Type:	Package
Title:	Companion to R for Plant Disease Epidemiology Book
Version:	0.1.0
Description:	Datasets and utility functions to support the book "R for Plant Disease Epidemiology" (R4PDE). It includes functions for quantifying disease, assessing spatial patterns, and modeling plant disease epidemics based on weather predictors. These tools are intended for teaching and research in plant disease epidemiology. Several functions are based on classical and contemporary methods, including those discussed in Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>.
License:	MIT + file LICENSE
Depends:	R (≥ 4.1.0)
Imports:	boot, car, cowplot, dplyr, ggplot2, igraph, interval, lubridate, nasapower, progress, purrr, rlang, stats, survival, tidyr
Suggests:	testthat, knitr, rmarkdown
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
URL:	https://github.com/emdelponte/r4pde
BugReports:	https://github.com/emdelponte/r4pde/issues
NeedsCompilation:	no
Packaged:	2025-06-27 18:58:21 UTC; emersondelponte
Author:	Emerson Del Ponte [aut, cre]
Maintainer:	Emerson Del Ponte <delponte@ufv.br>
Repository:	CRAN
Date/Publication:	2025-07-02 15:30:01 UTC

Analysis of foci structure and dynamics (AFSD)

Description

This function performs the analysis of a simple method introduced by Nelson (1996) and expanded by Laranjeira et al. (1998). The function assumes the dataframe supplied as input has columns 'x', 'y', and 'i', where 'x' and 'y' are spatial coordinates and 'i' is a disease indicator variable (1 if diseased, otherwise 0). The function performs several steps including filtering rows where 'i' is 1, converting to an adjacency matrix, and creating foci using igraph. It then calculates various statistics about the foci and returns these in a list.

Usage

AFSD(df)

Arguments

Value

A list containing: cluster_summary2: a dataframe summarizing the number and size of foci, and proportions of diseased plants. cluster_df: a dataframe containing foci information, including size and number of rows and columns in each foci. df_clustered: the original dataframe with an added 'focus_id' column, showing which foci each row belongs to.

See Also

Other Spatial analysis: BPL(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

# Generate a sample dataframe
set.seed(123)
df <- data.frame(x = sample(1:100, 500, replace = TRUE),
                 y = sample(1:100, 500, replace = TRUE),
                 i = sample(0:1, 500, replace = TRUE, prob = c(0.7, 0.3)))

# Perform the AFSD
result <- AFSD(df)

Binary Power Law Analysis for Spatial Disease Patterns

Description

This function calculates the Binary Power Law (BPL) parameters for spatial disease patterns, fits a linear model, and performs a hypothesis test for the slope.

Usage

BPL(data)

Arguments

Details

The function performs the following steps:

1. Summarizes the data by field to calculate the total number of observations (n_total), mean incidence (incidence_mean), observed variance (V), and binomial variance (Vbin).

2. Log-transforms the variances.

3. Fits a linear model to the log-transformed variances.

4. Tests the hypothesis that the slope of the linear model is equal to 1.

Value

A list containing the following elements:

• summary: A data frame summarizing the input data by field, including total observations (n_total), mean incidence (incidence_mean), observed variance (V), and binomial variance (Vbin).

• model_summary: A summary of the linear model fitted to the log-transformed variances.

• hypothesis_test: The result of the hypothesis test for the slope being equal to 1.

• ln_Ap: The intercept of the linear model, representing the natural logarithm of the parameter \( A_p \).

• slope: The slope of the linear model.

See Also

Other Spatial analysis: AFSD(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

# Example usage with a sample data frame
result <- BPL(FHBWheat)
print(result$summary)
print(result$model_summary)
print(result$hypothesis_test)
print(paste("ln(Ap):", result$ln_Ap))
print(paste("Slope (b):", result$slope))

BlastWheat dataset

Description

Wheat blast dataset with severity and weather covariates.

Usage

BlastWheat

Format

A data frame with the following columns:

heading

Date of heading

inc_mean

Mean incidence

index_mean

FHB index mean

latitude

Latitude coordinate

location

Experimental site name

longitude

Longitude coordinate

state

Brazilian state

study

Study ID or code

year

Crop year

yld_mean

Mean yield

Source

Del Ponte Lab internal data

BudBlightSoybean dataset

Description

Soybean bud blight incidence in experimental blocks.

Usage

BudBlightSoybean

Format

A data frame with the following columns:

block

Block number

time

Time point of assessment

treat

Treatment name

y

Incidence or severity value

Source

Del Ponte Lab internal data

Survival analysis for quantitative ordinal scale data.

