Exploratory Data Analysis (EDA)

Description

This function acts as a dynamic interface to call any available EDA function from the ILORA package. Users specify which function to execute and pass any required arguments to that function.

Usage

EDA(func, ...)

Arguments

Details

Available functions that can be called through EDA() include:

plot_species_on_map

Plots specified species on a map of India.

bar_plot

Bar plot showing counts of species across 5 invasion status categories.

species_count_plot

Returns a list of bar plots for growth habit and duration, plus summary tables.

introduction_pathways_plot

Pie chart showing species counts by introduction pathways.

plot_first_record_year_histogram

Histogram of species first record years within a given interval.

calculate_mrt

Calculates Minimum Residence Time (MRT) for species as current year minus first record year.

calculate_total_uses

Calculates total number of uses for each species.

calculate_market_metrics

Returns metrics for average seed/plant prices and species with highest/lowest prices.

visualize_native_range

Visualizes the native range of species on a global map.

visualize_naturalized_range

Visualizes the naturalized range of species on a global map.

Use ?function_name for detailed help on each individual function.

Value

The output of the specified EDA function. This could be a ggplot object, a Plotly object, a list of plots and summaries, or a data frame, depending on the function.

Examples

# Call the bar_plot function
EDA("bar_plot")

# Call plot_species_on_map with additional arguments
EDA("plot_species_on_map",
    species_names = c("Quercus robur L."),
    palette = "inferno",
    opacity = 0.9,
    highlight = FALSE)

ILORA: A package for retrieving species data from ILORA database

Description

Provides tools to access, query, and visualize species occurrence data from the Indian Alien Flora Information (ILORA) database. The package enables users to retrieve species names, explore available variables, and extract species-level data based on user-defined criteria. It also supports exploratory data analysis and visualization to facilitate ecological and biogeographical research. Further details about the database are available at https://ilora2020.wixsite.com/ilora2020.

Plot the Number of Species by Invasion Status

Description

Retrieves species data from the sp_categorization table in the ILORA database and creates a bar plot showing the number of species in each invasion status category. Categories include Invasive (In), Naturalized (Nt), Casual Aliens (CA), Cryptogenic (CG), and Native (N).

Usage

bar_plot()

Value

A ggplot object representing the number of species in each invasion status category.

Examples

# Generate a bar plot of species counts for each invasion status
bar_plot()

Calculate Market Metrics for Seed and Plant Prices

Description

Retrieves market data from the market_dynamics table in the ILORA database and computes key metrics for seed and plant prices. Metrics include average prices, and identification of species with the highest and lowest prices for seeds, nursery-live plants (NL), and plant-live plants (PL).

Usage

calculate_market_metrics()

Details

NL refers to Nursery-Live plants and PL refers to Plant-Live plants. The function calculates averages across all species recorded in the database and identifies extremes for each price category.

Value

A named list containing:

averages

A data frame with average seed, NL plant, and PL plant prices.

highest_seed

Data frame with species having the highest seed price.

lowest_seed

Data frame with species having the lowest seed price.

highest_nl_plant

Data frame with species having the highest NL plant price.

lowest_nl_plant

Data frame with species having the lowest NL plant price.

highest_pl_plant

Data frame with species having the highest PL plant price.

lowest_pl_plant

Data frame with species having the lowest PL plant price.

Examples

# Compute market metrics
market_metrics <- calculate_market_metrics()

# View average prices
market_metrics$averages

# View species with highest seed price
market_metrics$highest_seed

Calculate Minimum Residence Time (MRT) for Species

Description

Computes the Minimum Residence Time (MRT) for species based on their first recorded year in the ILORA database. MRT is defined as the difference between the current year and the species' first record year. Users can calculate MRT for specific species or all species.

Usage

calculate_mrt(species = "ALL")

Arguments

Value

A data frame with columns:

• acc_species_name: The accepted species name.

• first_year: The first recorded year of the species.

• mrt: Minimum Residence Time (current year minus first recorded year).

If a specified species is not found in the database, a message is printed indicating the missing species. Returns NULL if no matching records are found.

