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Aggregate metabolism data

Description

Aggregate a metabolism attribute from swmpr data by a specified time period and method

Usage

aggremetab(swmpr_in, ...)## S3 method for class 'swmpr'aggremetab(swmpr_in, by = "weeks", na.action = na.pass, alpha = 0.05, ...)

Arguments

Details

The function summarizes metabolism data by averaging across set periods of observation. Confidence intervals are also returned based on the specified alpha level. It is used withinplot_metab function to view summarized metabolism results. Data can be aggregated by'years','quarters','months', or'weeks' for the supplied function, which defaults to themean. The method of treating NA values for the user-supplied function should be noted since this may greatly affect the quantity of data that are returned.

Value

Returns an aggregated metabolismdata.frame if themetabolism attribute of the swmpr object is notNULL. Upper and lower confidence limits are also provided if the aggregation period was specified as a character string.

See Also

aggregate,aggreswmp,ecometab,plot_metab

Examples

## Not run: ## import water quality and weather datadata(apadbwq)data(apaebmet)## qaqc, combinewq <- qaqc(apadbwq)met <- qaqc(apaebmet)dat <- comb(wq, met)## estimate metabolismres <- ecometab(dat)## change aggregation period and alphaaggremetab(res, by = 'months', alpha = 0.1)## use a moving window average of 30 daysaggremetab(res, by = 30)## use a left-centered window insteadaggremetab(res, by = 30, sides = 1)## End(Not run)

Aggregate swmpr data

Description

Aggregate swmpr data by specified time period and method

Usage

aggreswmp(swmpr_in, ...)## S3 method for class 'swmpr'aggreswmp(  swmpr_in,  by,  FUN = function(x) mean(x, na.rm = TRUE),  params = NULL,  aggs_out = FALSE,  plot = FALSE,  na.action = na.pass,  ...)

Arguments

Details

The function aggregates parameter data for a swmpr object by set periods of observation and a user-supplied function. It is most useful for aggregating noisy data to evaluate trends on longer time scales, or to simply reduce the size of a dataset. Data can be aggregated by'years','quarters','months','weeks','days', or'hours' for the supplied function, which defaults to themean. A swmpr object is returned for the aggregated data, although the datetimestamp vector will be converted to a date object if the aggregation period is a day or longer. Days are assigned to the date vector if the aggregation period is a week or longer based on the round method forIDate objects. This approach was used to facilitate plotting using predefined methods for Date and POSIX objects.

The method of treating NA values for the user-supplied function should be noted since this may greatly affect the quantity of data that are returned (see the examples). Finally, the default argument forna.action is set tona.pass for swmpr objects to preserve the time series of the input data.

Value

Returns an aggregated swmpr object. QAQC columns are removed if included with input object. Ifaggs_out = TRUE, the originalswmpr object is returned with thedatetimestamp column formatted for the first day of the aggregation period fromby. Aggplot object of boxplot summaries is returned ifplot = TRUE.

See Also

aggregate

Examples

## Not run: ## get data, prepdata(apacpwq)dat <- apacpwqswmpr_in <- subset(qaqc(dat), rem_cols = TRUE)## get mean DO by quartersaggreswmp(swmpr_in, 'quarters', params = c('do_mgl'))## get a plot insteadaggreswmp(swmpr_in, 'quarters', params = c('do_mgl'), plot = T)## plots with other variablesp <- aggreswmp(swmpr_in, 'months', params = c('do_mgl', 'temp', 'sal'), plot = T)plibrary(ggplot2)p + geom_boxplot(aes(fill = var)) + theme(legend.position = 'none')## get variance of DO by years, remove NA when calculating variance## omit NA data in outputfun_in <- function(x)  var(x, na.rm = TRUE)aggreswmp(swmpr_in, FUN = fun_in, 'years') ## End(Not run)

Import current station records from the CDMO

Description

Import current station records from the CDMO starting with the most current date

Usage

all_params(station_code, Max = 100, param = NULL, trace = TRUE)

Arguments

Details

This function retrieves data from the CDMO through the web services URL. The computer making the request must have a registered IP address. Visit the CDMO web services page for more information:https://cdmo.baruch.sc.edu/webservices.cfm. Function is the CDMO equivalent ofexportAllParamsXMLNew but actually usesall_params_dtrng, which is a direct call toexportAllParamsDateRangeXMLNew.

Value

Returns a swmpr object, all available parameters including QAQC columns

See Also

all_params_dtrng,single_param

Examples

## Not run: ## all parameters for a station, most recentall_params('hudscwq')## End(Not run)

Get CDMO records within a date range

Description

Get station records from the CDMO within a date range

Usage

all_params_dtrng(station_code, dtrng, param = NULL, trace = TRUE, Max = NULL)

Arguments

Details

This function retrieves data from the CDMO through the web services URL. The computer making the request must have a registered IP address. Visit the CDMO web services page for more information:https://cdmo.baruch.sc.edu/webservices.cfm. This function is the CDMO equivalent ofexportAllParamsDateRangeXMLNew.Download time may be excessive for large requests.

Value

Returns a swmpr object, all parameters for a station type (nutrients, water quality, or meteorological) or a single parameter if specified. QAQC columns are not provided for single parameters.

See Also

all_params,single_param

Examples

## Not run: ## get all parameters within a date rangeall_params_dtrng('apaebwq', c('01/01/2013', '02/01/2013'))## get single parameter within a date rangeall_params_dtrng('apaebwq', c('01/01/2013', '02/01/2013'),    param = 'do_mgl')## End(Not run)

Example nutrient data for Apalachicola Bay Cat Point station.

Description

An example nutrient dataset for Apalachicola Bay Cat Point station. The data are aswmpr object that have been imported into R from csv files using theimport_local function. The raw data were obtained from the CDMO data portal but can also be accessed from a zip file created for this package. See the source below. The help file for theimport_local function describes how the data can be imported from the zip file. Attributes of the dataset includenames,row.names,class,station,parameters,qaqc_cols,date_rng,timezone, andstamp_class.

Usage

apacpnut

Format

Aswmpr object anddata.frame with 215 rows and 13 variables:

datetimestamp

POSIXct

po4f

num

f_po4f

chr

nh4f

num

f_nh4f

chr

no2f

num

f_no2f

chr

no3f

num

f_no3f

chr

no23f

num

f_no23f

chr

chla_n

num

f_chla_n

chr

Source

https://s3.amazonaws.com/swmpexdata/zip_ex.zip

Examples

data(apacpnut)

Example water quality data for Apalachicola Bay Cat Point station.

Description

An example water quality dataset for Apalachicola Bay Cat Point station. The data are aswmpr object that have been imported into R from csv files using theimport_local function. The raw data were obtained from the CDMO data portal but can also be accessed from a zip file created for this package. See the source below. The help file for theimport_local function describes how the data can be imported from the zip file. Attributes of the dataset includenames,row.names,class,station,parameters,qaqc_cols,date_rng,timezone, andstamp_class.

