Basic plotting of AOV, Conversion rate & Goal completion for a A/B Test across 3 domains. (#GoogleAnalytics and #ContentExperiments)

initiate_RGoogleAnalytics.r

# Load requirements to pull data from Google Analytics.
 
library(RGoogleAnalytics)
library(RColorBrewer)
library(scales)
library(lubridate)
library(ProjectTemplate)
library(ggplot2)
library(gridExtra)
library(plyr)
setwd("C:/Users/m.parzakonis/Google Drive/MyCodeRants/")
 
# CONNECT AND QUERY -----------------------------------------------------------
 
# Let's initiate a query instance to do the job
query <- QueryBuilder()
 
# Open Google's OAuth Playground in the default browser.
# You will need to exchange your Authorization Code for an Access Token.
access_token <- query$authorize()
# if you are clever enough to check the AutoRefresh on OAuth Playgournd you can use the following
# access_token <- "ya29.AHES6ZQEeVsVTg7_RcLjsUY_1hj_QfC0ZMVuagfOCgL_mm68AA"
 
ga <- RGoogleAnalytics()
 
# Get list of profiles and echo to the console. You will use the left-most number for the
# profile you want to query in the next step.
( ga.profiles <- ga$GetProfileData(access_token) )

multiplot.R

# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols:   Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  require(grid)
  
  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)
  
  numPlots = length(plots)
  
  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                     ncol = cols, nrow = ceiling(numPlots/cols))
  }
  
  if (numPlots==1) {
    print(plots[[1]])
    
  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
    
    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
      
      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}

plot_experiments_across_profiles.r

# Get list of profiles and echo to the console. You will use the left-most number for the
# profile you want to query in the next step.
( ga.profiles <- ga$GetProfileData(access_token) )
ga.profiles<-ga.profiles[c(39,67,1),]
my_start_date <-"2013-09-28"  # Query date range
my_end_date   <-"2013-10-09" 
 
# Create a filtering for getting specific data on the experiment across domains
filters<-c("ga:experimentId=~I1L9wLVESJen2wS_Cvt_KA","ga:experimentId=~3bo3cnAzTZux591CZabJDA","ga:experimentId=~_7ipfAJyRf2bMKCrXPfaaw")
 
for (i in 1:nrow(ga.profiles)) {
  print(filters[i])
  # Initiate a Query   
  query <- QueryBuilder()
  # Build the query string, use the profile by setting its index value 
  query$Init(start.date = my_start_date,
             end.date = my_end_date,
             dimensions = "ga:date,ga:experimentVariant,ga:browser",
             metrics = "ga:goal1Completions,ga:transactions,ga:visits,ga:transactionRevenue", 
             sort = "ga:date",
             filters = filters[i], 
             max.results = 1500,
             table.id = paste("ga:",ga.profiles$id[i],sep="",collapse=","),
             access_token=access_token)
  
  # Make a request to get the data from the API
  ga.data.1<- ga$GetReportData(query)
  
  # Merge files, create metrics, limit dataset to just days when tags firing
  ga.data.1$date <-ymd(ga.data.1$date)
  
  # Report Header  
  print(paste("The following graphs and tables are about",ga.profiles$name[i],"experiment performance"))
  
 
  
  # Build the graphs to use with multiplot()
  # REMINDER : Use print() to get ggplot/qplot in loops!
  a<-qplot(experimentVariant, transactionRevenue/transactions,data=ga.data.1, geom="boxplot" 
           , main=paste("AOV per Variant\n",ga.profiles$name[i]),xlab="Variant", ylab="AOV")
  b<-qplot(experimentVariant, goal1Completions/visits,data=ga.data.1, geom="boxplot" 
           , main=paste("Select a flight % per Variant\n",ga.profiles$name[i]),xlab="Variant", ylab="Select a flight (%)")
  c<-qplot(experimentVariant, transactions/visits,data=ga.data.1,geom="boxplot" 
           , main=paste("Conversion (%)\n",ga.profiles$name[i]),xlab="Variant", ylab="Conversion (%)")
  
  print(multiplot(a,b,c, cols=3))
  # The next two are ggplot grpahs and will be use without the multiplot()   
  d<-ggplot(ga.data.1, aes(x=date, y=(transactions/visits)*100, colour=experimentVariant)) 
  e<-ggplot(ga.data.1, aes(x=date, y=(transactionRevenue/transactions), colour=experimentVariant))
  print(d + 
          geom_point(alpha=.3) +
          geom_smooth(alpha=.2, size=1) +
          theme(legend.background = element_rect(fill="gray90", size=.5, linetype="dotted"))+
          labs(title = paste("Conversion (%) per Variant\n",ga.profiles$name[i]),x="Date",y="Conversion (%)"))
   print(e + 
           geom_point(alpha=.3) +
           geom_smooth(alpha=.2, size=1) +
           theme(legend.background = element_rect(fill="gray90", size=.5, linetype="dotted"))+
           labs(title = paste("AOV per Variant\n",ga.profiles$name[i]),x="Date",y="AOV"))
  
}