Annual Range Timeseries

Description

Assess variability within each season for a single year

Usage

annual_range(swmpr_in, ...)

## S3 method for class 'swmpr'
annual_range(
  swmpr_in,
  param = NULL,
  target_yr = NULL,
  criteria = NULL,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  criteria_lab = "WQ Threshold",
  plot_title = FALSE,
  plot = TRUE,
  ...
)

Arguments

Details

This function summarizes average daily values, average daily minimums/maximums, and absolute minimums/maximums across user-defined seasons for a target year (target_yr).

The user also has the option to add a threshold hold line using the criteria argument. Typically, this value is a water quality threshold, which is why criteria_lab defaults to 'WQ Threshold'. However, the user has the option to specify any other type of threshold they wish. when doing so, the value for criteria_lab should be changed accordingly.

Value

Returns a ggplot object

Author(s)

Julie Padilla

See Also

ggplot, assign_season, y_labeler

Examples

## get data, prep
data(elksmwq)
dat <- elksmwq

dat <- qaqc(elksmwq, qaqc_keep = c('0', '3', '5'))
do_plt <- annual_range(dat, param = 'do_mgl', target_yr = 2012)
do_plt <- annual_range(dat, param = 'do_mgl', target_yr = 2012, criteria = 2)

Assign seasons to SWMP sampling data

Description

Assign seasons to SWMPr sampling data on a monthly basis or user-defined seasonal basis

Usage

assign_season(
  data,
  season_grps = NULL,
  season_names = NULL,
  season_start = NULL,
  abb = TRUE
)

Arguments

Details

A helper function used by multiple data analyses to assign seasons to sampling data and to order the seasons. To assist with plotting, the seasons are assigned as factors. Seasons are assigned by first grouping the months into a list of season_grps and then specifying one name for each grouping using season_names. If season_grps is specified then season_names must also be defined. If neither argument is specified than the season assignments will default to monthly values. Using the season_start argument, the user can designate which season should be the first factor level. This assignment affects plot order for most functions. If season_start is not specified, then it will default to the first season in the list (January for monthly seasons and the first season in season_names for user-defined seasons).

Value

Returns a vector of ordered season factors.

Author(s)

Julie Padilla

Examples

data(elksmwq)
dat <- elksmwq

seas <- assign_season(dat$datetimestamp, abb = FALSE)
levels(seas)

seas <- assign_season(dat$datetimestamp, abb = TRUE)
levels(seas)

seas <- assign_season(dat$datetimestamp, season_start = 'Mar')
levels(seas)

seas <- assign_season(dat$datetimestamp, abb = FALSE, season_start = 'March')
levels(seas)

seas <- assign_season(dat$datetimestamp,
season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12)),
season_names = c('Winter', 'Spring', 'Summer', 'Fall'), season_start = 'Spring')
levels(seas)

seas <- assign_season(dat$datetimestamp, season_grps = list(c(10:12, 1:3), c(4:9)),
season_names = c('Wet', 'Dry'))
levels(seas)

Create background map

Description

Create a background map from a bounding box using Stamen Map tiles or a crude vector-based map.

Usage

base_map(
  bbox,
  bg_crs = 4326,
  vector_only = FALSE,
  maptype = "stamen_toner_lite",
  zoom = NULL,
  ...
)

Arguments

Details

A helper, or stand-alone, function to create background map based on based on raster map tiles retrieved with ggmap::get_stadiamap. If ggmap is unavailable, the function creates a basic map using county-level polygon files. This map is fairly crude and should be considered a placeholder.

Value

Returns a ggplot2 object.

Author(s)

Dave Eslinger

Examples

#Simple, low-zoom  map for testing
bound_box <- c(-77.393, 38.277, -75.553, 39.741)
(x <- base_map(bound_box, zoom = 7, maptype = 'stamen_toner_lite'))



# Default zoom map with terrain maptype.
y <- base_map(bound_box, maptype = 'stamen_terrain')

Spatial Data from Chesapeake Bay - Maryland

Description

Shapefile for Chesapeake Bay - Maryland reserve boundary

Usage

data(cbm_spatial)

Format

A sf object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

US County Map

Description

US County boundaries from the US Census Bureau's MAF/TIGER geographic database in EPSG:4269, with all data except state FIPS codes and land area removed.

Usage

data('counties_4269')

Format

A sf]{DataFrame} object

Source

US Census Bureau

References

United States Census Bureau. Data accessed from the US Census Bureau website: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_20m.zip; accessed 13 March 2020

Create a List of Flextable Objects

Description

Create a list of flextable objects to display Seasonal Kendall results in the NERRS reserve level template

Usage

create_sk_flextable_list(
  sk_result,
  stations,
  param,
  trend_col = c("#247BA0", "#A3DFFF", "#D9D9D9", "white"),
  font_col_default = "#444E65",
  font_sz_stn = 6,
  font_sz_result = 12,
  font_sz_head = 6,
  ht_head = 0.28,
  ht_body = 0.202,
  is_swmp = TRUE,
  stn_name = NULL,
  stn_abbrev = NULL,
  par_name = NULL
)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. Using the results from the reserve level trend analysis, create_sk_flextable_list creates a list of two flextable objects to be displayed in the NERRS reserve level template. The first flextable in the list contains the two-letter station IDs for each station and the full location name of each station. The second table lists the seasonal kendall results and the names of the parameters of interest.

Value

Returns a list of flextable objects

Author(s)

Julie Padilla

Create a Flextable Object of Reserve Names

Description

Create a flextable of reserve names for use with the NERRS national level template.

Usage

create_sk_national_ft_reserves(
  sk_result,
  font_sz_stn = 8,
  font_sz_head = 8,
  ht_head = 0.75,
  ht_body = 0.2
)

Arguments

Details

This function is intended for internal use with the NERRS national level reporting scripts. Using results from the reserve level trend analyses, create_sk_national_ft_reserves creates a flextable object of reserve names for display in the NERRS national level template.

Value

Returns a flextable object

Author(s)

Julie Padilla

Create a Flextable Object of Seasonal Kendall Results

Description

Create a flextable object to display Seasonal Kendall results for each reserve in the NERRS national level template

Usage

create_sk_national_ft_results(
  sk_result,
  param,
  font_sz_result = 12,
  font_sz_head = 8,
  ht_head = 0.375,
  ht_body = 0.2
)

Arguments

Details

This function is intended for internal use with the NERRS national level reporting scripts. Using results from the reserve level trend analyses, create_sk_national_ft_results creates a flextable object of seasonal kendall results to be displayed in the NERRS national level template.

Value

Returns a flextable object

Author(s)

Julie Padilla

Spatial Data from Elkhorn Slough

Description

Shapefile for Elkhorn Slough reserve boundary

Usage

data(elk_spatial)

Format

A sf object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

Nutrient Data from Elkhorn Slough - North Marsh Station

Description

Monthly nutrient data from Elkhorn Slough North Marsh station

Usage

data(elknmnut)

Format

A data.frame object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

Water Quality Data from Elkhorn Slough - South Marsh Station

Description

Water Quality data from Elkhorn Slough South Marsh station

Usage

data(elksmwq)

Format

A data.frame object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

Convert Parameter Abbreviations

Description

Convert SWMPr parameter abbreviations into formats appropriate for use with NERRS reserve level template flextable

Usage

ft_col_names(param)

Arguments

Details

A helper function used internally by create_sk_flextable_list to label flextable columns in the trend table for the reserve level report.