Usage

apacpwq

Format

Aswmpr object anddata.frame with 70176 rows and 25 variables:

datetimestamp

POSIXct

temp

num

f_temp

chr

spcond

num

f_spcond

chr

sal

num

f_sal

chr

do_pct

num

f_do_pct

chr

do_mgl

num

f_do_mgl

chr

depth

num

f_depth

chr

cdepth

num

f_cdepth

chr

level

num

f_level

chr

clevel

num

f_clevel

chr

ph

num

f_ph

chr

turb

num

f_turb

chr

chlfluor

num

f_chlfluor

chr

Source

https://s3.amazonaws.com/swmpexdata/zip_ex.zip

Examples

data(apacpwq)

Example water quality data for Apalachicola Bay Dry Bar station.

Description

An example water quality dataset for Apalachicola Bay Dry Bar station. The data are aswmpr object that have been imported into R from csv files using theimport_local function. The raw data were obtained from the CDMO data portal but can also be accessed from a zip file created for this package. See the source below. The help file for theimport_local function describes how the data can be imported from the zip file. Attributes of the dataset includenames,row.names,class,station,parameters,qaqc_cols,date_rng,timezone, andstamp_class.

Usage

apadbwq

Format

Aswmpr object anddata.frame with 70176 rows and 25 variables:

datetimestamp

POSIXct

temp

num

f_temp

chr

spcond

num

f_spcond

chr

sal

num

f_sal

chr

do_pct

num

f_do_pct

chr

do_mgl

num

f_do_mgl

chr

depth

num

f_depth

chr

cdepth

num

f_cdepth

chr

level

num

f_level

chr

clevel

num

f_clevel

chr

ph

num

f_ph

chr

turb

num

f_turb

chr

chlfluor

num

f_chlfluor

chr

Source

https://s3.amazonaws.com/swmpexdata/zip_ex.zip

Examples

data(apadbwq)

Example weather data for Apalachicola Bay East Bay station.

Description

An example weather dataset for Apalachicola Bay East Bay station. The data are aswmpr object that have been imported into R from csv files using theimport_local function. The raw data were obtained from the CDMO data portal but can also be accessed from a zip file created for this package. See the source below. The help file for theimport_local function describes how the data can be imported from the zip file. Attributes of the dataset includenames,row.names,class,station,parameters,qaqc_cols,date_rng,timezone, andstamp_class.

Usage

apaebmet

Format

Aswmpr object anddata.frame with 70176 rows and 23 variables:

datetimestamp

POSIXct

atemp

num

f_atemp

chr

rh

num

f_rh

chr

bp

num

f_bp

chr

wspd

num

f_wspd

chr

maxwspd

num

f_maxwspd

chr

wdir

num

f_wdir

chr

sdwdir

num

f_sdwdir

chr

totpar

num

f_totpar

chr

totprcp

num

f_totprcp

chr

cumprcp

num

f_cumprcp

chr

totsorad

num

f_totsorad

chr

Source

https://s3.amazonaws.com/swmpexdata/zip_ex.zip

Examples

data(apaebmet)

Calculate oxygen mass transfer coefficient

Description

Calculate oxygen mass transfer coefficient using equations in Thiebault et al. 2008. Output is used to estimate the volumetric reaeration coefficient for ecosystem metabolism.

Usage

calckl(temp, sal, atemp, wspd, bp, height = 10)

Arguments

Details

This function is used within theecometab function and should not be used explicitly.

Value

Returns numeric value for oxygen mass transfer coefficient (m d-1).

References

Ro KS, Hunt PG. 2006. A new unified equation for wind-driven surficial oxygen transfer into stationary water bodies. Transactions of the American Society of Agricultural and Biological Engineers. 49(5):1615-1622.

Thebault J, Schraga TS, Cloern JE, Dunlavey EG. 2008. Primary production and carrying capacity of former salt ponds after reconnection to San Francisco Bay. Wetlands. 28(3):841-851.

See Also

ecometab

Flag observations above/below detection limits

Description

Flag observations above/below detection limits

Usage

cens_id(swmpr_in, ...)## S3 method for class 'swmpr'cens_id(swmpr_in, flag_type = "both", select = NULL, ...)

Arguments

Details

Censored observations are identified in swmpr objects using the CDMO flags -4 or -5, indicating outside the low or high sensor range, respectively. Additional codes are identified including A (-2007) or SUL (2007-) for above and B (-2007), SBL (2007-), SCB (2007-, calculated) for below detection limits. The QAQC columns are searched for all parameters and replaced with the appropriate value indicating the detection limit as defined byflag_type. The default argumentflag_type = 'both' will recode the QAQC columns as -1, 0, or 1 indicating below, within, or above the detection limit. Settingflag_type = 'below' or'above' will convert the columns toTRUE/FALSE values indicating observations beyond the detection limit (either above or below,TRUE) or within the normal detection rangeFALSE. The output includes additional columns similar to those for QAQC flags, such that the column names for censored flags include ac_ prefix before the parameter name. Note that the function will of course not work if already processed withqaqc. QAQC columns are retained for additional processing.

The user should refer to the metadata or visually examine the observed data to identify the actual limit, which may change over time.

Value

Returns a swmpr object with additional columns for censored flag values and the appropriate flag type based on the input arguments. Censored flag columns are named with ac_ prefix.

See Also

qaqc

Examples

## get datadata(apacpnut)dat <- apacpnut## convert all qaqc columns to censored flags, -1 below, 0 within, 1 abovecens_id(dat)## T/F for above or within, note that none are abovecens_id(dat, flag_type = 'above')## T/F for below or withincens_id(dat, flag_type = 'below')

Combine swmpr data

Description

Combine swmpr data types for a station by common time series

Usage

comb(...)## S3 method for class 'swmpr'comb(..., timestep = 15, differ = NULL, method = "union")## Default S3 method:comb(..., date_col, timestep = 15, differ = NULL, method = "union")

Arguments

Details

Thecomb function is used to combine multiple swmpr objects into a single object with a continuous time series at a given step. Thetimestep function is used internally such thattimestep anddiffer are accepted arguments forcomb.

The function requires one or more swmpr objects as input as separate, undefined arguments. The remaining arguments must be called explicitly since an arbitrary number of objects can be used as input. In general, the function combines data by creating a master time series that is used to iteratively merge all swmpr objects. The time series for merging depends on the value passed to themethod argument. Passing'union' tomethod will create a time series that is continuous starting from the earliest date and the latest date for all input objects. Passing'intersect' tomethod will create a time series that is continuous from the set of dates that are shared between all input objects. Finally, a seven or eight character station name passed tomethod will merge all input objects based on a continuous time series for the given station. The specified station must be present in the input data. Currently, combining data types from different stations is not possible, excluding weather data which are typically at a single, dedicated station.

