Automating web analytics through Python

Web analytics is a fascinating domain and important target of data science. Seeing where people come from (geographical information), what they do (behavioral analyses), how they visit (device: mobile, tablet or workstation), and when they visit your website (time-related info, frequency etc), are all different metrics of webtraffic that have potential business value. Google Analytics is one of the available open-source tools that is highly used and well-documented. The current blog deals with the case how to implement web analytics in Python. I am enthusiastic about the options that are available inside Google Analytics. Google Analytics has a rich variety of metrics and dimensions available. It has a good visualization and an intuitive Graphic User Interface (GUI). However, in certain situations it makes sense to automate webanalytics and add advanced statistics and visualizations. In the current blog, I will show how to do that using Python.

The writexl package: zero dependency xlsx writer for R

We have started working on a new rOpenSci package called writexl. This package wraps the very powerful libxlsxwriter library which allows for exporting data to Microsoft Excel format.

Infographic-style charts using the R waffle package

Infographics. I’ve seen good examples. I’ve seen more bad examples. In general, I prefer a good chart to an infographic. That said, there’s a “genre” of infographic that I do think is useful, which I’ll call “if X were 100 Y”. A good example: if the world were 100 people. That method of showing proportions has been called a waffle chart and for extra “infographic-i-ness”, the squares can be replaced by icons. You want to do this using R? Of course you do. Here’s how. There’s not much more here than you’ll find at the Github home of the R packages, waffle and extrafont. I’ve just made it a little more step-by-step.

Naive Principal Component Analysis (using R)

Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set the those yourself, based on whatever prior information or purposes you had. I will tackle the naive method, mainly by following the guidelines in Field, Miles, and Field (2012), with updated code where necessary. This lecture material was also useful. The ‘naive’ approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables. I ascribe these to the ‘naive’ or formal approach because either or both could potentially be skipped in exceptional circumstances, where the purpose is not scientific, or where enough information exists in advance.

Combined Linear Congruential Generators with R

Combined linear congruential generators, as the name implies, are a type of PRNG (pseudorandom number generator) that combine two or more LCGs (linear congruential generators). The combination of two or more LCGs into one random number generator can result in a marked increase in the period length of the generator which makes them better suited for simulating more complex systems.