Description

Survival analysis for quantitative ordinal scale data.

Usage

CompMuCens(dat, scale, grade = TRUE, ckData = FALSE)

Arguments

Details

To assist plant pathologists in analyzing quantitative ordinal scale data and encourage the uptake of the interval-censored analysis method, Chiang and collaborators have developed this function and provided comprehensive explanation of the program code used to implement class ratings analyzed through this method in this repository: https://github.com/StatisticalMethodsinPlantProtection/CompMuCens According to results in the paper, the method can be applied to reduce the risk of type II errors when considering quantitative ordinal data, which are widely used in plant pathology and related disciplines.The function starts by converting the data into a censored data format and performs multiple pairwise comparisons to determine significance using the score statistic method.

Value

Returns a list containing the score statistic, hypothesis tests, adjusted significance level, and conclusion based on pairwise comparisons.

References

Chiang, K.S., Chang, Y.M., Liu, H.I., Lee, J.Y., El Jarroudi, M. and Bock, C., 2023. Survival Analysis as a Basis to Test Hypotheses When Using Quantitative Ordinal Scale Disease Severity Data. Phytopathology, in press. Available at: https://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-02-23-0055-R

See Also

Other Disease quantification: DSI(), DSI2()

Examples

# Entering your data as ordinal rating scores
trAs=c(5,4,2,5,5,4,4,2,5,2,2,3,4,3,2,2,6,2,2,4,2,4,2,4,5,3,4,2,2,3)
trBs=c(5,3,2,4,4,5,4,5,4,4,6,4,5,5,5,2,6,2,3,5,2,6,4,3,2,5,3,5,4,5)
trCs=c(2,3,1,4,1,1,4,1,1,3,2,1,4,1,1,2,5,2,1,3,1,4,2,2,2,4,2,3,2,2)
trDs=c(5,5,4,5,5,6,6,4,6,4,3,5,5,6,4,6,5,6,5,4,5,5,5,3,5,6,5,5,5,6)
# Data shaping into input format
inputData = data.frame(treatment=c(rep("A",30),rep("B",30),rep("C",30),
rep("D",30)), x=c(trAs, trBs, trCs, trDs))
# Perform analysis using CompMuCens() function
CompMuCens(dat=inputData, scale=c(0,3,6,12,25,50,75,88,94,97,100,100),ckData=TRUE)

Calculate the Disease Severity Index (DSI) (class for each unit)

Description

This function calculates the Disease Severity Index (DSI) based on the provided unit, class, and maximum class value. The DSI is computed by aggregating the classes, calculating weights by multiplying the frequency of each class by the class itself, and then dividing the sum of these weights by the product of the total number of entries and the maximum class value, then multiplying by 100.

Usage

DSI(unit, class, max)

Arguments

Value

Returns a single numeric value representing the DSI.

See Also

Other Disease quantification: CompMuCens(), DSI2()

Examples

# Example usage:
unit <- c(1, 2, 3, 4, 5, 6)
class <- c(1, 2, 1, 2, 3, 1)
max <- 3
DSI(unit, class, max)

Calculate the Disease severity Index (DSI) (frequency of each class)

Description

This function calculates the Disease Severity Index (DSI) given a vector of classes, a vector of frequencies, and a maximum possible class value. The DSI is calculated as a weighted sum of class values, where each class is multiplied by its corresponding frequency, then divided by the product of the total frequency and maximum class value, and finally multiplied by 100 to get a percentage.

Usage

DSI2(class, freq, max)

Arguments

Value

Returns a single numeric value representing the DSI.

See Also

Other Disease quantification: CompMuCens(), DSI()

Examples

DSI2(c(0, 1, 2, 3, 4), c(2, 0, 5, 0, 5), 4)

DidymellaWatermelon dataset

Description

Assessment of Didymella symptoms in watermelon plots.

Usage

DidymellaWatermelon

Format

A data frame with:

EW_row

Row position (east–west)

NS_col

Column position (north–south)

dap

Days after planting

severity

Disease severity

Source

Del Ponte Lab internal data

FHBWheat dataset

Description

Fusarium head blight quadrat assessments in wheat.

Usage

FHBWheat

Format

A data frame with:

field

Field identifier

i

Row position

n

Column position

quadrat

Quadrat ID

season

Crop season

Source

Del Ponte Lab internal data

FusariumBanana dataset

Description

Observations of Fusarium symptoms in banana fields.

Usage

FusariumBanana

Format

A data frame with:

field

Field ID

lat

Latitude

lon

Longitude

marker

Infection marker presence

Source

Del Ponte Lab internal data

RustSoybean dataset

Description

Soybean rust severity and field metadata.