Value

Returns a combined swmpr object

See Also

setstep

Examples

## get wq and met data as separate objects for the same stationswmp1 <- apacpnutswmp2 <- apaebmet## combine nuts and wq data by union, set timestep to 120 minutes## Not run: comb(swmp1, swmp2, timestep = 120, method = 'union')## End(Not run)

Simple trend decomposition

Description

Decompose data into trend, cyclical (e.g., daily, annual), and random components usingdecompose andts

Usage

decomp(dat_in, ...)## S3 method for class 'swmpr'decomp(  dat_in,  param,  type = "additive",  frequency = "daily",  start = NULL,  ...)## Default S3 method:decomp(  dat_in,  param,  date_col,  type = "additive",  frequency = "daily",  start = NULL,  ...)

Arguments

Details

This function is a simple wrapper to thedecompose function. Thedecompose function separates a time series into additive or multiplicative components describing a trend, cyclical variation (e.g., daily or annual), and the remainder. The additive decomposition assumes that the cyclical component of the time series is stationary (i.e., the variance is constant), whereas a multiplicative decomposition accounts for non-stationarity. By default, a moving average with a symmetric window is used to filter the cyclical component. Alternatively, a vector of filter coefficients in reverse time order can be supplied (seedecompose).

Thedecompose function requires a ts object with a specified frequency. Thedecomp function converts the input swmpr vector to a ts object prior todecompose. This requires an explicit input defining the frequency in the time series required to complete a full period of the parameter. For example, the frequency of a parameter with diurnal periodicity would be 96 if the time step is 15 minutes (24 hours * 60 minutes / 15 minutes). The frequency of a parameter with annual periodicity at a 15 minute time step would be 35040 (365 days * 24 hours * 60 minutes / 15 minutes). For simplicity, chr strings of'daily' or'annual' can be supplied in place of numeric values. A starting value of the time series must be supplied in the latter case. Use of thesetstep function is required to standardize the time step prior to decomposition.

Note that thedecompose function is a relatively simple approach and alternative methods should be investigated if a more sophisticated decomposition is desired.

Value

Returns a decomposed.ts object

References

M. Kendall and A. Stuart (1983) The Advanced Theory of Statistics, Vol. 3, Griffin. pp. 410-414.

See Also

decompose,ts,stl

Examples

## get datadata(apadbwq)swmp1 <- apadbwq## subset for daily decompositiondat <- subset(swmp1, subset = c('2013-07-01 00:00', '2013-07-31 00:00'))## decomposition and plottest <- decomp(dat, param = 'do_mgl', frequency = 'daily')plot(test)## dealing with missing valuesdat <- subset(swmp1, subset = c('2013-06-01 00:00', '2013-07-31 00:00'))## this returns an error## Not run: test <- decomp(dat, param = 'do_mgl', frequency = 'daily')## End(Not run)## how many missing values?sum(is.na(dat$do_mgl))## use na.approx to interpolate missing datadat <- na.approx(dat, params = 'do_mgl', maxgap = 10)## decomposition and plottest <- decomp(dat, param = 'do_mgl', frequency = 'daily')plot(test)

Decompose a time series

Description

The function decomposes a time series into a long-term mean, annual, seasonal and "events" component. The decomposition can be multiplicative or additive, and based on median or mean centering. Function and documentation herein are from archived wq package.

Usage

decompTs(  x,  event = TRUE,  type = c("add", "mult"),  center = c("mean", "median"))

Arguments

Details

The rationale for this simple approach to decomposing a time series, with examples of its application, is given by Cloern and Jassby (2010). It is motivated by the observation that many important events for estuaries (e.g., persistent dry periods, species invasions) start or stop suddenly. Smoothing to extract the annualized term, which can disguise the timing of these events and make analysis of them unnecessarily difficult, is not used.

A multiplicative decomposition will typically be useful for a biological community- or population-related variable (e.g., chlorophyll-a) that experiences exponential changes in time and is approximately lognormal, whereas an additive decomposition is more suitable for a normal variable. The default centering method is the median, especially appropriate for series that have large, infrequent events.

Ifevent = TRUE, the seasonal component represents a recurring monthly pattern and the events component a residual series. Otherwise, the seasonal component becomes the residual series. The latter is appropriate when seasonal patterns change systematically over time.

Value

A monthly time series matrix with the following individual time series:

References

Cloern, J.E. and Jassby, A.D. (2010) Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems.Estuaries and Coasts33, 230–241.

See Also

decomp_cj

Simple trend decomposition of monthly swmpr data

Description

Decompose monthly SWMP time series into grandmean, annual, seasonal, and event series as described in Cloern and Jassby 2010.

Usage

decomp_cj(dat_in, ...)## S3 method for class 'swmpr'decomp_cj(  dat_in,  param,  vals_out = FALSE,  event = TRUE,  type = c("add", "mult"),  center = c("mean", "median"),  ...)## Default S3 method:decomp_cj(  dat_in,  param,  date_col,  vals_out = FALSE,  event = TRUE,  type = c("add", "mult"),  center = c("mean", "median"),  ...)

Arguments

Details

This function is a simple wrapper to thedecompTs function in the archived wq package, also described in Cloern and Jassby (2010). The function is similar todecomp.swmpr (which is a wrapper todecompose) with a few key differences. Thedecomp.swmpr function decomposes the time series into a trend, seasonal, and random components, whereas the current function decomposes into the grandmean, annual, seasonal, and events components. For both functions, the random or events components, respectively, can be considered anomalies that don't follow the trends in the remaining categories.

Thedecomp_cj function provides only a monthly decomposition, which is appropriate for characterizing relatively long-term trends. This approach is meant for nutrient data that are obtained on a monthly cycle. The function will also work with continuous water quality or weather data but note that the data are first aggregated on the monthly scale before decomposition. Use thedecomp.swmpr function to decompose daily variation.

Value

Aggplot object ifvals_out = FALSE (default), otherwise a monthly time series matrix of classts.

References

Cloern, J.E., Jassby, A.D. 2010. Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems. Estuaries and Coasts. 33:230-241.

See Also

ts

Examples

## get datadata(apacpnut)dat <- apacpnutdat <- qaqc(dat, qaqc_keep = NULL)## decomposition of chl, values as data.framedecomp_cj(dat, param = 'chla_n', vals_out = TRUE)## decomposition of chl, ggplotdecomp_cj(dat, param = 'chla_n')## decomposition changing arguments passed to decompTsdecomp_cj(dat, param = 'chla_n', type = 'mult')## Not run: ## monthly decomposition of continuous datadata(apacpwq)dat2 <- qaqc(apacpwq)decomp_cj(dat2, param = 'do_mgl')## using the default method with a data framedat <- data.frame(dat)decomp_cj(dat, param = 'chla_n', date_col = 'datetimestamp')## End(Not run)

Ecosystem metabolism

Description

Estimate ecosystem metabolism using the Odum open-water method. Estimates of daily integrated gross production, total respiration, and net ecosystem metabolism are returned.

