value of k from 1 to k.max: sine(k 2 pi x) and cosine(k 2 pi x). The value of k.max is included as an attribute.

Note

Water-quality data commonly follow a sinusoidal variation throughout a yearly cycle. A Fourier series of order one to three is generally enough to adequately describe that variation for many constituents.

See Also

dectime

Examples

# compute the sine and cosine terms for quarters of 2002
fourier(2002 + (0:3)/4)
#           sin(k=1)      cos(k=1) 
# [1,]  3.54692e-014  1.00000e+000
# [2,]  1.00000e+000  7.08886e-013
# [3,] -3.65749e-013 -1.00000e+000
# [4,] -1.00000e+000 -3.78606e-013
# attr(, "k.max"):
# [1] 1
# Compare to 2 cycles per year:
fourier(2002 + (0:3)/4, 2)
#            sin(k=1)      cos(k=1)     sin(k=2) cos(k=2)
#[ 1,]  3.546924e-14  1.000000e+00 7.093848e-14        1
# [2,]  1.000000e+00  7.088855e-13 1.417771e-12       -1
# [3,] -3.657492e-13 -1.000000e+00 7.314983e-13        1
# [4,] -1.000000e+00 -3.786056e-13 7.572112e-13       -1
# attr(,"k.max")
# [1] 2

Description

Combine data from several rows into a single row based on common data in selected columns.

Usage

group2row(data, carryColumns, splitColumn, collectColumns)

Arguments

Details

The function group2row combines data from several rows into a single row. Certain columns in the input dataset are said to be "collected." Other columns may be "carried" into the output dataset by listing them in carryColumns. A new row will be created for each unique combination of values in the carryColumns. The output row consists of the carried columns plus new columns that are named by the unique values in the splitColumn concatenated with the names in the collectColumns. The number of columns in the output data frame is equal to the number of carryColumns plus the number of unique values in the splitColumn times the number of names in the collectColumns.

The strategy for collecting columns is to use a set of index values defined by the splitColumn. The maximum number of input rows collected for each output row is equal to the number of unique values defined in the splitColumn. The splitColumn is used to identify a column from the input data that contains output column information. If a row of input has a value in this column that matches one of the index values, then that row's data will be included in the output in the column positions corresponding to the matched index. The index values are concatenated with the input column names of the collected columns to derive output column names.

Examples

## Not run: 
library(smwrData)
data(QWstacked)
group2row(QWstacked, c("site_no", "sample_dt", "sample_tm"), "parm_cd", 
 c("result_va", "remark_cd"))

## End(Not run)

Description

Functions for transforming and back-transforming data using a hyperbolic function.

Usage

hyperbolic(x, factor = 0, scale = mean(x, na.rm = TRUE))

Ihyperbolic(x, factor = 0, scale)

Arguments

Details

If x contains missing values, then scale is computed after omitting the missing values and the output vector has a missing value wherever x has a missing value.

The basic equation for the hyberbolic transform is 1/(1 + (10^factor * x)/ scale). The basic equation is adjusted to produce fairly consistent values for small changes in factor and increase for increasing values in x.

The function hyperbolic computes the forward transform and the function Ihyperbolic computes the inverse [hyperbolic] transform, or back-transform.

Value

A numeric vector of the transformed or back-transformed values in x with an attribute "scale" of the values used for scale. The range of the values returned from hyperbolic is between 0 and 2 times scale.

Note

The original hyperbolic transform used a linear factor. The version in these functions uses the common log of the factor to make the factors easier to use.

When used with the default value for scale, factor values outside the range of +/- 3 have very little effect on the transform.

References

The use of a variable hyperbolic transform to help model the relations between stream water chemistry and flow was first described in:

Johnson, N.M., Likens, G.E., Borman, F.H., Fisher, D.W., and Pierce, R.S., 1969, A working model for the variation in stream water chemistry at the Hubbard Brook Experimental Forest, New Hampshire: Water Resources Research, v. 5, no. 6, p. 1353–1363.

See Also

boxCox

Examples

X.test <- c(1,4,9,16,25,36,49)
hyperbolic(X.test) # accept the defaults
hyperbolic(X.test, factor=1)
hyperbolic(X.test, factor=-1)

Description

Compute a basis for estimating hysteresis effects in some variable related to the argument x.

