**Survival Analysis With Generalized Models: Part II (time discretization, hazard rate integration and calculation of hazard ratios)**

In the second part of the series we will consider the time discretization that makes the Poisson GAM approach to survival analysis possible.

**Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)**

In the third part of the series on survival analysis with GAMs we will review the use of the baseline hazard estimates provided by this regression model.

**Survival Analysis With Generalized Additive Models : Part IV (the survival function)**

The ability of PGAMs to estimate the log-baseline hazard rate, endows them with the capability to be used as smooth alternatives to the Kaplan Meier curve. If we assume for the shake of simplicity that there are no proportional co-variates in the PGAM regression, then the quantity modeled corresponds to the log-hazard of the survival function.

**Graphics Window for MacOS and RStudio Server**

It’s been a recurring headache to get graphics windows to open seamlessly in MacOS and Windows, running RStudio locally and RStudio Server. A solution has been proposed by Professor David Zeitler. He has modified the openGraph function in the DBDA2E-utilities.R script. If you use a Mac, or you use RStudio Server, please check it out and let us know whether or not it works for you by entering a comment at the bottom of this post. To make the change, just open DBDA2E-utilities.R in an editing window. Comment out the current definition of openGraph (don’t delete it, in case you need to recover the original version). Insert the new function definition listed below, and be sure to save the edited version. Then source the script and try using openGraph or any existing script that calls it.

**Introducing Radiant: A shiny interface for R**

Radiant is a platform-independent browser-based interface for business analytics in R, based on the Shiny package.

**R: Think Bayes Locomotive Problem – Posterior Probabilities for Different Priors**

A railroad numbers its locomotives in order 1..N. One day you see a locomotive with the number 60. Estimate how many loco- motives the railroad has.

**Emojineering Part 1: Machine Learning for Emoji Trends**

In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we’ll discover the hidden semantics of emoji.

**Hands-on with dplyr**

This is meant to be an introduction to dplyr which covers dplyr basics, gets into a little bit of programming with dplyr and ends with brief mention of some gotchas and a benchmarking comparison to base for the split-apply strategy. You might also find Rstudio’s Data Wrangling Cheat Sheet featuring dplyr useful (this is also where I borrowed some of the images used in this tutorial). The dplyr package from Hadley Wickham is plain awesome. It features consistent and succinct syntax, is computationally fast and getting better with every release. The dplyr package has replaced many common more verbose R idioms which I had to previously rely upon for most common data analysis tasks. For example, many data analysis tasks involve the procedure of splitting the data set based on a grouping variable and then applying a function to each of the groups (split-apply). Lets say I want to calculate the median values for a few parameters for cars with different numbers of cylinders using the mtcars data set.