Usage

ecometab(dat_in, ...)## S3 method for class 'swmpr'ecometab(dat_in, depth_val = NULL, metab_units = "mmol", trace = FALSE, ...)## Default S3 method:ecometab(  dat_in,  tz,  lat,  long,  depth_val = NULL,  metab_units = "mmol",  trace = FALSE,  ...)

Arguments

Details

Input data include both water quality and weather time series, which are typically collected with independent instrument systems. For SWMP data, this requires merging water quality and meteorologyswmpr data objects using thecomb function (see examples). For the default method not using SWMP data, the inputdata.frame must have columns nameddatetimestamp (date/time column, asPOSIXct object),do_mgl (dissolved oxygen, mg/L),depth (depth, m),atemp (air temperature, C),sal (salinity, psu),temp (water temperature, C),wspd (wind speed, m/s), andbp (barometric pressure, mb).

The open-water method is a common approach to quantify net ecosystem metabolism using a mass balance equation that describes the change in dissolved oxygen over time from the balance between photosynthetic and respiration processes, corrected using an empirically constrained air-sea gas diffusion model (see Ro and Hunt 2006, Thebault et al. 2008). The diffusion-corrected DO flux estimates are averaged separately over each day and night of the time series. The nighttime average DO flux is used to estimate respiration rates, while the daytime DO flux is used to estimate net primary production. To generate daily integrated rates, respiration rates are assumed constant such that hourly night time DO flux rates are multiplied by 24. Similarly, the daytime DO flux rates are multiplied by the number of daylight hours, which varies with location and time of year, to yield net daytime primary production. Respiration rates are subtracted from daily net production estimates to yield gross production rates. The metabolic day is considered the 24 hour period between sunsets on two adjacent calendar days.

Areal rates for gross production and total respiration are based on volumetric rates normalized to the depth of the water column at the sampling location, which is assumed to be well-mixed, such that the DO sensor is reflecting the integrated processes in the entire water column (including the benthos). Water column depth is calculated as the mean value of the depth variable across the time series in theswmpr object. Depth values are floored at one meter for very shallow stations and 0.5 meters is also added to reflect the practice of placing sensors slightly off of the bottom. A user-supplied depth value can also be passed to thedepth_val argument, either as a single value that is repeated or as a vector equal in length to the number of rows in the input data. An accurate depth value should be used as this acts as a direct scalar on metabolism estimates.

The air-sea gas exchange model is calibrated with wind data either collected at, or adjusted to, wind speed at 10 m above the surface. The metadata should be consulted for exact height. The value can be changed manually using aheight argument, which is passed tocalckl.

A minimum of three records are required for both day and night periods to calculate daily metabolism estimates. Occasional missing values for air temperature, barometric pressure, and wind speed are replaced with the climatological means (hourly means by month) for the period of record using adjacent data within the same month as the missing data.

All DO calculations within the function are done using molar units (e.g., mmol O2 m-3). The output can be returned as mass units by settingmetab_units = 'grams' (i.e., 1mol = 32 g O2, which multiplies all estimates by 32 g mol-1/1000 mg/g). Input data must be in standard mass units for DO (mg L-1).

The specific approach for estimating metabolism with the open-water method is described in Caffrey et al. 2013 and references cited therein.

Value

Adata.frame of daily integrated metabolism estimates is returned. If aswmpr object is passed to the function, thisdata.frame is added to themetab attribute and the original object is returned. See the examples for retrieval from aswmpr object. The metabolismdata.frame contains the following:

date

The metabolic day, defined as the 24 hour period starting at sunrise (calculated usingmetab_day)

DOF_d

Mean DO flux during day hours, mmol m-2 hr-1. Day hours are calculated using themetab_day function.

D_d

Mean air-sea gas exchange of DO during day hours, mmol m-2 hr-1

DOF_n

Mean DO flux during night hours, mmol m-2 hr-1

D_n

Mean air-sea gas exchange of DO during night hours, mmol m-2 hr-1

Pg

Gross production, mmol m-2 d-1, calculated as ((DOF_d - D_d) - (DOF_n - D_n)) * day hours

Rt

Total respiration, mmol m-2 d-1, calculated as (DOF_n - D_n) * 24

NEM

Net ecosytem metabolism, mmol m-2 d-1, calculated as Pg + Rt

References

Caffrey JM, Murrell MC, Amacker KS, Harper J, Phipps S, Woodrey M. 2013. Seasonal and inter-annual patterns in primary production, respiration and net ecosystem metabolism in 3 estuaries in the northeast Gulf of Mexico. Estuaries and Coasts. 37(1):222-241.

Odum HT. 1956. Primary production in flowing waters. Limnology and Oceanography. 1(2):102-117.

Ro KS, Hunt PG. 2006. A new unified equation for wind-driven surficial oxygen transfer into stationary water bodies. Transactions of the American Society of Agricultural and Biological Engineers. 49(5):1615-1622.

Thebault J, Schraga TS, Cloern JE, Dunlavey EG. 2008. Primary production and carrying capacity of former salt ponds after reconnection to San Francisco Bay. Wetlands. 28(3):841-851.

See Also

calckl for estimating the oxygen mass transfer coefficient used with the air-sea gas exchange model,comb for combiningswmpr objects,metab_day for identifying the metabolic day for each observation in the time series,plot_metab for plotting the results, andaggremetab for aggregating the metabolism attribute.

Examples

## Not run: ## import water quality and weather data, qaqcdata(apadbwq)data(apaebmet)wq <- qaqc(apadbwq)met <- qaqc(apaebmet)## combinedat <- comb(wq, met)## output units in grams of oxygenres <- ecometab(dat, metab_units = 'grams')attr(res, 'metabolism')## manual input of integration depth## NA values must be filleddat_fill <- na.approx(dat, params = 'depth', maxgap = 1e6)depth <- dat_fill$depthres <- ecometab(dat, metab_units = 'grams', depth_val = depth)attr(res, 'metabolism')## use the default method for ecometab with a generic data frame## first recreate a generic object from the sample datacols <- c('datetimestamp', 'do_mgl', 'depth', 'atemp', 'sal', 'temp', 'wspd', 'bp')dat <- data.frame(dat)dat <- dat[, cols]res <- ecometab(dat, tz = 'America/Jamaica', lat = 29.67, long = -85.06)res## End(Not run)

Get colors for plots

Description

Get gradient default colors for plots

Usage

gradcols(col_vec = NULL)

Arguments

Details

This is a convenience function for retrieving a color palette. Palettes from RColorBrewer will use the maximum number of colors. The default palette is 'Spectral'.

Value

A character vector of colors in hexidecimal notation.

Plot swmpr using a histogram

Description

Plot a histogram showing the distribution of a swmpr parameter

Usage

## S3 method for class 'swmpr'hist(x, ...)

Arguments

Details

The swmpr method for histograms is a convenience function for the default histogram function. Conventional histogram methods also work well since swmpr objects are also data frames. The input data must contain only one parameter.

