Package 'tbeptools'

Description

Tampa Bay intertidal and supratidal land use and cover for Habitat Master Plan reporting

Usage

acres

Format

A data frame

Examples

## Not run: 
load(url("https://github.com/tbep-tech/hmpu-workflow/raw/master/data/acres.RData"))

save(acres, file = 'data/acres.RData', compress = 'xz')

## End(Not run)

Description

AMBI species group assignments for Tampa Bay benthic taxa

Usage

ambispp

Format

A data frame with 1983 rows and 5 variables:

TaxaListID

int, join key to taxacounts in benthicdata

NAME

chr, taxon name

AMBIGroupID

int, ecological group 1-5 for conventional AMBI (Gillett et al. 2015), NA if unclassified

TBAMBIGroupID

int, ecological group 1-5 for Tampa Bay-specific AMBI, NA if unclassified

AMBIListSource

chr, provenance of the AMBI group assignment (Gulf, National, or assumption)

Details

Ecological groups range from I (most sensitive to pollution) to V (most tolerant). The conventional AMBI groups (AMBIGroupID) follow published literature (Gillett et al. 2015), with Gulf of Mexico scores preferred over National scores, and family-level assumptions applied where species-level scores were unavailable. The Tampa Bay-specific groups (TBAMBIGroupID) are based on local sediment sample assignments.

This table is used as the species lookup in anlz_ambiscr to join ecological group assignments to observed taxa counts in benthicdata.

References

Gillett, D.J., Weisberg, S.B., Grayson, T., Hamilton, A., Hansen, V., Leppo, E.W., Pelletier, M.C., et al. (2015). Effect of ecological group classification schemes on performance of the AMBI benthic index in US coastal waters. Ecological Indicators, 50, 99-107. doi:10.1016/j.ecolind.2014.11.005

Examples

## Not run: 
library(readxl)
library(dplyr)

ambispp <- read_excel('data-raw/AMBI_EcoSource_ReportDeliverableToDK.xlsx') |>
  select(TaxaListID, NAME, AMBIGroupID, TBAMBIGroupID = TBAMBIGroupId, AMBIListSource)

save(ambispp, file = 'data/ambispp.RData', compress = 'xz')

## End(Not run)

Description

Get annual medians of AMBI scores by bay segment

Usage

anlz_ambimed(
  ambiscr,
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB", "All"),
  rev = FALSE,
  yrrng = c(1993, 2024),
  window = TRUE
)

Arguments

Details

An additional summary is provided for the entire bay as an unweighted summary across categories ("All").

Only sampling funded by TBEP and as part of the routine EPC benthic monitoring program are included in the final categories.

The default behavior is to use a rolling five-year window to calculate the percent of sites in each AMBI category by bay segment. This applies only to years 2005 and later, where the counts from the current year and the prior four years are summed to calculate the percentages. This is intended to help smooth out inter-annual variability due to reduced sampling effort from 2005 to present. If window = FALSE, then only single year values are used.

The AMBI category column (AMBICat or TBAMBICat) is based on the adjusted AMBI score (0-10 scale) using the classification thresholds defined in anlz_ambiscr.

Value

A data frame of annual percent composition by AMBI category and bay segment

Examples

ambiscr <- anlz_ambiscr(benthicdata)
anlz_ambimed(ambiscr)

Description

Get AMBI scores for benthic stations

Usage

anlz_ambiscr(benthicdata, type = c("AMBI", "AMBI-TB"))

Arguments

Details

This function calculates the AZTI Marine Biotic Index (AMBI) for benthic stations in Tampa Bay. The index assigns each taxon to one of five ecological groups (I = most sensitive, V = most tolerant to pollution) and computes a Biotic Coefficient (BC) from the proportion of taxa in each group.

The Biotic Coefficient is calculated as:

$BC = (0 \times \%G1 + 1.5 \times \%G2 + 3 \times \%G3 + 4.5 \times \%G4 + 6 \times \%G5) / 100$

where \

An adjusted AMBI score on a 0-10 scale is also returned:

$Adjusted AMBI = (7 - BC) \times (10 / 7)$

Higher adjusted scores indicate healthier benthic conditions. Azoic stations (empty samples, TaxaListID == 1942 in benthicdata) receive BC = 7 and adjusted score = 0.

Two variants are supported via the type argument. "AMBI" uses ecological group assignments from published literature (AMBIGroupID in ambispp). "AMBI-TB" uses Tampa Bay-specific assignments (TBAMBIGroupID in ambispp). The calculation is identical, only the group assignment column differs.

Value

A data frame of AMBI scores for each station, with columns from benthicdata stations joined to:

TotalGroupCount

int, total number of classified taxa records used in the calculation

PercentG1 - PercentG5

num, percentage of taxa records in each ecological group

BC

num, Biotic Coefficient (0-6 scale, or 7 for azoic)

AMBI or TBAMBI

num, adjusted AMBI score (0-10 scale)

SitePollutionClassification

chr, pollution category based on BC

BioticIndex

chr, biotic index value (0-6 or Azoic)

DominatingEcologicalGroup

chr, dominating ecological group based on BC

BenthicCommunityHealth

chr, community health descriptor based on BC

AMBICat or TBAMBICat

chr, adjusted AMBI category based on the 0-10 scale

References

Gillett, D.J., Weisberg, S.B., Grayson, T., Hamilton, A., Hansen, V., Leppo, E.W., Pelletier, M.C., et al. (2015). Effect of ecological group classification schemes on performance of the AMBI benthic index in US coastal waters. Ecological Indicators, 50, 99-107. doi:10.1016/j.ecolind.2014.11.005

Examples

anlz_ambiscr(benthicdata)
anlz_ambiscr(benthicdata, type = 'AMBI-TB')

Description

Get attainment categories for each year and bay segment using chlorophyll and light attenuation

Usage

anlz_attain(avedat, magdurout = FALSE, trgs = NULL)

Arguments

Value

A data.frame for each year and bay segment showing the attainment category

Examples

avedat <- anlz_avedat(epcdata)
anlz_attain(avedat)

Description

Get site attainment categories for chlorophyll or light attenuation

Usage

anlz_attainsite(
  avedatsite,
  thr = c("chla", "la"),
  trgs = NULL,
  yrrng = NULL,
  thrs = FALSE
)

Arguments

Details

This function is a simplication of the attainment categories returned by anlz_attain. Sites are only compared to the targets/thresholds that apply separately for chlorophyll or light attenuation.

Value

a data.frame for each year and site showing the attainment category

Examples

avedatsite <- anlz_avedatsite(epcdata)
anlz_attainsite(avedatsite)

Description

Estimate annual means for chlorophyll and secchi data

Usage

anlz_avedat(epcdata, partialyr = FALSE)

Arguments

Value

Mean estimates for chlorophyll and secchi

Examples

# view average estimates
anlz_avedat(epcdata)

Description

Estimate annual means by site for chlorophyll and secchi data

Usage

anlz_avedatsite(epcdata, partialyr = FALSE)

Arguments

Value

Mean estimates for chlorophyll and secchi

Examples

# view average estimates
anlz_avedatsite(epcdata)

Description

Assign threshold categories to Enterococcus data

Usage

anlz_enteromap(
  fibdata,
  yrsel = NULL,
  mosel = NULL,
  areasel = NULL,
  wetdry = FALSE,
  precipdata = NULL,
  temporal_window = NULL,
  wet_threshold = NULL,
  assf = FALSE
)

Arguments

Details

This function is based on anlz_fibmap, but is specific to Enterococcus data downloaded via read_importentero. It creates categories for mapping using show_enteromap. Optionally, if samples have been defined as 'wet' or not via anlz_fibwetdry, this can be represented via symbols on the map. Categories based on relevant thresholds are assigned to each observation. The categories are specific to Enterococcus in marine waters (class of 2 or 3M). A station is categorized into one of four ranges defined by the thresholds as noted in the cat column of the output, with corresponding colors appropriate for each range as noted in the col column of the output.

The areasel argument can indicate valid entries in the long_name column of enterodata. For example, use "Old Tampa Bay" for stations in the subwatershed of Old Tampa Bay, where rows in enterodata are filtered based on the the selection. All stations are returned if this argument is set as NULL (default). All valid options for areasel include "Old Tampa Bay", "Hillsborough Bay", "Middle Tampa Bay", "Lower Tampa Bay", "Boca Ciega Bay", or "Manatee River". One to any of the options can be used.

Value

A data.frame similar to fibdata if assf = FALSE with additional columns describing station categories and optionally filtered by arguments passed to the function. A sf object if assf = TRUE with additional columns for show_enteromap.

Examples

anlz_enteromap(enterodata, yrsel = 2020, mosel = 9)

# differentiate wet/dry samples in that time frame
anlz_enteromap(enterodata, yrsel = 2020, mosel = 9, wetdry = TRUE,
               temporal_window = 2, wet_threshold = 0.5)

# as sf object
anlz_enteromap(enterodata, assf = TRUE)

Description

Assign threshold categories to Fecal Indicator Bacteria (FIB) data

Usage

anlz_fibmap(fibdata, yrsel = NULL, mosel = NULL, areasel = NULL, assf = FALSE)

Arguments

Details

This function is used to create FIB categories for mapping using show_fibmap. Categories based on relevant thresholds are assigned to each observation. The categories are specific to E. coli or Enterococcus and are assigned based on the station class as freshwater (class as 1 or 3F) or marine (class as 2 or 3M), respectively. A station is categorized into one of four ranges defined by the thresholds as noted in the cat column of the output, with corresponding colors appropriate for each range as noted in the col column of the output.

The input fibdata object can be one returned by either read_importfib or read_importwqp.

The areasel argument can indicate valid entries in the area column of fibdata from read_importfib or mancofibdata, pascofibdata, polcofibdata, or hcesdfibdata from read_importwqp. For example, use either "Alafia River" or "Hillsborough River" to show stations for the corresponding river basins, where rows in fibdata are filtered based on the the selection. All valid options for areasel for fibdata include "Alafia River", "Hillsborough River", "Cockroach Bay", "East Lake Outfall", "Hillsborough Bay", "Little Manatee", "Lower Tampa Bay", "McKay Bay", "Middle Tampa Bay", "Old Tampa Bay", "Palm River", "Tampa Bypass Canal", or "Valrico Lake". Valid options for areasel if the input data are from read_importwqp are any that are present in the area column for the respective input datasets (mancofibdata, pascofibdata, polcofibdata, or polcofibdata). One to any of the options can be used.All stations are returned if this argument is set as NULL (default). Not all areas may be present based on the year/month selection.