Usage

hysteresis(x, step = 3)

Arguments

Value

A numeric vector that approximates the local trend in x.

Note

The basis for estimating hysteresis is the current value x minus the mean of the previous step values. The first step values in the output will be missing, and each missing value will result in step plus 1 missing values. This approximates the trend in x; if x is increasing in value over the previous step values, then the output will be positive and the greater the relative increase, the larger the output.

References

The use of hysteresis to help model the relations between stream water chemistry and flow are described in:

Garrett, J.D., 2012, Concentrations, loads, and yields of select constituents from major tributaries of the Mississippi and Missouri Rivers in Iowa, water years 2004-2008: U.S. Geological Survey Scientific Investigations Report 2012–5240, 61 p.

Wang, P., and Linker, L.C., 2008, Improvement of regression simulation in fluvial sediment loads: Journal of Hydraulic Engineering, v. 134, no. 10, p. 1,527–1,531.

See Also

anomalies

Examples

## Not run: 
library(smwrData)
data(Q05078770)
# Plot flow and hysteresis to show looping 
with(Q05078770, plot(log(FLOW), hysteresis(log(FLOW), 3), type="l"))

## End(Not run)

Description

Imports a comma-separated variable file to a data frame.

Usage

importCSV(file.name = "", tz = "")

Arguments

Details

All of the dates in a date column must have the same format as the first non-blank date in the column. Any date with a format different from that of the first non-blank date in the column will be imported as NA (missing value). Dates imported as class "Date" using a 4-digit year, 2-digit month, and 2-digit day with the period (.), hyphen (-), slash (/), or no separator. Time and date data are imported as class "POSIXct" and assumes the standard POSIX format for date and time.

Value

A data frame with one column for each data column in the CSV file.

Note

A NULL data frame is created if there are no data in the file.

See Also

read.csv, read.table, scan, as.Date, as.POSIXct

Examples

## Not run: 
## These datasets are available in smwrData as text files
TestDir <- system.file("misc", package="smwrData")
TestPart <- importCSV(file.path(TestDir, "TestPart.csv"))

## End(Not run)

Description

Imports a formatted, tab-delimited file to a data frame.

Usage

importRDB(file.name = "", date.format = NULL, tz = "",
  convert.type = TRUE)

Arguments

Details

All of the dates in a date column must have the same format as the first non-blank date in the column. Any date with a format different from that of the first non-blank date in the column will be imported as NA (missing value). By default, dates are imported as class "Date" using a 4-digit year, 2-digit month, and 2-digit day with the period (.), hyphen (-), slash (/), or no separator.

If a valid date.format is supplied, then the data are imported using as.POSIXct, and time information can be included in the data. If date.format is "none," then conversion of the date information is suppressed and the data are retained as character strings.

The value for tz should be a valid "Olson" format consisting typically of a continent and city. See timezone for a description of time zones. For the United States, use these time-zone specifications where daylight savings time is used:

Use these time specifications where daylight savings time is not used: #'

Value

A data frame with one column for each data column in the RDB file.

Note

A NULL data frame is created if there are no data in the file.

The header information contained in the RDB file is retained in the output dataset as comment.

If convert.type is TRUE, then non-numeric values, other than blanks, are converted to NaN (not a number) rather than NA (missing value) in numeric columns. NaN values are treated like NA values but can be identified using the is.nan function.

See Also

read.table, as.Date, as.POSIXct, comment

Examples

## Not run: 
## This dataset is available in smwrData as a text file
TestDir <- system.file("misc", package="smwrData")
TestFull <- importRDB(file.path(TestDir, "TestFull.rdb"))

## End(Not run)

Description

Inserts rows of missing values into a data frame for gaps in a sequence.

Usage

insertMissing(x, col, fill = FALSE)

Arguments

Value

A data frame like x, but with rows of missing values where there is a break in the sequence in column col.

Note

Setting fill to TRUE is useful for setting up datasets for fillMissing if there are gaps in the retrieved data. Setting fill to FALSE, the default, is useful for creating a break in the sequence for plotting the data.

