#' @title help_summarySE
#' @description Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
#' @param data data frame, Default: NULL
#' @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, Default: NULL
#' @param na.rm a boolean that indicates whether to ignore NA's, Default: FALSE
#' @param conf.interval the percent range of the confidence interval, Default: 0.95
#' @param .drop PARAM_DESCRIPTION, Default: TRUE
#' @return data frame
#' @details DETAILS
#' @examples
#' \dontrun{
#' if(interactive()){
#' #EXAMPLE1
#' }
#' }
#' @rdname help_summarySE
#' @export help_summarySE
help_summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(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)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- 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
)
# 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)
}
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