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#' plot_extreme
#'
#' Graphs a line or bar plot of a row with the highest metric in a matrix, produced by
#' \code{\link{daily_response}} or \code{\link{monthly_response}} functions. Bar plot is
#' drawn for monthly_response(), while for daily_response, line plot is produced.
#'
#' @param result_daily_response a list with three objects as produced by
#' daily_response function
#' @param title logical, if set to FALSE, no plot title is displayed
#' @param ylimits limit of the y axes. It should be given as ylimits = c(0,1)
#' @param reference_window character string, the reference_window argument describes,
#' how each calculation is referred. There are three different options: 'start'
#' (default), 'end' and 'middle'. If the reference_window argument is set to 'start',
#' then each calculation is related to the starting day of window. If the
#' reference_window argument is set to 'middle', each calculation is related to the
#' middle day of window calculation. If the reference_window argument is set to
#' 'end', then each calculation is related to the ending day of window calculation.
#' For example, if we consider correlations with window from DOY 15 to DOY 35. If
#' reference window is set to 'start', then this calculation will be related to the
#' DOY 15. If the reference window is set to 'end', then this calculation will be
#' related to the DOY 35. If the reference_window is set to 'middle', then this
#' calculation is related to DOY 25.
#' @param type the character string describing type of analysis: daily or monthly
#'
#' @return A ggplot2 object containing the plot display
#'
#' @examples
#' \donttest{
#' data(LJ_daily_temperatures)
#' data(example_proxies_1)
#' Example1 <- daily_response(response = example_proxies_1,
#' env_data = LJ_daily_temperatures, method = "lm", metric = "r.squared",
#' fixed_width = 90, previous_year = TRUE, row_names_subset = TRUE)
#' # plot_extreme(Example1)
#'
#' Example2 <- daily_response(response = example_proxies_1,
#' env_data = LJ_daily_temperatures, method = "brnn",
#' metric = "adj.r.squared", lower_limit = 50, upper_limit = 55, neurons = 1,
#' row_names_subset = TRUE, previous_year = TRUE)
#' # plot_extreme(Example2)
#'
#' # Example with negative correlations
#' data(data_TRW_1)
#' LJ_daily_temperatures_subset = LJ_daily_temperatures[-c(53:55), ]
#' Example3 <- daily_response(response = data_TRW_1,
#' env_data = LJ_daily_temperatures_subset, method = "lm", metric = "adj.r.squared",
#' lower_limit = 35, upper_limit = 40, previous_year = TRUE, row_names_subset = TRUE)
#' # plot_extreme(Example3)
#'
#' Example4 <- daily_response(response = example_proxies_1,
#' env_data = LJ_daily_temperatures, method = "lm",
#' metric = "r.squared", lower_limit = 30, upper_limit = 120, neurons = 1,
#' row_names_subset = TRUE, previous_year = TRUE)
#' # plot_extreme(Example4)
#' }
#'
#' @keywords internal
plot_extreme <- function(result_daily_response, title = TRUE, ylimits = NULL, reference_window = "start", type = "daily") {
# Short description of the function. It
# - extracts matrix (the frst object of a list)
# - in case of method == "cor" (second object of a list), calculates the
# highest minimum and maximum and compare its absolute values. If absolute
# minimum is higher than maximum, we have to plot minimum correlations.
# - query the information about windows width (row names of the matrix) and
# starting day of the highest (absolute) metric (column names of the matrix).
# - draws a ggplot line plot.
# This needs to be set to provide results in English language
Sys.setlocale("LC_TIME", "C")
# A) Extracting a matrix from a list and converting it into a data frame
result_daily_element1 <- data.frame(result_daily_response[[1]])