See Also

fillMissing, screenData

Examples

## Not run: 
library(smwrData)
data(Q05078470)
# Plot the original data
with(Q05078470[100:120, ], plot(DATES, FLOW, type="l"))
# Remove 3 rows from the data set
Q05078470 <- Q05078470[-(109:111), ]
# Plot the data--line drawn through the missing record
with(Q05078470[100:117, ], lines(DATES, FLOW, col="green"))
# Insert a missing record
Q05078470 <- insertMissing(Q05078470, "DATES")
# Now plot to show gap in line
with(Q05078470[100:118, ], lines(DATES, FLOW, col="blue"))

## End(Not run)

Description

Indicate which elements are missing.

Usage

## S3 method for class 'timeDay'
is.na(x)

Arguments

Value

A logical vector of the same length as its argument x, containing TRUE for those elements marked NA and FALSE otherwise.

Examples

is.na(as.timeDay(c("10:30", "11:00")))

Description

Tests if an object can be treated as a character, to name something; as a date; as a grouping variable, has distinct values; or as a number.

Usage

isCharLike(x)

isDateLike(x)

isGroupLike(x)

isNumberLike(x)

Arguments

Details

The function isCharLike tests whether x is of class "character" or "factor." The function isDateLike tests whether x is of class "Date" or "POSIXt." The function isGroupLike tests whether x is of class "character" or "factor" or if x is of type "integer" or "logical." The function isNumberLike tests whether x is of type "numeric" or of class "Date."

Value

A logical value TRUE if x meets the criteria, or FALSE if it does not.

Note

This function is most useful within other functions to control how that function handles a particular argument.

See Also

class, is.numeric, is.factor, is.character, is.integer, is.logical

Examples

## The first should be FALSE and the second TRUE
isCharLike(as.Date("2004-12-31"))
isCharLike("32")

## The first should be FALSE and the second TRUE
isDateLike(32)
isDateLike(as.Date("2004-12-31"))

## The first should be FALSE and the second TRUE
isGroupLike(as.Date("2004-12-31"))
isGroupLike(32)

## The first should be FALSE and the second TRUE
isNumberLike(as.Date("2004-12-31"))
isNumberLike(32)

Description

Get the length of a time-of-day object.

Usage

## S3 method for class 'timeDay'
length(x)

Arguments

Value

An integer of length 1 indicating the number of elements in x.

Examples

length(as.timeDay(c("10:30", "11:00")))

Description

Create a template meta file for a CSV file. The meta file is used by importCSV to define column types.

Usage

makeMeta(file.name = "")

Arguments

Value

The name of the meta file that was created.

Note

The meta file that is created will only contain column types of character, numeric, integer, and logical. It may need to be edited by the user to redefine the column types actually needed for the data, for example columns of class "Date," "POSIXct," or "factor."

See Also

importCSV

Description

A utility to help model.frame.default create the correct matrices when predicting from models with quadratic, hyperbolic, or boxCox terms. Used only internally.

Usage

## S3 method for class 'quadratic'
makepredictcall(var, call)

## S3 method for class 'hyperbolic'
makepredictcall(var, call)

## S3 method for class 'boxCox'
makepredictcall(var, call)

## S3 method for class 'scaleRng'
makepredictcall(var, call)

Arguments

Value

A replacement for call for the prediction variable.

Description

No mathematical functions, such as log or exp are allowed on time-of-day data. An error results when trying to use any mathematical function on objects of class "timeDay."

Usage

## S4 method for signature 'timeDay'
Math(x)

Arguments

Description

Merge two datasets based on the the nearest date between each observation.

Usage

mergeNearest(left, dates.left = "DATES", all.left = FALSE,
  suffix.left = "left", right, dates.right = "DATES",
  suffix.right = "right", Date.noon = TRUE, max.diff = "7 days")

Arguments

Details

The format for max.diff should be a numeric value followed by a description of the time span. The time span must be one of "secs," "mins," "hours," "days," or "weeks" for seconds, minutes, hours, days, or weeks, respectively.

Value

A data frame of the merged data with common column names renamed by the suffix arguments to avoid conflict.

Note

Water-quality data taken at a specific time frequently need to be merged with daily flow data or merged with other water-quality data such as replicate samples or samples of a different medium taken at about the same time, but having a different time stamp.

