Title: Calculate Pesticide Risk Metric (PRM) Values from Multiple Pesticides...Calc Them All

Version: 1.1.1

Description: Contains functions which can be used to calculate Pesticide Risk Metric values in aquatic environments from concentrations of multiple pesticides with known species sensitive distributions (SSDs). Pesticides provided by this package have all be validated however if the user has their own pesticides with SSD values they can append them to the pesticide_info table to include them in estimates.

Encoding: UTF-8

License: GPL (≥ 3)

RoxygenNote: 7.3.1

Imports: dplyr, lubridate, magrittr, MASS, stats, VGAM, plotly, zoo, DT

Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)

Config/testthat/edition: 3

Depends: R (≥ 2.10)

LazyData: true

VignetteBuilder: knitr

NeedsCompilation: no

Packaged: 2024年04月23日 13:44:40 UTC; Alexander

Author: Alexander Bezzina [aut, cre, cph], Jennifer Strauss [aut], Catherine Neelamraju [aut], Hayley Kaminski [aut]

Maintainer: Alexander Bezzina <alex.h.bezzina@gmail.com>

Repository: CRAN

Date/Publication: 2024年04月24日 14:30:05 UTC

lhs %>% rhs

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Burr_Type_III_Formula(concentration, scale, shape_location, shape_location_2)

concentration

The vector of concentration values for a selected pesticide, that has a Burr Type III shaped species sensitivity distribution, to run the equation on.

scale

The Burr Type III scale/b value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" data frame.

shape_location

The Burr Type III c/shape value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

shape_location_2

The Burr Type III k/shape value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

#Chlorpyrifos is used as its species sensitivity distribution fits Burr Type III
Chlorpyrifos <- c(0.000000001, 0.5, 2.7, 11)
Burr_Type_III_Formula(concentration = Chlorpyrifos,
scale = pesticide_info$scale[pesticide_info$pesticide == "Chlorpyrifos"],
shape_location = pesticide_info$shape_location[pesticide_info$pesticide == "Chlorpyrifos"],
shape_location_2 = pesticide_info$shape_location_2[pesticide_info$pesticide == "Chlorpyrifos"])

Canto_pesticides

Gamma_Formula(concentration, shape_location, scale)

concentration

The vector of concentration values for a selected pesticide, that has a Gamma shaped species sensitivity distribution, to run the equation on.

shape_location

The k/shape value for the Gamma equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale

The scale/theta value for the Gamma equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

gamma_pesticide_concentrations <- c(0.000000001, 0.5, 2.7, 11)
Gamma_Formula(concentration = gamma_pesticide_concentrations,
shape_location = 0.23, scale = 1.3)

Inverse_Weibull_Formula(concentration, shape_location, scale)

concentration

The vector of concentration values for a selected pesticide, that has a Inverse Weibull shaped species sensitivity distribution, to run the equation on.

shape_location

The Inverse Weibull shape/alpha value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale

The Inverse Weibull scale/beta value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

Hexazinone <- c(0.000000001, 0.5, 2.7, 11)
#Hexazinone is used as its species sensitivity distribution plots fits Inverse Weibull
Inverse_Weibull_Formula(concentration = Hexazinone,
shape_location = pesticide_info$shape_location[pesticide_info$pesticide == "Hexazinone"],
scale = pesticide_info$scale[pesticide_info$pesticide == "Hexazinone"])

Log_Gumbel_Formula(concentration, shape_location, scale)

concentration

The vector of concentration values for a selected pesticide, that has a Log Gumbel shaped species sensitivity distribution, to run the equation on.

shape_location

The mu/location value for the Log Gumbel equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale

The beta/scale value for the Log Gumbel equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

#This Example should produce roughly 1% and 5% PRM values
LogGumbel_pesticide_concentrations <- c(0.095957794, 0.245881898)
Log_Gumbel_Formula(concentration = LogGumbel_pesticide_concentrations,
shape_location = 0.9980581, scale = 2.188285)

Log_Logistic_Formula(concentration, scale, shape_location)

concentration

The vector of concentration values for a selected pesticide, that has a Log Logistic shaped species sensitivity distribution, to run the equation on.

scale

The Log Logistic alpha/scale value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

shape_location

The Log Logistic beta/shape value for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

Imazapic <- c(0.000000001, 0.5, 2.7, 11)
#Imazapic is used as its species sensitivity distribution plots fits Log Logistic
Log_Logistic_Formula(concentration = Imazapic,
scale = pesticide_info$scale[pesticide_info$pesticide == "Imazapic"],
shape_location = pesticide_info$shape_location[pesticide_info$pesticide == "Imazapic"])