Value

A data.frame similar to the input if assf = FALSE with additional columns describing station categories and optionally filtered by arguments passed to the function. A sf object if assf = TRUE with additional columns for show_fibmap.

Examples

# assign categories to all
anlz_fibmap(fibdata)

# filter by year, month, and area
anlz_fibmap(fibdata, yrsel = 2020, mosel = 7, areasel = 'Alafia River')

# as sf object
anlz_fibmap(fibdata, assf = TRUE)

Description

Analyze Fecal Indicator Bacteria categories over time by station or bay segment

Usage

anlz_fibmatrix(
  fibdata,
  yrrng = NULL,
  stas = NULL,
  bay_segment = NULL,
  lagyr = 3,
  subset_wetdry = c("all", "wet", "dry"),
  precipdata = NULL,
  temporal_window = NULL,
  wet_threshold = NULL,
  warn = TRUE
)

Arguments

Details

This function is used to create output for plotting a matrix stoplight graphic for FIB categories by station. Each station (or bay segment) and year combination is categorized based on the likelihood of fecal indicator bacteria concentrations exceeding some threshold in a given year. For Enterococcus (marine), the default threshold is 130 CFU / 100 mL in a given year. For E. coli (fresh), the default threshold is 410 CFU / 100 mL. The proportions are categorized as A, B, C, D, or E (Microbial Water Quality Assessment or MWQA categories) with corresponding colors, where the breakpoints for each category are <10\

If the input data are from read_importentero (baywide assessment), the results can be summarized by bay segment if bay_segment is not NULL. The stas argument is ignored and all stations within each bay segment watershed are used to evaluate the FIB categories.

yrrng can be specified several ways. If yrrng = NULL, the year range of the data for the selected changes is chosen. User-defined values for the minimum and maximum years can also be used, or only a minimum or maximum can be specified, e.g., yrrng = c(2000, 2010) or yrrng = c(2000, NA). In the latter case, the maximum year will be defined by the data.

The default stations if the input is from read_importfib (EPC data) and if bay_segment is NULL are those used in TBEP report #05-13 (https://drive.google.com/file/d/1MZnK3cMzV7LRg6dTbCKX8AOZU0GNurJJ/view) for the Hillsborough River Basin Management Action Plan (BMAP) subbasins. These include Blackwater Creek (WBID 1482, EPC stations 143, 108), Baker Creek (WBID 1522C, EPC station 107), Lake Thonotosassa (WBID 1522B, EPC stations 135, 118), Flint Creek (WBID 1522A, EPC station 148), and the Lower Hillsborough River (WBID 1443E, EPC stations 105, 152, 137). Other stations can be plotted using the stas argument.

Input from read_importwqp for Manatee County (21FLMANA_WQX), Pasco County (21FLPASC_WQX), or Polk County (21FLPOLK_WQX) FIB data can also be used. The function has not been tested for other organizations.

Value

A tibble object with FIB summaries by year and station including columns for the estimated geometric mean of Enterococcus (marine) or E. coli (fresh) concentrations (gmean), the proportion of samples exceeding 130 CFU / 100 mL (Enterococcus) or 410 CFU / 100 mL (exced), the count of samples (cnt), and a category indicating a letter outcome based on the proportion of exceedences (cat). Results can be summarized by bay segment if bay_segment is not NULL and the input data is from read_importentero.

See Also

show_fibmatrix

Examples

anlz_fibmatrix(fibdata)

# use different dataset
anlz_fibmatrix(enterodata, lagyr = 1)

# subset to only wet samples
anlz_fibmatrix(enterodata, lagyr = 1, subset_wetdry = "wet",
               temporal_window = 2, wet_threshold = 0.5)

# Manatee County data
anlz_fibmatrix(mancofibdata, lagyr = 1)

Description

Identify Fecal Indicator Bacteria samples as coming from a 'wet' or 'dry' time period

Usage

anlz_fibwetdry(fibdata, precipdata, temporal_window = 2, wet_threshold = 0.5)

Arguments

Details

This function allows the user to specify a threshold for declaring a sample to be taken after an important amount of rain over an important amount of days, and declaring it to be 'wet'. This is of interest because samples taken after significant precipitation (definitions of this vary, which is why the user can specify desired thresholds) are more likely to exceed relevant bacterial thresholds. Identifying samples as 'wet' or not allows for calculation of further indices for wet and dry subsets of samples.

Value

a data frame; the original fibdata data frame with three additional columns. rain_sampleDay is the total rain (inches) on the day of sampling, rain_total is the total rain (inches) for the period of time defined by temporal_window, and wet_sample is logical, indicating whether the rainfall for that station's catchment exceeded the amount over the time period specified in args.

Examples

entero_wetdry <- anlz_fibwetdry(enterodata, catchprecip)
head(entero_wetdry)

Description

Evaluate Habitat Master Plan progress for report card

Usage

anlz_hmpreport(acres, subtacres, hmptrgs)

Arguments

Details

The relevant output columns are targeteval and goaleval that indicate numeric values as -1 (target not met, trending below), 0 (target met, trending below), 0.5 (target not met, trending above), and 1 (target met, trending above).

Columns in the output are as follows:

year

Year of the assessment

metric

Habitat type assessed

Acres

Coverage estimate for the year

lacres

Coverage estimate for the previous set of available data

lyr

Year for the previous set of available data

category

Strata for the habitat type

Target

2030 target for the habitat type from the Habitat Master Plan

Goal

2050 goal for the habitat type from the Habitat Master Plan

acresdiff

Difference in acres for the current year and the previous set of available data

yeardiff

Difference in years for the current year and the previous set of available data

changerate

Acreage change per year for the current year relative to the previous set of available data

targetrate

Annual rate required to achieve the 2030 target

goalrate

Annual rate required to achieve the 2050 goal

targetprop

Proportion of target met for the current year

goalprop

Proportion of goal met for the current year

targeteval

A number indicating target status of the current year for the report card

goaleval

A number indicating goal status of the current year for the report card

The numbers in targeteval and goaleval are one of four values as -1, 0, 0.5, and 1. These numbers define the status for the assessment year:

-1

target or goal not met, trending below

0

target or goal met, trending below

0.5

target or goal not met, trending above

1

target or goal met, trending above

Value

A summarized data.frame appropriate for creating a report card

Examples

# view summarized data for report card
anlz_hmpreport(acres, subtacres, hmptrgs)

Description

Estimate hydrological estimates and adjustment factors for bay segments

Usage

anlz_hydroload(yrs, noaa_key = NULL, trace = FALSE)

Arguments

Details

This function uses rainfall and streamflow data from NOAA and USGS and requires an API key. See the webpage https://www.ncdc.noaa.gov/cdo-web/token for retrieving a NOAA token. This key can be added to the R environment file and called for later use, see the examples.

These estimates are used in annual compliance assessment reports produced by the Tampa Bay Nitrogen Management Consortium. Load estimates and adjustment factors are based on regression models in https://drive.google.com/file/d/11NT0NQ2WbPO6pVZaD7P7Z6qjcwO1jxHw/view?usp=drivesdk

Value

A data frame with hydrological load estimates by bay segments for the requested years

Examples

## Not run: 
# this function requires an API key
# save it to the R environment file (only once)
# save the key, do only once
cat("NOAA_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX\n",
   file=file.path(normalizePath("~/"), ".Renviron"),
   append=TRUE)

# retrieve the key after saving, may need to restart R
noaa_key <- Sys.getenv('NOAA_key')

# get estimates for 2021
anlz_hydroload(2021, noaa_key)


## End(Not run)

Description

Format raw IWR data

Usage

anlz_iwrraw(iwrraw, tidalcreeks, yr = 2025)

Arguments

Details

The function subsets the raw IWR data for ten years starting at the value in yr and subsets by the creek population in tidalcreeks. Select water quality parameters in masterCode are filtered and some of the names are combined for continuity.

Value

A data.frame with the formatted data

Examples

anlz_iwrraw(iwrraw, tidalcreeks, yr = 2025)

Description

Convert references csv to bib

Usage

anlz_refs(path)

Arguments

Value

A data frame with references formatted as bib entries

Examples

# input and format
path <- system.file('tbep-refs.csv', package = 'tbeptools')
bibs <- anlz_refs(path)

## Not run: 
# save output
 writeLines(bibs, 'formatted.bib')

## End(Not run)

Description

Add contaminant totals to sediment data

Usage

anlz_sedimentaddtot(
  sedimentdata,
  yrrng = c(1993, 2024),
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB"),
  funding_proj = "TBEP",
  param = NULL,
  pelave = TRUE
)

Arguments

Details

This function adds totals to the sedimentdata input for total PCBs, total DDT, total LMW PAH, total HMW PAH, and total PAH. Appropriate TEL/PEL values for the totals are also added.

Value

A data.frame object similar to the input, but filtered by the arguments and contaminant totals added. Replicate samples are also removed.

Examples

anlz_sedimentaddtot(sedimentdata)

Description

Get average concentrations for a sediment parameter by bay segment

Usage

anlz_sedimentave(
  sedimentdata,
  param,
  yrrng = c(1993, 2024),
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB"),
  funding_proj = "TBEP"
)

Arguments

Details

Summaries for all bay segments are returned by default. The averages and confidence intervals are specific to the year ranges in yrrng.

Value

A data.frame object with average sediment concentrations for the selected parameter by bay segment

See Also

show_sedimentave

Examples

anlz_sedimentave(sedimentdata, param = 'Arsenic')

Description

Get sediment PEL ratios at stations in Tampa Bay

Usage

anlz_sedimentpel(
  sedimentdata,
  yrrng = c(1993, 2024),
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB"),
  funding_proj = "TBEP"
)

Arguments

Details

Average PEL ratios for all contaminants graded from A to F for benthic stations monitored in Tampa Bay are estimated. The PEL is a measure of how likely a contaminant is to have a toxic effect on invertebrates that inhabit the sediment. The PEL ratio is the contaminant concentration divided by the Potential Effects Levels (PEL) that applies to a contaminant, if available. Higher ratios and lower grades indicate sediment conditions that are likely unfavorable for invertebrates. The station average combines the PEL ratios across all contaminants measured at a station and the grade applies to the average.

The grade breaks for the PEL ratio are 0.00756, 0.02052, 0.08567, and 0.28026, with lower grades assigned to the higher breaks.

Value

A data.frame object with average PEL ratios and grades at each station

See Also

show_sedimentpelmap

Examples

anlz_sedimentpel(sedimentdata)

Description

Get average concentrations for a sediment parameter by bay segment

Usage

anlz_sedimentpelave(
  sedimentdata,
  yrrng = c(1993, 2024),
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB"),
  funding_proj = "TBEP"
)

Arguments

Details

Summaries for all bay segments are returned by default. The averages and confidence intervals are specific to the year ranges in yrrng.

Value

A data.frame of the average of the average PEL ratios for each bay segment

See Also

show_sedimentpelave

Examples

anlz_sedimentpelave(sedimentdata)

Description

Analyze time series data split by date within years

Usage

anlz_splitdata(
  df,
  date_split,
  date_col = "date",
  value_col = "value",
  stats = list(avg = mean)
)

Arguments

Value

A tibble summarizing data for annual periods "before" and "after" the split date

Examples

# Create sample data
data <- data.frame(
  date = seq.Date(as.Date("2010-01-01"), as.Date("2020-12-31"), by = "month"),
  value = rnorm(132, mean = 10, sd = 2))

# Basic analysis with default statistics
split_date <- as.Date("2015-06-15")
anlz_splitdata(data, split_date, "date", "value")

Description

Analyze storm data split by date within years

Usage

anlz_splitstorms(
  df_hurricane,
  date_split,
  stats = list(sum = sum, avg = mean, n = length)
)

Arguments

Value

A tibble summarizing hurricanes for annual periods "before" and "after" the split date, with requested statistics computed for the scale values

Examples

# Create sample hurricane data
hurricanes <- data.frame(
  date_beg = as.Date(c(
    "1980-07-31", "1980-09-04", "1980-11-07",
    "1981-05-06", "1981-08-07", "1981-11-12")),
  scale = c(6, 1, 3, 1, 2, 1))

# Basic analysis with default statistics (sum, average and count)
split_date <- Sys.Date() - lubridate::years(1)
anlz_splitstorms(hurricanes, split_date)

# Analysis with custom statistics
anlz_splitstorms(hurricanes, split_date,
                     stats = list(
                       max = max,
                       min = min))

Description

Get annual medians of Tampa Bay Benthic Index scores by bay segment

Usage

anlz_tbbimed(
  tbbiscr,
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB", "All", "All (wt)"),
  rev = FALSE,
  yrrng = c(1993, 2024),
  window = TRUE
)

Arguments

Details

Additional summaries are provided for the entire bay, as a summary across categories ("All") and a summary weighted across the relative sizes of each bay segment ("All (wt)").

Only sampling funded by TBEP and as part of the routine EPC benthic monitoring program are included in the final categories.

The default behavior is to use a rolling five-year window to calculate the percent of sites in each TBBI category by bay segment. This applies only to years 2005 and later, where the counts from the current year and the prior four years are summed to calculate the percentages. This is intended to help smooth out inter-annual variability due to reduced sampling effort from 2005 to present. If window = FALSE, then only single year values are used.

Value

A data frame of annual medians by bay segment

Examples

tbbiscr <- anlz_tbbiscr(benthicdata)
anlz_tbbimed(tbbiscr)

Description

Get Tampa Bay Benthic Index scores

Usage

anlz_tbbiscr(benthicdata)

Arguments

Details

This function calculates scores for the TBBI based on station, taxa, and field sample data. The total TBBI scores are returned as TBBI and TBBICat, where the latter is a categorical description of the scores.

Value

A single data frame of TBBI scores for each site.

Examples

anlz_tbbiscr(benthicdata)

Description

Get annual averages of Tampa Bay Nekton Index scores by bay segment

Usage

anlz_tbniave(
  tbniscr,
  bay_segment = c("OTB", "HB", "MTB", "LTB"),
  rev = FALSE,
  perc = c(32, 46)
)

Arguments

Value

A data frame of annual averages by bay segment

Examples

tbniscr <- anlz_tbniscr(fimdata)
anlz_tbniave(tbniscr)

Description

Get all raw metrics for Tampa Bay Nekton Index

Usage

anlz_tbnimet(fimdata, all = FALSE)

Arguments

Details

All raw metrics are returned in addition to those required for the TBNI. Each row shows metric values for a station, year, and month where fish catch data were available.

Value

A data frame of raw metrics in wide fomat. If all = TRUE, all metrics are returned, otherwise only NumTaxa, BenthicTaxa, TaxaSelect, NumGuilds, and Shannon are returned.

Examples

anlz_tbnimet(fimdata)

Description

Get Tampa Bay Nekton Index scores

Usage

anlz_tbniscr(fimdata, raw = TRUE)

Arguments

Details

This function calculates raw and scored metrics for the TBNI, including NumTaxa, BenthicTaxa, TaxaSelect, NumGuilds, and Shannon. The total TBNI score is returned as TBNI_Score.

Value

A data frame of metrics and TBNI scores in wide format.

See Also

anlz_tbnimet

Examples

anlz_tbniscr(fimdata)

Description

Estimate tidal creek report card scores

Usage

anlz_tdlcrk(tidalcreeks, iwrraw, tidtrgs = NULL, yr = 2025)

Arguments

Value

A data.frame with the report card scores for each creek, as prioritize, investigate, caution, monitor, or no data

Examples

anlz_tdlcrk(tidalcreeks, iwrraw, yr = 2025)

Description

Estimate tidal creek water quality indicators to support report card scores

Usage

anlz_tdlcrkindic(tidalcreeks, iwrraw, yr = 2025, radar = FALSE)

Arguments

Details

Annual geometric means for additional water quality data available at each wbid, JEI combination. Florida trophic state index values are also estimated where data are available.

Nitrogen ratios are estimated for JEIs that cover source (upstream, freshwater) and tidal (downstream) WBIDs, defined as the ratio of concentrations between the two (i.e., ratios > 1 mean source has higher concentrations). Nitrogen ratios for a given year reflect the ratio of the median nitrogen concentrations when they were measured in both a source and tidal segment during the same day. Note that a ratio of one can be obtained if both the source and tidal segments are at minimum detection.

Indicators for years where more than 10\

If radar = TRUE, output is returned in a format for use with show_tdlcrkradar Specifically, results are calculated as the percentage of years where an indicator exceeds a relevant threshold. This only applies to the marine WBIDs of the tidal creeks (Florida DEP class 2, 3M). Six indicators are returned with percentage exceedances based on total nitrogen (tn_ind) greater than 1.1 mg/L, chlorophyll (chla_ind) greater than 11 ug/L, trophic state index (tsi_ind) greater than 55 (out of 100), nitrate/nitrite ratio between marine and upstream segments (nox_ind) greater than one, chlorophyll and total nitrogen ratios > 15, and percentage of years more where than ten percent of observations were below DO percent saturation of 42.

Value

A data.frame with the indicator values for each tidal creek

Examples

dat <- anlz_tdlcrkindic(tidalcreeks, iwrraw, yr = 2025)
head(dat)

Description

Get annual averages of seagrass frequency occurrence by bay segments and year

Usage

anlz_transectave(
  transectocc,
  bay_segment = c("OTB", "HB", "MTB", "LTB", "BCB"),
  total = TRUE,
  yrrng = c(1998, 2025),
  rev = FALSE
)

Arguments

Details

The focat column returned in the results shows a color category based on arbitrary breaks of the frequency occurrence estimates (foest) at 25, 50, and 75 percent. These don't necessarily translate to any ecological breakpoints.

Value

A data frame of annual averages by bay segment

Examples

## Not run: 
transect <- read_transect()

## End(Not run)
transectocc <- anlz_transectocc(transect)
anlz_transectave(transectocc)

Description

Get annual averages of seagrass frequency occurrence by bay segments, year, and species

Usage

anlz_transectavespp(
  transectocc,
  bay_segment = c("OTB", "HB", "MTB", "LTB", "BCB"),
  yrrng = c(1998, 2025),
  species = c("Halodule", "Syringodium", "Thalassia", "Ruppia", "Halophila", "Caulerpa",
    "Dapis", "Chaetomorpha"),
  total = TRUE,
  by_seg = FALSE
)

Arguments

Details

Frequency occurrence estimates are averaged across segments in bay_segment if by_seg = F, i.e., separate results by location are not returned. Results are retained by bay segment if by_seg = T.

Value

A data frame of annual averages by bay segment

Examples

## Not run: 
transect <- read_transect()

## End(Not run)
transectocc <- anlz_transectocc(transect)
anlz_transectavespp(transectocc)

Description

Get seagrass average abundance and occurrence across transects

Usage

anlz_transectocc(transect)

Arguments

Details

Abundance and frequency occurrence are estimated as in Sherwood et al. 2017, equations 1 and 2. In short, frequency occurrence is estimated as the number of instances a species was observed along a transect divided by the number of placements along a transect and average abundance was estimated as the sum of species-specific Braun-Blanquet scores divided by the number of placements along a transect. The estimates are obtained for all seagrass species including Caulerpa (attached macroalgae), Dapis (cyanobacteria), and Chaetomorpha (drift green algea), whereas all other attached and drift algae species are aggregated. Drift or attached macroalgae and cyanobacteria (Dapis) estimates may not be accurate prior to 2021. Values for total include total frequency occurrence only for seagrass species (Halodule, Syringodium, Thalassia, Ruppia, Halophila).

Value

A data frame with abundance and frequency occurrence estimates aggregated by species, transect, and date. The nsites column is the total number of placements that were sampled along a transect for a particular date.

References

Sherwood, E.T., Greening, H.S., Johansson, J.O.R., Kaufman, K., Raulerson, G.E. 2017. Tampa Bay (Florida, USA): Documenting seagrass recovery since the 1980's and reviewing the benefits. Southeastern Geographer. 57(3):294-319.

Examples

## Not run: 
transect <- read_transect()

## End(Not run)
anlz_transectocc(transect)

Description

Get attainment categories for a chosen year

Usage

anlz_yrattain(epcdata, yrsel, partialyr = FALSE)

Arguments

Value

A data.frame for the chosen year and all bay segments showing the bay segment averages for chloropyll concentration, light attenuations, segment targets, and attainment categories.