See Also

mergeQ

Examples

library(smwrData)
data(Q05078470)
data(QW05078470)
# Set the actual time of sampling in QW05078470
QW05078470 <- transform(QW05078470, DATES=DATES + as.timeDay(TIMES))
mergeNearest(QW05078470, right=Q05078470)
# Notice the difference in selected dates
mergeNearest(QW05078470, right=Q05078470, Date.noon=FALSE)

Description

Merges the flow (or other data) column from one or many daily-value datasets into a another dataset with one or more stations.

Usage

mergeQ(QWdata, STAID = "STAID", FLOW = "FLOW", DATES = "DATES",
  Qdata = NULL, Prefix = NULL, Plot = TRUE, ...)

Arguments

Details

More than one column can be specified for FLOW when merging a single station, and the flow data are specified in Qdata.

Value

A data frame like QWdata with an attached flow column(s).

Note

The station-identifier columns must be of class character.

There are fours ways to merge flow and water-quality data:

A dataset that contains data for a single site does not require a STAID column. Qdata must be supplied. This case must be used if the flow record is incomplete or does not cover the range of dates in QWdata; all other methods will fail if that is the case. See Example 1.

A dataset that contains data for one or more sites can be merged with a dataset that contains flow data for the sites in that first dataset. This method will fail if there is not a complete list of station identifiers in the flow dataset. See Example 2.

A dataset that contains data for one or more sites can be merged with flow datasets that have names based on STAID. The structure of the name must be some common prefix followed by the station identifier. The station identifier must conform to a valid name. This method will fill in missing values (NAs) if a dataset corresponding to a station identifier is not available. See Example 3.

A dataset that contains data for one or more sites can be merged with flow datasets that have arbitrary names. Station identifiers that do not conform to valid names must be quoted. This method will fail if there is not a complete list of station identifiers supplied as arguments. See Example 4.

The plot shows the joint distribution of the sampled flows and observed flows from the sampling time period. The quantile-quantile plots are used to assess whether the sampled and observed flows have the same distribution. If the distributions are the same, then the plot will be approximately a straight line (included as a reference line). The extreme points can have more variability than points toward the center. A plot that shows the upper end trailing upward indicates that the largest flows have been under sampled and the sampled data may not give a reliable estimate of loads.

See Also

mergeNearest

Examples

## Not run: 
library(smwrData)
data(Q05078470)
data(Q05078770)
data(Qall)
data(QW05078470)
data(QWall)
#                   Example 1
#
mergeQ(QW05078470, Qdata=Q05078470, Plot=FALSE)

#                   Example 2
#
mergeQ(QWall, FLOW="Flow", Qdata=Qall, Plot=FALSE)

#                   Example 3
#
mergeQ(QWall, Prefix="Q", Plot=FALSE)

#                   Example 4
# Note quotes required for station identifiers
mergeQ(QWall, "05078470"=Q05078470, "05078770"=Q05078770, Plot=FALSE)

## End(Not run)

Description

Display the contents of an object by pages.

Usage

more(x, n = 20L, ...)

Arguments

Value

The object x is returned invisibly. Sections of x of length n are displayed during the execution of the function.

Note

The function more is intended for interactive sessions. If used in a non-interactive session, it simply returns x invisibly.

Several keyboard commands can be used to view the contents of x. The function more will display n lines of x and wait for user input. Any of the following commands can be entered by the user; either upper- or lowercase letters are accepted.

q Quit

t Go to top of x

b Go to bottom of x

u Go up 1/2 page

p Go to previous page

d Go down 1/2 page

colName/pattern Search for pattern in the column named colName

h or ? Print help

any other letter Go down full page

Searching for a pattern in a column uses grep to search for the specified pattern in the character representation of the data in the column. This makes it possible to search columns that are not type character.

See Also

head, tail, grep

Description

Implements the Savitzky-Golay (Savitzky and Golay, 1964) filter on a regular series of data to compute a moving average.

Usage

movingAve(x, span = 3, order = 0, pos = "center")

Arguments

Value

A vector of the same length as x containing the averages.

Note

For odd values of span and pos equal to "center," order equal to 0 or 1 gives the same result.