Value

Returns a data.frame of user-specified results to be displayed

Author(s)

Julie Padilla

Filter Reformatted Seasonal Kendall Results

Description

Filters a dataframe of user-specified results for display in the NERRS reserve level report

Usage

generate_results_table(sk_result, stations, param)

Arguments

Details

A helper function used internally by create_sk_flextable_list to create a data.frame of user specified parameters to be displayed in the reserve level report.

Value

Returns a data.frame of user-specified results to be displayed

Author(s)

Julie Padilla

Filter Reformatted Seasonal Kendall Results

Description

Filters a dataframe of user-specified results for display in the NERRS reserve level report

Usage

generate_station_table(sk_result, stations)

Arguments

Details

Used internally by create_sk_flextable_list to create a data.frame of user specified parameters to be displayed

Value

Returns a data.frame of user-specified results to be displayed

Author(s)

Julie Padilla

Return a vector of geographically unique NERR Stations

Description

Creates an alphabetically sorted, vector of geographically unique stations for mapping

Usage

geographic_unique_stations(nerr_site_id)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts and is used along with res_local_map. It takes a vector of NERR site ids and only returns geographically unique locations.

Value

returns a vector of NERR stations

Author(s)

Julie Padilla

Examples

stns <- c('apacpnut', 'apacpwq', 'apadbnut', 'apadbwq', 'apaebmet',
'apaebnut', 'apaebwq', 'apaesnut', 'apaeswq')

geographic_unique_stations(stns)

Identify NERRS reserve from metadata

Description

Identify the NERRS reserve from metadata in the data file

Usage

get_reserve(data.file)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It determines the name of the full name of the NERRS reserve associated with the data in the user-specified data folder.

Value

Returns a character string of the full reserve name

Author(s)

Julie Padilla

Identify shapefile for NERRS reserve

Description

Identify the shapefile name associated with the reserve in the data file

Usage

get_shp_name(gis.file.loc)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It identifies the name of the shapefile associated with the NERRS reserve.

Value

Returns a character string of the shapefile for the reserve boundary

Author(s)

Julie Padilla

Return NERRS reserve site code based on data in the data file

Description

Identify the 3-letter NERRS reserve code from metadata in the data file

Usage

get_site_code(data.file)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It returns the 3-letter reserve code associated with the data in the user-specified data folder.

Value

Returns 3-letter, reserve site code as chr

Author(s)

Julie Padilla

Identify NERRS sampling locations from metadata

Description

Identify the latitude/longitude for sampling stations based on the metadata in the data file

Usage

get_site_coordinates(data.file, active = TRUE)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It returns the names, station codes, and coordinates associated with the data in the user-specified data folder.

Value

Returns a dataframe of station ids, station names, lat/long

Author(s)

Julie Padilla

Identify NERRS reserve stations from metadata

Description

Identify the NERRS reserve sampling stations based on the metadata in the data file

Usage

get_sites(
  data.file,
  type = c("wq", "nut", "met"),
  active = TRUE,
  primary = TRUE
)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It returns the sampling stations associated with the data in the user-specified data folder.

Value

Returns a character vector of reserve stations

Author(s)

Julie Padilla

Historical Daily Range Timeseries

Description

Compare daily averages for a target year to historical highs and lows

Usage

historical_daily_range(swmpr_in, ...)

## S3 method for class 'swmpr'
historical_daily_range(
  swmpr_in,
  param = NULL,
  hist_rng = NULL,
  target_yr = NULL,
  criteria = NULL,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  criteria_lab = "WQ Threshold",
  plot_title = FALSE,
  plot = TRUE,
  ...
)

Arguments

Details

This function compares the average daily minimums/maximums and absolute daily minimums/maximums from a historical range to the average daily value from a target year. If hist_rng is not specified then the minimum and maximum years within the data set will be used. If target_yr is not specified then only the results for the hist_rng will be returned.

The user also has the option to add a threshold line using the criteria argument. Typically, this value is a water quality threshold, which is why criteria_lab defaults to 'WQ Threshold'. However, the user has the option to specify any other type of threshold they wish. when doing so, the value for criteria_lab should be changed accordingly.

Value

Returns a ggplot object

Author(s)

Julie Padilla, Kimberly Cressman

See Also

ggplot, y_labeler

Examples

data(apacpwq)
dat <- apacpwq

dat <- qaqc(apacpwq, qaqc_keep = c('0', '3', '5'))
# with criteria
y <- historical_daily_range(dat, param = 'do_mgl', target_yr = 2013, criteria = 2)

# w/o criteria
z <- historical_daily_range(dat, param = 'do_mgl', target_yr = 2013)

# add a y label
zz <- z + labs(x = NULL, y = "Dissolved Oxygen (mg/L)")

Historical Monthly/Seasonal Range Timeseries

Description

Compare seasonal averages/minimums/maximums for a target year to historical seasonal averages/minimums/maximums

Usage

historical_range(swmpr_in, ...)

## S3 method for class 'swmpr'
historical_range(
  swmpr_in,
  param = NULL,
  hist_rng = NULL,
  target_yr = NULL,
  criteria = NULL,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  criteria_lab = "WQ Threshold",
  plot_title = FALSE,
  plot = TRUE,
  ...
)

Arguments

Details

This function summarizes average daily values and average daily minimums/maximums across user-defined seasons for a target year (target_yr) and for a historical range (hist_rng). If hist_rng is not specified then the minimum and maximum years within the data set will be used. If target_yr is not specified then only the results for the hist_rng will be returned.

The user also has the option to add a threshold hold line using the criteria argument. Typically, this value is a water quality threshold, which is why criteria_lab defaults to 'WQ Threshold'. However, the user has the option to specify any other type of threshold they wish. when doing so, the value for criteria_lab should be changed accordingly.

Value

Returns a ggplot object

Author(s)

Julie Padilla, Kimberly Cressman

See Also

ggplot, assign_season, y_labeler

Examples

data(elksmwq)

dat <- qaqc(elksmwq, qaqc_keep = c('0', '3', '5'))
# with criteria
y <- historical_range(dat, param = 'do_mgl', target_yr = 2013, criteria = 2)

# w/o criteria
z <- historical_range(dat, param = 'do_mgl', target_yr = 2013)

# add a y label
zz <- z + labs(x = NULL, y = "Dissolved Oxygen (mg/L)")

Import local CDMO data

Description

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

Usage

import_local_nut(path, station_code, collMethd = c(1, 2), trace = 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 using rem_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.

This function differs from import_local in that it allows for special handling of nutrient data. Using this function, the user can separate diel sampling data from low-tide sampling data using the collMthd argument.

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.

Author(s)

Marcus Beck, Julie Padilla (additional of collMthd argument) maintainer: Julie Padilla

See Also

all_params, all_params_dtrng, rem_reps, single_param

P-Value labels for Plotting

Description

Generate a dataframe of p-value labels based on p-values from linear regression

Usage

lm_p_labs(dat_in)

Arguments

Details

A helper function that returns a data.frame of p-value labels for use with the seasonal_dot. P-values are taken from linear regression lm.

Value

Returns data.frame for use with seasonal_dot

Author(s)

Julie Padilla, Dave Eslinger

See Also

lm

Load and format shapefile for reserve level map

Description

Load and format shapefile for use with res_local_map. If polygons are dissolved, the only attribute returned will be a count of the number of grouped polygons, otherwise, all attributes are retained.

Usage

load_shp_file(path, dissolve_boundaries = TRUE)

Arguments

Details

This function is intended for internal use with the NERRS reserve level reporting scripts. It loads a NERRS boundary shp file and dissolves unnecessary reserve boundaries. The resulting sf object is then used with res_sk_map and res_local_map

Value

Returns a sf object

Author(s)

Julie Padilla, Dave Eslinger

Reserve National Map with Seasonal Kendall Results

Description

Create a base map for NERRS reserves in ggplot with seasonal kendall results

Usage

national_sk_map(
  incl = c("contig", "AK", "HI", "PR"),
  highlight_states = NULL,
  sk_reserves = NULL,
  sk_results = NULL,
  sk_fill_colors = c("#444E65", "#A3DFFF", "#247BA0", "#0a0a0a"),
  agg_county = TRUE
)

Arguments

Details

Create a base map of the US with options for including AK, HI, and PR. The user can choose which states and NERRS reserves to highlight. An early 'sp'-based version of this function by Julie Padilla was developed, in part, from a blog post by Bob Rudis. The current sf-based version, by Dave Eslinger, uses an approach from the r-spatial tutorial by Mel Moreno and Mathieu Basille.

To ensure the proper plotting of results, the order of the results vector for sk_results should match the order of the reserves vector for sk_reserves.

Value

Returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger Maintainer: Dave Eslinger

References

Rudis, Bob. 2014. "Moving The Earth (well, Alaska & Hawaii) With R". rud.is (blog). November 16, 2014. https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/ Moreno, Mel, and Basille, Mathieu Basille. 2018. "Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 3: Layouts" r-spatial (blog). October 25, 2018. https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-3.html

Examples

##National map highlighting west coast and non-CONUS states and NERRS.
nerr_states_west <- c('02', '06', '41', '53', '72', '15')

nerrs_codes <- c('pdb', 'sos', 'sfb', 'elk', 'tjr', 'kac', 'job', 'hee')

nerrs_sk_results <- c('inc', 'inc', 'dec', 'insig', 'insuff', 'dec', 'inc', 'dec')

national_sk_map(highlight_states = nerr_states_west,
                sk_reserve = nerrs_codes, sk_results = nerrs_sk_results)

Boxplots of raw data by user-defined season for a target year

Description

Boxplots of raw data by user-defined season for a target year

Usage

raw_boxplot(swmpr_in, ...)

## S3 method for class 'swmpr'
raw_boxplot(
  swmpr_in,
  param = NULL,
  target_yr = NULL,
  criteria = NULL,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  plot_title = FALSE,
  ...
)

Arguments

Details

This function produces boxplots of raw, unaggregated data by user-specified season for year of interest

Value

A ggplot object

Author(s)

Julie Padilla

See Also

ggplot, assign_season, y_labeler

Examples

## get data, prep
data(elksmwq)
dat <- elksmwq

dat <- qaqc(elksmwq, qaqc_keep = c('0', '3', '5'))
raw_boxplot(dat, param = 'do_mgl')

Replace Inf/-Inf/NaN values

Description

Replace Inf, -Inf, and NaN in a matrix with NA

Usage

remove_inf_and_nan(x)

Arguments

Details

This function replaces Inf, -Inf, and NaN in a matrix with NA. It is used internally by several functions.

Value

Returns a matrix object

Author(s)

Julie Padilla

Local Reserve Map for Custom Stations

Description

Create a stylized reserve-level map of custom station locations for use with the reserve level reporting template

Usage

res_custom_map(
  stations,
  x_loc,
  y_loc,
  bbox,
  shp,
  station_labs = TRUE,
  station_col = NULL,
  lab_loc = NULL,
  bg_map = NULL,
  zoom = NULL,
  maptype = "stamen_toner_lite"
)

Arguments

Details

Creates a stylized, reserve-level base map. The user can specify the reserve and stations to plot. The user can also specify a bounding box. For multi-component reserves, the user should specify a bounding box that highlights the component of interest.

This function does not automatically detect conflicts between station labels. The lab_loc argument allows the user to specify "R" or "L" for each station to prevent labels from conflicting with each other.

This function is intended to be used with mapview::mapshot to generate a png for the reserve-level report.

Value

returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger

Examples

### set plotting parameters
stns <- c('Stn 1', 'Stn 2')
x_coords <- c(-121.735281, -121.750369)
y_coords <- c(36.850377, 36.806667)
shp_fl <- elk_spatial
bounding_elk <- c(-121.8005, 36.7779, -121.6966, 36.8799)
lab_dir <- c('L', 'R')

### Low zoom and default maptype plot (for CRAN testing, not recommended)
#    Lower zoom number gives coarser text and fewer features
(x_low <- res_custom_map(stations = stns, x_loc = x_coords, y_loc = y_coords,
                    bbox = bounding_elk, lab_loc = lab_dir, shp = shp_fl,
                    zoom = 10))


x_def <- res_custom_map(stations = stns, x_loc = x_coords, y_loc = y_coords,
                    bbox = bounding_elk, lab_loc = lab_dir, shp = shp_fl)

res_custom_map(stations = stns, x_loc = x_coords, y_loc = y_coords,
               bbox = bounding_elk, lab_loc = lab_dir,
               shp = shp_fl, station_col = c('red', 'green'))

Local Reserve Map With Seasonal Kendall Results for Custom Stations

Description

Create a stylized reserve-level map of seasonal kendall results from custom station locations for use with the reserve level reporting template

Usage

res_custom_sk_map(
  stations,
  x_loc,
  y_loc,
  sk_result = NULL,
  bbox,
  shp,
  station_labs = TRUE,
  lab_loc = NULL,
  bg_map = NULL,
  zoom = NULL,
  maptype = "stamen_toner_lite"
)

Arguments

Details

Creates a stylized, reserve-level base map for displaying seasonal kendall results from sk_seasonal. The user can specify the reserve and stations to plot. The user can also specify a bounding box. For multi-component reserves, the user should specify a bounding box that highlights the component of interest.

To display seasonal trends, the user must specify c('inc', 'dec', 'insig', 'insuff') for each station listed in the stations argument.