Log_Logistic_Log_Logistic_Formula(
 concentration,
 scale,
 shape_location,
 scale_2,
 shape_location_2,
 weight
)

concentration

The vector of concentration values for a selected pesticide, that has a Log Logistic Log Logistic shaped species sensitivity distribution, to run the equation on.

scale

The alpha/scale value for the first Log Logistic equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

shape_location

The beta/shape value for the first Log Logistic equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale_2

The alpha/scale value for the second Log Logistic equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

shape_location_2

The beta/shape value for the second Log Logistic equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

weight

The weight parameter for combining the two equations for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

#This Example should produce roughly 1% and 5% PRM values
LogL_LogL_pesticide_concentrations <- c(0.00341453, 0.009854566)
Log_Logistic_Log_Logistic_Formula(concentration = LogL_LogL_pesticide_concentrations,
scale = 0.5823392, shape_location = -3.499604, scale_2 = 1.144555,
shape_location_2 = 1.100755, weight = 0.3585467)

Log_Normal_Formula(concentration, shape_location, scale)

concentration

The vector of concentration values for a selected pesticide, that has a Log Normal shaped species sensitivity distribution, to run the equation on.

shape_location

The mu/location value for the Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale

The sigma/scale value for the Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

LogN_pesticide_concentrations <- c(0.000000001, 0.5, 2.7, 11)
Log_Normal_Formula(concentration = LogN_pesticide_concentrations,
shape_location = 0.23, scale = 1.3)

Log_Normal_Log_Normal_Formula(
 concentration,
 shape_location,
 scale,
 shape_location_2,
 scale_2,
 weight
)

concentration

The vector of concentration values for a selected pesticide, that has a Log Normal Log Normal species sensitivity distribution, to run the equation on.

shape_location

The mu/shape value for the first Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale

The sigma/scale value for the first Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

shape_location_2

The mu/shape value for the second Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

scale_2

The sigma/scale value for the second Log Normal equation for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

weight

The weight parameter for combining the two equations for the selected pesticide. These can be found in the "pesticide_info" data frame provided in this package. If you are including other pesticides you will need to append them with their respective distribution variables to the "pesticide_info" table.

#This Example should produce roughly 1% and 5% PRM values
LogN_LogN_pesticide_concentrations <- c(4.79E-05, 0.000225588)
Log_Normal_Log_Normal_Formula(concentration = LogN_LogN_pesticide_concentrations,
shape_location = -5.596431, scale = 2.061943,
shape_location_2 = 0.01174954, scale_2 = 0.9134796, weight = 0.5733126)

PRM_DT(PRM_data, fill_cols = NULL, colour_cols = NULL)

PRM_data

a data frame of either daily average or wet season PRM values

fill_cols

A vector of column names of pesticide groups to fill colour with corresponding PRM risk category

colour_cols

A vector of column names of pesticide groups to colour text with corresponding PRM risk category

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
Canto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Canto_pesticides_LOR_treated)
PRM_DT(PRM_data = Canto_daily_PRM, fill_cols = "Total PRM",
colour_cols = c("PSII Herbicide PRM", "Other Herbicide PRM", "Insecticide PRM"))

add_your_own_pesticide(
 pesticides,
 relative_LORs,
 pesticide_types,
 distribution_types,
 shape_locations = NA,
 shape_location_2s = NA,
 scales = NA,
 scale_2s = NA,
 weights = NA,
 pesticide_info = CalcThemAll.PRM::pesticide_info
)

pesticides

A vector of pesticide names

relative_LORs

A vector of relative limit of reporting replacement values

pesticide_types

A vector of the new pesticide's types

distribution_types

A vector of the new pesticide's species sensitivity distribution types

shape_locations

A vector of shape/location values (if applicable, else put NA)

shape_location_2s

A vector of secondary shape/location values (if applicable, else put NA)

scales

A vector of scale values (if applicable, else put NA)

scale_2s

A vector of secondary scale values (if applicable, else put NA)

weights

A vector of weight values (if applicable, else put NA)

pesticide_info

A data set to add pesticides too

new <- add_your_own_pesticide(pesticides = "Poison", relative_LORs = 0.023,
pesticide_types = "Poison", distribution_types = "Log-Normal", scales = 0.09,
shape_locations = 0.014)
multiple_new <- add_your_own_pesticide(pesticides = c("Poison", "Acid", "Sludge"),
relative_LORs = c(0.03, 0.01, 0.5), pesticide_types = c("Poison", "Acid", "Sludge"),
distribution_types = c("Log-Normal", "Log-Logistic Log-Logistic", "Burr Type III"),
scales = c(0.3, 0.002, 2),
scale_2s = c(NA, 0.04, NA), shape_locations = c(1, 0.07, 3),
shape_location_2s = c(NA, 0.14, 2.3), weights = c(NA, 0.08, NA))

calculate_daily_average_PRM(
 LOR_treated_data,
 include_PAF = FALSE,
 pesticide_info = CalcThemAll.PRM::pesticide_info
)