Usage

RustSoybean

Format

A data frame with:

detection

Detection score or date

epidemia

Epidemic phase

latitude

Latitude

local

Location name

longitude

Longitude

planting

Planting date or stage

severity

Disease severity

Source

Del Ponte Lab internal data

SpatialAggregated dataset

Description

Simulated aggregated spatial binary disease pattern.

Usage

SpatialAggregated

Format

A data frame with:

x

x-coordinate

y

y-coordinate

Source

Simulated example

SpatialRandom dataset

Description

Simulated random spatial binary disease pattern.

Usage

SpatialRandom

Format

A data frame with:

x

x-coordinate

y

y-coordinate

Source

Simulated example

WhiteMoldSoybean dataset

Description

National dataset of white mold severity and yield.

Usage

WhiteMoldSoybean

Format

A data frame with:

country

Country name

elevation

Field elevation

elevation_class

Elevation class

harvest_year

Year of harvest

inc

Incidence

inc_check

Check plot incidence

inc_class

Incidence class

location

Location name

region

Geographical region

scl

Soybean canopy layer

season

Crop season

state

State name

study

Study identifier

treat

Treatment applied

yld

Yield

yld_check

Yield of untreated check

yld_class

Yield class

Source

Del Ponte Lab internal data

Count the Number of Ones in Subareas of a Matrix

Description

This function takes a binary matrix (0s and 1s) and divides it into rectangular subareas, counting the number of ones in each. Subareas are defined by the number of rows and columns specified by the user. If the matrix dimensions are not perfectly divisible by the subarea size, edge subareas may be smaller.

Usage

count_subareas(matrix_data, sub_rows, sub_cols)

Arguments

Value

A matrix where each cell corresponds to a subarea and contains the count of ones.

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

set.seed(123)
mat <- matrix(sample(c(0, 1), 12 * 16, replace = TRUE), nrow = 16, ncol = 12)
count_matrix <- count_subareas(mat, sub_rows = 3, sub_cols = 3)
print(count_matrix)

Random Subgrid Sampling of a Binary Matrix

Description

Randomly samples submatrices (quadrats) of specified size from a binary matrix, and returns the positions, submatrices, and count of 1s in each sampled quadrat.

Usage

count_subareas_random(matrix_data, sub_rows = 3, sub_cols = 3, n_samples = 100)

Arguments

Value

A list of sampled subgrids. Each element is a list with:

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Fit Gradient Models to Data

Description

This function fits three gradient models (exponential, power, and modified power) to given data. It then ranks the models based on their R-squared values and returns diagnostic plots for each model.

Usage

fit_gradients(data, C = 1)

Arguments

Value

A list containing:

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

x <- c(0.8, 1.6, 2.4, 3.2, 4, 7.2, 12, 15.2, 21.6, 28.8)
Y <- c(184.9, 113.3, 113.3, 64.1, 25, 8, 4.3, 2.5, 1, 0.8)
grad1 <- data.frame(x = x, Y = Y)
library(ggplot2)
mg <- fit_gradients(grad1, C = 0.4)
mg$plot_power_original +
  labs(title = "", x = "Distance from focus (m)", y = "Count of lesions")

Fetch NASA POWER Data for Multiple Locations with a Progress Bar

Description

This function downloads daily NASA POWER data for specified weather variables over a specified number of days around a given date column for multiple locations. It includes a progress bar to show the download progress.

Usage

get_nasapower(
  data,
  days_around,
  date_col,
  pars = c("T2M", "RH2M", "PRECTOTCORR", "T2M_MAX", "T2M_MIN", "T2MDEW")
)

Arguments

Details

The function uses the get_power function from the nasapower package to fetch weather data for a range of dates around the specified date column for each location. A progress bar is shown during the data download process, and the results are combined into a single data frame.

Value

A data frame with the downloaded weather data from NASA POWER, combined for all specified locations. Includes a new variable study indicating the study identifier from the input data. Returns an empty data frame if no data is retrieved.

See Also

Other Disease modeling: windowpane()

Test for Spatial Join Count Statistics

Description

The function join_count calculates spatial join count statistics for a binary matrix, identifying patterns of aggregation or randomness.

Usage

join_count(matrix_data, verbose = TRUE)

Arguments

Details

The function conducts an analysis by first counting the occurrence of specific sequences ("01 or 10" and "11" - equivalent to HD and DD) in the binary matrix. It then calculates expected values, standard deviations, and Z-scores to determine the spatial randomness or aggregation. The analysis considers both horizontal and vertical adjacency (rook case) in the matrix.