In general, there is no reason to use polynomial orders greater than 2, and pos should always be set to "center" for polynomial orders greater than 1 to avoid strange behavior due to end effects.

The weights for the averages are computed based on linear model theory (Savitzky and Golay, 1964; Wood and Hockens, 1970). Wood and Hockens (1970) also discuss some artifacts resulting from smoothing.

References

Savitzky, A., and Golay, M.J.E., 1964, Smoothing and differentiation of data by simplified least squares procedures: Analytical Chemistry, v. 36, no. 8, p. 1627–1639.

Wood, L.C., and Hockens, S.N., 1970, Least squares smoothing operators: Geophysics, v. 35, no. 6, p. 1005–1019.

See Also

filter, diff, movingDiff

Examples

## Construct a simple valley
movingData <- abs(seq(-5, 5))
movingAve(movingData, span=5)
movingAve(movingData, span=5, order=2)

Description

Filter a regular series of data to compute a moving difference.

Usage

movingDiff(x, span = 1, pos = "end")

Arguments

Value

A vector of the same length as x containing the differences.

See Also

filter, diff, movingAve

Examples

# Construct a simple valley
movingData <- abs(seq(-5, 5))
movingDiff(movingData, span=1)
# Compare to diff:
diff(movingData)

Description

Converts missing values (NAs) to or from a user specified value.

Usage

na2miss(x, to = -99999)

miss2na(x, from = -99999)

Arguments

Value

An object like x with each target value replaced by the specified value.

Note

The function na2miss converts missing values (NA) to the value to and is useful to prepare a vector for export and subsequent use by software external to R that does not handle NAs.
The function miss2na converts the value from to NA and can be used to recode data imported from external software that uses a special value to indicate missing values.

See Also

is.na, sub

Examples

## Construct simple substitutions
na2miss(c(1, 2, 3, NA, 5, 6))

Description

Find the local maxima in a vector.

Usage

peaks(x, span = 3, ties = "first", ends = TRUE)

Arguments

Details

Possible values for ties include "none," which treats sequential tied values as individual values; all other values can be thought of as collapsing sequential tied values—"first," "middle," or "last" identify the first, middle, or last, respectively, of a sequence of ties as the peak if appropriate.

Value

A vector matching x of logical values indicating whether the corresponding element is a local maximum or not.

Note

A peak is defined as an element in a sequence that is strictly greater than all other elements within a window of width span centered at that element. As such, setting ties to "none" has the effect of not identifying peaks with sequential tied values.

See Also

max

Examples

# Note the effect of missing values
peaks(c(1:6,5,4,NA,4,6,9,NA))
peaks(c(1:6,NA,5,4,NA,4,6,9,NA))
# Note the effect of ties
peaks(c(1:6,6,6,5,4,3,4,6,9))
peaks(c(1:6,6,6,5,4,3,4,6,9), ties="none")

Description

Return the value associated with test from the supplied vectors.

Usage

pick(test, ..., .pass = test, na = NA)

Arguments

Details

If test is logical, then if test is TRUE, return the first argument in ..., otherwise return the second argument.
If test is numeric, then return that value in the list defined by ...
If test is character, then return that value in the list defined by ..., which must be named in the call.
If test is NA, then return the value specified by na.

Value

A vector of the same length as test and data values from the values list defined by ... The mode of the result will be coerced from the values list defined by ...

Note

This function is designed to replace nested ifelse expressions. See Examples. It is different from switch in that the value selected from the possible alternatives is selected by the values in test rather than by a single value.

See Also

ifelse, switch

Examples

## Create the test vector
testpick <- c(1,2,3,1)
## Nested ifelse
ifelse(testpick == 1, 1,
 ifelse(testpick == 2, 3,
   ifelse(testpick == 3, 5, NA)))
## Results by pick:
pick(testpick, 1, 3, 5)
## Create a test vector of character data
testpick <- c("a","b","c","a")
pick(testpick, a=1, b=3, c=5)

Description

Print an object of class "timeDay."

Usage

## S3 method for class 'timeDay'
print(x, ...)

Arguments

Value

The object x is returned invisibly.

Side Effect

The object x is printed.

Note

The object is printed using the format that created the object in as.timeDay.

