R/summarySE.R

In happyrabbit/DataScienceR: Data Science Using R

#' Summarizes data.
#' @description  Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
#' @param data a data frame.
#' @param measurevar the name of a column that contains the variable to be summariezed
#' @param groupvars a vector containing names of columns that contain grouping variables
#' @param na.rm a boolean that indicates whether to ignore NA's
#' @param conf.interval the percent range of the confidence interval (default is 95%)
#' @param pce_less_than_zero if TRUE, function will report the percentage of observation that is < 0
#'
#' @author Hui Lin, \email{longqiman@gmail.com}
#' @examples
#' \dontrun{
#' data("SegData")
#' summarySE(SegData, measurevar = "income", na.rm = T)
#' }
#'
#' @export

summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE, pce_less_than_zero=FALSE) {
  require(plyr)

  # 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)
  }

  qx<-function(x,q){

  sort( na.omit(x) )[round(length( na.omit(x) )*q,0)]->res
    return(res)
  }
  # This is does the summary; it's not easy to understand...
  datac <- ddply(data, groupvars, .drop=.drop,
                 if (pce_less_than_zero)
                 .fun= function(xx, col, na.rm) {
                   c( N    = length2(xx[,col], na.rm=na.rm),
                      mean = mean   (xx[,col], na.rm=na.rm),
                      pct = mean   (xx[,col]>0, na.rm=na.rm),
                      sd   = sd     (xx[,col], na.rm=na.rm),
                      q25   = qx(xx[,col],0.25) ,
                      q75   = qx(xx[,col],0.75) ,
                      q5   = qx(xx[,col],0.05) ,
                      q95   = qx(xx[,col],0.95) ,
                      q15   = qx(xx[,col],0.15) ,
                      q85   = qx(xx[,col],0.85) ,
                      pce_down= mean(xx[,col]<0)
                   )
                 }
                 else
                   .fun= function(xx, col, na.rm) {
                     c( N    = length2(xx[,col], na.rm=na.rm),
                        mean = mean   (xx[,col], na.rm=na.rm),
                        pct = mean   (xx[,col]>0, na.rm=na.rm),
                        sd   = sd     (xx[,col], na.rm=na.rm),
                        q5   = qx(xx[,col],0.05) ,
                        q15   = qx(xx[,col],0.15) ,
                        q25   = qx(xx[,col],0.25) ,
                        q75   = qx(xx[,col],0.75) ,
                        q85   = qx(xx[,col],0.85) ,
                        q95   = qx(xx[,col],0.95)

                     )
                   },
                 measurevar,
                 na.rm
  )

  # Rename the "mean" column
  datac <- rename(datac, c("mean"=measurevar))
  datac$se <- datac$sd / sqrt(datac$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, datac$N-1)
  datac$ci <- datac$se * ciMult
  return(datac)
}