R/summarySYM.R

In ACSoupir/Endophyte: Endophyte Summarizing Tool

#' Creates a summary table for a metric that is set using measurevar
#'
#' @import plyr
#'
#' @author Alex Soupir
#'
#' @param data A dataset that has raw data to be summarized
#' @param measurevar A column heading that is to be summarized
#' @param change The row label that is to be used to compare to (control)
#' @param groupvars The column heading(s) for summary (isolate)
#' @param na.rm Set true to not include cells with missing values
#' @return Table with summarized information about \code{measurevar} sorted by \code{groupvars}
#' @export
#' @examples
#' summarySYM(plant_merged, measurevar="Root_Length", change="Control", groupvars=c("Isolate","Nutrient"), na.rm=TRUE)
#' summary_table <- summarySYM(plant_merged, measurevar="Root_Length", groupvars="Isolate", na.rm=TRUE)

summarySYM <- function(data=NULL, measurevar, change=NULL, groupvars=NULL, na.rm=FALSE,
                                conf.interval=temp, .drop=TRUE) {

  # New version of length which can handle NA's: if na.rm==T, don't count them
  length2 <- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
  }

  # This does the summary. For each group's data frame, return a vector with
  # N, mean, median, and sd
  data2 <- ddply(data, groupvars, .drop=.drop,
                 .fun = function(xx, col) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = mean   (xx[[col]], na.rm=na.rm),
                     median = median(xx[[col]], na.rm=na.rm),
                     sd   = sd     (xx[[col]], na.rm=na.rm)
                   )
                 },
                 measurevar
  )

  data2$se <- data2$sd / sqrt(data2$N)  # Calculate standard error of the mean

  # Confidence interval multiplier for standard error
  # Calculate t-statistic for confidence interval:
  # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  ciMult <- qt(conf.interval/2 + .5, data2$N-1)
  data2$ci <- data2$se * ciMult

  #sets the variable in the table to what you want the
  i=1
  if(!is.null(change)){
    while(change != data2[i,1]){
      i=i+1
    }

  #calculates the change for each variable set before
  data2$change <- ((data2[,"mean"] - data2[i,"mean"]) / data2[i,"mean"])

  }

  # Rename the "mean" column
  data2 <- rename(data2, c("mean" = measurevar))

  #run dunnTest for nonparametric comparisons
  data3 <-

  #returns the new table
  # use "<- summarySYM()" to set global table
  return(data2)
}

#library("devtools")
#library(roxygen)
#document()