LOR_treated_data

A data set of LOR treated pesticide concentration values in individual columns that match the pesticide names in the "pesticide_info" data frame. This data set should also include a "Date", "Sampling Year" and "Site Name" column.

include_PAF

If "TRUE" Percentage Affected Fraction values are included in the output along with Daily PRM in a list format. These values can be useful for plotting relative individual pesticide contribution to overall PRM, however most will not need this so default is "FALSE".

pesticide_info

The reference table which contains all relevant information for calculations. It is recommended that the "pesticide_info" data set included in this package be used and if you wish to include more pesticides you can appended them with the relevant information to this table. If you are creating your own table you must ensure that the pesticide name column is title "pesticide" and the relative LOR replacement column is "relative_LOR" for the function to run.

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
Canto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Canto_pesticides_LOR_treated)
head(Canto_daily_PRM)

calculate_wet_season_average_PRM(
 daily_PRM_data,
 PRM_group = "Total PRM",
 imputations = 1000,
 min_sampling_days = 12,
 wet_season_length = 182
)

daily_PRM_data

A data set of calculated daily average PRM values. This data set should also include a "Date", "Sampling Year" and "Site Name" column.

PRM_group

This specifies the name of the column to run the calculations on. The daily average calculations gives PRM for each pesticide type and total in different columns so this selects which to run. "Total" is set as the default as it is the PRM of all pesticides.

imputations

This sets the number of imputations to run. The more imputations the greater the reliability, however it also increases calculation time. You can increase imputations beyond 1000 however the improvement of the confidence interval on imputed values may not be sufficient to warrant increased computing time. We recommend 1000 :)

min_sampling_days

This is the minimum number of sampling days a site-year combination must have to calculate a wet season average PRM. No less than 12 is the default (1 for each month) for reliability but more is recommended.

wet_season_length

The length of the wet season in days.

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
Canto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Canto_pesticides_LOR_treated)
Celestial_City_2019_2020_daily_PRM <- Canto_daily_PRM %>%
dplyr::filter(`Site Name` == "Celestial City" & `Sampling Year` == "2019-2020")
CC2019_2020_wet_season_PSII_PRM <- calculate_wet_season_average_PRM(daily_PRM_data =
Celestial_City_2019_2020_daily_PRM, PRM_group = "PSII Herbicide PRM")
CC2019_2020_wet_season_PSII_PRM

find_Sampling_Year(dates, wet_season_split = 7)

dates

A date vector of sampling dates. Must be in yyyy-mm-dd format.

wet_season_split

The first month of the sampling year in numeric e.g. July = 7. July (7) is used as the default as this is the first month of the Queensland wet season.

dates <- as.Date(c("2014-03-04", "2014-12-30", "2015-06-12"))
sampling_years <- find_Sampling_Year(dates) #cut of date

find_season(wet_season_start_dates, sampling_dates, wet_season_length = 182)

wet_season_start_dates

A vector of dates signifying the first day of the wet season for site year combinations.

sampling_dates

A date vector of sampling dates. Must be in yyyy-mm-dd format.

wet_season_length

The length of the wet season in days.

dates <- as.Date(c("2014-12-04", "2014-10-30", "2015-11-12"))
wet_start_dates <- as.Date(c("2014-10-04", "2014-12-30", "2015-09-12"))
Seasons <- find_season(wet_start_dates, sampling_dates = dates)
#cut of date for the sampling year will be last day of June

find_wet_season_end(wet_season_start, wet_season_length = 182)

wet_season_start

A vector of dates signifying the first day of the wet season for site year combinations.

wet_season_length

The length of the wet season in days.

wet_season_start_dates <- as.Date(c("2014-10-04", "2014-12-30", "2015-09-12"))
wet_season_end_dates <- find_wet_season_end(wet_season_start_dates)
#cut of date for the sampling year will be last day of June

imputation_beta(impute_variable, wet_season_length = 182)

impute_variable

The variable you wish to impute.

