#!/usr/bin/Rscript   
getwd()
setwd("~/git/mosaic_2020/")
getwd()
############################################################
# TASK: boxplots with stats
############################################################
doMyPlots <- function(df) {
    mediators = levels(as.factor(df$mediator))
    
    plots <- list()
    
    for (i in mediators) {
      cat("Plotting:", i, "\n")
      single=df[df$mediator==i,]
      
      max_y = max(single$value, na.rm = T)
      cat("Plotting:", i, "max_y:", max_y, "\n")
    
      #----------
      # boxplot
      #----------
      p = ggplot(single)+ geom_boxplot(aes(x = timepoint
                                           , y = value
                                           , color = obesity
                                           #, palette = c("#00BFC4", "#F8766D")
                                           ))+
        scale_colour_manual(values=c("#00BFC4", "#F8766D")) + 
        scale_y_log10()
      theme(axis.text.x = element_text(size = 15)
            , axis.text.y = element_text(size = 15
                                         , angle = 0
                                         , hjust = 1
                                         , vjust = 0)
            , axis.title.x = element_blank()
            , axis.title.y = element_blank()
            , legend.position = "none"
            , plot.subtitle = element_text(size = 20, hjust = 0.5)
            , plot.title = element_text(size = 20, hjust = 0.5)) +
        labs(title = i)
      
      #--------
      # stats
      #---------
      stat_df <- single %>%
        group_by(timepoint, mediator) %>%
        wilcox_test(value ~ obesity, paired = F) %>%
        add_significance("p")
      stat_df$p_format = round(stat_df$p, digits = 3)
      
      stat_df <- stat_df %>%
        add_xy_position(x = "timepoint", dodge = 0.8)
      cat("printing p")
      p2 = p + stat_pvalue_manual(stat_df
                                   #, y.position = max_y
                                   , label = "{p_format} {p.signif}"
                                   , hide.ns = T
                                   , tip.length = 0)+
        scale_y_continuous(expand = expansion(mult = c(0.05, 0.25)))
      plots[[i]] <- p2
      
    }
    return(plots)
  }

###############################################################
#============
# test
#============
source("boxplot_linear.R")
#=============
# Output:
#=============
outfile_bp = paste0("boxplots_TEST", ".pdf")
output_boxplot_stats = paste0(outdir_plots, outfile_bp); output_boxplot_stats
#pdf(output_boxplot_stats, width=22, height=16)

med_names = c("eotaxin3", "il12p70", "itac", "il13")
lf_test = lf_fp_npa[lf_fp_npa$mediator%in%med_names,]

foo=doMyPlots(lf_test)
#baz=cowplot::plot_grid(plotlist=foo, align = 'hv', ncol=2, nrow=2)
baz
#dev.off()
##################################################################
#=========================
# entire sample_type data
#=========================
my_sample_type = "NPA"
outfile_bp = paste0("boxplots_stats_", my_sample_type, ".pdf")
output_boxplot_stats = paste0(outdir_plots, outfile_bp); output_boxplot_stats
pdf(output_boxplot_stats, width=22, height=16)

#===========================
# combine: ggpubr::ggarrange
#===========================
plots_npa = doMyPlots(lf_fp_npa)
npa_plot = ggpubr::ggarrange(plotlist = plots_npa
                             , align = "hv"
                             , ncol = 7
                             , nrow = 5
                             , common.legend = T)
#npa_plot
npa_plot_annot = annotate_figure(npa_plot
                , top = text_grob(my_sample_type, color = "purple", face = "bold", size = 14)
                , bottom = text_grob("Mosaic data\nFlu positive adults (n=133)"
                                   , color = "blue"
                                   , hjust = 1, x = 0.98, face = "italic", size = 10)
                , left = text_grob("Levels (pg/ml)", color = "black", rot = 90, size = 18)
                #, right = "I'm done, thanks :-)!",
                #, fig.lab = "Figure 1", fig.lab.face = "bold"
)
npa_plot_annot
dev.off()