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Data Analysis with R and Python

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Article from ADMIN 25/2015

By Andreas Möller

The statistical programming language R dissects its database in a masterful way, and you can embed your R in Python using the Rpy2 interface.

Large volumes of data are most useful if you can study them with intensive data analysis. The open source language, R [1], is a powerful tool for evaluating an existing database. The R language offers a variety of statistical functions, but R can do more.

This article shows how to use R for a sample application that evaluates comet data. An Apache web server visualizes the results of statistical reports – with the help of web technologies such as HTML, JavaScript, jQuery [3], and CSS3, which Python creates in combination with R and the MongoDB [4] database.

Comet Rising

Figure 1 shows how the report generator of the sample application displays the comet data in Firefox. The selection list at the top lets the user select a report variant. If you click on Send, JavaScript sends an HTTP request to the Apache web server, which then generates the report.

Figure 1: The comet data application in Firefox.

The Python scripts first save the comet data in a MongoDB database. Scripts then parse the data and draw on R to create the report in the form of a graphic, which ends up as a PNG file in a public directory on

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