Introducing Twitter.jl

This is possibly the latest “announcement” of a package ever, given that Twitter.jl has existed on METADATA for nearly a year now, but that’s how things go sometimes. Here’s how to get started with Twitter.jl.

Hello, World!

If ‘Hello, World!’ is the canonical example of getting started with a programming language, the Twitter API is becoming the first place to start for people wanting to learn about APIs. Authenticating with the Twitter API using Julia is similar to using the R or Python packages, except that rather than doing the OAuth “dance”, Twitter.jl takes all four authentication values in one function:

using Twitter

apikey = "q8Qw7WJTVP..."
apisecret = "FIichPpGJxiOssN..."
accesstoken = "98689850-v0zZNr..."
accesstokensecret = "w7bDg9K0c493T..."


All four of these values can be found after registering at the Twitter Developer page and creating an application. Having all four values in your script is less secure than just providing the api key and api secret, but in the future, I’ll likely implement the full OAuth “handshake”. One thing to keep in mind with this function as it currently works is that no validation of your credentials is performed; the only thing this function does is define a global variable twittercred for later use by the various functions that create the OAuth headers. To shout “Hello, World!” to all of your Twitter followers, you can use the following code:

post_status_update("Hello, World!")

General Package/Function Structure

From the example above, you can see that the function naming follows the Twitter REST API naming convention, with the HTTP verb first and the endpoint as the remainder of the function name. As such, it’s a good idea at this early package state to have the Twitter documentation open while using this package, so that you can quickly find the methods you are looking for.

For each function/API endpoint, I’ve gone through and determined which parameters are required; these are required arguments in the Julia functions. For all other options, each function takes a second optional Dict{String, String} for any option shown in the Twitter documentation. While this Dict structure allows for ultimate flexibility (and quick definition of functions!), I do realize that it’s less than optimal that you don’t know what optional arguments each Twitter endpoint allows.

As an example, suppose you wanted to search for tweets containing the hashtag #julialang. The minimum function call is as follows:

julia_tweets = get_search_tweets("#julialang")

By default, the API will return the 15 most recent tweets containing the #julialang hashtag. To return the most recent 100 tweets (the maximum per API ‘page’), you can pass the “count” parameter via the Options Dict:

julia_tweets_100 = get_search_tweets("#julialang"; options = {"count" => "100"})

Composite Types and DataFrames definitions

The Twitter API is structured into 4 return data types (Places, Users, Tweets, and Entities), and I’ve mimicked these types using Julia Composite Types. As such, most functions in Twitter.jl return an array of specific type, such as Array{TWEETS,1} from the prior #julialang search example. The benefit to defining custom types for the returned Twitter data is that rudimentary DataFrame methods have also been defined:

df = DataFrame(julia_tweets_100)

I describe these DataFrames as ‘rudimentary’ as they parse the top level of JSON into columns, which results in some DataFrame columns having complex data types such as Dict() (and within the Dict(), nested Dicts!). As a running theme in this post, this is something I hope to get around to improving in the future.

Want to Get Started Developing Julia? Start Here!

One of the common questions I get asked is how to get started with Julia, both from a learning perspective and from a package development perspective. Hacking away on the core Julia codebase is great if you have the ability, but the code can certainly be intimidating (the people are quite friendly though). Creating a package isn’t necessarily hard, but you have to think about an idea you want to implement. The third alternative is…

…improve the Twitter package! If you go to the GitHub page for Twitter.jl, you’ll see a long list of TODO items that need to be worked on. The hardest part (building the OAuth headers) has already been taken care of. What’s left is re-factoring the code for simplification, factoring out the OAuth code in general into a new Julia library (also partially started), then building the Streaming API functions, cleaning up the DataFrame methods to remove the Dict column types, paging through API results…and so-on.

So if any of you are on the sidelines wanting to get some practice on developing packages, without needing to worry about learning Astrophysics first, I’d love to collaborate. And if any Julia programming masters want to collaborate, well that’s great too. All help and pull requests are welcomed.

In the meantime, hopefully some of you will find this package useful for natural language processing, social networking analysis or even creating bots 😉

RSiteCatalyst Version 1.4.2 Release Notes

RSiteCatalyst version 1.4.2 is now available on CRAN. This update was primarily bug fixes with one additional feature added.

  1. Fixed QueueRanked function to allow multiple SAINT classifications to be specified. This allows for breaking down a SAINT classification with another SAINT classification, such as breaking down tracking codes by marketing channel and by campaign
  2. Fixed bug in internal function, to allow for using the same element multiple times in a QueueRanked function call. This was a necessary fix for allowing multiple SAINT classifications in #1
  3. Exported previous internal function SubmitJsonQueueReport to allow for submitting JSON requests directly to the Adobe Analytics API without all of the R function scaffolding. This approximates the same functionality as the Adobe API Explorer.