Examples

# defaults to current year
anlz_yrattain(epcdata, yrsel = 2025)

Description

Benthic data for the Tampa Bay Benthic Index current as of 20251225

Usage

benthicdata

Format

A nested tibble with 3 rows and 2 variables:

name

chr identifying the dataset as stations, fieldsamples, or taxacounts

value

list of dataframes for each dataset

Details

Index the corresponding list element in the value column to view each dataset. For example, the stations data in the first row can be viewed as benthicdata$value[[1]].

Examples

## Not run: 
# location to download data
path <- '~/Desktop/benthic.zip'

# load and assign to object
benthicdata <- read_importbenthic(path, download_latest = TRUE, remove = TRUE)

save(benthicdata, file = 'data/benthicdata.RData', compress = 'xz')


## End(Not run)

Description

Terrain basemap

Usage

bsmap

Format

A ggmap object

Description

Catchments and radar pixels (for precip) of selected Enterococcus stations

Usage

catchpixels

Format

A data frame with two columns:

station

a character string of the Water Quality Portal station name

pixel

a number; each row is a single pixel from the Southwest Florida Water Management District's radar-estimated rainfall shapefile

Details

Generated by data-raw/catchpixels-raw.R (view on github: https://github.com/tbep-tech/tbeptools/blob/master/data-raw/catchpixels-raw.R)

Description

Daily precip by catchment for selected Enterococcus stations

Usage

catchprecip

Format

A data frame with three columns:

station

a character string of the Water Quality Portal station name

date

a date

rain

a number; inches of rain for that date, averaged across all pixels in the station's catchment

Details

Daily precipitation data for multiple years, provided by the Southwest Florida Water Management District, were downloaded at the pixel level and averaged to the catchment level for key Enterococcus sampling stations. Created using read_importrainmany.

Source

Southwest Florida Management District radar-estimated daily rainfall data, ftp://ftp.swfwmd.state.fl.us/pub/radar_rainfall/Daily_Data/

Examples

## Not run: 
catchprecip <- read_importrainmany(1995:2025, quiet = F)

save(catchprecip, file = 'data/catchprecip.RData')

## End(Not run)

Description

Spatial data object of minor subbasins (dbasins) in the Tampa Bay watershed

Usage

dbasin

Format

A simple features sf object (POLYGON) with 436 features and 26 fields, +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs

Examples

library(sf)
plot(st_geometry(dbasin))

Description

Enterococcus data from 53 key Enterococcus stations since 1995

Usage

enterodata

Format

A data frame with 6885 rows and 16 columns:

date

date, sample date

yr

numeric, year of sample date

mo

numeric, month of sample date

time

character, sample time

time_zone

character, sample time zone

long_name

character, long name of bay segment subwatershed

bay_segment

character, short name of bay segment subwatershed

station

character, sample station

entero

numeric, Enterococcus concentration

entero_censored

logical, whether entero value was below the laboratory MDL, minimum detection limit

MDL

numeric, minimum detection limit at the time of processing

entero_units

character, units of measurement for entero

qualifier

qualifier codes associated with sample

LabComments

lab comments on sample

Latitude

numeric, latitude in decimal degrees

Longitude

numeric, longitude in decimal degrees

Details

A dataset containing Enterococcus from 53 stations in the TBEP watershed since 1995.

Source

Water Quality Portal, https://waterqualitydata.us

Examples

## Not run: 
enterodata <- read_importentero(startDate = '1995-01-01', endDate = '2025-12-31')

save(enterodata, file = 'data/enterodata.RData')

## End(Not run)

Description

All bay data as of 20260203

Usage

epcdata

Format

A data frame with 29041 rows and 26 variables:

bay_segment

chr

epchc_station

num

SampleTime

POSIXct

yr

num

mo

num

Latitude

num

Longitude

num

Total_Depth_m

num

Sample_Depth_m

num

tn

num

tn_q

chr

sd_m

num

sd_raw_m

num

sd_q

chr

chla

num

chla_q

chr

Sal_Top_ppth

num

Sal_Mid_ppth

num

Sal_Bottom_ppth

num

Temp_Water_Top_degC

num

Temp_Water_Mid_degC

num

Temp_Water_Bottom_degC

num

Turbidity_JTU-NTU

num

Turbidity_Q

num

Color_345_F45_PCU

num

Color_345_F45_Q

num

Examples

## Not run: 
xlsx <- '~/Desktop/epcdata.xlsx'
epcdata <- read_importwq(xlsx, download_latest = TRUE)

nrow(epcdata)
ncol(epcdata)

save(epcdata, file = 'data/epcdata.RData', compress = 'xz')

## End(Not run)

Description

All Fecal Indicator Bacteria (FIB) data as of 20260302

Usage

fibdata

Format

A data frame with 31081 rows and 18 variables:

area

chr

epchc_station

num

class

chr

SampleTime

POSIXct

yr

num

mo

num

Latitude

num

Longitude

num

Total_Depth_m

num

Sample_Depth_m

num

ecoli

num

ecoli_q

chr

entero

num

entero_q

chr

fcolif

num

fcolif_q

chr

totcol

num

totcol_q

chr

Details

This dataset includes FIB data from the Environmental Protection Commission where station class is marine (3M, 2) and Enterococcus data is present or the station class is freshwater (3F, 1) and E. coli data is present. The data is formatted from the raw data loaded from read_importfib.

Examples

## Not run: 
xlsx <- tempfile(fileext = '.xlsx')
fibdata <- read_importfib(xlsx, download_latest = TRUE)

nrow(fibdata)
ncol(fibdata)

save(fibdata, file = 'data/fibdata.RData')

file.remove(xlsx)

## End(Not run)

Description

FIM data for Tampa Bay Nekton Index current as of 09032025

Usage

fimdata

Format

A data frame with 53969 rows and 19 variables:

Reference

chr

Sampling_Date

Date

Latitude

num

Longitude

num

Zone

chr

Grid

int

NODCCODE

chr

Year

num

Month

num

Total_N

num

ScientificName

chr

Include_TB_Index

chr

Hab_Cat

chr

Est_Cat

chr

Est_Use

chr

Feeding_Cat

chr

Feeding_Guild

chr

Selected_Taxa

chr

bay_segment

chr

Examples

## Not run: 
csv <- '~/Desktop/TampaBay_NektonIndexData.csv'

fimdata <- read_importfim(csv, download_latest = TRUE)

save(fimdata, file = 'data/fimdata.RData', compress = 'xz')


## End(Not run)

Description

Spatial data object of FIM stations including Tampa Bay segments

Usage

fimstations

Format

A simple features sf object (POINT) with 8099 features and 2 fields, +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs

Reference

num

bay_segment

chr

Examples

## Not run: 
# file path
csv <- '~/Desktop/fimraw.csv'

# load and assign to object
fimstations <- read_importfim(csv, download_latest = FALSE, locs = TRUE)
save(fimstations, file = 'data/fimstations.RData', compress = 'xz')

## End(Not run)

Description

Hillsborough County Environmental Services Division (ESD) FIB data as of 20260302

Usage

hcesdfibdata

Format

A data frame with 1264 rows and 13 variables:

hcesd_station

chr, Station name

SampleTime

POSIXct, Date/time of sampling

class

chr, Waterbody class (Fresh, Marine)

yr

num, Year of sampling

mo

num, Month of sampling

Latitude

num, Latitude, WGS84

Longitude

num, Latitude, WGS84

Sample_Depth_m

num, Depth of sample, meters

var

chr, Variable name (ecoli, entero

val

num, Value of variable

uni

num, Units of variable

qual

num, Qualifier code

area

chr, Location name

Details

This dataset includes FIB data from Hillsborough County Environmental Services Division (21FLHESD_WQX) where station class is marine (estuary) and Enterococcus data is present or the station class is freshwater (stream/river, reservoir) and E. coli data is present. The data is formatted from the raw data loaded from read_importwqp.

Examples

## Not run: 
hcesdfibdata <- read_importwqp('21FLHESD_WQX', type = 'fib')

nrow(hcesdfibdata)
ncol(hcesdfibdata)

save(hcesdfibdata, file = 'data/hcesdfibdata.RData')

## End(Not run)

Description

Habitat Master Plan targets and goals

Usage

hmptrgs

Format

A data frame showing 2030 targets and 2050 goals

Examples

## Not run: 
library(dplyr)

load(url("https://github.com/tbep-tech/hmpu-workflow/raw/master/data/trgs.RData"))

hmptrgs <- trgs %>%
   rename(Goal2050 = Target2050)

save(hmptrgs, file = 'data/hmptrgs.RData', compress = 'xz')

## End(Not run)

Description

Florida Department of Environmental Protection, Impaired Waters Rule, Run 67

Usage

iwrraw

Format

A data frame 592464 rows and 11 variables

Details

File was created using workflow at https://tbep-tech.github.io/tidalcreek-stats/Creek_select_tbeptools, example below is old and for Run 61.

Examples

## Not run: 
library(dplyr)

load(file = '../../02_DOCUMENTS/tidal_creeks/iwrraw_run61.RData')
iwrraw <- sf::st_set_geometry(iwrraw, NULL) %>%
  rename(JEI = jei)
save(iwrraw, file = 'data/iwrraw.RData', compress = 'xz')

## End(Not run)

Description

Manatee County FIB data as of 20260302

Usage

mancofibdata

Format

A data frame with 1928 rows and 13 variables:

manco_station

chr, Station name

SampleTime

POSIXct, Date/time of sampling

class

chr, Waterbody class (Fresh, Marine)

yr

num, Year of sampling

mo

num, Month of sampling

Latitude

num, Latitude, WGS84

Longitude

num, Latitude, WGS84

Sample_Depth_m

num, Depth of sample, meters

var

chr, Variable name (ecoli, entero

val

num, Value of variable

uni

num, Units of variable

qual

num, Qualifier code

area

chr, Location name based on USF Water Alas waterbody name

Details

This dataset includes FIB data from Manatee County Department of Natural Resources where station class is marine (estuary) and Enterococcus data is present or the station class is freshwater (stream/river, reservoir) and E. coli data is present. The data is formatted from the raw data loaded from read_importwqp.

