Showing weighted.summarySE function to aggregate weighted means with standard errors

weighted.SummarySE

require(ggplot2)
require(ggthemes)
require(plyr)
require(stringr)
require(Rmisc)
require(lubridate)
require(popbio)  #betaval - to create random beta variables

#### Experiment setting
rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) }
getDates <- function(d) {as.Date(Sys.Date() -d)}

bouncerate <- replicate(100, betaval(.5, sd=.3))
user <-  rnorm2(100, 30, 10)
Date <- unlist(llply(1:100, function(x){getDates(x)}))

df <- data.frame(Date=as.Date(Date, origin = as.Date("1970-01-01")), BounceRate = bouncerate, Sessions=user)
df$wday <- wday(df$Date, abbr = T , label = T)

## create the well formated data frame to use in ggplot
dfc1 <- summarySE(df, measurevar = "BounceRate", groupvars = "wday")

p1<-ggplot(dfc1, aes(wday, BounceRate))   + geom_point() +  geom_errorbar(aes(ymin=BounceRate-se, ymax=BounceRate+se), width=.1)
p1 + theme_economist(base_size = 16) + ylab("Unweighted Bounce-rate")  + xlab("")

############ adapt function
weighted.summarySE <- function(data=NULL, measurevar,  groupvars=NULL, weights, na.rm=FALSE,
                      conf.interval=.95, .drop=TRUE) {
  
  # 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)
  }
  
  #weighted - SD function!
  w.sd <- function(x, w,na.rm=TRUE )  ( (sum(w*x*x, na.rm=na.rm)/sum(w, na.rm=na.rm)) - weighted.mean(x,w, na.rm=na.rm)^2 )^.5
  
  
  # This does the summary. For each group's data frame, return a vector with
  datac <- ddply(data, groupvars,
                 .fun = function(xx, col, weights) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = weighted.mean(xx[[col]], xx[[weights]], na.rm=na.rm),
                     sd   = w.sd(xx[[col]], xx[[weights]], na.rm=na.rm)
                   )
                 },
                 measurevar, weights
  )
  
  # 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)
}


### the correct way to plot the means!

dfc2 <- weighted.summarySE(df, measurevar = "BounceRate", groupvars = "wday", weights = "Sessions")
p1<-ggplot(dfc2, aes(wday, BounceRate))   + geom_point() +  geom_errorbar(aes(ymin=BounceRate-se, ymax=BounceRate+se), width=.1)
p1 + theme_economist(base_size = 16) + ylab("Weighted Bounce-rate")  + xlab("")