For the most part, this isn’t a release that most people will notice any differences from version 1.4.1. That said, special thanks go out to Jason Morgan (@framingeinstein) for identifying the two bugs that were fixed AND submitting fixes.

Feature Requests/Bugs

As always, if you come across bugs or have feature requests, please continue to use the RSiteCatalyst GitHub Issues page to submit issues. Don’t worry about cluttering up the page with tickets, please fill out a new issue for anything you encounter (with code you’ve already tried and is failing), unless you are SURE that it is the same problem someone else is facing.

And finally, like I end every blog post about RSiteCatalyst, please note that I’m not an Adobe employee. Please don’t send me your API credentials, expect immediate replies (especially for you e-commerce folks sweating the holiday season!) or ask to set up phone calls to troubleshoot your problems. This is open-source software…Willem Paling and I did the hard part writing it, you’re expected to support yourself as best as possible unless you believe you’re encountering a bug. Then use GitHub.

Destroy Your Data Using Excel With This One Weird Trick!

All you pie-chart haters are wishing I used one here

All you pie-chart haters are wishing I used one here.

I often use Twitter as a place to vent about the horribleness of Excel, from the product itself to analyses its UI and workflow influences. Admittedly, some of this is snobbish preference: if everyone used my preferred tools, then the world would be a better place! But let me back off my snobbishness a bit and just say this: please feel free to use any tool you want, up to and including pencil-and-paper…JUST.STOP.USING.EXCEL.

Excel arbitrarily destroys data for fun, as evidenced by the example below.

Who Gives A ‘F’ About Seconds? I’m 10 minutes Late Everywhere!

CSV files have many flaws, but at least they are just plain text. It doesn’t take any special software to read them and you can open and close them without loss of fidelity…except if you open them with Excel.

Suppose you have a CSV file with timestamps in ISO8601 format. Depending on which text editor you use, it might look something like this:


Now, let’s open our file in Excel:


The first thing you might notice is that not only does Excel change the date formatting in the file to be more “‘Murica!”, they don’t even have the courtesy to use one of their existing date or time formats! And rather than keep the date the way it was, or standardize the dates to the way the rest of the world writes them, or even keep fixed-width columns, Excel feels like it should also hide the seconds! Makes sense…seconds are for other people to see, if/when they highlight an individual cell.

So, you’ve opened this file, but can’t remember if you made any changes outside of applying auto-width to the columns. The data still looks right, so you hit ‘Save’ when prompted by Excel. But you remember that your favorite programmer asked for a CSV file, and it’s already a CSV file, so you hit save, ignore the ‘features’ Excel brags about and email it back to your co-worker. Here’s what they receive:


Reading this back in our plain-text editor, we can now see we have a loss of fidelity of between 37 and 47 seconds on each cell of data. Whereas Excel keeps track of your timestamps while you’re in a SPREADSHEET, if you save as plain text, Excel assumes you want to keep the format it automatically applied to your data (automatically! silently!), and thus, destroys your file. In what world would you not care about seconds in your timestamps?

Remember, this mis-feature occurs even if the only thing you do is open a plain-text file in Excel and hit save. No other Excel actions are needed to destroy your data.

Excel: Only The Proper Tool If You Don’t Care

If you don’t care about using the proper tool for analytics, don’t want to learn something new, don’t want numerical accuracy, hate visually interesting graphics, don’t need reproducibility…use Excel. For everything else, there’s everything else. Don’t be a VLOOKUP guru, use SQL. Don’t store your data in Excel just because it allows for a million rows, use a database. If you need point-and-click graphics, at least spring for Tableau so the defaults look nicer.

Or, learn to code using open-source languages for a total licensing cost of $0. Every analyst would get value from knowing one open-source analytics language, even topically, so that you can write simple calculation scripts and document your thought process. A side benefit is that by coding, you can also use version control like Git or SVN. Then, you can have different versions of thought, and the next analyst down the line can see how your analysis has evolved.

And while I’m ranting, a special message for all you ‘top-tier’ analytics consultants out there: you should know SEVERAL of the common analytics languages. If you do your “analysis” in Excel, you are a hack or you are just providing reporting for $300/hr. Use better tools, your clients deserve better. I have infinitely more respect for someone who delivers a sloppy set of slides and a documented R script than someone who knows who to put drop-shadows on MS Office documents and makes fancy decks. You are being judged not just by the C-Suite, but also by snobs like me. And when contract renewal time comes around, they do ask my opinion and I do make comments on how sophisticated your toolset was that you used (or lack thereof if you’re using Excel).

It’s nearly 2015, do better. Stop Using Excel.

  • 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
  • Google Analytics Individual Qualification (IQ) - Passed!
  • Google Analytics SEO reports: Not Ready For Primetime?
  • An Afternoon With Edward Tufte
  • Google Analytics Custom Variables: A Page-Level Example
  • Xchange 2011: Think Tank and Harbor Cruise
  • Google Analytics for WordPress: Two Methods
  • WordPress Stats or Google Analytics? Yes!
  • 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