Examples

## Not run: 
mancofibdata <- read_importwqp('21FLMANA_WQX', type = 'fib')

nrow(mancofibdata)
ncol(mancofibdata)

save(mancofibdata, file = 'data/mancofibdata.RData')

## End(Not run)

Description

Pasco County FIB data as of 20260302

Usage

pascofibdata

Format

A data frame with 255 rows and 13 variables:

pasco_station

chr, Station name

SampleTime

POSIXct, Date/time of sampling

class

chr, Waterbody class (Fresh, Marine)

yr

num, Year of sampling

mo

num, Month of sampling

Latitude

num, Latitude, WGS84

Longitude

num, Latitude, WGS84

Sample_Depth_m

num, Depth of sample, meters

var

chr, Variable name (ecoli, entero

val

num, Value of variable

uni

num, Units of variable

qual

num, Qualifier code

area

chr, Location name

Details

This dataset includes FIB data from Pasco County (21FLPASC_WQX) where station class is marine (estuary) and Enterococcus data is present or the station class is freshwater (stream/river, reservoir) and E. coli data is present. The data is formatted from the raw data loaded from read_importwqp.

Examples

## Not run: 
pascofibdata <- read_importwqp('21FLPASC_WQX', type = 'fib')

nrow(pascofibdata)
ncol(pascofibdata)

save(pascofibdata, file = 'data/pascofibdata.RData')

## End(Not run)

Description

Phytoplankton data current as of 20260204

Usage

phytodata

Format

A nested tibble with 41390 rows and 8 variables:

epchc_station

chr

Date

Date

name

chr

units

chr

count

num

yrqrt

Date

yr

num

mo

Ord.factor

Examples

## Not run: 
# location to download data
path <- tempfile(fileext = '.xlsx')

# load and assign to object
phytodata <- read_importphyto(path, download_latest = TRUE)

nrow(phytodata)
ncol(phytodata)

save(phytodata, file = 'data/phytodata.RData', compress = 'xz')

file.remove(path)

## End(Not run)

Description

Polk County FIB data as of 20260302

Usage

polcofibdata

Format

A data frame with 996 rows and 13 variables:

polco_station

chr, Station name

SampleTime

POSIXct, Date/time of sampling

class

chr, Waterbody class (Fresh, Marine)

yr

num, Year of sampling

mo

num, Month of sampling

Latitude

num, Latitude, WGS84

Longitude

num, Latitude, WGS84

Sample_Depth_m

num, Depth of sample, meters

var

chr, Variable name (ecoli, entero

val

num, Value of variable

uni

num, Units of variable

qual

num, Qualifier code

area

chr, Location name

Details

This dataset includes FIB data from Polk County (21FLPOLK_WQX) where station class is marine (estuary) and Enterococcus data is present or the station class is freshwater (stream/river, reservoir) and E. coli data is present. The data is formatted from the raw data loaded from read_importwqp.

Examples

## Not run: 
polcofibdata <- read_importwqp('21FLPOLK_WQX', type = 'fib')

nrow(polcofibdata)
ncol(polcofibdata)

save(polcofibdata, file = 'data/polcofibdata.RData')

## End(Not run)

Description

Download latest file from epchc.org

Usage

read_dlcurrent(locin, download_latest = TRUE, urlin)

Arguments

Details

The local copy is checked against a temporary file downloaded from the location specified by urlin. The local file is replaced with the downloaded file if the MD5 hashes are different.

Value

The local copy specified in the path by locin is overwritten by the new file is not current or download_latest = TRUE. The function does nothing if download_latest = FALSE.

Examples

## Not run: 
locin <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'
urlin1 <- 'https://epcbocc.sharepoint.com/:x:/s/Share/'
urlin2 <- 'EYXZ5t16UlFGk1rzIU91VogBa8U37lh8z_Hftf2KJISSHg?e=8r1SUL'
urlin <- paste0(urlin1, urlin2, '&download=1')
read_dlcurrent(locin = locin, urlin = urlin)

## End(Not run)

Description

Format benthic data for the Tampa Bay Benthic Index

Usage

read_formbenthic(pathin)

Arguments

Details

Function is used internally within read_importbenthic

Value

A nested tibble of station, field sample, and taxa data

Examples

## Not run: 

# location to download data
path <- '~/Desktop/benthic.zip'

# download
urlin1 <- 'https://epcbocc.sharepoint.com/:x:/s/Share/'
urlin2 <- 'EQUCWBuwCNdGuMREYAyAD1gBKC98mYtCHMWX0FYLrbT4KA?e=nDfnnQ'
urlin <- paste0(urlin1, urlin2, '&download=1')
read_dlcurrent(path, download_latest = TRUE, urlin = urlin)

# unzip
tmppth <- tempfile()
utils::unzip(path, exdir = tmppth, overwrite = TRUE)

# format benthic data
read_formbenthic(pathin = tmppth)

# remove temporary path
unlink(tmppth, recursive = TRUE)


## End(Not run)

Description

Format Fecal Indicator Bacteria (FIB) data

Usage

read_formfib(datin, all = FALSE)

Arguments

Details

Formats input data from read_importepc appropriate for FIB results, see the details in read_importfib for more more information.

Value

A lightly formatted data.frame with FIB data

See Also

read_importfib, read_importepc

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'

# load raw data and assign to object
epcall <- read_importepc(xlsx, download_latest = T)

# final formatting
fibdata <- read_formfib(epcall)

## End(Not run)

Description

Format FIM data for the Tampa Bay Nekton Index

Usage

read_formfim(datin, locs = FALSE)

Arguments

Details

Function is used internally within read_importfim

Value

A formatted data.frame with FIM data if locs = FALSE, otherwise a simple features object if locs = TRUE

See Also

read_importfim

Examples

# file path
url <- 'https://raw.githubusercontent.com/tbep-tech/tbni-proc/master/data/'
fl <- 'TampaBay_NektonIndexData.csv'
csv <- url(paste0(url, fl))

datin <- read.csv(csv)

# load and assign to object
fimdata <- read_formfim(datin)

Description

Format phytoplankton data

Usage

read_formphyto(datin)

Arguments

Details

Only seven taxonomic groups are summarized. Pyrodinium bahamense, Karenia brevis, Tripos hircus, Pseudo-nitzschia sp., and Pseudo-nitzschia pungens are retained at the species level. Diatoms are summarized at the class level using Bacillariophyceae, Coscinodiscophyceae, and Mediophyceae and Cyanobacteria are summarized at the phylum level using Cyanobacteriota. All other taxa are grouped into an "other" category.

Value

A formatted data.frame with phytoplankton count data

See Also

read_importphyto

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/phyto_data.xlsx'

# load and assign to object
phytodata <- read_importphyto(xlsx, download_latest = TRUE)

## End(Not run)

Description

Format sediment data

Usage

read_formsediment(pathin)

Arguments

Details

Function is used internally within read_importsediment

Value

A data.frame of sediment data for Tampa Bay

Examples

## Not run: 

# location to download data
path <- '~/Desktop/sediment.zip'

# download
urlin1 <- 'https://epcbocc.sharepoint.com/:x:/s/Share/'
urlin2 <- 'Ef9utuKCHD9LliarsOPKCJwB5kxgCObf0tY5x5wX20JQUA?e=DuTseb'
urlin <- paste0(urlin1, urlin2, '&download=1')
read_dlcurrent(path, download_latest = TRUE, urlin = urlin)

# unzip
tmppth <- tempfile()
utils::unzip(path, exdir = tmppth, overwrite = TRUE)

# format sediment data
read_formsediment(pathin = tmppth)

# remove temporary path
unlink(tmppth, recursive = TRUE)


## End(Not run)

Description

Format seagrass transect data from Water Atlas

Usage

read_formtransect(jsn, training = FALSE, raw = FALSE)

Arguments

Details

Shoot density is reported as number of shoots per square meter and is corrected for the quadrat size entered in the raw data. Shoot density and blade height (cm) are based on averages across random observations at each transect point that are entered separately in the data form. Abundance is reported as a numeric value from 0 - 5 for Braun-Blanquet coverage estimates.

If raw = TRUE, the unformatted data are returned. The default is to use formatting that allows the raw data to be used with the downstream functions. The raw data may have extra information that may be of use outside of the plotting functions in this package.

Value

data frame in long format

Examples

library(jsonlite)

## Not run: 
# all transect data
url <- 'https://tampabay.wateratlas.usf.edu/seagrass-transect-data-portal/api/assessments/all__use-with-care'
jsn <- fromJSON(url)
trndat <- read_formtransect(jsn)

## End(Not run)

# training transect data
url <- 'https://tampabay.wateratlas.usf.edu/seagrass-transect-data-portal/api/assessments/training'
jsn <- fromJSON(url)
trndat <- read_formtransect(jsn, training = TRUE)

Description

Format water quality data

Usage

read_formwq(datin, all = FALSE)

Arguments

Details

Secchi data VOB depths or secchis < 0.5 ft from bottom are assigned NA, function is used internally within read_importwq

Value

A lightly formatted data.frame with chloropyll and secchi observations

See Also

read_importwq, read_importepc

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'

# load raw data and assign to object
epcall <- read_importepc(xlsx, download_latest = T)

# final formatting
epcdata <- read_formwq(epcall)

## End(Not run)

Description

Format data and station metadata from the Water Quality Portal

Usage

read_formwqp(res, sta, org, type, trace = F)

Arguments

Details

This function is used by read_importwqp to combine, format, and process data (res) and station metadata (sta) obtained from the Water Quality Portal for the selected county and data type. The resulting data frame includes the date, time, station identifier, latitude, longitude, variable name, value, unit, and quality flag. Manatee County FIB data (21FLMANA_WQX) will also include an area column indicating the waterbody name as used by the USF Water Atlas, with some area aggregations. Other county-level FIB data will have a similar column.

Value

A data frame containing formatted water quality and station metadata

See Also

read_importwqp

Examples

## Not run: 
url <- list(
  Result = "https://www.waterqualitydata.us/data/Result/search?mimeType=csv&zip=no",
  Station = "https://www.waterqualitydata.us/data/Station/search?mimeType=csv&zip=no"
)

headers <- c(
  "Content-Type" = "application/json",
  "Accept" = "application/zip"
)

body <- list(
  organization = c("21FLMANA_WQX"),
  sampleMedia = c("Water"),
  characteristicType = c("Information", "Nutrient", "Biological, Algae, Phytoplankton,
    Photosynthetic Pigments"),
  providers = c("STORET"),
  siteType = c("Estuary")
)

res <- url[['Result']] %>%
  httr::POST(httr::add_headers(headers), body = jsonlite::toJSON(body)) %>%
  httr::content('text') %>%
  read.csv(text = .)

sta <- url[['Station']] %>%
  httr::POST(httr::add_headers(headers), body = jsonlite::toJSON(body)) %>%
  httr::content('text') %>%
  read.csv(text = .)

