rga_countries_speed_data.R

require(googleVis)
require(plyr)
# We need to aggreagate data across countries first. teh function below does a nice job and many more
# GroupBy function -------------------------------------------------------
## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   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%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
  require(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 is does the summary; it's not easy to understand...
  datac <- ddply(data, groupvars, .drop=.drop,
                 .fun= function(xx, col, na.rm) {
                   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,
                 na.rm
  )
  
  # 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)
}


# Actual Viz --------------------------------------------------------------
## We need to create an aggregation of the dataset to get the everage/median for the plot
require(googleVis)
input<- summarySE(final_dataset, measurevar="avgPageLoadTime",groupvars="country")
select<- input[which(input$country!="<NA>"),]
Map<- data.frame(select$country, select$avgPageLoadTime)
names(Map)<- c("Country", "avgPageLoadTime")
Geo=gvisGeoMap(Map, locationvar="Country", numvar="avgPageLoadTime",
               options=list(height=350, dataMode='regions'))
plot(Geo)