How about some visual takeaways from the IMF’s World Economic Outlook? Recently I prepared two nifty data visualizations with Tableau that I like to share with you.
These visualizations allow you to explore plenty of economical data, including IMF staff estimates until 2020. Don’t forget to choose “Units” after switching “Subject” on the right-side bar. A detailed description on each subject is displayed below.
Recently Tableau released an exciting new feature: R integration via RServe. Tableau with R seems to bring my data science toolbox to the next level! In this tutorial I’m going to walk you through the installation and connecting Tableau with RServe. I will also give you an example of calling an R function with a parameter from Tableau to visualize the results in Tableau.
1. Install and start R and RServe
You can download base R from r-project.org. Next, invoke R from the terminal to install and run the RServe package:
[Update 26 Jun 2016]: Tableau 8.1 screenshots were updated with Tableau 10.0 (Beta) screenshots due to my upcoming Advanced Analytics session at TC16, which is going to reference back to this blog post.
I became a Python geek and GnuPlot maniac since I joined CERN around three years ago. I have to admit, however, that I really enjoy the flexibility of D3.js, and its capability to render histograms directly in the web browser.
The following example loads a CSV file, which includes 10,000 dimuon events (i.e. events containing two muons) from the CMS detector, and displays the distribution of the invariant mass M (in GeV, in bins of size 0.1 GeV):
Feel free to download the sample CSV dataset here.
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