#' @title Give a summary of a data frame (Deprecated)
#'
#' @description Gives count, mean, standard deviation, standard error of the mean, and confidence
#' interval (default 0.95).
#'
#' @param data 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 0.95)
#' @param .drop should empty fields be dropped
#'
#' @return Data frame with count, mean, standard devation, standard error of the mean and
#' confidence interval.
#'
#' @export
#' @importFrom plyr ddply rename
#' @importFrom stats sd qt
#'
#' @author Rico Derks
#' @references www.cookbook-r.com
summary_se <- function(data = NULL,
measurevar,
groupvars = NULL,
na.rm = FALSE,
conf.interval = 0.95,
.drop = TRUE) {
.Deprecated(
new = "",
package = "Rcpm",
msg = "The function summary_se is deprecated. There are much nicer solutions available. Have a look at the skimr package!"
)
# 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 <- plyr::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),
sd = stats::sd(xx[[col]], na.rm = na.rm)
)
},
measurevar)
# Rename the "mean" column
datac <- plyr::rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is 0.95, use 0.975 (above/below), and use df = N-1
ciMult <- stats::qt(conf.interval / 2 + 0.5, datac$N - 1)
datac$ci <- datac$se * ciMult
return(datac)
}
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