Value

returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger

Examples

### set plotting parameters
stns <- c('Stn 1', 'Stn 2')
x_coords <- c(-121.735281, -121.750369)
y_coords <- c(36.850377, 36.806667)
shp_fl <- elk_spatial
bounding_elk <- c(-121.8005, 36.7779, -121.6966, 36.8799)
lab_dir <- c('R', 'L')
trnds <- c('inc', 'dec')

### Low zoom and default maptype plot (for CRAN testing, not recommended)
#    Lower zoom number gives coarser text and fewer features
(x_low <- res_custom_sk_map(stations = stns, x_loc = x_coords,
                  sk_result = trnds, y_loc = y_coords,
                  bbox = bounding_elk, lab_loc = lab_dir,
                  shp = shp_fl, zoom = 10))

### Default zoom and maptype plot
x_def <- res_custom_sk_map(stations = stns, x_loc = x_coords,
                  sk_result = trnds, y_loc = y_coords,
                  bbox = bounding_elk, lab_loc = lab_dir,
                  shp = shp_fl)

### Higher zoom number gives more details, but may not be visible
x_14 <- res_custom_sk_map(stations = stns, x_loc = x_coords,
                  sk_result = trnds, y_loc = y_coords,
                  bbox = bounding_elk, lab_loc = lab_dir,
                  shp = shp_fl, zoom = 14)


### Different maptypes may be used.  All may not be available.
#   Note that zoom and maptype interact, so some experimentation may be
#   required.
x_terrain <- res_custom_sk_map(stations = stns, x_loc = x_coords,
                  sk_result = trnds, y_loc = y_coords,
                  bbox = bounding_elk, lab_loc = lab_dir,
                  shp = shp_fl, maptype = 'stamen_terrain')

Local Reserve Map

Description

Create a stylized reserve-level map for use with the reserve level reporting template

Usage

res_local_map(
  nerr_site_id,
  stations,
  bbox,
  shp,
  station_labs = TRUE,
  lab_loc = NULL,
  bg_map = NULL,
  zoom = NULL,
  maptype = "stamen_toner_lite"
)

Arguments

Details

Creates a stylized, reserve-level base map. The user can specify the reserve and stations to plot. The user can also specify a bounding box. For multi-component reserves, the user should specify a bounding box that highlights the component of interest.

This function does not automatically detect conflicts between station labels. The lab_loc argument allows the user to specify "R" or "L" for each station to prevent labels from conflicting with each other.

This function is intended to be used with mapview::mapshot to generate a png for the reserve-level report.

Value

returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger

Examples

## a compact reserve
### set plotting parameters
stations <-
sampling_stations[(sampling_stations$NERR.Site.ID == 'elk'
          & sampling_stations$Status == 'Active'
          & sampling_stations$isSWMP == "P"), ]$Station.Code
          to_match <- c('wq', 'met')
stns <- stations[grep(paste(to_match, collapse = '|'), stations)]
shp_fl <- elk_spatial
bounding_elk <- c(-121.8005, 36.7779, -121.6966, 36.8799)
lab_dir <- c('L', 'R', 'L', 'L', 'L')
labs <- c('ap', 'cw', 'nm', 'sm', 'vm')

### Low zoom and default maptype plot (for CRAN testing, not recommended)
#    Lower zoom number gives coarser text and fewer features
(x_low <- res_local_map('elk', stations = stns, bbox = bounding_elk,
                   lab_loc = lab_dir, shp = shp_fl,
                   zoom = 10))


### Default zoom and maptype
x_def  <- res_local_map('elk', stations = stns, bbox = bounding_elk,
                   lab_loc = lab_dir, shp = shp_fl,
                   zoom = 10)

### A multicomponent reserve (show two different bounding boxes)
#    set plotting parameters
stations <- sampling_stations[(sampling_stations$NERR.Site.ID == 'cbm'
            & sampling_stations$Status == 'Active'
            & sampling_stations$isSWMP == "P"), ]$Station.Code
            to_match <- c('wq', 'met')
stns <- stations[grep(paste(to_match, collapse = '|'), stations)]
shp_fl <- cbm_spatial
bounding_cbm_1 <- c(-77.393, 38.277, -75.553, 39.741)
bounding_cbm_2 <- c(-76.8,  38.7, -76.62,  38.85)
lab_dir <- c('L', 'R', 'L', 'L', 'L')
labs <- c('ap', 'cw', 'nm', 'sm', 'vm')

### plot
y <- res_local_map('cbm', stations = stns, bbox = bounding_cbm_1,
                   lab_loc = lab_dir, shp = shp_fl)

z <- res_local_map('cbm', stations = stns, bbox = bounding_cbm_2,
                   lab_loc = lab_dir, shp = shp_fl)

Reserve National Map

Description

Create a base map for NERRS reserves in ggplot

Usage

res_national_map(
  incl = c("contig", "AK", "HI", "PR"),
  highlight_states = NULL,
  highlight_reserves = NULL,
  agg_county = TRUE
)

Arguments

Details

Create a base map of the US with options for including AK, HI, and PR. The user can choose which states and NERRS reserves to highlight. An early 'sp'-based version of this function by Julie Padilla was developed, in part, from a blog post by Bob Rudis. The current sf-based version, by Dave Eslinger, uses an approach from the r-spatial tutorial by Mel Moreno and Mathieu Basille.

Value

Returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger Maintainer: Dave Eslinger

References

Rudis, Bob. 2014. "Moving The Earth (well, Alaska & Hawaii) With R". rud.is (blog). November 16, 2014. https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/ Moreno, Mel, and Basille, Mathieu Basille. 2018. "Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 3: Layouts" r-spatial (blog). October 25, 2018. https://www.r-spatial.org/r/2018/10/25/ggplot2-sf-3.html

Examples

##National map highlighting states with NERRS
nerr_states <- c('01', '02', '06', '10', '12', '13', '15'
, '23', '24', '25', '27', '28', '33', '34', '36', '37', '39'
, '41', '44', '45', '48', '51', '53', '55', '72')

res_national_map(highlight_states = nerr_states)


#' ##Just the national map
res_national_map()

##National map highlighting west coast states and NERRS (including AK)
nerr_states_west <- c('02', '06', '41', '53')

nerrs_codes <- c('pdb', 'sos', 'sfb', 'elk', 'tjr', 'kac')

res_national_map(highlight_states = nerr_states_west, highlight_reserve = nerrs_codes)

Local Reserve Map With Seasonal Kendall Results

Description

Create a stylized reserve-level map of seasonal kendall results for use with the reserve level reporting template

Usage

res_sk_map(
  nerr_site_id,
  stations,
  sk_result = NULL,
  bbox,
  shp,
  station_labs = TRUE,
  lab_loc = NULL,
  bg_map = NULL,
  zoom = NULL,
  maptype = "stamen_toner_lite"
)

Arguments

Details

Creates a stylized, reserve-level base map for displaying seasonal kendall results from sk_seasonal. The user can specify the reserve and stations to plot. The user can also specify a bounding box. For multi-component reserves, the user should specify a bounding box that highlights the component of interest.

To display seasonal trends, the user must specify c('inc', 'dec', 'insig', 'insuff') for each station listed in the stations argument.

Value

returns a ggplot object.