# Do we have monthly or daily data?
type <- type
# GLobl variable
final_list <- NULL
# With the following chunk, overall_maximum and overall_minimum values of
# result_daily_element1 matrix are calculated.
overall_max <- max(result_daily_element1, na.rm = TRUE)
overall_min <- min(result_daily_element1, na.rm = TRUE)
# absolute vales of overall_maximum and overall_minimum are compared and
# one of the following two if functions is used
# There are unimportant warnings produced:
# no non-missing arguments to max; returning -Inf
# Based on the answer on the StackOverlow site:
# https://stackoverflow.com/questions/24282550/no-non-missing-arguments-warning-when-using-min-or-max-in-reshape2
# Those Warnings could be easily ignored
if ((abs(overall_max) >= abs(overall_min)) == TRUE) {
# maximum value is located. Row indeces are needed to query information
# about the window width used to calculate the maximum. Column name is
# needed to query the starting day.
max_result <- suppressWarnings(which.max(apply(result_daily_element1,
MARGIN = 2, max, na.rm = TRUE)))
plot_column <- max_result
plot_column_source <- plot_column
max_index <- which.max(result_daily_element1[, names(max_result)])
row_index <- row.names(result_daily_element1)[max_index]
temporal_vector <- unlist(result_daily_element1[max_index, ])
temporal_vector <- data.frame(temporal_vector)
calculated_metric <- round(max(temporal_vector, na.rm = TRUE), 3)
# Here we remove missing values at the end of the temporal_vector.
# It is important to remove missing values only at the end of the
# temporal_vector!
row_count <- nrow(temporal_vector)
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count - 1
}
# To check if the last row is a missing value
if (is.na(temporal_vector[nrow(temporal_vector), ] == TRUE)) {
temporal_vector <- temporal_vector[-c((row_count + 1):(row_count +
delete_rows)), ]
}
temporal_vector <- data.frame(temporal_vector)
}
if ((abs(overall_max) < abs(overall_min)) == TRUE) {
# minimum value is located. Row indeces are needed to query information
# about the window width used to calculate the minimum. Column name is
# needed to query the starting day.
min_result <- suppressWarnings(which.min(apply(result_daily_element1,
MARGIN = 2, min, na.rm = TRUE)))
plot_column <- min_result
plot_column_source <- plot_column
min_index <- which.min(result_daily_element1[, names(min_result)])
row_index <- row.names(result_daily_element1)[min_index]
temporal_vector <- unlist(result_daily_element1[min_index, ])
temporal_vector <- data.frame(temporal_vector)
calculated_metric <- round(min(temporal_vector, na.rm = TRUE), 3)
# Here we remove missing values
# We remove missing values at the end of the temporal_vector.
# It is important to remove missing values only at the end of the
# temporal_vector!
row_count <- nrow(temporal_vector)
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count - 1
}
# To check if the last row is a missing value
if (is.na(temporal_vector[nrow(temporal_vector), ] == TRUE)) {
temporal_vector <- temporal_vector[-c((row_count + 1):(row_count +
delete_rows)), ]
}
temporal_vector <- data.frame(temporal_vector)
}
# In case of previous_year == TRUE, we calculate the day of a year
# (plot_column), considering 366 days of previous year.
if (ncol(result_daily_element1) > 366 & plot_column > 366) {
plot_column_extra <- plot_column %% 366
} else {
plot_column_extra <- plot_column
}
# The final plot is being created. The first part of a plot is the same,
# the second part is different, depending on temporal.vector, plot_column,
# method and metric string stored in result_daily_response. The second part
# defines xlabs, xlabs and ggtitles.
# The definition of theme
journal_theme <- theme_bw() +
theme(axis.text = element_text(size = 16, face = "bold"),
axis.title = element_text(size = 18), text = element_text(size = 18),
plot.title = element_text(size = 16, face = "bold"))
if (title == FALSE){
journal_theme <- journal_theme +
theme(plot.title = element_blank())
}
# Here we define a data frame of dates and corresponing day of year (doi). Later
# this dataframe will be used to describe tht optimal sequence of days
if (ncol(result_daily_element1) < 367){
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes <- data.frame(doy = doy, date = date)
} else {
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes <- data.frame(doy = doy, date = date)
date_codes$date <- paste0(date_codes$date, "*")
doy <- seq(1:366)
date <- seq(as.Date('2013-01-01'),as.Date('2013-12-31'), by = "+1 day")
date[366] <- as.Date('2015-12-31')
date <- format(date, "%b %d")
date_codes2 <- data.frame(doy = doy, date = date)
date_codes <- rbind(date_codes, date_codes2)
