Real-time Reporting with the Adobe Analytics API

Starting with version 1.3.1 of RSiteCatalyst, you can now access the real-time reporting capabilities of the Adobe Analytics API through a familiar R interface. Here’s how to get started…


Before using the real-time reporting capabilities of Adobe Analytics, you first need to indicate which metrics and elements you are interested in seeing in real-time. To see which reports are already set up for real-time access on a given report suite, you can use the GetRealTimeConfiguration() function:

#Get Real-Time reports that already set up
realtime_reports <- GetRealTimeConfiguration("<reportsuite>")

It’s likely the case that the first time you set this up, you’ll already see a real-time report for ‘Instances-Page-Site Section-Referring Domain’. You can leave this report in place, or switch the parameters using SaveRealTimeConfiguration().


If you want to add/modify which real-time reports are available in a report suite, you can use the SaveRealTimeConfiguration() function:

SaveRealTimeConfiguration("<report suite>",
  metric1 = "instances",
  elements1 = c("page", "referringdomain", "sitesection"),

  metric2 = "revenue",
  elements2 = c("referringdomain", "sitesection")

  metric3 = "orders",
  elements3 = c("products")

Up to three real-time reports are available to be stored at any given time. Note that you can mix-and-match what reports you want to modify, you don’t have to submit all three reports at a given time. Finally, keep in mind that it can take up to 15 minutes for the API to incorporate your real-time report changes, so if you don’t get your data right away don’t keep re-submitting the function call!


Once you have your real-time reports set up in the API, you can use the GetRealTimeReport() function in order to access your reports. There are numerous parameters for customization; selected examples are below.

Minimum Example - Overtime Report

The simplest function call for a real-time report is to create an Overtime report (monitoring a metric over a specific time period):

rt <- GetRealTimeReport("<report suite>", "instances")

The result of this call will be a DataFrame having 15 rows of one minute granularity for your metric. This is a great way to monitor real-time orders & revenue during a flash sale, see how users are accessing a landing page for an email marketing campaign or any other metric where you want up-to-the-minute status updates.

Granularity, Offset, Periods

If you want to have a time period other than the last 15 minutes, or one minute granularity is too volatile for the metric you are monitoring, you can add additional arguments to modify the returned DataFrame:

rt2 <- GetRealTimeReport("<reportsuite>",
                  periodMinutes = "5",
                  periodCount = "12",
                  periodOffset = "10"

For this function call, we will receive instances for the last hour (12 periods) of five minute granularity, with a 10 minute offset (meaning, now - 10 minutes ago is the first time period reported).

Single Elements

Beyond just monitoring a metric over time, you can specify an element such as page to receive your metrics by:

                  periodMinutes = "9",
                  periodCount = "3")

This function call will return Instances by Page, for the last 27 minutes (3 rows/periods per page, 9 minute granularity…just because!). Additionally, there are other arguments such as algorithm, algorithmArgument, firstRankPeriod and floorSensitivity that allow for creating reports similar to what is provided in the Real-Time tab in the Adobe Analytics interface.

Currently, even through the Adobe Analytics API supports real-time reports with three breakdowns, only one element breakdown is supported by RSiteCatalyst; it is planned to extend these functions in RSiteCatalyst to full support the real-time capabilities in the near future.

From DataFrame to Something ‘Shiny’

If we’re talking real-time reports, we’re probably talking about dashboarding. If we’re talking about R and dashboarding, then naturally, ggvis/Shiny comes to mind. While providing a full ggvis/Shiny example is beyond the scope of this blog post, it’s my hope to provide a working example in a future blog post. Stay tuned!

  • RSiteCatalyst Version 1.4.16 Release Notes
  • Using RSiteCatalyst With Microsoft PowerBI Desktop
  • RSiteCatalyst Version 1.4.14 Release Notes
  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • ODSC webinar: End-to-End Data Science Without Leaving the GPU
  • PyData NYC 2018: End-to-End Data Science Without Leaving the GPU
  • Data Science Without Leaving the GPU
  • Getting Started With OmniSci, Part 2: Electricity Dataset
  • Getting Started With OmniSci, Part 1: Docker Install and Loading Data
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS