R/screenIsotopeData3sources.R

In clquezada/tRophicPosition: Bayesian Trophic Position Estimation with Stable Isotopes

# Internal function that plots the data
#
# This function is intended to be used within the package. For details refer to
# \code{\link{screenIsotopeData}}. This function plots 3 baselines coupled with
# the siar version of tRophicPosition.
#
# @param isotopeData a named list composed at least of 6 vectors, dNb1, dCb1,
# dCc, dNc, deltaN and deltaC.
# @param density a character that might plot the density function.
#
#

screenIsotopeData3sources <- function (isotopeData = NULL, density = "both" ) {

  #library(RColorBrewer)
  #library(ggplot2)
  #library(gridExtra)
  #library(ggExtra)

  # Since this function is called from inside the package, we already checked
  # that IsotopeData is not null. But still we check this in case we decide the
  # user can use this function from outside.
  if (!is.null(isotopeData)){

    #First we create a dataframe, reordering the IsotopeData
    #Check the class of IsotopeData
    #if (class(IsotopeData))
    df <- toStacked(isotopeData, baselines = 3)

    #And now we calculate mean and standard deviation for baselines and consumer
    df.b1 <- df[which(df$Factor=="Baseline 1"),c("d13C","d15N")]
    a <- lapply(df.b1, mean)
    names(a) <- c("mean_d13C", "mean_d15N")
    b <- lapply(df.b1, stats::sd)
    names(b) <- c("sd_d13C", "sd_d15N")
    b1.meansSDs <- data.frame(a, b, Factor="Baseline 1")

    df.b2 <- df[which(df$Factor=="Baseline 2"),c("d13C","d15N")]
    a <- lapply(df.b2, mean)
    names(a) <- c("mean_d13C", "mean_d15N")
    b <- lapply(df.b2, stats::sd)
    names(b) <- c("sd_d13C", "sd_d15N")
    b2.meansSDs <- data.frame(a, b, Factor="Baseline 2")

    df.b3 <- df[which(df$Factor=="Baseline 3"),c("d13C","d15N")]
    a <- lapply(df.b3, mean)
    names(a) <- c("mean_d13C", "mean_d15N")
    b <- lapply(df.b3, stats::sd)
    names(b) <- c("sd_d13C", "sd_d15N")
    b3.meansSDs <- data.frame(a, b, Factor="Baseline 3")

    df.sc <- df[which(df$Factor=="Secondary consumer"),c("d13C","d15N")]
    a <- lapply(df.sc, mean)
    names(a) <- c("mean_d13C", "mean_d15N")
    b <- lapply(df.sc, stats::sd)
    names(b) <- c("sd_d13C", "sd_d15N")
    sc.meansSDs <- data.frame(a, b, Factor="Secondary consumer")

    df2 <- rbind(sc.meansSDs, b1.meansSDs, b2.meansSDs, b3.meansSDs)

    #Here we set up the title and x and y labels (in case that in the future we
    #want to modify through the user. Same applies for the colors)
    xlab <- expression(paste(delta^{13}, "C (\u2030)"))
    ylab <- expression(paste(delta^{15}, "N (\u2030)"))
    palette <- "Set1"
    type <- "qual"

    #At some point we need to take these three ggplots to an outside function
    #get the limits
    x_min <- min(df$d13C)
    x_max <- max(df$d13C)

    y_min <- min(df$d15N)
    y_max <- max(df$d15N)


    p1 <- biPlot(df, df2, ylab, xlab, p = "p1")


    p2 <- biPlot(df, p = "p2")

    p3 <- biPlot(df, p = "p3")

    if (density == "none") {

      p1

    } else if (density == "both") {

      gridExtra::grid.arrange(gridExtra::arrangeGrob(p2, ncol = 2,
                                                     widths=c(3,1)),
                              gridExtra::arrangeGrob(p1, p3, ncol=2,
                                                     widths=c(3,1)),
                              heights=c(1,3))

    } else if (density == "above") {

      gridExtra::grid.arrange(gridExtra::arrangeGrob(p2, p1, nrow=2,
                                                     heights=c(1,3)))

    } else if (density == "right") {

      gridExtra::grid.arrange(gridExtra::arrangeGrob(p1, p3, ncol=2,
                                                     widths=c(3,1)))

    } else {

      warning("We expect that density has only four values:
                'none', 'both', 'above' or 'right'.")}

  }

}