See Also

hist

Examples

## get datadata(apadbwq)dat <- subset(apadbwq, select = 'do_mgl')## histogram using swmpr methodhist(dat)## change axis labels, plot titlehist(dat, xlab = 'Dissolved oxygen', main = 'Histogram of DO')## plot using default methodhist(dat$do_mgl)

Import local CDMO data

Description

Import local data that were obtained from the CDMO through the zip downloads feature

Usage

import_local(  path,  station_code,  trace = FALSE,  collMethd = c("1", "2"),  keep_qaqcstatus = FALSE)

Arguments

Details

The function is designed to import local data that were downloaded from the CDMO outside of R. This approach works best for larger data requests, specifically those from the zip downloads feature in the advanced query section of the CDMO. The function may also work using data from the data export system, but this feature has not been extensively tested. The downloaded data will be in a compressed folder that includes multiple .csv files by year for a given data type (e.g., apacpwq2002.csv, apacpwq2003.csv, apacpnut2002.csv, etc.). The import_local function can be used to import files directly from the compressed folder or after the folder is decompressed. In the former case, the requested files are extracted to a temporary directory and then deleted after they are loaded into the current session. An example dataset is available online to illustrate the format of the data provided through the zip downloads feature. See the link below to access these data. All example datasets included with the package were derived from these raw data.

Occasionally, duplicate time stamps are present in the raw data. The function handles duplicate entries differently depending on the data type (water quality, weather, or nutrients). For water quality and nutrient data, duplicate time stamps are simply removed. Note that nutrient data often contain replicate samples with similar but not duplicated time stamps within a few minutes of each other. Replicates with unique time stamps are not removed but can be further processed usingrem_reps. Weather data prior to 2007 may contain duplicate time stamps at frequencies for 60 (hourly) and 144 (daily) averages, in addition to 15 minute frequencies. Duplicate values that correspond to the smallest value in the frequency column (15 minutes) are retained.

Ifkeep_qaqcstatus = TRUE, thehistorical andprovisionalplus columns are retained in the output. These two columns include integer values as 0 or 1. From the CDMO web page, a value of 0 in thehistorical column indicates that the data have not been through final QAQC by the CDMO. A value of 1 indicates that the data have been through final tertiary review at the CDMO and posted as the final authoritative data. A value of 0 in theprovisionalplus column indicates that the data have been through the automated flagging process (primary QAQC) only and have not been checked by the Reserve. A value of 1 in theprovisionalplus column indicates that the data have been through secondary QAQC at the Reserve using Excel macros (provided by the CDMO) to further QAQC the data.

Zip download request through CDMO:https://cdmo.baruch.sc.edu/aqs/zips.cfm

Example dataset:https://s3.amazonaws.com/swmpexdata/zip_ex.zip

Value

Returns a swmpr object with all parameters and QAQC columns for the station. The full date range in the raw data are also imported.

See Also

all_params,all_params_dtrng,rem_reps,single_param

Examples

## Not run: ## this is the path for csv example files, decompressedpath <- 'C:/this/is/my/data/path'## import, do not include file extensionimport_local(path, 'apaebmet') ## this is the path for csv example files, zipped folderpath <- 'C:/this/is/my/data/path.zip'## import, do not include file extensionimport_local(path, 'apaebmet') ## End(Not run)

Map a reserve

Description

Create a map of all the stations in a reserve

Usage

map_reserve(nerr_site_id, text_sz = 6, text_col = "black", f = 0.2)

Arguments

Details

This function is a simple wrapper to functions in the ggmap package which returns a map of all of the stations at a NERRS reserve. Thef argument may have to be chosen through trial and error depending on the spatial extent of the reserve. A local data file included with the package is used to get the latitude and longitude values of each station. The files includes only active stations as of January 2015.

Value

Aggplot object for plotting.

See Also

get_map,ggmap,ggplot

Examples

## Not run: map_reserve('jac')## End(Not run)

Identify metabolic days in a time series

Description

Identify metabolic days in a time series based on sunrise and sunset times for a location and date. The metabolic day is considered the 24 hour period between sunsets for two adjacent calendar days. The function calls thegetSunlightTimes function from the suncalc package.

Usage

metab_day(dat_in, ...)## Default S3 method:metab_day(dat_in, tz, lat, long, ...)

Arguments

Details

This function is only used withinecometab and should not be called explicitly.

See Also

ecometab,getSunlightTimes

Linearly interpolate gaps

Description

Linearly interpolate gaps in swmpr data within a maximum size

Usage

## S3 method for class 'swmpr'na.approx(object, params = NULL, maxgap, ...)

Arguments

Details

A common approach for handling missing data in time series analysis is linear interpolation. A simple curve fitting method is used to create a continuous set of records between observations separated by missing data. A required argument for the function ismaxgap which defines the maximum gap size for interpolation. The ability of the interpolated data to approximate actual, unobserved trends is a function of the gap size. Interpolation between larger gaps are less likely to resemble patterns of an actual parameter, whereas interpolation between smaller gaps may be more likely to resemble actual patterns. An appropriate gap size limit depends on the unique characteristics of specific datasets or parameters.

Value

Returns a swmpr object. QAQC columns are removed if included with input object.

See Also

na.approx

Examples

data(apadbwq)dat <- qaqc(apadbwq)dat <- subset(dat, select = 'do_mgl',  subset = c('2013-01-22 00:00', '2013-01-26 00:00'))# interpolate, maxgap of 10 recordsfill1 <- na.approx(dat, params = 'do_mgl', maxgap = 10)# interpolate maxgap of 30 recordsfill2 <- na.approx(dat, params = 'do_mgl', maxgap = 30)# plot for comparisonpar(mfrow = c(3, 1))plot(fill1, col = 'red', main = 'Interpolation - maximum gap of 10 records')lines(dat)plot(fill2, col = 'red', main = 'Interpolation - maximum gap of 30 records')lines(dat)

Plot multiple SWMP time series on the same y-axis

Description

Plot multiple SWMP time series on the same y-axis, aka overplotting

Usage

overplot(dat_in, ...)## S3 method for class 'swmpr'overplot(  dat_in,  select = NULL,  subset = NULL,  operator = NULL,  ylabs = NULL,  xlab = NULL,  cols = NULL,  lty = NULL,  lwd = NULL,  pch = NULL,  type = NULL,  ...)## Default S3 method:overplot(  dat_in,  date_var,  select = NULL,  ylabs = NULL,  xlab = NULL,  cols = NULL,  lty = NULL,  lwd = NULL,  inset = -0.15,  cex = 1,  xloc = "top",  yloc = NULL,  pch = NULL,  type = NULL,  ...)

Arguments

Details

One to many SWMP parameters can be plotted on the same y-axis to facilitate visual comparison. This is commonly known as overplotting. The building blocks of this function includeplot,legend,axis, andmtext.