Author(s)

Julie Padilla, Dave Eslinger

Examples

## A compact reserve

### set plotting parameters
stations <- sampling_stations[(sampling_stations$NERR.Site.ID == 'elk'
     & sampling_stations$Status == 'Active'
     & sampling_stations$isSWMP == "P"), ]$Station.Code
     to_match <- c('wq')
stns <- stations[grep(paste(to_match, collapse = '|'), stations)]
shp_fl <- elk_spatial
bounding_elk <- c(-121.8005, 36.7779, -121.6966, 36.8799)
trnds <- c('inc', 'dec', 'insuff', 'insig')

### Low zoom and default maptype plot (for CRAN testing, not recommended)
#    Lower zoom number gives coarser text and fewer features
(x_low <- res_sk_map('elk', stations = stns, sk_result = trnds,
                 bbox = bounding_elk, shp = shp_fl,
                 zoom = 10))



### Default zoom and maptype
x_def <- res_sk_map('elk', stations = stns, sk_result = trnds,
                 bbox = bounding_elk, shp = shp_fl)

### Higher zoom number gives more details, but those may not be visible
x_14 <- res_sk_map('elk', stations = stns, sk_result = trnds,
                 bbox = bounding_elk, shp = shp_fl,
                 zoom = 14)

### Different maptypes may be used.
x_terrain <- res_sk_map('elk', stations = stns, sk_result = trnds,
                 bbox = bounding_elk, shp = shp_fl,
                 maptype = 'stamen_terrain')
### A multicomponent reserve (showing two different bounding boxes)

#  set plotting parameters
stations <- sampling_stations[(sampling_stations$NERR.Site.ID == 'cbm'
         & sampling_stations$Status == 'Active'
         & sampling_stations$isSWMP == "P"), ]$Station.Code
         to_match <- c('wq')
stns <- stations[grep(paste(to_match, collapse = '|'), stations)]
shp_fl <- cbm_spatial
bounding_cbm_1 <- c(-77.393, 38.277, -75.553, 39.741)
bounding_cbm_2 <- c(-76.8,  38.7, -76.62,  38.85)
trnds <- c('inc', 'insuff', 'dec', 'insig')

#   plot
y <- res_sk_map('cbm', stations = stns, sk_result = trnds,
                 bbox = bounding_cbm_1, shp = shp_fl)

z <- res_sk_map('cbm', stations = stns, sk_result = trnds,
                 bbox = bounding_cbm_2, shp = shp_fl)

NERRS Sampling Location Data Frame

Description

Create a data.frame of selected NERRS locations for plotting with res_national_map

Usage

reserve_locs(incl = c("contig", "AK", "HI", "PR"), subset_reserve = NULL)

Arguments

Details

A helper function used to generate a data.frame of selected reserve locations for use with res_national_map.

Value

Returns a data.frame for internal use with res_national_map

Author(s)

Julie Padilla, Dave Eslinger

Detailed NERRS site data

Description

Metadata on NERRS stations provided by the Central Data Management Office (CDMO) when data is downloaded

Usage

data(sampling_stations)

Format

A data.frame object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

A Backup of Detailed NERRS site data

Description

A backup of the current metadata on NERRS stations provided by the Central Data Management Office (CDMO) created when new data is loaded

Usage

data(sampling_stations_backup)

Format

A data.frame object

Source

CDMO

References

NOAA National Estuarine Research Reserve System (NERRS). System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: https://www.nerrsdata.org/; accessed 08 October 2016

Cumulative Bar Plot

Description

Cumulative bar plot over a historic range

Usage

seasonal_barplot(swmpr_in, ...)

## S3 method for class 'swmpr'
seasonal_barplot(
  swmpr_in,
  param = NULL,
  hist_rng = NULL,
  log_trans = FALSE,
  converted = FALSE,
  hist_avg = TRUE,
  bar_position = "stack",
  season_facet = FALSE,
  plot_title = FALSE,
  plot = TRUE,
  ...
)

Arguments

Details

This function uses barplots to summarize parameters that are best viewed on a cumulative basis (e.g., precipitation). Data are aggregated on a seasonal and annual basis.

There are two ways to make interannual comparisons: on an aggregate basis and on a seasonal basis. If the argument season_facet = FALSE then parameter totals from each season will be added together to compose one, multi-color bar.If season_facet = TRUE then parameter totals from each season separated into multiple plots for easier intra-season comparison across years.

Value

A ggplot object

Author(s)

Julie Padilla

See Also

ggplot, assign_season, y_labeler

Examples

data(apaebmet)
dat <- qaqc(apaebmet, qaqc_keep = c('0', '3', '5'))

x <- seasonal_barplot(dat, param = 'totprcp'
                      , season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12))
                      , season_names = c('Winter', 'Spring', 'Summer', 'Fall')
                      , hist_avg = TRUE
                      , converted = FALSE)


# return a table instead of a figure
y <- seasonal_barplot(dat, param = 'totprcp'
                      , season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12))
                      , season_names = c('Winter', 'Spring', 'Summer', 'Fall')
                      , converted = FALSE
                      , plot = FALSE)

## divide plot into seasonal facets
z <- seasonal_barplot(dat, param = 'totprcp'
                      , season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12))
                      , season_names = c('Winter', 'Spring', 'Summer', 'Fall')
                      , season_facet = TRUE
                      , hist_avg = TRUE
                      , converted = FALSE)

## convert from mm to in
dat$totprcp <- dat$totprcp / 25.4

x1 <- seasonal_barplot(dat, param = 'totprcp'
                      , season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12))
                      , season_names = c('Winter', 'Spring', 'Summer', 'Fall')
                      , hist_avg = TRUE
                      , converted = TRUE)

Seasonal boxplots

Description

Annual time series for year of interest on top of long-term percentiles

Usage

seasonal_boxplot(swmpr_in, ...)

## S3 method for class 'swmpr'
seasonal_boxplot(
  swmpr_in,
  param = NULL,
  hist_rng = NULL,
  target_yr = NULL,
  criteria = NULL,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  criteria_lab = "WQ Threshold",
  stat_lab = "Average",
  plot_title = FALSE,
  plot = TRUE,
  FUN = function(x) mean(x, na.rm = TRUE),
  ...
)

Arguments

Details

This function uses boxplots to summarize statistics calculated on a daily basis across user-defined seasons for all years within the historic range (hist_rng). If hist_rng is not specified then the minimum and maximum years within the data set will be used. The summary statistics used to generate the boxplots are ggplot2 defaults: the center of the box is a median, and the lower/upper limits of the box are the 25-th and 75-th percentiles. The whiskers extend to the furthest data point within 1.5 * inter-quartile range (IQR). The dots beyond the whiskers are data points that are greater than 1.5 * IQR. If the user selects a target_yr, then a median summary statistic value will be plotted as a point against the boxplots.

Using the FUN argument, the user can specify the daily summary statistic to use. Commonly used statistics are min(x, na.rm = TRUE), mean(x, na.rm = TRUE), and max(x, na.rm = TRUE). After specifying FUN, the user should also specify stat_lab, which is used to construct appropriate legend labels.

The user also has the option to add a threshold hold line using the criteria argument. Typically, this value is a water quality threshold, which is why criteria_lab defaults to 'WQ Threshold'. However, the user has the option to specify any other type of threshold they wish. when doing so, the value for criteria_lab should be changed accordingly.

