Live Earthquakes App

It's awesome when you are asked to build a product demo and you end up building something you actually use yourself.

That is what happened to me with the Live Earthquake Shiny App.  A few months ago, as part of the JHU Data Science Specialization course, I was tasked to build a data product demo using the shiny package in R. I'd already had some experience with shiny, but this time I wanted to build an app showing real-time data. Something people would like to monitor regularly and see if something special happened during the last couple of days.

I am not at all an expert on earthquakes, but I thought this would make a great use case for a real-time data visualization. And now, every time I hear from the news of a new earthquake episode I go to double check it in my app and see what else is going on.

The app does the following:

• Retrieve the latest version of data available from USGS website. Data comes in a .csv file and reports quakes for the past 7 days (check the exact URL in the R code).
• Subset the dataset in case the user chooses to see only data from yesterday.
• Plot earthquake data on a world map using the leaflet library.
• Calculate a few basic metrics like max and number of occurrences.
• Force a manual refresh of data if the user press the button “Update Data”.

Here is a snapshot of the app. You can use the app here in shinyapps.io server.

Also you check out the code here.

If you click on each circle some basic info about the quake are shown: place, time, magnitude and depth. Circles colors are based on the magnitude (the darker the stronger). If you wonder how I classified them from minor to strong below is the scale I used:

Hope you'll have a chance to explore it! Enjoy.

Actionable Data Analysis for Ecommerce Products

Managing an Ecommerce shop backed with proper transactional/warehouse database and a digital analytics collection platform (e.g. Google Analytics) means having access to lots and lots of data. The types of analysis you can do are uncountable. It depends of course on the business question you need to answer.

However, the unique added value of your analysis is very often  represented by how much actionable their results are for the business. In this post I am going to demonstrate a few examples of actionable analysis you can do with your ecommerce business data.

I will take some data from the Google Merchandise Site (there is a free GA demo account) and use Tableau to create the visualizations.

How to Upgrade R version in Windows. The easy way recommended on CRAN

Today I have found myself needing to upgrade R. Main reason was that my current version  R-3.2.1 did not support some new graphic packages. To install these new packages I needed at least a R-3.3 version.

After a bit of initial hesitation (will I lose my packages during the new installation? etc. etc.) I finally took some courage and decided to follow the official documentation on CRAN. Everything worked just fine and I have now installed the latest available R version on CRAN: at the time I write this post it´s R-3.3.1.

The upgrading process was really easy, so I thought to share it step by step. Enjoy :)

Analyzing Stack Overflow questions and tags with the StackLite dataset

The guys at Stack Overflow have recently released a very interesting dataset containing the entire history of questions made by users since the beginning of the site, back in 2008. It's called StackLite and it contains, for each Stack Overflow question the following data:

• Question ID
• Creation Date
• Closed Date (when applicable)
• Deletion Date (when applicable)
• Score
• Owner user ID
• Number of answers
• Tags 

As David Robinson explains in his introductory post, the Stacklite dataset is designed to be easy to read and analysed with any programming language or statistical tool. A fantastic resource if you are a data analyst/scientist and want to crunch some real data! 

I thought to give it a go and perform some exploratory analysis using R. More specifically, I am going to answer the following business questions:

• What are the most popular tags?
• How many questions have more than one tag?
• What is the overall closure rate for the site and which tags present higher values?
• How much time it takes, on average, to close a question?
• Which tags tend to have higher/lower score?
• And in particular: how data science languages perform on the above questions?

Google Analytics makes Demo Account available to all

Playing with GA data is much much easier now.

Last week biggest news was definitely Google making a Demo Google Analytics Account available to everyone. As the word "demo" says, the main purpose is demonstrating all the features and reports GA offers, and become a learning platform for analysts. But it´s actually real numbers! All the data available come from the Google Merchandise Store (which sells Google branded merchandise), so you can apply your favorite algorithm, find valuable insights from the data and show off your analytics skills to others.

Click on this link to access the GA Demo Account.

• If you already have a Google Analytics account, Google will add the demo account to it (then you can access it via the Home tab in Google Analytics).
• If you do not have a Google Analytics account, it will create one for you in association with your Google account (yes you need a Google account first) and add the demo account to it.

What can you do with the GA Demo Account?

Where to Live in Barcelona in a Dashboard

Sometimes data can tell a story much faster and effectively than many words. That's why I´ve decided to start sharing more data stories via this blog, hoping to both:

1. address specific topics readers want to dive in (often these will not be data-people, they would be new to my blog, probably coming after googling a specific questions e.g. "which are the best boroughs to live in Barcelona?").
2. showcase data visualization tools and best practices to present your data (these are data-people, yes you my regular readers, you might like to see a tool in action).

Enhance your Blog Measurement with these Google Analytics Calculated Metrics

Google Analytics has recently incorporated a new powerful feature that offers more flexibility for measuring your own business objectives. I am talking about calculated metrics.

In this post I am going to suggest a list of calculated metrics that you can easily configure in Google Analytics to better measure your blog performance.

As a blogger, when it comes to measure performance of my content, I am very focused on measuring readers engagement with the content I publish. Also, I am constantly looking to increase my readers base, giving my blog more exposure and acquiring new subscribers. Here is an outline of my measurement plan using Google Analytics (I highly recommend this read if you are new on the concept of digital measurement plan).

The new calculated metrics feature gives me the opportunity to customize my own measurement plan. How?