Publications by Data * Science + R

Coloring the world – Extracting user specific color palettes from Tableau Workbooks

22.06.2014

If you read some of my last blog post you may notice that R got a new companion called Tableau. Tableau is an easy to use and mighty BI toolbox for visualizing all kinds of data and I suggest everybody to give it a trial. One of the things that I like very much is that it gives you all the options to create simple graphics instantly and on the ot...

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Deep Down Below – Using in-database analytics from within Tableau (with MADlib)

28.09.2014

Introduction Using Tableau for visualizing all kinds of data is quite a joy, but it’s not that strong on build-in analytics or predictive features. Tableaus integration of R was a huge step in the right direction (and I love it very much – see here, here and here) but still has some limitations (e.g. no RAWSQL like functions to work on a row ...

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Change Point Detection in Time Series with R and Tableau

01.01.2015

Introduction Happy new year to all of you. Even if you still fight with the aftereffects of your new year’s party, the following is something that may help in getting you more active because that’s it what this blog post is about – Activity. Regardless of the business you are working in, I bet that customer activity is something that matter...

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How to Use R for Connecting Neo4j and Tableau (A Recommendation Use Case)

06.03.2015

Introduction Year is just a little bit more than two months old and we got the good news from Tableau – beta testing for version 9.0 started. But it looks like that one of my most favored features didn’t manage to be part of the first release – the Tableau Web Data Connector (it’s mentioned in Christian Chabot keynote at 01:18:00 you can ...

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Scatter Plots with Marginal Densities – An Example for Doing Exploratory Data Analysis with Tableau and R

13.07.2015

Introduction One of the first stages in most data analysis projects is about exploring the data at hand. During this stage the analyst tries to get familiar with his dataset by looking at summary statistics, feature distributions and relationships between different attributes – just to name the key tasks. It is a really important procedure bef...

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