Value

Returns a ggplot object or a data.frame if plot = FALSE

Author(s)

Julie Padilla

See Also

ggplot, assign_season

Examples

dat <- elksmwq
dat <- qaqc(dat, qaqc_keep = c('0', '3', '5'))

x <-
  seasonal_boxplot(dat, param = 'do_mgl')

y <-
    seasonal_boxplot(dat, param = 'do_mgl', target_yr = 2015,
    season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12)),
    season_names = c('Winter', 'Spring', 'Summer', 'Fall'),
    season_start = 'Spring')

z_min <-
   seasonal_boxplot(dat, param = 'do_mgl',
   stat_lab = 'Minimum', FUN = function(x) min(x, na.rm = TRUE))

z_max <-
   seasonal_boxplot(dat, param = 'do_mgl',
   stat_lab = 'Maximum', FUN = function(x) max(x, na.rm = TRUE))

Seasonal Dot Plot

Description

Plot average/min/max seasonal values faceted by season

Usage

seasonal_dot(swmpr_in, ...)

## S3 method for class 'swmpr'
seasonal_dot(
  swmpr_in,
  param = NULL,
  lm_trend = FALSE,
  lm_lab = FALSE,
  free_y = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  plot_title = FALSE,
  plot = TRUE,
  ...
)

Arguments

Details

This function summarizes minimum, mean, and maximum values calculated on a seasonal basis to allow for easier intra-season comparisons over time.

lm_trend = TRUE adds a linear regression to the plot, and lm_lab = TRUE will add p-values from the linear regression to the plot. If the p-values are significant (p < 0.05) then the text will appear in bold. lm_lab text is color coded to match with the corresponding dots.

Value

Returns a ggplot object

Author(s)

Julie Padilla, Dave Eslinger

See Also

ggplot, assign_season, y_labeler

Examples

dat_wq <- elksmwq
#dat_wq <- subset(dat_wq, subset = c('2010-01-01 0:00', '2017-01-01 0:00'))
dat_wq <- qaqc(dat_wq, qaqc_keep = c(0, 3, 5))

x <-
  seasonal_dot(dat_wq, param = 'do_mgl'
               , lm_trend = TRUE
               , lm_lab = TRUE
               , plot_title = TRUE)


y <-
  seasonal_dot(dat_wq, param = 'do_mgl'
               , lm_trend = FALSE
               , lm_lab = FALSE
               , plot_title = TRUE)

z <-
  seasonal_dot(dat_wq, param = 'do_mgl'
               , lm_trend = TRUE
               , lm_lab = FALSE
               , plot_title = TRUE)


dat_nut <- elknmnut
dat_nut <- subset(dat_nut, subset = c('2007-01-01 0:00', '2017-01-01 0:00'))
dat_nut <- qaqc(dat_nut, qaqc_keep = c(0, 3, 5))

x1 <-
  seasonal_dot(dat_nut
               , param = 'chla_n'
               , season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12))
               , season_names = c('Winter', 'Spring', 'Summer', 'Fall')
               , season_start = 'Spring'
               , lm_trend = FALSE
               , lm_lab = FALSE
               , plot_title = TRUE)

y1 <-
  seasonal_dot(dat_nut, param = 'chla_n'
               , lm_trend = TRUE
               , lm_lab = FALSE
               , plot_title = TRUE)

z1 <-
  seasonal_dot(dat_nut, param = 'chla_n'
               , lm_trend = TRUE
               , lm_lab = TRUE
               , plot_title = TRUE)

Set reasonable date breaks labels

Description

Select reasonable labels for breaks used in scale_x_datetime

Usage

set_date_break_labs(rng)

Arguments

Details

A helper function for easier date label setting

Value

Returns a chr string for date_labels

Author(s)

Julie Padilla

See Also

set_date_breaks, scale_x_datetime

Set reasonable date breaks

Description

Select reasonable breaks for scale_x_datetime

Usage

set_date_breaks(rng)

Arguments

Details

A helper function for easier date label setting

Value

Returns a chr string for date_breaks

Author(s)

Julie Padilla

See Also

set_date_break_labs, scale_x_datetime

Select reasonable minor breaks for scale_x_datetime

Description

Select reasonable minor breaks for scale_x_datetime

Usage

set_date_breaks_minor(rng)

Arguments

Details

A helper function for easier date label setting

Value

Returns a chr string for date_breaks

Author(s)

Dave Eslinger, Julie Padilla

See Also

set_date_break_labs, scale_x_datetime

Seasonal Kendall Analysis for Seasonal Data

Description

Non-parametric test for monotonic seasonal trends

Usage

sk_seasonal(swmpr_in, ...)

## S3 method for class 'swmpr'
sk_seasonal(
  swmpr_in,
  param = NULL,
  alpha = 0.05,
  data_min = 5,
  envStats_summary = FALSE,
  stat_lab = "Average",
  FUN = function(x) mean(x, na.rm = TRUE),
  ...
)

Arguments

Details

This function performs a seasonal kendall test on seasonally aggregated values using kendallSeasonalTrendTest.

Data are aggregated on a user-specified seasonal basis using the FUN argument. For example, using default settings, sk_seasonal would perform a seasonal kendall test on average monthly values. However, if the user set FUN = min(x, na.rm = TRUE) then a seasonal kendall would be performed on monthly minimum values.

If EnvStats_summary = TRUE then the detailed output summary from kendallSeasonalTrendTest will be returned. If EnvStats_summary = FALSE then an abbreviated summary will be returned in a data.frame. The abbreviated summary contains the station name, the type of statistic used to summarize the data on a seasonal basis (specified by stat_lab), and the following results from kendallSeasonalTrendTest: tau, slope, p-value for the chi-square test, and the p-value for the trend test.

Value

Returns a data.frame object or a summary from EnvStats::kendallSeasonalTrendTest

Author(s)

Julie Padilla

See Also

assign_season, y_labeler, kendallSeasonalTrendTest

Examples

dat_wq <- elksmwq
dat_wq <- qaqc(dat_wq, qaqc_keep = c(0, 3, 5))

x <- sk_seasonal(dat_wq, param = 'temp')

Tidy Seasonal Kendall Results

Description

Tidy results from kendallSeasonalTrendTest

Usage

sk_tidy(data, station, param, stat, alpha = 0.05)

Arguments

Details

A helper function used by sk_seasonal to return a table of tidied values.

Value

Returns a data.frame of results from kendallSeasonalTrendTest

Author(s)

Julie Padilla

Standard Parameter Check

Description

Determine if a parameter is one of the standard SWMP parameters

Usage

std_param_check(param)

Arguments

Details

A helper function used internally by several plotting functions to determine if parameter has a standard y-axis label. To accommodate the needs of the reserve-level annual report, this function also recognizes dissolved organic phosphorus (DIP) and dissolved inorganic nitrogen (DIN) as standard parameters.

Value

Returns TRUE or FALSE

Author(s)

Julie Padilla

Examples

std_param_check('do_mgl')


std_param_check('nitrogen')

Summarise Hand-off Files from Reserve Level Reports

Description

Summarise the seasonal kendall results from reserve level report hand-off files

Usage

summarise_handoff_files(path, param, res_region = NULL)

Arguments

Details

This function is intended for use with the NERRS national level reporting scripts. It returns a data.frame that summarises the result of the reserve level seasonal kendall trend analyses found in the hand-off files generated by the reserve level reporting scripts. The summary groups reserves into regional classifications based on user-specified regions given in res_region.

Value

Returns a data.frame

Author(s)

Julie Padilla

Water Quality Threshold Plot For Parameters With Criteria

Description

Observed data compared against user-defined water quality thresholds

Usage

threshold_criteria_plot(swmpr_in, ...)