}
# Here, there is a special check if optimal window width is divisible by 2 or not.
if (as.numeric(row_index)%%2 == 0){
adjustment_1 = 0
adjustment_2 = 1
} else {
adjustment_1 = 1
adjustment_2 = 2
}
if (reference_window == "start"){
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[plot_column_source, 2]),"-",
as.character(date_codes[plot_column_source + as.numeric(row_index) - 1, 2]))
} else if (reference_window == "end") {
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[plot_column_source - as.numeric(row_index) + 1, 2]),"-",
as.character(date_codes[plot_column_source, 2]))
} else if (reference_window == "middle") {
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[(round2((plot_column_source - as.numeric(row_index)/2)) - adjustment_1), 2]),"-",
as.character(date_codes[(round2((plot_column_source + as.numeric(row_index)/2)) - adjustment_2), 2]))
}
# in the next chunk, warnings are supressed. At the end of the vector,
# there are always missing values, which are a result of changing window
# width calclulations. Those warnings are not important and do not affect
# our results at all
# Function to find the longest non-NA range
max_length <- 0
current_length <- 0
for (value_temp in temporal_vector[,1]) {
if (!is.na(value_temp)) {
current_length <- current_length + 1
} else {
if (current_length > max_length) {
max_length <- current_length
}
current_length <- 0
}
}
# Check at the end in case the longest sequence is at the end of the vector
longest_sequence <- max(max_length, current_length)
if (longest_sequence > 1){
final_plot <- suppressWarnings(
ggplot(temporal_vector, aes(y = temporal_vector,
x = seq(1, length(temporal_vector)))) + geom_line(linewidth = 1.2) +
geom_vline(xintercept = plot_column, col = "red") +
scale_x_continuous(breaks = sort(c(seq(0, nrow(temporal_vector), 50)), decreasing = FALSE),
labels = sort(c(seq(0, nrow(temporal_vector), 50)))) +
scale_y_continuous(limits = ylimits) +
annotate("label", label = as.character(calculated_metric),
y = calculated_metric, x = plot_column + 15) +
annotate("label", label = paste("Day", as.character(plot_column), sep = " "),
y = min(temporal_vector, na.rm = TRUE) + 0.2*min(temporal_vector, na.rm = TRUE), x = plot_column + 15) +
journal_theme)
} else {
final_plot <- suppressWarnings(
ggplot(temporal_vector, aes(y = temporal_vector,
x = seq(1, length(temporal_vector)))) + geom_point() +
geom_vline(xintercept = plot_column, col = "red") +
scale_x_continuous(breaks = sort(c(seq(0, nrow(temporal_vector), 50)), decreasing = FALSE),
labels = sort(c(seq(0, nrow(temporal_vector), 50)))) +
scale_y_continuous(limits = ylimits) +
annotate("label", label = as.character(calculated_metric),
y = calculated_metric, x = plot_column + 15) +
annotate("label", label = paste("Day", as.character(plot_column), sep = " "),
y = min(temporal_vector, na.rm = TRUE) + 0.2*min(temporal_vector, na.rm = TRUE), x = plot_column + 15) +
journal_theme)
}
# If previous_year = TRUE, we add a vertical line with labels of
# previous and current years
if (ncol(result_daily_element1) > 366) {
final_plot <- final_plot +
annotate(fontface = "bold", label = 'Previous Year', geom = 'label',
x = 366 - ncol(result_daily_element1) / 12.8,
y = calculated_metric - (calculated_metric/5)) +
annotate(fontface = "bold", label = 'Current Year', geom = 'label',