# combine and format
read_formwqp(res, sta, '21FLMANA_WQX', type = 'wq')

## End(Not run)

Description

Download and import benthic data for Tampa Bay

Usage

read_importbenthic(path, download_latest = FALSE, remove = FALSE)

Arguments

Details

This function downloads and unzips a folder of base tables used to calculate the benthic index from https://epcbocc.sharepoint.com/:f:/s/Share/EtOJfziTTa9FliL1oROb9OsBRZU-nO60fu_0NRC162hHjQ?e=4gUXgJ.

Index the corresponding list element in the value column to view each dataset. For example, the stations data in the first row can be viewed as benthicdata$value[[1]].

Value

A nested tibble of station, taxa, and field sample data.

Examples

## Not run: 
# location to download data
path <- '~/Desktop/benthic.zip'

# load and assign to object
benthicdata <- read_importbenthic(path, download_latest = TRUE)


## End(Not run)

Description

Download Enterococcus data from the Water Quality Portal

Usage

read_importentero(stas = NULL, startDate, endDate)

Arguments

Details

Retrieves Enterococcus sample data from selected stations and date range from the Water Quality Portal, https://www.waterqualitydata.us

Value

a data frame containing one row for each sample. Columns returned are:

date

date, sample date

yr

numeric, year of sample date

mo

numeric, month of sample date

time

character, sample time

time_zone

character, sample time zone

long_name

character, long name of bay segment subwatershed

bay_segment

character, short name of bay segment subwatershed

station

character, sample station

entero

numeric, Enterococcus concentration

entero_censored

logical, whether entero value was below the laboratory MDL, minimum detection limit

MDL

numeric, minimum detection limit at the time of processing

entero_units

character, units of measurement for entero

qualifier

qualifier codes associated with sample

LabComments

lab comments on sample

Latitude

numeric, latitude in decimal degrees

Longitude

numeric, longitude in decimal degrees

Examples

## Not run: 
# stations to download
stas <- c('21FLHILL_WQX-101',
'21FLHILL_WQX-102',
'21FLHILL_WQX-103')

# download and read the data
entero_in <- read_importentero(stas = stas, startDate = '2023-01-01', endDate = '2023-02-01')

head(entero_in)


## End(Not run)

Description

Download and/or import local water quality file for internal use

Usage

read_importepc(xlsx, download_latest = FALSE, na = c("", "NULL"))

Arguments

Details

Loads the "RWMDataSpreadsheet" worksheet from the file located at xlsx. The file is downloaded from https://epcbocc.sharepoint.com/:x:/s/Share/EYXZ5t16UlFGk1rzIU91VogBa8U37lh8z_Hftf2KJISSHg?e=8r1SUL&download=1. The files can be viewed at https://epcbocc.sharepoint.com/:f:/s/Share/EiypSSYdsEFCi84Sv_6-t7kBUYaXiIqN0B1n2w57Z_V3kQ?e=NdZQcU.

This function is used internally by read_importwq and read_importfib because both use the same source data from the Environmental Protection Commission of Hillsborough County.

Value

An unformatted data.frame from EPC

See Also

read_importwq, read_importfib

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'

# load and assign to object
epcall <- read_importepc(xlsx, download_latest = T)

## End(Not run)

Description

Load local water quality file for Fecal Indicator Bacteria (FIB)

Usage

read_importfib(xlsx, download_latest = FALSE, na = c("", "NULL"), all = TRUE)

Arguments

Details

Loads the "RWMDataSpreadsheet" worksheet from the file located at xlsx. The file is downloaded from https://epcbocc.sharepoint.com/:x:/s/Share/EYXZ5t16UlFGk1rzIU91VogBa8U37lh8z_Hftf2KJISSHg?e=8r1SUL&download=1. The files can be viewed at https://epcbocc.sharepoint.com/:f:/s/Share/EiypSSYdsEFCi84Sv_6-t7kBUYaXiIqN0B1n2w57Z_V3kQ?e=NdZQcU.

Returns FIB data including E. coli, Enterococcus, Fecal Coliform, and Total Coliform concentrations and waterbody class (freshwater as 1 or 3F, marine as 2 or 3M) for stations and sample dates.

Values are returned for E. coli (ecoli), Enterococcus (entero), Fecal Coliform (fcolif), and Total Coliform (totcol). Values shown are # of colonies per 100 mL of water (#/100mL). Qualifier columns for each are also returned with the _q suffix. Qualifier codes can be interpreted from the source spreadsheet.

Concentrations noted with < or > in the raw data are reported as is, with only the numeric value shown. Samples with this notation can be determined from the qualifier columns.

If all = FALSE, only stations with AreaName in the source data as Hillsborough River, Hillsborough River Tributary, Alafia River, Alafia River Tributary, Lake Thonotosassa, Lake Thonotosassa Tributary, and Lake Roberta.

Value

A data.frame of formatted FIB data.

See Also

read_formfib

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'

# load and assign to object
fibdata <- read_importfib(xlsx, download_latest = T)


## End(Not run)

Description

Load local FIM data for the Tampa Bay Nekton Index

Usage

read_importfim(csv, download_latest = FALSE, locs = FALSE)

Arguments

Details

Data downloaded from ftp://ftp.floridamarine.org/users/fim/tmac/NektonIndex/TampaBay_NektonIndexData.csv.

Value

A formatted data.frame with FIM data if locs = FALSE, otherwise a simple features object if locs = TRUE

See Also

read_formwq, read_importphyto

Examples

## Not run: 
# file path
csv <- '~/Desktop/fimraw.csv'

# load and assign to object
fimdata <- read_importfim(csv, download_latest = TRUE)

## End(Not run)

Description

Load local phytoplankton cell count file

Usage

read_importphyto(xlsx, download_latest = FALSE, na = c("", "NULL"))

Arguments

Details

Phytoplankton cell count data downloaded from https://epcbocc.sharepoint.com/:x:/s/Share/ETAfRQ5drmRHntDd1O8s3FQB180Fumed4nQ99w-OIVDxrA?e=eSmtxD&download=1

Value

A data.frame of formatted water quality data.

See Also

read_importwq

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/phyto_data.xlsx'

# load and assign to object
phytodata <- read_importphyto(xlsx, download_latest = T)

## End(Not run)

Description

Download and crop the latest weather rasters from Parameter-elevation Relationship on Independent Slopes Model (PRISM). Summarize by zone and save as CSV.

Usage

read_importprism(
  vars = c("tmin", "tmax", "tdmean", "ppt"),
  vars_ytd = c("ppt"),
  date_beg = as.Date("1981-01-01"),
  date_end = Sys.Date(),
  bbox = sf::st_bbox(sf_zones),
  dir_tif = sub("\\.[^.]*$", "", zonal_csv),
  pfx_tif = "prism_daily_",
  sf_zones,
  fld_zones,
  zonal_fun = "mean",
  zonal_csv,
  redo_zonal = F,
  verbose = F
)

Arguments

Details

The Parameter-elevation Relationship on Independent Slopes Model (PRISM) is a combined dataset consisting of ground gauge station and RADAR products, at a 4km grid resolution covering the contiguous United States from 1981 to present.

Variables

The available variables (vars) are:

• tmin: minimum temperature (ºC)

• tmax: maximum temperature (ºC)

• tmean: mean temperature (ºC; tmean = mean(tmin, tmax))

• tdmean: mean dew point temperature (ºC)

• ppt: total precipitation (mm; rain and snow)

• vpdmin: daily minimum vapor pressure deficit

• vpdmax: daily maximum vapor pressure deficit

Updates

The daily PRISM data is available over three stability levels, or 8 versions given by the update schedule:

• stable: data is considered final and will not change (before 6 months)

• provisional: data is considered final but may be updated (previous 6 months)

• early: data is preliminary and may be updated (this month)

Processing details

1. The PRISM update schedule is downloaded and processed in a data frame to understand the latest date and variable available, along with version and date updated.

2. Any existing rasters in dir_tif are fetched based on a common naming structure for the raster file name (⁠prism_daily_{month}-{day}.tif⁠) and layer names (⁠{date}_{variable}_v{version}-{date_updated}⁠) into a data frame.

3. Based on intersecting above with the requested dates, any missing or more recently updated variable-date PRISM rasters are downloaded and cropped to the bounding box (bbox) and written as GeoTIFFs (*.tif). Layers are renamed to include extra information on {version} (1-8) and {date_udpated}.

4. Summary statistics (based on sf_zones and zonal_fun) are calculated on the cropped PRISM daily rasters.

More on PRISM

For more on Parameter-elevation Relationship on Independent Slopes Model (PRISM), see:

• PRISM model methods (Daly et al., 2008)

• PRISM dataset descriptions

• PRISM download methods information (FTP, web services)

Value

data frame with the following columns:

• {fld_zones} column(s) specified from input sf_zones

• {zonal_fun}: value of input function (e.g. "mean") summarizing raster to zone

• date: date of PRISM daily raster

• variable: PRISM variable

• version: PRISM version

• date_updated: date of PRISM daily raster update

Examples

## Not run: 
# setup output directory and table
dir_tif   <- system.file("prism", package = "tbeptools")
zonal_csv <- system.file("prism/_zones.csv", package = "tbeptools")

# run function for Tampa Bay watersheds for first 3 days and 4 variables
d <- read_importprism(
  vars      = c("tdmean", "ppt"),
  date_beg  = as.Date("1981-01-01"),
  date_end  = as.Date("1981-01-02"),
  dir_tif   = dir_tif,
  sf_zones  = tbsegshed,
  fld_zones = "bay_segment",
  zonal_csv = zonal_csv)

# show raster files, layers and plot
tifs <- list.files(dir_tif, pattern = ".tif$", full.names = T)
basename(tifs)
r <- terra::rast(tifs[1])
r
names(r)
terra::plet(
  r[[3]],
  main  = names(r)[3],
  col   = "Spectral",
  tiles = "CartoDB.DarkMatter")

# show summary by zone
d

## End(Not run)

Description

Download daily precip data and summarise by station catchment

Usage

read_importrain(curyr, catchpixels, mos = 1:12, quiet = T)

Arguments

Details

Data from the Southwest Florida Water Management District's (SWFWMD) ftp site: ftp://ftp.swfwmd.state.fl.us/pub/radar_rainfall/Daily_Data/

Value

data.frame with station, date, rain columns as a daily average (inches) for all pixels in a catchment

Examples

## Not run: 
read_importrain(2021, catchpixels, quiet = F)

## End(Not run)

Description

Run read_importrain for multiple years

Usage

read_importrainmany(yrs, quiet = FALSE, sleep = 5)

Arguments

Details

This function is a simple wrapper for read_importrain to download multiple years of rainfall data from the Southwest Florida Water Management District's (SWFWMD) ftp site: ftp://ftp.swfwmd.state.fl.us/pub/radar_rainfall/Daily_Data/. The function will pause for sleep seconds between years to avoid overloading the server.