## S3 method for class 'swmpr'
threshold_criteria_plot(
  swmpr_in,
  param = NULL,
  rng = NULL,
  thresholds = NULL,
  threshold_labs = c("Good", "Fair", "Poor"),
  threshold_cols = c("#ABD9E9", "#FFFFCC", "#FEC596"),
  crit_threshold = NULL,
  log_trans = FALSE,
  monthly_smooth = FALSE,
  plot_title = FALSE,
  ...
)

Arguments

Details

This function visualizes exceedances of numeric criteria which are specified using thresholds. Suggested numeric criteria for several parameters (dissolved oxygen, dissolved inorganic phosphorus, dissolved inorganic nitrogen, and chlorophyll-a) can be found in the USEPA National Coastal Condition Report (2012).

If the parameter of interest does not have numeric criteria, then threshold_percentile_plot is recommended.

Value

Returns a ggplot object

Author(s)

Julie Padilla

References

United States Environmental Protection Agency (USEPA). 2012. "National Coastal Condition Report IV." https://www.epa.gov/national-aquatic-resource-surveys/national-coastal-condition-report-iv-2012

See Also

ggplot,y_labeler

Examples

data(apacpwq)
dat_wq <- apacpwq

dat_wq <- qaqc(dat_wq, qaqc_keep = c(0, 3, 5))

## Due to the volume of instantaneous data, these plots are a bit slow
x <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , monthly_smooth = TRUE
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9'))


y <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9'))

z <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Fair', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9')
                 , monthly_smooth = TRUE)

## A few examples with only two thresholds
x1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 2)

                  # A dummy blank ('') value must be added as a threshold label
                 , threshold_labs = c('Poor', '', 'Good')
                 , threshold_cols = c('#FEC596', '#FFFFCC', '#ABD9E9')
                 , monthly_smooth = TRUE)

y1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(5, 5)

                 # A dummy blank ('') value must be added as a threshold label
                 , threshold_labs = c('Poor', '', 'Good')
                 , threshold_cols = c('#FEC596', '#FEC596', '#ABD9E9')
                 , monthly_smooth = TRUE)

z1 <-
  threshold_criteria_plot(dat_wq, param = 'do_mgl'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Poor', 'Good', 'Poor')
                 , threshold_cols = c('#FEC596', '#ABD9E9', '#FEC596')
                 , monthly_smooth = TRUE)


data(apacpnut)
dat_nut <- apacpnut

dat_nut <- qaqc(dat_nut, qaqc_keep = c(0, 3, 5))
dat_nut <- rem_reps(dat_nut)

x2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor'))


y2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor'))

## Nutrient plots are not capable of accidentally displaying any kind of smooth
z2 <-
  threshold_criteria_plot(dat_nut, param = 'chla_n'
                 , rng = 2012
                 , thresholds = c(2, 5)
                 , threshold_labs = c('Good', 'Fair', 'Poor')
                 , monthly_smooth = TRUE)

Tabulate Threshold Exceedances

Description

Tabulate user-specified threshold exceedances

Usage

threshold_identification(swmpr_in, ...)

## S3 method for class 'swmpr'
threshold_identification(
  swmpr_in,
  param,
  parameter_threshold,
  threshold_type,
  time_threshold = NULL,
  ...
)

Arguments

Details

This function creates tabular summary of events when a user-specified threshold is exceeded.

Before using this function, the user must apply setstep to normalize the datetimestamp time step.

For MET and WQ data, the user must specify time_threshold. This argument is the minimum duration that an event must last in order to be counted. For example, if time_threshold = 2, param = "do_mgl", parameter_threshold = 2, and threshold_type = "<" then dissolved oxygen must be lower than 2 mg/L for more than two hours or the event will not be summarized in the final table. For NUT parameters, all exceedances are included in the tabular summary.

Recommended thresholds for chlorophyll-a, dissolved inorganic nitrogen, dissolved inorganic phosphorus, and dissolved oxygen can be found in the National Coastal Condition Assessment 2010 (USEPA 2016)

Value

Returns a data frame of threshold exceedances by parameter

Author(s)

Julie Padilla

References

United States Environmental Protection Agency (USEPA). 2015. "National Coastal Condition Assessment 2010". EPA 841-R-15-006. https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030

Examples

data("apacpwq")
wq <- apacpwq

dat_wq <- qaqc(wq, qaqc_keep = c(0, 3, 5))
dat_wq <- setstep(dat_wq)

wq_pars<- threshold_identification(dat_wq, param = c('do_mgl', 'ph', 'temp')
                          , parameter_threshold = c(2, 5, 30)
                          , threshold_type = c('<', '<', '>'), time_threshold = 2)


wq_par<- threshold_identification(dat_wq, param = c('do_mgl')
                          , parameter_threshold = c(2)
                          , threshold_type = c('<'), time_threshold = 2)


## time_threshold and setstep are not necessary for monthly parameters
data("apacpnut")
nut <- apacpnut

dat_nut <- qaqc(nut, qaqc_keep = c(0, 3, 5))

nut_pars <- threshold_identification(dat_nut, param = c('chla_n', 'po4f')
                          , parameter_threshold = c(10, 0.01)
                          , threshold_type = c('>', '>'))

nut_par <- threshold_identification(dat_nut, param = c('chla_n')
                          , parameter_threshold = c(10)
                          , threshold_type = c('>'))

nut_err <- threshold_identification(dat_nut, param = c('chla_n')
                          , parameter_threshold = c(30)
                          , threshold_type = c('>'))

Threshold Percentile Plot

Description

Observed data compared against user-defined percentiles

Usage

threshold_percentile_plot(swmpr_in, ...)

## S3 method for class 'swmpr'
threshold_percentile_plot(
  swmpr_in,
  param = NULL,
  hist_rng = NULL,
  target_yr = NULL,
  percentiles = c(0.05, 0.95),
  free_y = FALSE,
  by_month = FALSE,
  log_trans = FALSE,
  converted = FALSE,
  plot_title = FALSE,
  ...
)

Arguments

Details

This function provides an alternative to threshold_criteria_plot. For parameters that may not have numeric threshold criteria, a percentile threshold can be used instead. For a one-tailed analysis, the 90-th percentile is recommended. For a two-tailed analysis, the 5-th and 95-th percentiles are recommended.

Using by_month, the user can specify whether the percentiles should be calculated on a monthly basis or by using the entire data set.