x = 366 + ncol(result_daily_element1) / 13.5,
y = calculated_metric -(calculated_metric/5)) +
geom_vline(xintercept = 366, size = 1)
}
# Here we define titles. They differ importantly among methods and arguments
# in the final output list from daily_response() function
if (result_daily_response[[2]] == "cor"){
y_lab <- "Correlation Coefficient"
} else if (result_daily_response[[2]] == "pcor"){
y_lab <- "Partial Correlation Coefficient"
} else if (result_daily_response[[3]] == "r.squared"){
y_lab <- "Explained Variance"
} else if (result_daily_response[[3]] == "adj.r.squared"){
y_lab <- "Adjusted Explained Variance"
}
if (ncol(result_daily_element1) > 366){
x_lab <- "Day of Year (Including Previous Year)"
} else if (ncol(result_daily_element1) <= 366){
x_lab <- "Day of Year"
}
if (reference_window == 'start' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nStarting Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'start' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nStarting Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'start' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("\nStarting Day of Optimal Window Width: Day ",
plot_column_extra)}
if (reference_window == 'end' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nEnding Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'end' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nEnding Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'end' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("\nEnding Day of Optimal Window Width: Day ",
plot_column_extra)}
if (reference_window == 'middle' && plot_column > 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nMiddle Day of Optimal Window Width: Day ",
plot_column_extra, " of Current Year")}
if (reference_window == 'middle' && plot_column <= 366 && ncol(result_daily_element1) > 366){
reference_string <- paste0("\nMiddle Day of Optimal Window Width: Day ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'middle' && plot_column <= 366 && ncol(result_daily_element1) <= 366){
reference_string <- paste0("\nMiddle Day of Optimal Window Width: Day ",
plot_column_extra)}
optimal_window_string <- paste0("\nOptimal Window Width: ", as.numeric(row_index),
" Days")
optimal_calculation <- paste0("\nThe Highest ", y_lab,": " , calculated_metric)
period_string <- paste0("Analysed Period: ", result_daily_response[[4]])
if (result_daily_response[[2]] == 'cor'){
method_string <- paste0("\nMethod: Correlation Coefficient (", result_daily_response[[3]], ")")
} else if (result_daily_response[[2]] == 'pcor'){
method_string <- paste0("\nMethod: Partial Correlation Coefficient (", result_daily_response[[3]], ")")
} else if (result_daily_response[[2]] == 'lm'){
method_string <- paste0("\nMethod: Linear Regression")
} else if (result_daily_response[[2]] == 'brnn'){
method_string <- paste0("\nMethod: ANN with Bayesian Regularization")
}
final_plot <- final_plot +
ggtitle(paste0(period_string, method_string, optimal_calculation,
optimal_window_string, reference_string, Optimal_string)) +
xlab(x_lab) +
ylab(y_lab)
if (type == "monthly"){
if (reference_window == "start"){
x_lab_reference <- "Starting"
} else if (reference_window == "end") {
x_lab_reference <- "Ending"
}
# Plural or singular?
if (as.numeric(row_index) == 1){
month_string <- " Month"
} else {
month_string <- " Months"