Value

A data frame identical to that returned by read_importrain with the years requested

Examples

## Not run: 
read_importrainmany(c(2021, 2022), quiet = F)

## End(Not run)

Description

Under NOAA Tides and Currents, there is the NOAA Center for Operational Oceanographic Products and Services (CO-OPS). This function uses the CO-OPS Data Retrieval API to extract see level data by station.

Usage

read_importsealevels(
  path_csv,
  download_latest = TRUE,
  df_stations = sealevelstations[, c("station_id", "station_name")],
  api_url = "https://api.tidesandcurrents.noaa.gov/api/prod/datagetter",
  beg_int = 19010101,
  end_int = as.integer(format(lubridate::today(), "%Y%m%d")),
  product = "monthly_mean",
  datum = "stnd",
  time_zone = "lst",
  units = "metric"
)

Arguments

Value

Given the default arguments in (and especially product = "monthly_mean"), this function returns a data frame from reading path_csv (updated if download_latest = TRUE or newly written if path_csv does not exist) having the following fields:

• station_id: integer column from input argument df_stations

• station_name: character column from input argument df_stations

• date: first of the month given by year and month from API output

• year: year of the data

• month: month of the data

• mhhw: Mean Higher-High Water

• mhw: Mean High Water

• msl: Mean Sea Level

• mtl: Mean Tide Level

• mlw: Mean Low Water

• mllw: Mean Lower-Low Water

• dtl: Mean Diurnal Tide Level

• gt: Great Diurnal Range

• mn: Mean Range of Tide

• dhq: Mean Diurnal High Water Inequality

• dlq: Mean Diurnal Low Water Inequality

• hwi: Greenwich High Water Interval (in Hours)

• lwi: Greenwich Low Water Interval (in Hours)

• highest: Highest Tide

• lowest: Lowest Tide

• inferred: A flag that when set to 1 indicates that the water level value has been inferred

For more details on these output data columns, see About Tidal Datums | NOAA Tides & Currents.

Examples

read_importsealevels(tempfile("sealevels", fileext=".csv"))

Description

Download and import sediment data for Tampa Bay

Usage

read_importsediment(path, download_latest = FALSE, remove = FALSE)

Arguments

Details

This function downloads and unzips a folder of results tables from https://epcbocc.sharepoint.com/:u:/s/Share/Ef9utuKCHD9LliarsOPKCJwB5kxgCObf0tY5x5wX20JQUA?e=DuTseb&download=1 (viewable at https://epcbocc.sharepoint.com/:f:/s/Share/EtOJfziTTa9FliL1oROb9OsBRZU-nO60fu_0NRC162hHjQ?e=4gUXgJ).

The row entries for columns "BetweenTELPEL" and "ExceedsPEL" for rows where the "Qualifier" column is "U" or "T" (below detection, not detected) are assigned NA, regardless of the entry in the source data.

Value

A data.frame of sediment data for Tampa Bay

Examples

## Not run: 
# location to download data
path <- '~/Desktop/sediment.zip'

# load and assign to object
sedimentdata <- read_importsediment(path, download_latest = TRUE)


## End(Not run)

Description

Load local water quality file

Usage

read_importwq(xlsx, download_latest = FALSE, na = c("", "NULL"), all = FALSE)

Arguments

Details

Loads the "RWMDataSpreadsheet" worksheet from the file located at xlsx. The file is downloaded from https://epcbocc.sharepoint.com/:x:/s/Share/EYXZ5t16UlFGk1rzIU91VogBa8U37lh8z_Hftf2KJISSHg?e=8r1SUL&download=1. The files can be viewed at https://epcbocc.sharepoint.com/:f:/s/Share/EiypSSYdsEFCi84Sv_6-t7kBUYaXiIqN0B1n2w57Z_V3kQ?e=NdZQcU.

Water quality parameters returned by default are total nitrogen (tn), Secchi depth (sd), chlorophyll-a (chla), salinity (top, mid, and bottom depths, Sal_ prefix), water temperature (top, mid, and bottom depths, Temp_Water_ prefix), turbidity (Turbidity_JTU-NTU), and water color (Color_345_F45). Additional qualifier columns for each that include the _Q suffix are also returned, excluding salinity and water temperature. All other water quality parameters and qualifiers can be returned by setting all = T.

Value

A data.frame of formatted water quality data.

See Also

read_formwq, read_importphyto

Examples

## Not run: 
# file path
xlsx <- '~/Desktop/RWMDataSpreadsheet_ThroughCurrentReportMonth.xlsx'

# load and assign to object
epcdata <- read_importwq(xlsx, download_latest = T)

# get all water quality parameters
epcdataall <- read_importwq(xlsx, download_latest = T, all = T)

## End(Not run)

Description

Import data from the Water Quality Portal

Usage

read_importwqp(org, type, trace = F)

Arguments

Details

This function retrieves data from the Water Quality Portal API (https://www.waterqualitydata.us/) for selected counties in or around the Tampa Bay watershed. The type of data returned are defined by the type argument as either "wq" or "fib" for water quality of Fecal Indicator Bacteria data, respectively.

The org argument retrieves data for the specific organization. Valid entries for org include "21FLCOSP_WQX" (City of St. Petersburg), "21FLDOH_WQX" (Florida Department of Health), "21FLHILL_WQX" (Hillsborough County), "21FLMANA_WQX" (Manatee County), "21FLPASC_WQX" (Pasco County), "21FLPDEM_WQX" (Pinellas County), "21FLPOLK_WQX" (Polk County), "21FLTPA_WQX" (Florida Department of Environmental Protection, Southwest District), or "21FLHESD_WQX" (Hillsborough County Environmental Services Division). The naming convention follows the Organization ID in the Water Quality Portal.

The function fetches results and station metadata, combines and formats them using the read_formwqp function, and returns the processed data as a data frame. Parameters are specific to the type argument.

Value

A data frame containing the imported data for the selected county

See Also

read_formwqp

Examples

## Not run: 
# get Manatee County water quality data
mancodata <- read_importwqp(org = '21FLMANA_WQX', type = 'wq', trace = T)

# get Pinellas County FIB data
pincodata <- read_importwqp(org = '21FLPDEM_WQX', type = 'fib', trace = T)

## End(Not run)

Description

Import data from the Water Atlas API

Usage

read_importwqwa(
  dataSource,
  parameter,
  start_date = NULL,
  end_date = NULL,
  trace = TRUE
)

Arguments

Details

This function retrieves sampling data from the Water Atlas API (https://dev.api.wateratlas.org/redoc/index.html#tag/Sampling-Data/operation/StreamSamplingData) using the specified data source and parameter. Optional start and end dates can be provided to filter the data by date range. The function processes the NDJSON response stream and returns a data frame of sampling records.

See util_importwqwa for retrieving metadata such as available data sources and parameters.

Value

A data frame containing the imported sampling data

Examples

read_importwqwa('WIN_21FLPDEM', 'Chla_ugl', '2023-01-01', '2023-02-01')

Description

Retrieve water quality data from the Florida Department of Environmental Protection's Watershed Information Network (WIN)

Usage

read_importwqwin(
  start_date,
  end_date,
  org_id,
  verbose = FALSE,
  max_retries = 5
)

Arguments

Details

This function implements util_importwqwin iteratively to retrieve water quality results for the specified organization ID and start date. Data are retrieved using the API at https://prodapps.dep.state.fl.us/dear-watershed/swagger-ui/index.html.

Value

A data frame containing the water quality data

Examples

## Not run: 
dat <- read_importwqwin("2025-01-15", "2025-02-15", "21FLMANA", verbose = TRUE)
head(dat)

## End(Not run)

Description

Scan directory for rasters (*.tif) and parse PRISM layer names into columns of a data frame.

Usage

read_prism_rasters(dir_tif)

Arguments

Details

Any existing rasters in dir_tif are scanned based on a common naming structure for the raster file name (⁠prism_daily_{month}-{day}.tif⁠) and layer names (⁠{date}_{variable}_v{version}-{date_updated}⁠) into a data frame.

For more on the Parameter-elevation Relationship on Independent Slopes Model (PRISM), see read_importprism().

Value

A data frame with columns for:

• path_tif path to GeoTIFF

• lyr layer name

• date date of modeled weather data

• md month-day

• variable variable of modeled weather data

• version version (1-8)

• date_updated date the model was updated

Examples

dir_tif <- system.file("prism", package = "tbeptools")
read_prism_rasters(dir_tif)

Description

Import JSON seagrass transect data from Water Atlas

Usage

read_transect(training = FALSE, raw = FALSE)

Arguments

Details

The function imports a JSON file from the USF Water Atlas. If training = TRUE, a dataset from the TBEP training survey is imported from https://tampabay.wateratlas.usf.edu/seagrass-transect-data-portal/api/assessments/training. If training = FALSE, the entire transect survey database is imported from https://tampabay.wateratlas.usf.edu/seagrass-transect-data-portal/api/assessments/all__use-with-care.

Abundance is reported as a numeric value from 0 -5 for Braun-Blanquet coverage estimates, blade length is in cm, and short shoot density is number of shoots per square meter. The short density is corrected for quadrat size included in the raw data.

If raw = TRUE, the unformatted data are returned. The default is to use formatting that allows the raw data to be used with the downstream functions. The raw data may have extra information that may be of use outside of the plotting functions in this package.

Value

data frame

Examples

## Not run: 
# get training data
transect <- read_transect(training = TRUE)

# import all transect data
transect <- read_transect()

## End(Not run)

Description

Seagrass coverage by year

Usage

seagrass

Format

A data frame used to create the flagship seagrass coverage graphic:

Year

int

Acres

num

Hectares

num

Details

Original data are from the Southwest Florida Water Management District and available online at https://data-swfwmd.opendata.arcgis.com/. Totals are for all of Tampa Bay.

