dvnorm_paper_BW.R

###nmprep.r
##Goal: To collate tables of missing data contained within nonclinical raw data obtained on 23rd March 2016
##Note: Based heavily off of datacheck_cyt_script2.r -> Richards code

# Remove any previous objects in the workspace
  rm(list=ls(all=TRUE))
  graphics.off()

# Set the working directory
  master.dir <- "E:/Hughes/Data"
  scriptname <- "nmprep_clin"
  setwd(master.dir)

# Load libraries
  library(ggplot2)
  library(doBy)
  library(Hmisc)
  library(plyr)
  library(grid)
  library(reshape)
  library(stringr)
  library(scales)

# Source utility functions file
  source("E:/Hughes/functions_utility.r")

# Customize ggplot2 theme - R 2.15.3
  theme_bw2 <- theme_set(theme_bw(base_size = 22))
  theme_bw2 <- theme_update(plot.margin = unit(c(1, 0.5, 3, 0.5), "lines"),
    axis.title.x = element_text(size = 18, vjust = 0),
    axis.title.y = element_text(size = 18, vjust = 0, angle = 90),
    strip.text.x = element_text(size = 16),
    strip.text.y = element_text(size = 16, angle = 90),
    legend.title = element_text(size = 18),
    legend.text = element_text(size = 16))

# Organise working and output directories
  working.dir <- paste(master.dir,"RAW_Clinical",sep="/")
  workspacefilename <- paste(getwd(),"/",scriptname,".RData", sep="")

  output.dir <- paste(working.dir,"/",scriptname,"_Output",sep="")
  if(!file.exists(output.dir)){
	dir.create(output.dir)
  }

  filename <- paste(output.dir,"nmprep_flagged.csv",sep="/")
  nmprep <- read.csv(filename, na.strings = ".")


  locf <- function (x) {
  #Last observation carried forward
  #Finds an NA and carries forward the previous value
    good <- !is.na(x)
    positions <- seq(length(x))
    good.positions <- good * positions
    last.good.position <- cummax(good.positions)
    last.good.position[last.good.position == 0] <- NA
    x[last.good.position]
  }

  nmprep$DOSE <- locf(nmprep$AMT)
  nmprep$DVNORM <- nmprep$DV/nmprep$DOSE

  bin_cuts <- c(0.52, 1.02, 2.02, 3.02, 5.02, 8.02, 49)
  nmprep$TADBIN <- cut2(nmprep$TAD, cuts = bin_cuts, levels.mean = T)
  levels(nmprep$TADBIN)[length(bin_cuts)] <- 24
  nmprep$TADBIN <- as.numeric(paste(nmprep$TADBIN))

  dose_bins <- c(8, 26, 80)
  nmprep$DOSEf <- cut2(nmprep$DOSE, cuts = dose_bins)
  levels(nmprep$DOSEf) <- c("<10mg", "10-25mg", ">25mg")

  p <- NULL
  p <- ggplot(aes(x = TAD, y = DVNORM*1000), data = nmprep)
  p <- p + geom_point(colour = "#999999", shape = 1)
  p <- p + stat_summary(aes(x = TADBIN, y = DVNORM*1000, linetype = DOSEf), fun.y = median,
    geom = "line", size = 1.2)
  p <- p + scale_y_log10("Dose Normalised Concentrations (ng/mL)\n", labels = comma)
  p <- p + xlab("\nTime After Last Dose (hours)")
  p

  ggsave("dvnormplotBW.png", width = 17.4, height = 15.7, units = c("cm"))
  ggsave("dvnormplotBW.eps", width = 17.4, height = 15.7, units = c("cm"),
    dpi = 1200, device = cairo_ps, fallback_resolution = 1200)