Value

A comprehensive, rich-text formatted string of results that includes:

• Statistical counts of specific binary sequences (e.g., "01 or 10", "11")

• Expected counts under the assumption of Complete Spatial Randomness (CSR)

• Standard deviations and Z-scores (ZHD for "01 or 10" sequences, ZDD for "11" sequences)

• Interpretation of whether the spatial distribution for each sequence type is "Aggregated" or "Not Aggregated" based on Z-scores

• A summary explaining the implications of these statistics and patterns

The return value aims to provide a clear understanding of the spatial arrangement's characteristics, aiding in further spatial analysis or research.

References

Madden, L. V., Hughes, G., & van den Bosch, F. (2007). The Study of Plant Disease Epidemics. The American Phytopathological Society.

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), fit_gradients(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Runs Test

Description

Perform a runs test on the input data to test for clustering or randomness.

Usage

oruns_test(x)

Arguments

Value

an r4pde.oruns object.

An r4pde.oruns object is a list containing:

• U, number of runs,

• EU, expected number of runs,

• sU, standard deviation of the expected number of runs

• Z, Z-score of the observed number of runs,

• pvalue, the p-value of the Z-score, and

• result, the test result of either "aggregation or clustering" or "randomness"

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

oruns_test(c(1, 0, 1, 1, 0, 1, 0, 0, 1, 1))

Boustrophedon Run Test for Binary Matrix

Description

Applies the ordinary runs test to a binary matrix using boustrophedon-style traversal. The function supports two modes: row-wise and column-wise boustrophedon. Each traversal flattens the matrix into a 1D sequence which is then tested using oruns_test.

Usage

oruns_test_boustrophedon(mat)

Arguments

Value

A list with two elements:

See Also

oruns_test

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_byrowcol(), plot_AFSD()

Runs Test for Each Row and Column of a Binary Matrix

Description

Applies the ordinary runs test to each row and column of a binary matrix individually.

Usage

oruns_test_byrowcol(mat)

Arguments

Value

A list with four elements:

See Also

oruns_test

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), plot_AFSD()

Plot ASFD

Description

This function creates a tile plot of the foci (cluster) identified by the AFSD function. It colors each cell in a foci and labels the centroid of each cluster with the foci ID. The 'ggplot2' package is used for the plot, and will be automatically installed if not already present.

Usage

plot_AFSD(df)

Arguments

Value

A ggplot object with the scatter plot of foci (clusters).

See Also

Other Spatial analysis: AFSD(), BPL(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol()

Examples

df <- data.frame(x = sample(1:100, 500, replace = TRUE),
                 y = sample(1:100, 500, replace = TRUE),
                 i = sample(0:1, 500, replace = TRUE, prob = c(0.7, 0.3)))

# Perform the AFSD
result <- AFSD(df)
# Plot the foci
plot_AFSD(result[[3]])

Custom ggplot2 theme based on cowplot::theme_half_open

Description

This function creates a new ggplot2 theme by modifying the cowplot::theme_half_open theme. It sets a custom font size and changes the panel background color to gray96.

Usage

theme_r4pde(font_size = 16)

Arguments

Value

A ggplot2 theme object.

Window Pane for Epidemiological Analysis

Description

This function calculates summary statistics within specified windows around a given end date in a dataset, facilitating epidemiological analysis. It allows backward, forward, or both directions of window calculations based on a user-defined variable and window lengths.

Usage

windowpane(
  data,
  end_date_col,
  date_col,
  variable,
  summary_type,
  threshold = NULL,
  window_lengths,
  direction = "backward",
  group_by_cols = NULL,
  date_format = "%Y-%m-%d"
)

Arguments

Value

A data frame with the calculated summary values for each window.

See Also

Other Disease modeling: get_nasapower()

Windowpane Tests for Correlation Analysis

Description

This function performs bootstrapped correlation analysis for multiple predictors against a response variable. It applies the Simes method for global significance testing and calculates individual correlations, p-values, and bootstrap statistics.

Usage

windowpane_tests(
  data,
  response_var,
  corr_type = "spearman",
  R = 1000,
  global_alpha = 0.05,
  individual_alpha = 0.005
)

Arguments

Details

The function calculates correlations between the response variable and each predictor in the data frame, using bootstrapping to generate mean, standard deviation, and median estimates of the correlation. The Simes correction is applied to control for multiple testing, providing a global p-value (Pg). The function also returns the maximum observed correlation.

Value

A list containing the following elements: