R/lipidPlot.R

In GitShepard1/lipidtools:

#' @title plots lipidonomic data based on specifications set by parse_raw and summarise_lipids functions
#'
#' @description  This combines head with a number of columns
#' @param object takes an object generated by summarise_lipids function
#' @import ggplot2
#' @import tidyverse
#' @import purrr
#' @import magrittr
#' @export lipidPlot
#' @return NULL

lipidPlot = function(object){

  add_scale = function(scaling_factor=1.35){

    if (object@yvariable == "mean_sum"){

      ymin = 0; ymax = max(object@treatSummary$mean_sum) * scaling_factor
      scale_y_continuous(expand = c(0, 0), limits = c(0,ymax))

    } else if (object@yvariable == "diff"){

      scale_y_continuous()

    } else if (object@yvariable == "percent"){

      scale_y_continuous(expand = c(0, 0),limits = c(0, 1))
    }

  }
  add_errorbars = function(){

    geom_errorbar(
      data = object@treatSummary,
      aes(ymin = mean_sum - se_sum, ymax = mean_sum + se_sum),
      width = 0.3,
      size = 0.5)

  }
  add_lines = function(){

    len = length(object@groupSummary$treat %>% unique)
    if(len%%2 == 0){


      lapply(seq(len/6 + 0.5, len*2 + 0.5, len*2 / 12),
             function(x) geom_vline(aes(xintercept=x), linetype=2, alpha=0.2) )

    } else {

      return(NULL)

    }

  }
  add_statistics = function(){

    if(length(object@treatSummary$TREATMENT %>% unique) == 2){

      (object@treatSummary %>%
         group_by(REGION) %>%
         filter(mean_sum == max(mean_sum)) %>%
         left_join(object@results, by = 'REGION') %>%
         cbind(xposition = seq(1, nrow(object@treatSummary),2)) %>%
         dplyr::mutate(geom = map2(xposition, stars, function(i,s) {

           yposition = (mean_sum + se_sum)*1.1

           list(
             geom_path(data = data.frame(x=c(rep(i,2), rep(i+1,2)),
                                         y=c(rep(yposition, 4))),
                       size = 1,
                       aes(x=x,y=y)),

             annotate("text",
                      x=xposition + 0.5,
                      y=yposition + 35,

                      label=s)
           )

         })))$geom

    } else {

      NULL

    }



  }
  saturation_legend = function(position = c(0.9,0.8), justification = c(1,-0.1)){
    theme(legend.justification=justification,
          legend.position=position,
          axis.text.x=element_blank(),
          axis.text.y=element_blank(),
          axis.ticks=element_blank(),
          axis.title.x=element_blank(),
          axis.title.y=element_blank())
  }


  generate_plot = function(){

    object@treatSummary %>%
      ggplot(aes(get(object@xvariable), mean_sum)) +
      geom_bar(
        data = object@groupSummary,
        aes(x = get(object@xvariable), y = get(object@yvariable), fill = reorder(FILL_BY, ORDER)),
        stat = "identity"
      ) +
      theme_bw() +
      theme(panel.grid.major = element_blank(),
            panel.grid.minor = element_blank(),
            panel.background = element_rect(colour = "black", size=1),
            legend.title=element_blank()
      ) +
      labs(x = '', y = object@ytitle) +
      scale_x_discrete(limits = object@xorder) +
      scale_fill_manual(values = object@colours) +
      add_scale() +
      { if(object@yvariable == 'mean_sum'){ # START mean_sum block
        if(object@grouping == "CHAIN"){
          list(
            add_errorbars(),
            add_lines(),
            add_statistics()
          )
        } else if (object@grouping == "SATURATION"){
          list(
            add_scale(),
            add_errorbars(),
            saturation_legend()
          )
        }

      } else if(object@yvariable == 'diff' & object@grouping == "SATURATION"){

        saturation_legend()

      }
      }

  }


  return(generate_plot())

}