Value

An R plot created using base graphics

See Also

subset

Examples

## import datadata(apacpwq)dat <- qaqc(apacpwq)## plotoverplot(dat)## a truly heinous plotoverplot(dat, select = c('depth', 'do_mgl', 'ph', 'turb'),  subset = c('2013-01-01 0:0', '2013-02-01 0:0'), lwd = 2) ## Not run: ## change the type argument if plotting discrete and continuous dataswmp1 <- apacpnutswmp2 <- apaebmetdat <- comb(swmp1, swmp2, timestep = 120, method = 'union')overplot(dat, select = c('chla_n', 'atemp'), subset = c('2012-01-01 0:0', '2013-01-01 0:0'),  type = c('p', 'l'))## End(Not run)

Dissolved oxygen at saturation

Description

Finds dissolved oxygen concentration in equilibrium with water-saturated air. Function and documentation herein are from archived wq package.

Usage

oxySol(t, S, P = NULL)

Arguments

Details

Calculations are based on the approach of Benson and Krause (1984), using Green and Carritt's (1967) equation for dependence of water vapor partial pressure ont andS. Equations are valid for temperature in the range 0-40 C and salinity in the range 0-40.

Value

Dissolved oxygen concentration in mg/L at 100% saturation. IfP = NULL, saturation values at 1 atm are calculated.

References

Benson, B.B. and Krause, D. (1984) The concentration and isotopic fractionation of oxygen dissolved in fresh-water and seawater in equilibrium with the atmosphere.Limnology and Oceanography29, 620-632.

Green, E.J. and Carritt, D.E. (1967) New tables for oxygen saturation of seawater.Journal of Marine Research25, 140-147.

Get parameters of a given type

Description

Get parameter column names for each parameter type

Usage

param_names(param_type = c("nut", "wq", "met"))

Arguments

Details

This function is used internally within several functions to return a list of the expected parameters for a given parameter type: nutrients, water quality, or meteorological. It does not need to be called explicitly.

Value

Returns a named list of parameters for theparam_type. The parameter names are lower-case strings of SWMP parameters and corresponding qaqc names ('f_' prefix)

Parse web results for swmpr

Description

Parsing function for objects returned from CDMO web services

Usage

parser(resp_in, parent_in = "data")

Arguments

Details

This function parses XML objects returned from the CDMO server, which are further passed toswmpr. It is used internally by the data retrieval functions, excludingimport_local. The function does not need to be called explicitly.

Value

Returns adata.frame of parsed XML nodes

Plot swmpr data

Description

Plot a time series of parameters in a swmpr object

Usage

## S3 method for class 'swmpr'plot(x, type = "l", ...)## S3 method for class 'swmpr'lines(x, ...)

Arguments

Details

The swmpr method for plotting is a convenience function for plotting a univariate time series. Conventional plotting methods also work well since swmpr objects are also data frames. See the examples for use with different methods.

See Also

plot

Examples

## get datadata(apadbwq)swmp1 <- apadbwq## subsetdat <- subset(swmp1, select = 'do_mgl',  subset = c('2013-07-01 00:00', '2013-07-31 00:00'))## plot using swmpr method, note default line plotplot(dat)## plot using formula methodplot(do_mgl ~ datetimestamp, dat)## plot using default, add linesplot(dat, type = 'n')lines(dat, col = 'red')

Plot ecosystem metabolism for a swmpr object

Description

Plot gross production, total respiration, and net ecosystem metabolism for a swmpr object.

Usage

plot_metab(swmpr_in, ...)## S3 method for class 'swmpr'plot_metab(  swmpr_in,  by = "months",  alpha = 0.05,  width = 10,  pretty = TRUE,  ...)

Arguments

Details

A plot will only be returned if themetabolism attribute for theswmpr object is notNULL. Daily metabolism estimates are also aggregated into weekly averages. Accepted aggregation periods are'years','quarters','months','weeks', and'days' (if no aggregation is preferred).

By default,pretty = TRUE will return aggplot object with predefined aesthetics. Settingpretty = FALSE will return the plot with minimal modifications to theggplot object. Use the latter approach for easier customization of the plot.

Value

Aggplot object which can be further modified.

See Also

aggremetab,ecometab

Examples

## Not run: ## import water quality and weather datadata(apadbwq)data(apaebmet)## qaqc, combinewq <- qaqc(apadbwq)met <- qaqc(apaebmet)dat <- comb(wq, met)## estimate metabolismres <- ecometab(dat)## plotplot_metab(res)## change alpha, aggregation period, widthsplot_metab(res, by = 'quarters', alpha = 0.1, widths = 0)## plot daily raw, no aestheticsplot_metab(res, by = 'days', pretty = FALSE)## note the difference if aggregating with a moving window averageplot_metab(res, by = 30)## End(Not run)

Create a plot of data for a single year overlaid on historical data.

Description

A line for a single year is plotted over ribbons ofquantiles for historical data.

Usage

plot_quants(swmpr_in, ...)## S3 method for class 'swmpr'plot_quants(  swmpr_in,  paramtoplot,  yr,  yrstart,  yrend,  yaxislab = NULL,  yrcolor = "red3",  bgcolor1 = "lightgray",  bgcolor2 = "gray65",  maintitle = NULL,  ...)

Arguments

Details

The plot is based on aggregates of daily average values for the entire time series. Quantiles (min, 25%, 75%, max) for each individual calendar day (01/01, 01/02, ... 12/31) are used to generate a ribbon plot of historical data and the selected year inyr is plotted as a line over the ribbon for historical context.

required packages: dplyr, lubridate, ggplot2, tibble

Value

A aggplot2 object.

Author(s)

Kimberly Cressman, Marcus Beck

Examples

# qaqcdat <- qaqc(apacpwq)# generate a plot of salinity for 2013 overlaid on 2012-2013 dataplot_quants(dat, 'sal', yr = 2013, yrstart = 2012, yrend = 2013)# change some of the defaultsplot_quants(dat, 'sal', yr = 2013, yrstart = 2012, yrend = 2013,  bgcolor1 = 'lightsteelblue2', bgcolor2 = 'lightsteelblue4',  yaxislab = 'Salinity (psu)')

Plot graphical summaries of SWMP data

Description

Plot graphical summaries of SWMP data for individual parameters, including seasonal/annual trends and anomalies

Usage

plot_summary(swmpr_in, ...)## S3 method for class 'swmpr'plot_summary(  swmpr_in,  param,  colsleft = c("lightblue", "lightgreen"),  colsmid = "lightblue",  colsright = c("lightblue", "lightgreen", "tomato1"),  base_size = 11,  years = NULL,  plt_sep = FALSE,  sum_out = FALSE,  fill = c("none", "monoclim", "interp"),  ...)