See Also

timeDay-class, as.timeDay

Description

Computes orthogonal polynomials of degree 2 (Cohn and others, 1992). Used primarily in a linear regression formula.

Usage

quadratic(x, center = NULL)

Arguments

Value

A matrix of two columns—the centered value of x and its square.

Note

If center is specified, then the polynomials will not necessarily be orthogonal. If used in a linear regression formula, then the coefficient of the linear term is the slope at center.
The function quadratic differs from poly in that the data are not scaled, so the regression coefficients are directly interpretable in terms of the units of x.

References

Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D., and Summers, R.M., 1992, The validity of a simple statistical model for estimating fluvial constituent loads—An empirical study involving nutrient loads entering Chesapeake Bay: Water Resources Research, v. 28, no. 5, p. 937–942.

See Also

poly

Examples

## first and second orthogonal polynomials for the sequence from 1 to 10
quadratic(seq(10))

Description

Import data arranged on lines into a list.

Usage

readList(file, names = TRUE, sep = "", nlines = 1, convert = NULL)

Arguments

Value

A list with one component for each nlines in the input file.

See Also

as

Examples

# Make a 3-line example dataset with component names A, B, and C.
cat("A 1 2 3 4\nB 5 6 7\nC 8 9\n", file="readList.test")
# Read the example dataset
readList("readList.test")

Description

Converts a specified value to another value.

Usage

recode(x, from, to)

## S3 method for class 'factor'
recode(x, from, to)

## S3 method for class 'integer'
recode(x, from, to)

## S3 method for class 'character'
recode(x, from, to)

## S3 method for class 'numeric'
recode(x, from, to)

Arguments

Value

An object like vector with each target value replaced by the specified value.

Note

When used on numeric (type "double"), the recode function uses an approximate match within a small tolerance range to avoid mismatches due to computations.
The function sub offers greater flexibility than recode for replacing parts of text instead of the complete text.

See Also

sub, na2miss, miss2na

Examples

XT <- c(1, 2, 0, 4)
recode(XT, 0, 3)

Description

Some time-series analyses require data that are uniformly spaced in time. This function will construct a regular series from randomly spaced data using any of several user-definable methods.

Usage

regularSeries(x, times, period = "month", which = "middle", begin, end,
  k.period = 1)

Arguments

Details

For regularSeries, if there is no observation during a period, then that value is set to NA. If there is one observation, then the value is set to the value of that single observation. The value of which controls how periods with multiple observations are handled. Three character strings are recognized for selecting a single value: "earliest" selects the earliest observation in the period, "middle" selects the observation closest to the middle of the period, and "latest" selects the latest observation in the period. If which is not one of these, then it should be the name of a function such as mean or median.

Value

The function regularSeries returns a data frame with the following columns:

Examples

## Not run: 
library(smwrData)
data(QW05078470)
with(QW05078470, regularSeries(P00665, DATES))
# there should be no values for season numbers 2, 5, or 10

## End(Not run)

Description

Transforms numeric data to a specified range.

Usage

scaleRng(x, Min = 0, Max = 1, x.range = range(x, na.rm = TRUE))

IscaleRng(x, Min, Max, x.range)

Arguments

Details

The function scaleRng maps the minimum of x.range to Min and the maximum of x.range to Max and uses linear interpolation for other values in x.

Value

A numeric vector scaled to the specified range.

Note

Some applications recommend or require data scaled to a consistent range. The function scaleRng will do that and can be used to back-transform the data.

Examples

## simple case with back-transform
x.tmp <- print(scaleRng(c(1.2, 2.3, 3.4, 5.6)))
IscaleRng(x.tmp)
## now set the expected ranges
x.tmp <- print(scaleRng(c(1.2, 2.3, 3.4, 5.6), x.range=c(1, 6)))
IscaleRng(x.tmp)

Description

Screens data to determine if a value is reported for each date by calendar or water year.

Usage

screenData(dates, values, type = "DV", year = "calendar", printit = TRUE)

Arguments

Details

Missing values are permitted in either dates or values. Those missing values are tallied in the completeness of record.

Value

For type = "DV," a matrix of the counts of missing values, either coded as NA or not in the dataset, for each month and each year within the range of dates.

For type = "IV," a matrix of the counts of observed values for each month and each year within the range of dates.

References

This function is based on the screen program described in:
Rutledge, A.T., 2007, Program user guide for RECESS: at https://water.usgs.gov/ogw/recess/UserManualRECESS.pdf.

Examples

library(smwrData)
data(Q05078770)
# this should indicate no missing values.
with(Q05078770, screenData(DATES, FLOW))
# There should be missing values shown for:
#months 10-12 in water year 2003 (October - December, 2002), and
#months 1-9 of water year 2004.  
with(Q05078770, screenData(DATES, FLOW, year="w"))

Description

Functions for transforming and back-transforming data using an s-shaped curve.

Usage

sCurve(x, location = 0, scale = 1, shape = 1)

IsCurve(x, location = 0, scale = 1, shape = 1)

Arguments

Details

The basic equation for the s-curve is z/(1 + abs(z)^shape)^(1/shape), where z is scale*(x-location).

The function sCurve computes the forward transform and the function IsCurve computes the inverse [sCurve] transform, or back-transform.

Value

A numeric vector of the transformed or back-transformed values in x.

Note

The sCurve function is related to the hyperbolic function in that both can represent mixing models for flow in stream water chemistry. The sCurve function is more flexible when there are distinct upper and lower limits to the concentration. The hyperbolic function is more flexible for open-ended concentrations for either high or low flows. Also, sCurve would typically use log-transformed values for flow.

See Also

hyperbolic

Examples

## Not run: 
# Basic changes to the s-curve
curve(sCurve(x), -5,5, ylim=c(-1,1))
# Shift to left
curve(sCurve(x, location=1), -5,5, add=TRUE, col="red")
# increase slope
curve(sCurve(x, scale=2), -5,5, add=TRUE, col="cyan")
# increase rate
curve(sCurve(x, shape=2), -5,5, add=TRUE, col="purple")

## End(Not run)

Description

Create categories for any definitions of seasons by month and day.

Usage

seasons(x, breaks, Names = paste("Season Ending ", breaks, sep = ""))

Arguments

Details

The default names for the seasons are of the form "Season Ending ...," where ... is derived from breaks.

Value

A factor of seasonal categories.

See Also

month

Examples

## Just two seasons
seasons(as.Date(c("2001-03-31", "2001-06-30", "2001-09-30")), breaks=c("June", "December"))
## The equivalent using mm/dd format
seasons(as.Date(c("2001-03-31", "2001-06-30", "2001-09-30")), breaks=c("06/30", "12/31"))
## Not run: 
# Apply to a real dataset
library(smwrData)
data(QW05078470)
transform(QW05078470, Seas=seasons(DATES, breaks=c("June", "December")))

## End(Not run)

Description

Collapse a numeric sequence into a compact form that represents continuous ranges and discontinuous values.

Usage

seqCollapse(x, sequential = "-", skips = ", ")

Arguments

Value

A character string that represents that data in x in a compact form. If x is empty, then "" is returned.

Note

This function is commonly used to express years in a compact form.

See Also

paste

Examples

# A single value
seqCollapse(1968)
# A single, continuous range of values
seqCollapse(1968:1992)
# A collection of continuous and individual values
seqCollapse(c(1968:1992, 1998, 2002, 2006:2012))

Description

Create a new file or object name from an old name with an optional new suffix.

Usage

setFileType(filename, type = "tmp", replace = FALSE)

Arguments

Value

A character string like filename but with a new suffix.

Note

This function is designed as a support function for many functions.

Author(s)

Dave Lorenz, original coding by Jim Slack, U.S. Geological Survey retired.

Examples

# Replace the .dat suffix with .txt
setFileType("TestName.dat", "txt", replace=TRUE)

Description

Set the time-zone information for dates and times.

Usage

setTZ(x, TZ, force.stz = FALSE)

Arguments

Details

The time-zone information should be a standard name like those described in http://en.wikipedia.org/wiki/List_of_zoneinfo_time_zones. For the convenience of users in the United States, correct conversion is provided for the time-zone codes of EST, EDT, CST, CDT, MST, MDT, PST, PDT, AKST, AST, AKDT, ADT, HAST, and HST. However, time data in States like Arizona, where savings time is never used, would use time-zone information specified like " America/Phoenix" to avoid the possibility of setting savings time when it is not appropriate.