Examples

## Not run: 

seagrass <- structure(list(
   Year = c(1950L, 1982L, 1988L, 1990L, 1992L, 1994L, 1996L,
        1999L, 2001L, 2004L, 2006L, 2008L, 2010L, 2012L, 2014L,
        2016L, 2018L, 2020L, 2022L, 2024L),
   Acres = c(40420, 21650, 23285, 25226, 25753, 26518, 26916,
        24841, 26078, 27021, 28299, 29647, 32897, 34642, 40294.71,
        41655.16, 40651.55, 34298, 30137, 31563),
   Hectares = c(16357.39, 8761.44, 9423.11, 10208.6, 10421.87,
        10731.45, 10892.52, 10052.8, 10553.39, 10935.01, 11452.2,
        11997.72, 13312.94, 14019.27, 16306.69, 16857.25, 16451.1,
        13880, 12196, 12773)
 ), class = "data.frame", row.names = c(NA, -20L))

save(seagrass, file = 'data/seagrass.RData', compress = 'xz')


## End(Not run)

Description

Stations in Tampa Bay with sea level data through CO-OPS Data Retrieval API for use with read_importsealevels().

Usage

sealevelstations

Format

A data frame with 4 rows and 5 columns:

• station_id integer station identifier

• station_name character station name

• longitude double longitude

• latitude double latitude

• date_est date established

Description

Sediment data for the Tampa Bay current as of 20251210

Usage

sedimentdata

Format

A data.frame with 231727 rows and 24 variables:

ProgramId

int

ProgramName

chr

FundingProject

chr

yr

int

AreaAbbr

chr

StationID

int

StationNumber

chr

Latitude

num

Longitude

num

Replicate

chr

SedResultsType

chr

Parameter

chr

ValueAdjusted

num

Units

chr

Qualifier

chr

MDLnum

num

PQLnum

num

TEL

num

PEL

num

BetweenTELPEL

chr

ExceedsPEL

chr

PreparationDate

chr

AnalysisTimeMerge

chr

PELGrade

Factor

Examples

## Not run: 
# location to download data
path <- '~/Desktop/sediment.zip'

# load and assign to object
sedimentdata <- read_importsediment(path, download_latest = TRUE, remove = TRUE)

save(sedimentdata, file = 'data/sedimentdata.RData', compress = 'xz')


## End(Not run)

Description

Seagrass management areas for Tampa Bay

Usage

sgmanagement

Format

A simple features sf object (MULTIPOLYGON) with 30 features and 1 field, +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs

areas

int

Details

These polygons are seagrass management areas for Tampa Bay that provide a finer division of areas within major segments (tbseg) having relevance for locations of seagrass beds.

Examples

## Not run: 
library(sf)
library(dplyr)
library(tools)

# NAD83(HARN) / Florida West (ftUS)
# same as sgseg
prj <- 2882

# create sf object of boundaries
sgmanagement <- st_read(
  dsn = '~/Desktop/TBEP/GISboundaries/Seagrass_Management_Areas/TBEP_SG_MA_FINAL_Projectfix.shp',
  drivers = 'ESRI Shapefile'
  ) %>%
  select(areas = TBEP_SG_MA) %>%
  st_zm() %>%
  st_transform(prj)

# save
save(sgmanagement, file = 'data/sgmanagement.RData', compress = 'xz')


## End(Not run)

Description

Seagrass segment reporting boundaries for southwest Florida

Usage

sgseg

Format

A simple features sf object (POLYGON) with 22 features and 1 field, +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs

segment

chr

Details

These polygons are used by Southwest Florida Water Management District for summarizing seagrass coverage estimates by major coastal and estuarine boundaries.

Examples

## Not run: 
library(sf)
library(dplyr)
library(tools)

# create sf object of boundaries
# make sure projection does not change
sgseg <- st_read(
  dsn = '~/Desktop/TBEP/GISboundaries/Seagrass_Segment_Boundaries/Seagrass_Segment_Boundaries.shp',
  drivers = 'ESRI Shapefile'
  ) %>%
  select(segment = SEAGRASSSE) %>%
  mutate(
     segment = tolower(segment),
     segment = case_when(
        segment == 'terra ciea bay' ~ 'Terra Ceia Bay',
        T ~ segment
     ),
     segment = toTitleCase(segment)
  )

# save
save(sgseg, file = 'data/sgseg.RData', compress = 'xz')


## End(Not run)

Description

Plot a matrix of AMBI scores over time by bay segment

Usage

show_ambimatrix(
  ambiscr,
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB", "All"),
  yrrng = c(1993, 2024),
  window = TRUE,
  alph = 1,
  txtsz = 2.5,
  family = "sans",
  rev = FALSE,
  position = "top",
  plotly = FALSE,
  width = NULL,
  height = NULL
)

Arguments

Details

An additional summary is provided for the entire bay as an unweighted summary across categories ("All").

The default behavior is to use a rolling five-year window to calculate the percent of sites in each AMBI category by bay segment. This applies only to years 2005 and later, where the counts from the current year and the prior four years are summed to calculate the percentages. This is intended to help smooth out inter-annual variability due to reduced sampling effort from 2005 to present. If window = FALSE, then only single year values are used.

The color scale reflects the adjusted AMBI classification (0-10 scale): Unpolluted (dark green), Slightly Polluted (light green), Meanly Polluted (yellow), Heavily Polluted (orange), and Extremely Polluted (red).

The matrix tile for each bay segment and year shows the dominant AMBI category based on the plurality of sites. The dominant category is the one with the highest proportion of sites in that year and bay segment.

Value

A ggplot object showing trends over time in AMBI scores for each bay segment if plotly = FALSE, otherwise a plotly object

Examples

ambiscr <- anlz_ambiscr(benthicdata)
show_ambimatrix(ambiscr)

Description

Create a summary table of AMBI category percentages by bay segment

Usage

show_ambitab(
  ambiscr,
  bay_segment = c("OTB", "HB", "MTB", "LTB", "TCB", "MR", "BCB"),
  yrrng = c(1993, 2024)
)

Arguments

Details

Only sampling funded by TBEP and as part of the routine EPC benthic monitoring program are included. Azoic stations are excluded from counts and percentages. The year range filter applies to all years from yrrng[1] through yrrng[2].

Column header colours match those used in show_ambimatrix: Unpolluted (dark green), Slightly Polluted (light green), Meanly Polluted (yellow), Heavily Polluted (orange), and Extremely Polluted (red).

Value

A flextable object showing the count and percentage of sites in each AMBI category by bay segment over the requested year range. The bottom row ("All") summarises across all included bay segments.

Examples

ambiscr <- anlz_ambiscr(benthicdata)
show_ambitab(ambiscr)

Description

Plot mean AMBI scores over time by bay segment

Usage

show_ambitrend(
  ambiscr,
  ambiscr_tb = NULL,
  bay_segment = c("HB", "OTB", "MTB", "LTB", "TCB", "MR", "BCB", "All"),
  yrrng = c(1993, 2024),
  yscl = TRUE,
  plotly = FALSE,
  width = NULL,
  height = NULL
)

Arguments

Details

The background of the plot is shaded by AMBI pollution category using the adjusted score thresholds (0-10 scale, higher = healthier): Unpolluted (8.29-10, dark green), Slightly Polluted (5.29-8.29, light green), Meanly Polluted (2.89-5.29, yellow), Heavily Polluted (1.39-2.89, orange), and Extremely Polluted (0-1.39, red).

Only sampling funded by TBEP and as part of the routine EPC benthic monitoring program are included.

If both ambiscr and ambiscr_tb are provided, both series are shown on the same plot with dark grey used for the first series and black for the second. The AMBI variant for each input is detected automatically from the column names (AMBI or TBAMBI).

Value

A ggplot object showing mean adjusted AMBI scores by year if plotly = FALSE, otherwise a plotly object

Examples

ambiscr    <- anlz_ambiscr(benthicdata)
ambiscr_tb <- anlz_ambiscr(benthicdata, type = 'AMBI-TB')
show_ambitrend(ambiscr, ambiscr_tb)

Description

Create a table for the annual management outcome assessments for chlorophyll-a and light attenuation by bay segment

Usage

show_annualassess(
  epcdata,
  yrsel,
  partialyr = F,
  caption = F,
  family = "Arial",
  txtsz = 12,
  width = NULL
)

Arguments

Value

A flextable object showing the segment-averaged chlorophyll-a and light attenuation for the selected year, with bay segment names colored by the management outcome used in show_matrix.

Examples

show_annualassess(epcdata, yrsel = 2025)
show_annualassess(epcdata, yrsel = 2025, caption = TRUE)

Description

Plot monthly chlorophyll or light attenuation values for a bay segment

Usage

show_boxplot(
  epcdata,
  param = c("chla", "la"),
  yrsel = NULL,
  yrrng = c(1975, 2025),
  ptsz = 0.5,
  bay_segment = c("OTB", "HB", "MTB", "LTB"),
  trgs = NULL,
  family = "sans",
  labelexp = TRUE,
  txtlab = TRUE,
  partialyr = FALSE,
  points = TRUE
)

Arguments

Details

Points not included in yrsel are plotted over the box plots using position_jitter. Use ptsz = -1 to suppress. The dotted line in the plot shows the large exceedance value.

Value

A ggplot object

Examples

show_boxplot(epcdata, bay_segment = 'OTB')

Description

Make a bar plot for transect training group comparisons

Usage

show_compplot(
  transect,
  yr,
  site,
  species = c("Halodule", "Syringodium", "Thalassia", "Halophila", "Ruppia"),
  varplo = c("Abundance", "Blade Length", "Short Shoot Density"),
  base_size = 18,
  xtxt = 10,
  size = 1
)

Arguments

Value

A ggplot object

Examples

transect <- read_transect(training = TRUE)
show_compplot(transect, yr = 2025, site = '3', species = 'Halodule', varplo = 'Abundance')

Description

Map Enterococcus results by month, year, and location

Usage

show_enteromap(
  fibdata,
  yrsel,
  mosel,
  areasel = NULL,
  wetdry = FALSE,
  precipdata = NULL,
  temporal_window = NULL,
  wet_threshold = NULL,
  addsta = FALSE
)

Arguments