Arguments

Details

This function creates several graphics showing seasonal and annual trends for a given swmp parameter. Plots include monthly distributions, monthly anomalies, and annual anomalies in multiple formats. Anomalies are defined as the difference between the monthly or annual average from the grand mean. Monthly anomalies are in relation to the grand mean for the same month across all years. All data are aggregated for quicker plotting. Nutrient data are based on monthly averages, wheras weather and water quality data are based on daily averages. Cumulative precipitation data are based on the daily maximum.

Individual plots can be obtained ifplt_sep = TRUE. Individual plots for elements one through six in the list correspond to those from top left to bottom right in the combined plot.

Summary data for the plots can be obtained ifsum_out = TRUE. This returns a list with three data frames with namessum_mo,sum_moyr, andsum_mo. The data frames match the plots as follows:sum_mo for the top left, bottom left, and center plots,sum_moyr for the top right and middle right plots, andsum_yr for the bottom right plot.

Missing values can be filled using the long-term average across years for each month (fill = 'monoclim') or as a linear interpolation between missing values usingna.approx (fill = 'interp'). The monthly average works well for long gaps, but may not be an accurate representation of long-term trends, i.e., real averages may differ early vs late in the time series if a trend exists. The linear interpolation option is preferred for small gaps.

Value

A graphics object (Grob) of multipleggplot objects, otherwise a list of individualggplot objects ifplt_sep = TRUE or a list with data frames of the summarized data ifsum_out = TRUE.

See Also

ggplot

Examples

## import datadata(apacpnut)dat <- qaqc(apacpnut)## plotplot_summary(dat, param = 'chla_n', years = c(2007, 2013))## get individaul plotsplots <- plot_summary(dat, param = 'chla_n', years = c(2007, 2013), plt_sep = TRUE)plots[[1]] # top leftplots[[3]] # middleplots[[6]] # bottom right## get summary dataplot_summary(dat, param = 'chla_n', year = c(2007, 2013), sum_out = TRUE)

Create a wind rose

Description

Create a wind rose from met data

Usage

plot_wind(swmpr_in, ...)## S3 method for class 'swmpr'plot_wind(  swmpr_in,  years = NULL,  angle = 45,  width = 1.5,  breaks = 5,  paddle = FALSE,  grid.line = 10,  max.freq = 30,  cols = "GnBu",  annotate = FALSE,  main = NULL,  type = "default",  between = list(x = 1, y = 1),  par.settings = NULL,  strip = NULL,  ...)

Arguments

Details

This function is a convenience wrapper towindRose. Most of the arguments are taken directly from this function.

Thetype argument can be used for temporal divisions of the plot. Options include the entire year (type = "default"), seasons (type = "season"), months (type = "month"), or weekdays (type = "weekday"). Combinations are also possible (seewindRose).

Value

A wind rose plot

Author(s)

Kimberly Cressman, Marcus Beck

Examples

plot_wind(apaebmet)

QAQC filtering for SWMP data

Description

QAQC filtering for SWMP data obtained from retrieval functions, local and remote

Usage

qaqc(swmpr_in, ...)## S3 method for class 'swmpr'qaqc(swmpr_in, qaqc_keep = "0", trace = FALSE, ...)

Arguments

Details

The qaqc function is a simple screen to retain values from the data with specified QAQC flags, described online:https://cdmo.baruch.sc.edu/data/qaqc.cfm. Each parameter in the swmpr data typically has a corresponding QAQC column of the same name with the added prefix 'f_'. Values in the QAQC column specify a flag from -5 to 5. Generally, only data with the '0' QAQC flag should be used, which is the default option for the function. Data that do not satisfy QAQC criteria are converted toNA values. Additionally, simple filters are used to remove obviously bad values, e.g., wind speed values less than zero or pH values greater than 12. Erroneous data entered as -99 are also removed. Processed data will have QAQC columns removed, in addition to removal of values in the actual parameter columns that do not meet the criteria.

The data are filtered by matching the flag columns with the character string provided byqaqc_keep. A single combined string is created by pasting each element together using the '|' operator, then using partial string matching withgrepl to compare the actual flags in the QAQC columns. Values that can be passed to the function are those described online:https://cdmo.baruch.sc.edu/data/qaqc.cfm.

Value

Returns a swmpr object withNA values for records that did not matchqaqc_keep. QAQC columns are also removed.

See Also

qaqcchk

Examples

## Not run: ## get datadata(apadbwq)dat <- apadbwq## retain only '0' and '-1' flagsqaqc(dat, qaqc_keep = c('0', '-1'))## retain observations with the 'CSM' error codeqaqc(dat, qaqc_keep = 'CSM')## End(Not run)

Summary of QAQC flags in SWMP data

Description

Summary of the number of observations with a given QAQC flag for each parameter column of a swmpr object

Usage

qaqcchk(swmpr_in)## S3 method for class 'swmpr'qaqcchk(swmpr_in)

Arguments

Details

Viewing the number of observations for each parameter that are assigned to a QAQC flag may be useful for deciding how to process the data qith qaqc. Theqaqcchk function can be used to view this information. Consult the online documentation for a description of each QAQC flag:https://cdmo.baruch.sc.edu/data/qaqc.cfm

Value

Returns adata.frame with columns for swmpr parameters and row counts indicating the number of observations in each parameter assigned to a flag value.

See Also

qaqc

Examples

## get datadata(apadbwq)dat <- apadbwq## view the number observations in each QAQC flagqaqcchk(dat)

Remove replicates in nutrient data

Description

Remove replicates in SWMP nutrient data to keep approximate monthly time step

Usage

rem_reps(swmpr_in, ...)## S3 method for class 'swmpr'rem_reps(swmpr_in, FUN = function(x) mean(x, na.rm = TRUE), ...)

Arguments

Details

Raw nutrient data obtained from the CDMO will usually include replicate samples that are taken within a few minutes of each other. This function combines nutrient data that occur on the same day. Thedatetimestamp column will always be averaged for replicates, but the actual observations will be combined based on the user-supplied function which defauls to the mean. Other suggested functions include themedian,min, ormax. The entire function call including treatment ofNA values should be passed to theFUN argument (see the examples). The function is meant to be used afterqaqc processing, although it works with a warning if QAQC columns are present.

Value

Returns a swmpr object for nutrient data with no replicates.

See Also

qaqc

Examples

## get nutrient datadata(apacpnut)swmp1 <- apacpnut# remove replicate nutrient datarem_reps(swmp1)# use different function to aggregate replicatesfunc <- function(x) max(x, na.rm = TRUE)rem_reps(swmp1, FUN = func)

Format a swmpr time vector

Description

Create a continuous time vector at set time step for a swmpr object

Usage

setstep(dat_in, ...)## S3 method for class 'swmpr'setstep(dat_in, timestep = 15, differ = NULL, ...)## Default S3 method:setstep(dat_in, date_col, timestep = 15, differ = NULL, ...)