Recommended thresholds for chlorophyll-a, dissolved inorganic nitrogen, dissolved inorganic phosphorus, and dissolved oxygen can be found in the National Coastal Condition Assessment 2010 (USEPA 2016)

Value

Returns a ggplot object

Author(s)

Julie Padilla

References

United States Environmental Protection Agency (USEPA). 2015. "National Coastal Condition Assessment 2010". EPA 841-R-15-006. https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030

See Also

ggplot

Examples

dat_wq <- qaqc(elksmwq, qaqc_keep = c(0, 3, 5))
dat_wq <- subset(dat_wq, subset = '2007-01-01 0:00', operator = '>=')

x <-
  threshold_percentile_plot(dat_wq, param = 'do_mgl'
                           , hist_rng = c(2013, 2014), by_month = FALSE)


y <-
  threshold_percentile_plot(dat_wq, param = 'do_mgl', percentiles = c(0.95)
                           , hist_rng = c(2013, 2014), target_yr = 2014
                           , by_month = FALSE)

x2 <-
  threshold_percentile_plot(dat_wq, param = 'do_mgl'
                           , hist_rng = c(2013, 2014), by_month = TRUE)

y2 <-
  threshold_percentile_plot(dat_wq, param = 'do_mgl'
                           , hist_rng = c(2013, 2014), by_month = TRUE
                           , target_yr = 2014)


dat_nut <- qaqc(elknmnut, qaqc_keep = c(0, 3, 5))
dat_nut <- subset(dat_nut, subset = '2007-01-01 0:00', operator = '>=')
dat_nut <- rem_reps(dat_nut)

x3 <-
  threshold_percentile_plot(dat_nut, param = 'chla_n'
                           , hist_rng = c(2007, 2014), by_month = FALSE)

y3 <-
  threshold_percentile_plot(dat_nut, param = 'chla_n'
                           , hist_rng = c(2007, 2014), by_month = FALSE
                           , target_yr = 2016)

Summary Plots for Threshold Identification

Description

Summary plots for threshold identification analysis

Usage

threshold_summary(swmpr_in, ...)

## S3 method for class 'swmpr'
threshold_summary(
  swmpr_in,
  param = NULL,
  summary_type = c("month", "season", "year"),
  parameter_threshold = NULL,
  threshold_type = NULL,
  time_threshold = NULL,
  converted = FALSE,
  pal = "Set3",
  plot_title = FALSE,
  plot = TRUE,
  label_y_axis = TRUE,
  ...
)

Arguments

Details

This function provides a graphical or tabular summary of the results from threshold_identification. The user can summarize results on a monthly, seasonal, or annual basis by specifying summary_type = c('month', 'season', 'year'). If summary_type = 'season', then the user should also define season, season_names, and season_start, as required by assign_season. The user can specify 'month' for nutrient parameters, but this is not recommended and will produce a warning.

Recommended thresholds for chlorophyll-a, dissolved inorganic nitrogen, dissolved inorganic phosphorus, and dissolved oxygen can be found in the National Coastal Condition Assessment 2010 (USEPA 2016)

Value

Returns a ggplot object (if plot = TRUE) or a dataframe (if plot = FALSE)

Author(s)

Julie Padilla

References

United States Environmental Protection Agency (USEPA). 2015. "National Coastal Condition Assessment 2010". EPA 841-R-15-006. https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030

See Also

assign_season, ggplot, threshold_identification, scale_fill_brewer

Examples

## Water quality examples
data(apacpwq)
dat_wq <- qaqc(apacpwq, qaqc_keep = c(0, 3, 5))
dat_wq <- SWMPr::setstep(dat_wq)

x <-
  threshold_summary(dat_wq, param = 'do_mgl', parameter_threshold = 2
  , threshold_type = '<', time_threshold = 2, summary_type = 'month'
  , plot_title = TRUE)


y <-
  threshold_summary(dat_wq, param = 'do_mgl', parameter_threshold = 2,
  threshold_type = '<', time_threshold = 2, summary_type = 'season',
  season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12)),
  season_names = c('Winter', 'Spring', 'Summer', 'Fall'),
  season_start = 'Winter',
  plot_title = TRUE)

## Nutrient examples
dat_nut <- qaqc(apacpnut, qaqc_keep = c(0, 3, 5))

x <-
  threshold_summary(dat_nut, param = 'chla_n',
  parameter_threshold = 10,
  threshold_type = '>', summary_type = 'month',
  plot_title = TRUE)

y <-
  threshold_summary(dat_nut, param = 'chla_n', parameter_threshold = 10,
  threshold_type = '>', summary_type = 'season',
  season_grps = list(c(1,2,3), c(4,5,6), c(7,8,9), c(10, 11, 12)),
  season_names = c('Winter', 'Spring', 'Summer', 'Fall'),
  season_start = 'Winter', plot_title = TRUE)

 z <-
   threshold_summary(dat_nut, param = 'chla_n', parameter_threshold = 10,
   threshold_type = '>', summary_type = 'year',
   plot_title = TRUE, plot = TRUE)

Generate Plot Title Based on NERR Site ID

Description

Generate a plot title based on SWMP station abbreviation

Usage

title_labeler(nerr_site_id)

Arguments

Details

A helper function used internally by several plotting functions to generate plot titles.

Value

Returns character vector

Author(s)

Julie Padilla

Examples

ttl <- title_labeler('elkapwq')

Update reserve sampling stations

Description

Script to modify the internal sampling_sites.rda file for adding new reserves or for making changes to existing sampling station locations.

Usage

update_sampling_stations(file_path, file_name = "sampling_stations.csv")

Arguments

Details

This is a standalone function used to replace the internal SWMPrExtension sampling sites data table used by the get_sites.R function. It reads a csv-formatted file of all NERRS SWMP stations that the user has downloaded from the CDMO SWMP station website: https://cdmo.baruch.sc.edu/data/swmp-stations/.

This downloaded csv-formatted file and its location are the only input arguments to update_sampling_station(). The current station information is loaded from data/sampling_stations.rda, a copy of which is written out as data/sampling_stations_backup.rda, and the new data file is read, formatted appropriately, and written out as data/sampling_stations.rda.

Note: This function need only be run when new reserves are added, stations are moved, etc.

Value

Returns TRUE on a successful run, FALSE on a failure.

Author(s)

Dave Eslinger

Examples

# Provide a bad file name to get error message
x <- update_sampling_stations("data","bad_file_name.csv")
print(x)

US State Map

Description

US state boundaries from the US Census Bureau's MAF/TIGER geographic database in EPSG:4269, with all data except state FIPS codes and land area removed.

Usage

data('us_4269')

Format

A sf]{DataFrame} object

Source

US Census Bureau

References

United States Census Bureau. Data accessed from the US Census Bureau website: https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_20m.zip; accessed 13 March 2020

Generate y-axis Label Based on SWMP Parameter Abbreviation

Description

Generate a y-axis label based on SWMP parameter abbreviation and threshold criteria

Usage

y_count_labeler(
  param,
  parameter_threshold,
  threshold_type,
  time_threshold = NULL,
  converted = FALSE
)

Arguments

Details

A helper function used internally by several plotting functions to generate y-axis labels. This function does not convert sample results from metric to english. It only adjusts the units in the y-axis label.

Value

Returns character vector or an unevaluated expression

Author(s)

Julie Padilla

Examples

y_lab <- y_count_labeler(param = 'do_mgl', parameter_threshold = 2
, threshold_type = '<', time_threshold = 2, converted = FALSE)

Generate y-axis Label Based on SWMP Parameter Abbreviation

Description

Generate a y-axis label based on SWMP parameter abbreviation

Usage

y_labeler(param, converted = FALSE)

Arguments

Details

A helper function used internally by several plotting functions to generate y-axis labels. This function does not convert sample results from metric to english. It only adjusts the units in the y-axis label.

Value

Returns character vector or an unevaluated expression

Author(s)

Julie Padilla

Examples

y_lab <- y_labeler('do_mgl')