Examples

# Calculate MRT for all species
all_species_mrt <- calculate_mrt(species = "ALL")

# Calculate MRT for specific species
species <- c("Species A", "Species B", "Species C")
specific_species_mrt <- calculate_mrt(species)

Calculate Total Number of Uses for Each Species

Description

Computes the total number of uses for species based on the economic_uses table in the ILORA database. It sums across all use-related columns for each species.

Usage

calculate_total_uses(species = NULL)

Arguments

Value

A data frame with columns:

• acc_species_name: The accepted species name.

• acc_species_id: Internal species ID.

• total_uses: Sum of all recorded uses across relevant economic use columns.

If no matching records are found, a message is printed and NULL is returned.

Examples

# Calculate total uses for all species
all_species_uses <- calculate_total_uses()

# Calculate total uses for specific species
species_list <- c("Quercus robur L.", "Azadirachta indica")
specific_species_uses <- calculate_total_uses(species_list)

Generate a Color Palette

Description

Returns a vector of colors based on a specified palette name. Supports built-in R palettes, RColorBrewer palettes, viridis palettes, and custom diverging palettes.

Usage

generate_palette(n, palette)

Arguments

Value

Character vector of color hex codes.

Examples

# Generate 5 colors from rainbow palette
generate_palette(5, "rainbow")

# Generate 10 colors from viridis palette
generate_palette(10, "viridis")

Retrieve Data for One or More Species from ILORA Database

Description

Fetches either specific variables across tables or an entire table for one or more species from the ILORA database.

Usage

get_data(species_name, variables = NULL, table_name = NULL)

Arguments

Value

A data frame containing the requested species data. Returns an error if species or variables/table are not found.

Examples

# Retrieve specific variables
get_data("Quercus robur L.", variables = c("Longitude", "Latitude"))

# Retrieve all columns from a specific table
get_data("Quercus robur L.", table_name = "habitat")

Retrieve Comprehensive Species Information from ILORA Database

Description

This function fetches detailed data for one or more species from multiple ILORA database tables. It retrieves taxonomy, invasion status, general information, native range, introduction pathways, uses, market data, habitat, naturalized range, occurrence and distribution, geography, and climate.

Usage

get_species_details(species_name)

Arguments

Value

A structured list containing data frames for each category:

taxonomy

Taxonomic classification.

invasion_status

Invasion status of the species.

general_info

General species information.

native_range

Native range at TDWG level 2.

introduction

Introduction pathway and first record date in India.

uses

Economic uses across different sectors.

market_info

Market metrics such as plant prices.

habitat

Habitat information.

naturalized_range

Naturalized range at TDWG level 2.

occurrence_distribution

List containing occurrence and distribution data frames.

geography

List containing LULC, anthrome, and ecoregion data frames.

climate

Climate-related data.

Examples

species_details <- get_species_details(c("Azadirachta indica", "Quercus robur L."))
species_details$taxonomy
species_details$market_info

Retrieve Species Names from ILORA Database

Description

This function connects to the ILORA database and retrieves all species names available in the species_name table. It returns them as a character vector in alphabetical order.

Usage

get_species_names()

Value

A character vector containing all species names in the ILORA database. If no species are found, returns an empty character vector.

Examples

species_names <- get_species_names()
head(species_names)

Retrieve Table Names from ILORA Database

Description

This function connects to the ILORA database and returns all table names in the public schema, excluding system tables.

Usage

get_table_names()

Value

A character vector of table names. Returns an empty vector if no tables are found.

Examples

# Retrieve all user tables
get_table_names()

Retrieve Column Names by Table from ILORA Database

Description

This function connects to the ILORA database and retrieves all column names for tables in the public schema. Columns are grouped by table name.

Usage

get_variable_names(include_id = FALSE)

Arguments

Value

A named list. Each element is a character vector of column names for a table. If no tables/columns are found, returns an empty list.

Examples

# Retrieve variables excluding ID columns
get_variable_names()

# Retrieve variables including ID columns
get_variable_names(include_id = TRUE)

Open ILORA Variable Description PDF

Description

Opens the ILORA variable description PDF file (ILORA_HowToRead.pdf) in the system's default PDF viewer. This guide helps users understand the variables and structure of the ILORA dataset.