wet_season_length

The length of the wet season in days.

imputation_kernel(impute_variable, wet_season_length = 182)

impute_variable

The variable you wish to impute.

wet_season_length

The length of the wet season in days.

pesticide_info

plot_daily_PRM(
 daily_PRM_data,
 wet_season_start = NULL,
 wet_season_length = 182,
 PRM_group = "Total PRM",
 title = FALSE,
 legend = "Numerical"
)

daily_PRM_data

A data set of calculated daily average PRM values for a single site and sampling year. This data set should also include a "Date", "Sampling Year" and "Site Name" column.

wet_season_start

The date of the start of the wet season for this site sampling year. If not applicable leave as NA

wet_season_length

The length of the wet season in days.

PRM_group

This specifies the name of the column to plot. The daily average calculations gives PRM for each pesticide type and a total in different columns. "Total" is set as the default as it is the PRM value for all pesticides.

title

TRUE or FALSE value to include a title.

legend

Does the legend show "Numerical" or "Categorical" values for PRM values on the plot.

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
Canto_daily_PRM <- calculate_daily_average_PRM(LOR_treated_data = Canto_pesticides_LOR_treated)
Violet_Town_2017_2018_PRM <- Canto_daily_PRM %>%
dplyr::filter(.data$`Sampling Year` == "2017-2018" & .data$`Site Name` == "Violet Town")
plot_daily_PRM(Violet_Town_2017_2018_PRM, "2017-10-02", PRM_group = "Total PRM")

plot_wet_season_window(wet_season_start = 0, wet_season_length = 182)

wet_season_start

The date of the start of the wet season for this site sampling year.

wet_season_length

The length of the wet season in days.

shape <- plot_wet_season_window(wet_season_start = "2017-08-01")

treat_LORs(
 sample_data,
 pesticide_info = CalcThemAll.PRM::pesticide_info,
 treatment_method = "Zero"
)

sample_data

A single observation containing a concentration value for each pesticide being used in the metric. LOR values should be in "<0.05" format and no values should be empty "".

pesticide_info

The reference table which contains all relevant information for calculations. It is recommended that the "pesticide_info" dataset included in this package be used and if you wish to include more or less pesticides you can appended them with the relevant information to this table. If you are creating your own table you must ensure that the pesticide name column is title "pesticides" and the relative LOR replacement column is "relative_LOR" for the function to run.

treatment_method

Select how to treat the LOR values with either "WQI" representing the Queensland Department of Environment & Science Water Quality Monitoring & Investigations team's method for replacing LORs or "Zero" which replaces them with a negligible numeric value. Zero is the default here as this function on its own only treats a single observation and therefore the first detection in the WQI method cannot be used.

first_sample <- Canto_pesticides[1,] #this selects only the first row (sample)
LOR_treated_first_sample <- treat_LORs(sample_data = first_sample,
pesticide_info = CalcThemAll.PRM::pesticide_info, treatment_method = "Zero")
print(LOR_treated_first_sample)

treat_LORs_all_data(
 raw_data,
 pesticide_info = CalcThemAll.PRM::pesticide_info,
 wet_season_split = 7,
 treatment_method = "WQI"
)

raw_data

A data set of raw pesticide concentration values in individual columns that match the pesticide names in the "pesticide_info" data frame. This data set should also include a "Date" column and "Site Name" column. A reference data set can be seen in the "Canto_pesticides" data frame provided in this package, your data should mirror these column headings.

pesticide_info

The reference table which contains all relevant information for calculations. It is recommended that the "pesticide_info" data set included in this package be used and if you wish to include more or less pesticides you can appended them with the relevant information to this table. If you are creating your own table you must ensure that the pesticide name column is title "pesticides" and the relative LOR replacement column is "relative_LOR" for the function to run.

wet_season_split

The first month of the sampling year in numeric e.g. July = 7. July (7) is used as the default as this is the first month of the Queensland wet season. This is only required for the LOR replacement method and if needed.

treatment_method

Select how to treat the LOR values with either the default "WQI" representing the Queensland Department of Environment & Science Water Quality Monitoring & Investigations team's method for replacing LORs or "Zero" which replaces them with a negligible numeric value.

Canto_pesticides_LOR_treated <- treat_LORs_all_data(raw_data = Canto_pesticides,
pesticide_info = CalcThemAll.PRM::pesticide_info)
head(Canto_pesticides_LOR_treated)