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Brook Trout eDNA and Environmental Data

Description

This dataset contains information on brook trout detections usingenvironmental DNA (eDNA) and environmental parameters collected from varioussites in Ontario, Canada. The data was sourced from a scientific studycomparing eDNA sampling methods with electrofishing to detect Brook troutpopulations.

Usage

BrookTrout

Format

A dataframe with 10 variables and multiple rows (one row per sample):

Backpack

Character. The type of eDNA sampler: "OSMOS" or "ANDe".

Site

Integer. The site number where the sample was taken.

eFishCatch

Integer. The number of fish caught via electrofishing.

AirTemp

Numeric. Air temperature in degrees Celsius.

WaterTemp

Numeric. Water temperature in degrees Celsius.

pH

Numeric. pH level of the water sample.

DissolvedOxygen

Numeric. Dissolved oxygen concentration in mg/L.

Conductivity

Numeric. Conductivity in uS/cm.

VolumeFiltered

Numeric. Volume of water filtered in litres.

eDNAConc

Numeric. eDNA concentration in copies per microlitre.

Source

Adapted from Nolan, K. P., Loeza-Quintana, T., Little, H. A., et al.(2022). Detection of brook trout in spatiotemporally separate locationsusing validated eDNA technology.Journal of Environmental Studies andSciences, 13, 66-82.doi:10.1007/s13412-022-00800-x

Examples

data(BrookTrout)summary(BrookTrout)plot(eDNAConc ~ Site, data = BrookTrout)

Compare Association Rule Sets and Find Their Intersections

Description

Compares multiple sets of association rules, identifies intersections,and optionally displays the results or writes them to a CSV file.

Usage

compare_rules(..., display = TRUE, filename = NULL)

Arguments

Value

A list containing the intersections of the provided rule sets.

Examples

library(arules)data(BrookTrout)# Discretize the BrookTrout datasetdiscrete_bt <- dtize_df(BrookTrout, cutoff = "mean")# Generate the first set of rules with a confidence threshold of 0.5rules1 <- apriori(  discrete_bt,  parameter = list(supp = 0.01, conf = 0.5, target = "rules"))# Generate the second set of rules with a higher confidence threshold of 0.6rules2 <- apriori(  discrete_bt,  parameter = list(supp = 0.01, conf = 0.6, target = "rules"))# Compare the two sets of rules and display the intersectionscompare_rules(  r1 = rules1,  r2 = rules2,  display = TRUE)# If `filename = "intersections.csv"`, the data is saved in a .csv file

Discretize a Numeric Column

Description

Discretizes a numeric vector into categories based on specified cutoff points.The function handles missing values, allows for infinite bounds, and supportspredefined cutoffs such as the mean or median.

Usage

dtize_col(  column,  cutoff = "median",  labels = c("low", "high"),  include_right = TRUE,  infinity = TRUE,  include_lowest = TRUE,  na_fill = "none")

Arguments

Value

A factor with the same length ascolumn, where each value is categorized based on the cutoffs.

Examples

data(BrookTrout)# Example with predefined cutoffsdiscrete_water_temp <- dtize_col(  BrookTrout$eDNAConc, cutoff=13.3,  labels=c("low", "high"),  infinity=TRUE)# Example with median as cutoffdiscrete_pH <- dtize_col(BrookTrout$pH, cutoff="median")# Example with missing value imputationfilled_col <- dtize_col(  c(1, 2, NA, 4, 5),  cutoff = "mean",  include_right=FALSE,  na_fill = "mean")

Discretize Dataframe Columns

Description

Discretizes numeric columns of a dataframe based on specified splitting criteria,and handles missing values using specified imputation methods.

Usage

dtize_df(  data,  cutoff = "median",  labels = c("low", "high"),  include_right = TRUE,  infinity = TRUE,  include_lowest = TRUE,  na_fill = "none",  m = 5,  maxit = 5,  seed = NULL,  printFlag = FALSE)

Arguments

Value

A dataframe with numeric columns discretized and missing values handled based on the specified imputation method.

Examples

data(BrookTrout)# Example with median as cutoffmed_df <- dtize_df(  BrookTrout,  cutoff="median",  labels=c("below median", "above median"))# Example with mean as cutoffmean_df <- dtize_df(  BrookTrout,  cutoff="mean",  include_right=FALSE)# Example with missing value imputationair <- dtize_df(  airquality,  cutoff="mean",  na_fill="pmm",  m=10,  maxit=10,  seed=42)

Create an Euler Diagram for Association Rules

Description

Generates an Euler diagram visualization for up to 4 sets of association rules.The function displays the relationships between rule sets with customizable colors, transparency, and labels.

Usage

rule_euler(  rules,  fill_color = NULL,  fill_alpha = 0.5,  stroke_color = "black",  stroke_size = 1,  title = NULL,  name_color = "black",  name_size = 12,  text_color = "black",  text_size = 11,  show_legend = FALSE,  legend_position = "bottom",  nrow = NULL,  ncol = NULL)

Arguments

Value

Aplot object displaying the Euler diagram visualization.

Examples

library(arules)data(BrookTrout)# Discretize the BrookTrout datasetdiscrete_bt <- dtize_df(BrookTrout, cutoff = "median")# Generate the first set of rules with a confidence threshold of 0.5rules1 <- apriori(  discrete_bt,  parameter = list(supp = 0.01, conf = 0.5, target = "rules"))# Generate the second set of rules with a higher confidence threshold of 0.6rules2 <- apriori(  discrete_bt,  parameter = list(supp = 0.01, conf = 0.6, target = "rules"))# Create an Euler diagram to visualize the intersections between the rule setsrule_euler(  rules = list(conf0.5 = rules1, conf0.6 = rules2),  title = "Euler Diagram of BrookTrout Rule Sets",  fill_color = c("#7832ff", "lightgreen"),  stroke_color = "darkblue")

Create a Heatmap for Association Rules

Description

Generates a heatmap visualization of association rules,showing relationships between antecedents and consequents based on a specified metric.

Usage

rule_heatmap(  rules,  metric = "confidence",  graph_title = "",  graph_title_size = 14,  x_axis_title = "Antecedents",  x_axis_title_size = 12,  x_axis_text_size = 11,  x_axis_text_angle = 45,  y_axis_title = "Consequents",  y_axis_title_size = 12,  y_axis_text_size = 11,  y_axis_text_angle = 0,  legend_title = metric,  legend_text_size = 8,  legend_position = "right",  low_color = "lightblue",  high_color = "navy",  include_zero = FALSE)

Arguments

Value

Aggplot object representing the heatmap visualization of the association rules.

Examples

library(arules)library(tidyr)data(BrookTrout)# Discretise datadiscrete_bt <- dtize_df(BrookTrout, cutoff="median")# Generate rulesrules <- apriori(  discrete_bt,  parameter = list(supp = 0.01, conf = 0.5, target = "rules"),  appearance = list(rhs="eDNAConc=high"))# Subset ruleset (too many rules won't fit on the heatmap)rules <- rules %>%  subset(!is.redundant(., measure = "confidence")) %>%  subset(is.significant(., alpha = 0.05)) %>%  sort(by = c("confidence", "lift", "support"))# Create a heatmap of the rules using confidence as the metricrule_heatmap(  rules,  metric = "confidence",  graph_title = "Confidence Heatmap")# Create a heatmap of the rules using lift as the metricrule_heatmap(  rules,  metric = "lift",  graph_title = "Lift Heatmap",  low_color = "#D4A221",  high_color = "darkgreen")