Value

Data like x, but with times adjusted by the time-zone information.

Note

The time-zone information is a characterisitic of the data and not of each individual value. If the data in x come from different time zones, then a time zone is selected from the data and used as the base—the dates in x are correctly converted to the selected time zone and a warning is issued.

See Also

as.POSIXct

Examples

TestDts <- as.POSIXct(c("2010-05-28 09:50:00", "2010-11-29 15:20:00"))
setTZ(TestDts, c("PDT", "PST"))
# Try setting to different time zones
setTZ(TestDts, c("PDT", "CST"))

Description

Returns a vector like the input, but with the position of the data shifted up or down.

Usage

shiftData(x, k = 1, fill = NA, circular = FALSE)

Arguments

Value

A vector like x, with data shifted in position.

See Also

lag

Examples

shiftData(1:5, k=1)
# [1] NA  1  2  3  4
shiftData(1:5, k=1, circ=TRUE)
# [1] 5 1 2 3 4

Description

Display the time of day.

Usage

## S4 method for signature 'timeDay'
show(object)

Arguments

Value

The object is printed and returned invisibly.

Description

Compute percentage or proportion of elements in a composition.

Usage

sumComposition(x, ..., Range = 100)

Arguments

Details

Missing values are permitted in x or ... and result in missing values for the row in the output.

Value

A matrix with columns matching all of the data in x and ...{} with rows summing to Range.

Note

This function is designed to meet a very simple need in some applications like constructing data for Piper (Piper, 1944) or trilinear diagrams. For more in-depth manipulations of compositional data, the user is directed to the compositions or other similar package.

References

Piper, A.M., 1944, A graphical procedure in the geochemical interpretation of water analyses: Transactions of the American Geophysical Union, v. 25, p. 914-923.

Examples

# Create tiny dataset
TinyCations <- data.frame(Ca=c(32, 47, 28), Mg=c(10,12,15), Na=c(7, 5, 7))
sumComposition(TinyCations)

Description

Class "timeDay" describes the time of day without any reference to the date. The data are stored as seconds since midnight.

Slots

time

the time of day in seconds since midnight.

format

a character string indicating the format to display the time of day.

Objects from the Class

Objects can be created by calls of the form as.timeDay(time, format).

Examples

showClass("timeDay")

Description

Constructs a data frame from the count data in a contingency table, using the column and row names as classes.

Usage

untable(x, rows = "Rows", cols = "Columns", counts = FALSE)

Arguments

Value

A data frame containing two columns named from rows and cols and an optional column named "Counts" if counts is set to TRUE.

Note

The output for this function can be used for input to contingency table analysis functions that require a data frame rather than a contingency table. To convert a column from factor to ordered, use the ordered function.

See Also

ordered

Examples

## Create a small synthetic data matrix
mdat <- matrix(seq(6), nrow = 2, ncol=3,
   dimnames = list(c("row1", "row2"), c("C.1", "C.2", "C.3")))
untable(mdat)

Description

Create an ordered factor or numeric values from a vector of dates based on the water year.

Usage

waterYear(x, numeric = FALSE)

Arguments

Value

An ordered factor or numeric vector corresponding to the water year.

Note

The water year is defined as the period from October 1 to September 30. The water year is designated by the calendar year in which it ends. Thus, the year ending September 30, 1999, is the "1999 water year."

See Also

year

Examples

library(smwrData)
data(QW05078470)
## Return an ordered factor
waterYear(QW05078470$DATES)

Description

Identifies the row and column numbers (indexes) of TRUE values in a logical matrix.

Usage

whichRowCol(x, which = "both")

Arguments

Value

A matrix of the row and column number if which is "both." Otherwise a named vector of the row number, if which is "row," or column number, if which is "col." Only the first character is needed.

Note

Some comparisons, %in% for example, will return a vector rather than a matrix and cause whichRowCol to fail.

See Also

row, col, which

Examples

# Simple case to find a single value
whichRowCol(matrix(1:20, ncol=4) == 16)
# Where are the missing values in a data set?
library(smwrData)
data(MenomineeMajorIons)
whichRowCol(sapply(MenomineeMajorIons, is.na))