Arguments

Details

The setstep function formats a swmpr object to a continuous time series at a given time step. This function is not necessary for most stations but can be useful for combining data or converting an existing time series to a set interval. The first argument of the function,timestep, specifies the desired time step in minutes starting from the nearest hour of the first observation. The second argument,differ, specifies the allowable tolerance in minutes for matching existing observations to user-defined time steps in cases where the two are dissimilar. Values fordiffer that are greater than one half the value of timestep are not allowed to prevent duplication of existing data. Likewise, the default value for differ is one half the time step. Rows that do not match any existing data within the limits of the differ argument are not discarded. Output from the function can be used withsubset and to create a time series at a set interval with empty data removed.

Value

Returns a data object for the specified time step

See Also

comb

Examples

## Not run: ## import datadata(apaebmet)dat <- apaebmet## convert time series to two hour invervals## tolerance of +/- 30 minutes for matching existing datasetstep(dat, timestep = 120, differ = 30)## convert a nutrient time series to a continuous time series## then remove empty rows and columnsdata(apacpnut)dat_nut <- apacpnutdat_nut <- setstep(dat_nut, timestep = 60)subset(dat_nut, rem_rows = TRUE, rem_cols = TRUE)## End(Not run)

Get CDMO records for a single parameter

Description

Get stations records from the CDMO for a single parameter starting with the most current date

Usage

single_param(station_code, param, Max = 100, trace = TRUE)

Arguments

Details

This function retrieves data from the CDMO through the web services URL. The computer making the request must have a registered IP address. Visit the CDMO web services page for more information:https://cdmo.baruch.sc.edu/webservices.cfm. This function is the CDMO equivalent ofexportSingleParamXML.

Value

Returns a swmpr object with one parameter. QAQC columns are not provided.

See Also

all_params,all_params_dtrng

Examples

## Not run: ## single parameter for a station, most recentsingle_param('hudscwq', 'do_mgl')## End(Not run)

Obtain metadata for all stations

Description

Obtain adata.frame of metadata for all SWMP stations.

Usage

site_codes()

Details

This function retrieves data from the CDMO web services. The computer making the request must have a registered IP address. Visit the CDMO web services page for more information:https://cdmo.baruch.sc.edu/webservices.cfm. This is the CDMO equivalent ofexportStationCodesXML.

Value

Adata.frame of SWMP metadata

Examples

## Not run: ## retrieve metadata for all sitessite_codes()## End(Not run)

Obtain metadata for a single reserve

Description

Get metadata for all the stations at a single SWMP reserve

Usage

site_codes_ind(nerr_site_id)

Arguments

Details

This function retrieves data from the CDMO web services. The computer making the request must have a registered IP address. Visit the CDMO web services page for more information:https://cdmo.baruch.sc.edu/webservices.cfm. This function is the CDMO equivalent ofNERRFilterStationCodesXMLNew.

Value

An abbreviateddata.frame of the SWMP metadata for the requested site

Examples

## Not run: ## retrieve metadata for all stations at a sitesite_codes_ind('apa')## End(Not run)

Smooth swmpr data

Description

Smooth swmpr data with a moving window average

Usage

smoother(x, ...)## Default S3 method:smoother(x, window = 5, sides = 2, ...)## S3 method for class 'swmpr'smoother(x, params = NULL, ...)

Arguments

Details

Thesmoother function can be used to smooth parameters in a swmpr object using a specified window size. This method is a simple wrapper tofilter. The window argument specifies the number of observations included in the moving average. The sides argument specifies how the average is calculated for each observation (see the documentation forfilter). A value of 1 will filter observations within the window that are previous to the current observation, whereas a value of 2 will filter all observations within the window centered at zero lag from the current observation. The params argument specifies which parameters to smooth.

Value

Returns a filtered swmpr object. QAQC columns are removed if included with input object.

See Also

filter

Examples

## import datadata(apadbwq)swmp1 <- apadbwq## qaqc and subset imported datadat <- qaqc(swmp1)dat <- subset(dat, subset = c('2012-07-09 00:00', '2012-07-24 00:00'))## filtertest <- smoother(dat, window = 50, params = 'do_mgl')## plot to see the differenceplot(do_mgl ~ datetimestamp, data = dat, type = 'l')lines(test, select = 'do_mgl', col = 'red', lwd = 2)

Locations of NERRS sites

Description

Location of NERRS sites in decimal degrees and time offset from Greenwich mean time. Only active sites are included. Sites are identified by five letters indexing the reserve and site names. The dataset is used to plot locations with themap_reserve function and to identify metabolic days with theecometab function. Created from sampling_locations.csv provided by CDMO.

Usage

stat_locs

Format

Adata.frame object with 161 rows and 4 variables:

station_code

chr

latitude

numeric

longitude

numeric

gmt_off

int

See Also

ecometab,map_reserve

Subset a swmpr object

Description

Subset a swmpr object by a date range, parameters, or non-empty values

Usage

## S3 method for class 'swmpr'subset(  x,  subset = NULL,  select = NULL,  operator = NULL,  rem_rows = FALSE,  rem_cols = FALSE,  ...)

Arguments

Details

This function is used to subset swmpr data by date and/or a selected parameter. The date can be a single value or as two dates to select records within the range. The former case requires a binary operator input as a character string passed to the argument, such as> or<. The subset argument for the date(s) must also be a character string of the format YYYY-mm-dd HH:MM for each element (i.e.,%Y-%m-%d %H:%M in POSIX standards). Be aware that an error may be returned using this function if the subset argument is in the correct format but the calendar date does not exist, e.g.'2012-11-31 12:00'. The function can also be used to remove rows and columns that do not contain data, which may be useful after processing with other functions.

Value

Returns a swmpr object as a subset of the input. The original object will be returned if no arguments are specified.

See Also

subset

Examples

## get datadata(apaebmet)dat <- apaebmet## subset records greater than or equal to a datesubset(dat, subset = '2013-01-01 0:00', operator = '>=')## subset records within a date range, select two parameterssubset(dat, subset = c('2012-07-01 6:00', '2012-08-01 18:15'),   select = c('atemp', 'totsorad'))

Create a swmpr object

Description

Wrapper for creating a swmpr object

Usage

swmpr(stat_in, meta_in)

Arguments

Details

This function is a simple wrapper tostructure that is used internally within other functions to create a swmpr object. The function does not have to be used explicitly. Attributes of a swmpr object includenames,row.names,class,station,parameters,qaqc_cols,cens_cols,date_rng,timezone,stamp_class,metabolism (if present), andmetab_units (if present).

Value

Returns a swmpr object to be used with S3 methods

Format SWMP datetimestamp

Description

Format the datetimestamp column of SWMP data

Usage

time_vec(chr_in = NULL, station_code, tz_only = FALSE)

Arguments

Details

This function formats the datetimestamp column of SWMP data to thePOSIXct format and the correct timezone for a station. Note that SWMP data do not include daylight savings and the appropriate location based on GMT offsets is used for formatting. This function is used internally within data retrieval functions and does not need to be called explicitly.

Value

Returns a POSIX vector iftz_only is true, otherwise the timezone for a station is returned as a chr string