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78 lines
3.1 KiB
R
78 lines
3.1 KiB
R
# Installing missing dependencies
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dependencies <- c("plyr")
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missing_packages <- dependencies[!(dependencies %in% installed.packages()[, "Package"])]
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if(length(missing_packages)) install.packages(missing_packages)
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rm(missing_packages,dependencies)
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summarySE <- function(data=NULL, measurevar=NULL, statistic="mean", groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
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# #<---------------------------->
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# # You must include this section when:
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# # Distributing, Using and/or Modifying this code.
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# # Please read and abide by the terms of the included LICENSE.
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# # Copyright 2018, Deepankar Chakroborty, All rights reserved.
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# # Author : Deepankar Chakroborty (https://github.com/dchakro)
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# # Report issues: https://github.com/dchakro/shared_Rscripts/issues
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# # License: https://github.com/dchakro/shared_Rscripts/blob/master/LICENSE
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# # Adapted from: http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
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# # PURPOSE:
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# # Summarizes data by returning count, mean, standard deviation,
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# # standard error of the mean, and confidence interval (default 95%)
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# # for a given data frame based on grouping variables
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# # data: a data frame.
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# # PARAMETERS
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# # measurevar: the name of a column that contains the variable to be summariezed
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# # groupvars: a vector containing names of columns that contain grouping variables
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# # na.rm: a boolean that indicates whether to ignore NA's
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# # conf.interval: the percent range of the confidence interval (default is 95%)
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# #<---------------------------->
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# a version of length which can handle NA's: if na.rm==T, don't count them
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length2 <- function (x, na.rm=FALSE) {
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if (na.rm) sum(!is.na(x))
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else length(x)
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}
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# This does the summary. For each group's data frame, return a vector with
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# N, mean, and sd
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if(statistic=="mean"){
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datac <- plyr::ddply(data, groupvars, .drop=.drop,
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.fun = function(xx, col) {
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c(N = length2(xx[[col]], na.rm=na.rm),
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mean = mean (xx[[col]], na.rm=na.rm),
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sd = sd (xx[[col]], na.rm=na.rm)
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)
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},
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measurevar
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)
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# Rename the "mean" column
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datac <- plyr::rename(datac, c("mean" = measurevar))
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}
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if(statistic=="median"){
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datac <- plyr::ddply(data, groupvars, .drop=.drop,
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.fun = function(xx, col) {
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c(N = length2(xx[[col]], na.rm=na.rm),
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median = median (xx[[col]], na.rm=na.rm),
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sd = sd (xx[[col]], na.rm=na.rm)
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)
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},
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measurevar
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)
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# Rename the "median" column
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datac <- plyr::rename(datac, c("median" = measurevar))
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}
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# Calculate standard error of the mean
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datac$se <- datac$sd / sqrt(datac$N)
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# Confidence interval multiplier for standard error
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# Calculate t-statistic for confidence interval:
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# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
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ciMult <- qt(conf.interval/2 + .5, datac$N-1)
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datac$ci <- datac$se * ciMult
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return(datac)
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}
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