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# TODO: summary methods for single and list
#
# This file contains common methods (plot/print/summary/as.data.frame) used for
# generic indicators objects (without significance assessment).
#
# Plot methods
# --------------------------------------------------
# NOTE: we do not document the args as they are already included by another
# function in the generic_spews doc file
#
#' @rdname generic_spews
#'
#' @param x A \code{generic_spews} object (as provided by the
#' \code{generic_spews} function).
#'
#' @param along A vector providing values over which the indicator trend
#' will be plotted. If \code{NULL} then the values are plotted sequentially
#' in their original order.
#'
#' @details Note that the produced plot is adjusted depending on whether
#' \code{along} is numeric or not.
#'
#'
#' @method plot generic_spews
#' @export
plot.generic_spews <- function(x, along = NULL, ...) {
if ( 'generic_spews_single' %in% class(x) ) {
stop('I cannot plot a trend with only one value !')
}
new_data <- as.data.frame(x)
plot.generic_spews_test(new_data, along, display_null = FALSE)
}
# Print and Summary methods
# --------------------------------------------------
# This function works for both list and single object
#'@export
summary.generic_spews <- function(object, ...) {
cat('Generic Spatial Early-Warnings\n')
cat('\n')
display_size_info(object)
cat('\n')
# Format output table
output <- as.data.frame(object)
output <- reshape2::dcast(output, replicate ~ indicator, value.var = 'value')
names(output) <- c('Mat. #', 'Mean', 'Moran\'s I', 'Skewness', 'Variance')
print.data.frame(output, row.names = FALSE, digits = DIGITS)
cat('\n')
cat('Use as.data.frame() to retrieve values in a convenient form\n')
}
# Print is currently identical to summary()
#'@export
print.generic_spews <- function(x, ...) {
summary.generic_spews(x, ...)
}
# As data.frame methods
# --------------------------------------------------
#'@export
as.data.frame.generic_spews_list <- function(x, ...) {
df <- plyr::ldply(x, function(x) { as.data.frame(x[['results']]) })
df[ ,'replicate'] <- seq.int(length(x))
# Extract and reorder the data.frame
df <- df[ ,c('replicate', 'mean', 'moran', 'skewness', 'variance')]
tidyr::gather_(df, 'indicator', 'value',
c('mean', 'moran', 'skewness', 'variance'),
factor_key = TRUE)
}
#'@export
as.data.frame.generic_spews_single <- function(x, ...) {
as.data.frame.generic_spews_list(list(x))
}
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