}
# In case of previous_year == TRUE, we calculate the day of a year
# (plot_column), considering 366 days of previous year.
if (ncol(result_daily_response[[1]]) >= 12 & plot_column > 12) {
plot_column_extra <- plot_column %% 12
} else {
plot_column_extra <- plot_column
}
if (reference_window == 'start' && plot_column > 12 && ncol(result_daily_response[[1]]) <= 24){
reference_string <- paste0("\nStarting Month of Optimal Window Width: Month ",
plot_column_extra, " of Current Year")}
if (reference_window == 'start' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 24){
reference_string <- paste0("\nStarting Month of Optimal Window Width: Month ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'start' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 12){
reference_string <- paste0("\nStarting Month of Optimal Window Width: Month ",
plot_column_extra)}
if (reference_window == 'end' && plot_column > 12 && ncol(result_daily_response[[1]]) <= 24){
reference_string <- paste0("\nEnding Month of Optimal Window Width: Month ",
plot_column_extra, " of Current Year")}
if (reference_window == 'end' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 24){
reference_string <- paste0("\nEnding Month of Optimal Window Width: Month ",
plot_column_extra, " of Previous Year")}
if (reference_window == 'end' && plot_column <= 12 && ncol(result_daily_response[[1]]) <= 12){
reference_string <- paste0("\nEnding Month of Optimal Window Width: Month ",
plot_column_extra)}
optimal_window_string <- paste0("\nOptimal Window Width: ", as.numeric(row_index),
month_string)
# Here we define a data frame of months. Later
# this dataframe will be used to describe tht optimal sequence of days
if (ncol(result_daily_response[[1]]) > 12){
date_codes <- c("Jan*", "Feb*", "Mar*", "Apr*", "May*", "Jun*", "Jul*", "Aug*", "Sep*", "Oct*", "Nov*", "Dec*",
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
} else if (ncol(result_daily_response[[1]]) <= 12){
date_codes <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
}
if (reference_window == "start"){
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[plot_column_source]),"-",
as.character(date_codes[plot_column_source + as.numeric(row_index) - 1]))
} else if (reference_window == "end") {
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[plot_column_source - as.numeric(row_index) + 1]),"-",
as.character(date_codes[plot_column_source]))
} else if (reference_window == "middle") {
Optimal_string <- paste("\nOptimal Selection:",
as.character(date_codes[(round2((plot_column_source - as.numeric(row_index)/2)) - adjustment_1)]),"-",
as.character(date_codes[(round2((plot_column_source + as.numeric(row_index)/2)) - adjustment_2)]))
}
if (as.numeric(row_index == 1)){
Optimal_string <- substr(Optimal_string, 1, nchar(Optimal_string)-6)
}
if ((plot_column_extra) > 12){
}
if (ncol(result_daily_response[[1]]) <= 12){
window_widths <- seq(1, length(temporal_vector))
row_count <- 1
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count + 1
}
color_values <- rep("grey50", sum(!is.na(temporal_vector)))
color_values[ plot_column - row_count + 1] <- "red"
months <- c("J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D")
final_plot <- suppressWarnings(
ggplot(temporal_vector, aes(y = temporal_vector,
x = seq(1, length(temporal_vector)))) +
geom_col() +
geom_hline(yintercept = 0) +
scale_x_continuous(breaks = sort(c(seq(1, 12, 1)), decreasing = FALSE),
labels = months) +
scale_y_continuous(limits = ylimits) +
annotate("label", label = as.character(calculated_metric),
y = calculated_metric, x = plot_column) +
xlab(paste0(x_lab_reference, " Month of Calculation and ",as.character(as.numeric(row_index)) ," Consecutive", month_string)) +
ylab(y_lab) +
ggtitle(paste0(period_string, method_string, optimal_calculation,
optimal_window_string, reference_string, Optimal_string)) +
journal_theme)
} else if (ncol(result_daily_response[[1]]) > 12){
months <- c("J*", "F*", "M*", "A*", "M*", "J*", "J*", "A*", "S*", "O*", "N*", "D*",
"J", "F", "M", "A", "M", "J", "J", "A", "S", "O", "N", "D")
window_widths <- seq(1, nrow(temporal_vector))
row_count <- 1
delete_rows <- 0
while (is.na(temporal_vector[row_count, ] == TRUE)){
delete_rows <- delete_rows + 1
row_count <- row_count + 1
}
color_values <- rep("grey50", sum(!is.na(temporal_vector)))
color_values[ plot_column - row_count + 1] <- "red"
final_plot <- suppressWarnings(
ggplot(temporal_vector, aes(y = temporal_vector, x = seq(1, length(temporal_vector)))) +
geom_col() +
scale_x_continuous(breaks = sort(c(seq(1, 24, 1)), decreasing = FALSE),
labels = months) +
annotate("label", label = as.character(calculated_metric), y = calculated_metric, x = plot_column) +
xlab(paste0(x_lab_reference, " Month of Calculation (Including Previous Year) and ",as.character(as.numeric(row_index)) ,
" Consecutive", month_string)) +
ylab(y_lab) +
scale_fill_discrete(guide = 'none') +
geom_hline(yintercept = 0) +
scale_y_continuous(limits = ylimits) +
ggtitle(paste0(period_string, method_string, optimal_calculation,
optimal_window_string, reference_string, Optimal_string)) +
journal_theme)
}
}
final_plot
}
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