Usage

ilora_variable_guide()

Details

This function uses utils::browseURL() to open the PDF file. Depending on the user's system configuration, the file may open in a web browser, external PDF viewer, or within RStudio's viewer pane.

Value

No return value. This function is called for its side effect of opening the PDF file.

Examples

ilora_variable_guide()

Species Count Plot by Introduction Pathways

Description

Retrieves species data from the introduction table in the ILORA database, counts distinct species for each reported introduction pathway, and generates a 3D pie chart using Plotly. The summary table of counts is also printed.

Usage

introduction_pathways_plot()

Value

A plotly object representing a 3D pie chart of species counts by introduction pathway. The summary table of top pathways is printed as a message.

Examples

# Generate pie chart for species introduction pathways
plot <- introduction_pathways_plot()
plot  # Displays the Plotly pie chart

Histogram of Species First Record Year

Description

Retrieves first record years of species from the introduction table in the ILORA database within a specified year interval and plots a histogram using ggplot2.

Usage

plot_first_record_year_histogram(start_year, end_year, binwidth = NULL)

Arguments

Value

A ggplot object representing the histogram of species first record years.

Examples

# Plot histogram for species first records between 1990 and 2000
plot_first_record_year_histogram(start_year = 1990, end_year = 2000)

Plot Latitude and Longitude of Species on a Map of India

Description

Visualizes occurrences of one or more species on a map of India using their latitude and longitude data from the ILORA database.

Usage

plot_species_on_map(
  species_names,
  custom_colors = NULL,
  palette = "viridis",
  opacity = 0.7,
  highlight = FALSE
)

Arguments

Details

Plots multiple species occurrences with either a specified palette or custom colors. Ensures spatial coordinates align with India's shapefile CRS.

Value

A ggplot object displaying the species occurrences on a map of India.

Examples

# Plot multiple species with custom colors
plot_species_on_map(
  c("Cyanus segetum Hill", "Cuphea llavea Lex."),
  custom_colors = c("orange", "blue")
)

# Plot a single species using the heat palette and reduced opacity
plot_species_on_map("Quercus robur L.", palette = "heat", opacity = 0.6)

# Plot a species using custom colors and viridis palette
plot_species_on_map("Avena sativa L.", custom_colors = c("orange"), palette = "viridis")

Species Count Plot by Growth Habit and Duration

Description

Retrieves species data from the general_information table in the ILORA database, calculates the number of distinct species for each growth habit and duration category, and generates bar plots to visualize these counts. Summary tables are also included.

Usage

species_count_plot()

Value

A list containing:

plot_growthhabit

A ggplot object showing species counts by growth habit.

plot_duration

A ggplot object showing species counts by duration.

growthhabit_summary

A data frame summarizing species counts by growth habit.

duration_summary

A data frame summarizing species counts by duration.

Examples

# Generate and display plots for growth habit and duration
plots <- species_count_plot()
print(plots$plot_growthhabit)
print(plots$plot_duration)

Visualize Native Range of Species on a Global Map

Description

This function retrieves the native range of one or more species from the ILORA database and plots their distribution on a global map using TDWG Level 2 regions.

Usage

visualize_native_range(species_name)

Arguments

Details

The function joins the native range data from the ILORA database with a TDWG Level 2 shapefile to display species’ native regions. Each species is color-coded, and overlapping ranges are handled to avoid duplicate polygons.

Value

A ggplot2 object showing the native range of the specified species.

Examples

# Visualize native range of specific species
visualize_native_range(c("Cyanus segetum Hill", "Avena sativa L."))

Visualize Naturalized Range of Species on a Global Map

Description

This function retrieves the naturalized range of one or more species from the ILORA database and plots their distribution on a global map using TDWG Level 2 regions.

Usage

visualize_naturalized_range(species_name)

Arguments

Details

The function joins the naturalized range data from the ILORA database with a TDWG Level 2 shapefile to display species’ naturalized regions. Each species is color-coded.

Value

A ggplot2 object showing the naturalized range of the specified species.

Examples

# Visualize naturalized range of specific species
visualize_naturalized_range(c("Cyanus segetum Hill", "Avena sativa L."))