Because it's Friday: Shake that globe

It’s been a while since a major earthquake has overtaken the headlines, but as this animation shows (source data here), major earthquakes aren’t actually all that rare: it’s just (relatively) rare that they occur in heavily populated areas. It’s a really nice information design: the size of the circle represents the magnitude of each earthquake, and the color represents its depth. The way the circles appear and then slowly shrink away is a great way of visualizing the geographic impact of these inherently short-lived incidents. I also like the way it’s represented on an actual globe, even though half of the data is obscured at any one time, as it gives a better sense of the geographic relationships. Nonetheless, NOAA also produced a whole-earth projection version of the same animation: That’s all from us for this…
Original Post: Because it's Friday: Shake that globe

R 3.4.1 "Single Candle" released

The R core team announced today the release of R 3.4.1 (codename: Single Candle). This release fixes a few minor bugs reported after the release of R 3.4.0, including an issue sometimes encountered when attempting to install packages on Windows, and problems displaying functions including Unicode characters (like “日本語”) in the Windows GUI. The other fixes are mostly relevant to packages developers and those building R from source, and you can see the full list in the announcement linked below. At the time of writing, Windows builds are already available at the main CRAN cloud mirror, and the Debian builds are out, but Mac builds aren’t there quite yet (unless you want to build from source). Binaries for all platforms should propagate across the mirror network over the next couple of days. r-announce mailing list: R 3.4.1 is released Related …
Original Post: R 3.4.1 "Single Candle" released

R 3.4.1 "Single Candle" released

The R core team announced today the release of R 3.4.1 (codename: Single Candle). This release fixes a few minor bugs reported after the release of R 3.4.0, including an issue sometimes encountered when attempting to install packages on Windows, and problems displaying functions including Unicode characters (like “日本語”) in the Windows GUI. The other fixes are mostly relevant to packages developers and those building R from source, and you can see the full list in the announcement linked below. At the time of writing, Windows builds are already available at the main CRAN cloud mirror, and the Debian builds are out, but Mac builds aren’t there quite yet (unless you want to build from source). Binaries for all platforms should propagate across the mirror network over the next couple of days. r-announce mailing list: R 3.4.1 is released
Original Post: R 3.4.1 "Single Candle" released

Stan Weekly Roundup, 30 June 2017

Here’s some things that have been going on with Stan since the last week’s roundup Stan® and the logo and were was granted U.S. Trademark Registrations No. 5,222,891 and U.S. Serial Number: 87,237,369. Hard to feel special when there were millions of products ahead of you. Trademarked names are case insensitive and they required a black-and-white image, shown here. Peter Ellis, a data analyst working for the New Zealand government, posted a nice case study, State-space modelling of the Australian 2007 federal election. His post is intended to “replicate Simon Jackman’s state space modelling [from his book and pscl package in R] with house effects of the 2007 Australian federal election.” Masaaki Horikoshi provides Stan programs on GitHub for the models in Jacques J.F. Commandeur and Siem Jan Koopman’s book Introduction to State Space Time Series Analysis. Sebastian Weber…
Original Post: Stan Weekly Roundup, 30 June 2017

Data Visualization with googleVis exercises part 5

Candlestick, Pie, Gauge, Intensity Charts In the fifth part of our journey we will meet some special but more and more usable types of charts that googleVis provides. More specifically you will learn about the features of Candlestick, Pie, Gauge and Intensity Charts. Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin! Answers to the exercises are available here. Package & Data frame As you already know, the first thing you have to do is install and load the googleVis package with:install.packages(“googleVis”)library(googleVis) Secondly we will create an experimental data frame which will be used for our charts’ plotting. You can create it with:co=data.frame(country=c(“US”, “GB”, “BR”),population=c(15,17,19),size=c(33,42,22)) NOTE: The charts are created locally by your browser. In case they are…
Original Post: Data Visualization with googleVis exercises part 5

HexJSON HTMLWidget for R, Part 3

In HexJSON HTMLWidget for R, Part 1 I described a basic HTMLwidget for rendering hexJSON maps using d3-hexJSON, and HexJSON HTMLWidget for R, Part 2 described updates for supporting colour. Having booked off today for emergency family cover that turned out not to be required, I had another stab at the package, so it now supports the following additional features… Firstly, I had a go at popping some “base” hexjson files into a location within the package from which I could load them (checkin). Based on a crib from here, which suggests putting datafiles into an extdata folder in the package inst/ folder, from where devtools::build() makes them available in the built package root directory. hexjsonbasefiles With the files in place, we can use any base hexjson files included included in the package as the basis for hexmaps. I…
Original Post: HexJSON HTMLWidget for R, Part 3

Why Artificial Intelligence and Machine Learning?

[unable to retrieve full-text content]The next step for any artificial intelligence or machine learning solution is to specify ​how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
Original Post: Why Artificial Intelligence and Machine Learning?

Applying Deep Learning to Real-world Problems

[unable to retrieve full-text content]In this blog post I shared three learnings that are important to us at Merantix when applying deep learning to real-world problems. I hope that these ideas are helpful for other people who plan to use deep learning in their business.
Original Post: Applying Deep Learning to Real-world Problems

Cubic and Smoothing Splines in R

Splines are a smooth and flexible way of fitting Non linear Models and learning the Non linear interactions from the data.In most of the methods in which we fit Non linear Models to data and learn Non linearities is by transforming the data or the variables by applying a Non linear transformation.Cubic Splines Cubic Splines with knots(cutpoints) at (xi_K , K = 1, 2… k) is a piece-wise cubic polynomial with continious derivatives upto order 2 at each knot. They have continuous 1st and 2nd derivative. The order of continuity is = ( (d – 1) ) , where (d) is the degree of polynomial. Now we can represent the Model with truncated power Basis function (b(x)). What happens is that we transform the variables (X_i) by applying a Basis function (b(x)) and fit a model using these…
Original Post: Cubic and Smoothing Splines in R