devicePerformanceAnalysis: Device Performance Analysis

In erenturunc/AnalyticsMiner: What the package does (short line)

Usage

devicePerformanceAnalysis(ClientID, ClientSecret, ViewID, StartDate, EndDate, SplitDaywise = T)

Arguments

ClientID
ClientSecret
ViewID
StartDate
EndDate
SplitDaywise

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (ClientID, ClientSecret, ViewID, StartDate, EndDate, 
    SplitDaywise = T) 
{
    checkPackages("RGoogleAnalytics", "httpuv", "ggplot2", "scales", 
        "sqldf")
    library(httpuv)
    library(ggplot2)
    library(RGoogleAnalytics)
    library(scales)
    library(sqldf)
    token <- Auth(ClientID, ClientSecret)
    ValidateToken(token)
    query.list <- Init(start.date = toString(StartDate), end.date = toString(EndDate), 
        dimensions = "ga:date,ga:deviceCategory", metrics = "ga:sessions,ga:uniquePageviews,ga:goalCompletionsAll,ga:uniquePurchases,ga:transactionRevenue", 
        max.results = 10000, sort = "ga:date", table.id = ViewID)
    ga.query <- QueryBuilder(query.list)
    ga.data <- GetReportData(ga.query, token, split_daywise = SplitDaywise)
    ga.data$date <- as.Date(ga.data$date, format = "%Y%m%d")
    p_sessions <- ggplot(ga.data, aes(x = date, y = sessions, 
        group = deviceCategory, color = deviceCategory)) + geom_line() + 
        labs(title = "Sessions by date", x = "Dates", y = "Sessions") + 
        theme_classic()
    p_sessions <- p_sessions + theme(axis.text.x = element_text(angle = 90, 
        hjust = 1))
    p_sessions <- p_sessions + theme_classic()
    p_sessions <- p_sessions + stat_smooth(method = "lm")
    p_uniquePageviews <- ggplot(ga.data, aes(x = date, y = uniquePageviews, 
        group = deviceCategory, color = deviceCategory)) + geom_line() + 
        labs(title = "Unique PageViews by date", x = "Dates", 
            y = "Unique PageViews") + theme_classic()
    p_uniquePageviews <- p_uniquePageviews + theme(axis.text.x = element_text(angle = 90, 
        hjust = 1))
    p_uniquePageviews <- p_uniquePageviews + theme_classic()
    p_uniquePageviews <- p_uniquePageviews + stat_smooth(method = "lm")
    p_goalCompletionsAll <- ggplot(ga.data, aes(x = date, y = goalCompletionsAll, 
        group = deviceCategory, color = deviceCategory)) + geom_line() + 
        labs(title = "Goal completions by date", x = "Dates", 
            y = "Goal Completions") + theme_classic()
    p_goalCompletionsAll <- p_goalCompletionsAll + theme(axis.text.x = element_text(angle = 90, 
        hjust = 1))
    p_goalCompletionsAll <- p_goalCompletionsAll + theme_classic()
    p_goalCompletionsAll <- p_goalCompletionsAll + stat_smooth(method = "lm")
    p_uniquePurchases <- ggplot(ga.data, aes(x = date, y = uniquePurchases, 
        group = deviceCategory, color = deviceCategory)) + geom_line() + 
        labs(title = "Purchases by date", x = "Dates", y = "Purchase") + 
        theme_classic()
    p_uniquePurchases <- p_uniquePurchases + theme(axis.text.x = element_text(angle = 90, 
        hjust = 1))
    p_uniquePurchases <- p_uniquePurchases + theme_classic()
    p_uniquePurchases <- p_uniquePurchases + stat_smooth(method = "lm")
    p_transactionRevenue <- ggplot(ga.data, aes(x = date, y = transactionRevenue, 
        group = deviceCategory, color = deviceCategory)) + geom_line() + 
        labs(title = "Revenue by date", x = "Dates", y = "Revenue") + 
        theme_classic()
    p_transactionRevenue <- p_transactionRevenue + theme(axis.text.x = element_text(angle = 90, 
        hjust = 1))
    p_transactionRevenue <- p_transactionRevenue + theme_classic()
    p_transactionRevenue <- p_transactionRevenue + stat_smooth(method = "lm")
    multiplot(p_sessions, p_uniquePageviews, p_goalCompletionsAll, 
        p_uniquePurchases, p_transactionRevenue, cols = 2)
    data_mobile <- sqldf("select * from [ga.data] where deviceCategory == 'mobile'")
    model_mobile <- lm(formula = sessions ~ date, data = data_mobile)
    predictionDate_mobile = data.frame(date = c("20161101", "20161102"))
    predictionDate_mobile$date <- as.Date(predictionDate_mobile$date, 
        format = "%Y%m%d")
    predictedValues_mobile = predict(model_mobile, predictionDate_mobile)
  }

erenturunc/AnalyticsMiner documentation built on May 20, 2019, 4:09 p.m.