R/plot.mean_comparisons_model_local_foreign.R

In priviere/PPBstats: An R package to perform analysis found within PPB programmes

#' Get ggplot to visualize output from \code{\link{mean_comparisons.check_model_local_foreign}}
#'
#' @description
#' \code{plot.mean_comparisons_model_local_foreign} returns ggplot to visualize outputs from \code{\link{mean_comparisons.check_model_local_foreign}}
#'
#' @param x Output from \code{\link{mean_comparisons.check_model_local_foreign}}
#'
#' @param nb_parameters_per_plot number of parameter per plot to display
#'
#' @param ... further arguments passed to or from other methods
#'
#' @details
#' S3 method.
#' See in the book for mo arere details \href{https://priviere.github.io/PPBstats_book/intro-agro.html#section-freq}{here}
#'
#' @return
#' A list with barplot.
#' For each element of the list, there are as many graph as needed with \code{nb_parameters_per_plot} parameters per graph.
#' Letters are displayed on each bar. Parameters that do not share the same letters are different regarding type I error (alpha) and alpha correction.
#' The error I (alpha) and the alpha correction are displayed in the title.
#' \itemize{
#'  \item version:location : mean comparison for local foreign for each location and year
#'  \item germplasm : mean comparison for germplasm
#'  \item location : mean comparison for location
#'  \item year : mean comparison for year
#'  \item version : mean comparison for local and foreign
#'  }
#'
#' @author Pierre Riviere and Baptiste Rouger
#'
#' @seealso \code{\link{mean_comparisons.check_model_local_foreign}}
#' 
#' @import dplyr
#' @import plyr
#'
#' @export
#'
plot.mean_comparisons_model_local_foreign <- function(
                                                      x,
                                                      nb_parameters_per_plot = 8, ...
                                                      ){
    parameter = means = stars = NULL # to avoid no visible binding for global variable
    
    d = x[["data_ggplot_LSDbarplot_version:location"]]
    d = dplyr::arrange(d, means)
    d$max = max(d$means, na.rm = TRUE)
    d$split = add_split_col(d, nb_parameters_per_plot)
    d_split = plyr:::splitter_d(d, .(split))

    STARS = unique(d[,c("parameter", "stars")])
    y = tapply(d$means, d$parameter, mean, na.rm = TRUE)
    y = y + (max(y) * 0.2)
    STARS$means = y[STARS$parameter]

    fac = "version:location"
    variable = x$info$variable

    out_fac_inter = lapply(d_split, function(dx, fac, STARS, variable){
        p = ggplot(dx, aes(x = parameter, y = means))
        p = p + geom_bar(aes(fill = version), position = "dodge", stat = "identity")
        p = p + geom_text(data = STARS, aes(label = stars))
        p = p + ggtitle(paste(fac, "\n alpha = ", dx[1, "alpha"], "; alpha correction :", dx[1, "alpha.correction"]))
        p = p + xlab("") + theme(axis.text.x = element_text(angle = 90)) + coord_cartesian(ylim = c(0, dx[1,"max"])) + ylab(variable)
        return(p)
    }, fac, STARS, variable)
    names(out_fac_inter) = fac

    out_fac_single = plot_mean_comparisons_freq_anova(x, variable = x$info$variable, nb_parameters_per_plot)

    out = c(out_fac_inter, out_fac_single)
    return(out)
}