Move summarySE.R from gitlab.utu.fi MTT calc

summarySE.R

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