Hafidz Zulkifli

Traditionally in R, an H2O instance that is created using h2o.init() will be shared by everyone else. This poses a problem since because the resources (threads) are shared - everyone can't basically do work at the same time. While h2o.init() itself has a function to specify which port and ip, it is currently buggy since you can't initiate an instance without using the 54321 port. To work around this, you'll need to initiate an h2o instance externally from unix. From there, you can then connect directly to your defined instance.In addition, connecting to an instance in this manner provides another layer of protection, since you're no longer relying on libraries that are loaded in R (libraries that are loaded in R can be removed by other users using RStudio). You may use the following command to: 1. Create h2o instance externally 2. Connect to your h2o instance from R. So basically from R; launchH2O system(command = launchH2O, intern =FALSE, wait=FALSE) h2o....

Below is a great course by Stanford University on Natural Language Processing. There hasn't been any recent sessions as of late in Coursera, but you can still access the archive at this  link. I'm currently working on my capstone for the John Hopkins Data Science Specialization, where we're asked to build a data product that is able to predict the next sets of word based on what users type into a textbox - similar to stuff like Google Autocomplete or Swiftkey. Pretty psyched about it - looking forward to the challenge! :)

One of the reason I like to join these online courses are that it gives you the chance to meet with people from different backgrounds, industries and countries. In this particular post, I'm quite amazed with the dedication and thought that was put in by one of my classmates. The assignments required that we create our own data product using R - so that we become a wholesome data science practitioner - we acquire data, process, model, document, and create data products for others to consume. It's one thing for doing assignments for the sake of completing the course, it's another to produce a beauty such as the above. You may explore the Shiny app here at this link , and have a look at the forked source code here . I've forked it since I know I'll be making use of this in times to come.

Lately I've been studying Shiny and how to use in R. It's a cool arsenal to have while using R as you can really quickly develop a data product right from R itself. Of course you could say load the data up in tools like Tableau or Qlik Sense are have a much cooler/sexier visualisation - but that's not the point I'm trying to bring here. For a quick preview of what I managed to conjure up with Shiny, pop over to Social Network  . Couldn't help it - it just had to be a network graph - again :P. A real sucker for graphs I am. Anyways, what it aims to demonstrate are how from a social network graph like that, you can derive the centralities (degree, closeness etc) and from there - the roles of each nodes based on how they are connected to each other. I'll not be making the claim that it's correct in any way - it's just something that I've picked up from Drew Conway, based on his presentation on Socio-Terrorism . For more details - check ou...

The below excerpt are taken from  this page.  Copying it here for future reference in finding the right R packages for different types of analysis - god knows it's hard to find the right packages in R. :) Several add-on packages implement ideas and methods developed at the borderline between computer science and statistics - this field of research is usually referred to as machine learning. The packages can be roughly structured into the following topics: Neural Networks  : Single-hidden-layer neural network are implemented in package  nnet  (shipped with base R). Package  RSNNS  offers an interface to the Stuttgart Neural Network Simulator (SNNS). An interface to the FCNN library allows user-extensible artificial neural networks in package  FCNN4R . Recursive Partitioning  : Tree-structured models for regression, classification and survival analysis, following the ideas in the CART book, are implemented in  rpart  (shipped w...

There's basically two ways that I know of to clear up memory in R. 1. rm( )  2. gc() The first one basically removes your variable of vector or data frame from your workspace. But somehow rather the memory can sometimes (or in my case, all the time) still be consumed by R based on Task Manager. That's when garbage collection ( gc() ) comes in handy.  For more info on gc() , use ?gc

Excerpt from the article Cross Validation techniques in R: A brief overview of some methods, packages, and functions for assessing prediction models  by Dr. Jon Starkweather. Cross validation is useful for overcoming the problem of over-fitting. Over-fitting is one aspect of the larger issue of what statisticians refer to as shrinkage (Harrell, Lee, & Mark, 1996). Over-fitting is a term which refers to when the model requires more information than the data can provide. For example, over-fitting can occur when a model which was initially fit with the same data as was used to assess fit. Much like exploratory and confirmatory analysis should not be done on the same sample of data, fitting a model and then assessing how well that model performs on the same data should be avoided For more info, refer to the actual article .

Notes below are documented for future reference should I ever need to use them again someday. A pharmaceutical company is interested in testing a potential blood pressure lowering medication. Their first examination considers only subjects that received the medication at baseline then two weeks later. The data are as follows (SBP in mmHg) Subject Baseline Week 2 1 140 132 2 138 135 3 150 151 4 148 146 5 135 130 Consider testing the hypothesis that there was a mean reduction in blood pressure? Give the P-value for the associated two sided T test. (Hint, consider that the observations are paired). Solution: baseline c ( 140 , 138 , 150 , 148 , 135 ) week2 c ( 132 , 135 , 151 , 146 , 130 ) round ( t.test ( baseline , week2 , paired = TRUE ) $ p.value , 3 ) ## [1] 0.087 Researchers conducted a blind taste test of Coke versus Pepsi. Each of four people was asked which of two blinded drinks given in random order that they preferred. The data was such that 3 of the...

As usual, the notes below are documented for future reference should I ever need to use them again someday. Also to note, most of the exercises this time around requires that you’d be able to generate a random data set in a precise manner - else you wouldn’t get the same answer as what was provided in the exercises. You’d think that R already has a function on that but sadly no. Due to the small sample sizes in most of the questions, rnorm wouldn’t usually give you an accurate result. It’d instead give you something like this: a rnorm ( 10 , 5 , 1 ) mean ( a ) ## [1] 5.081355 sd ( a ) ## [1] 0.7624848 Lucky for me, I was able to find this nice function right here  Stack Overflow rnorm2 function ( n , mean , sd ) { mean + sd * scale ( rnorm ( n ) ) } It’s very similar to rnorm - only more precise and behaves exactly as you’d expect a random number generator to behave. Let’s take it for a test run. b rnorm2 ( 10 , 5 , 1 ) mean ( b ) ## [1] 5 sd ( b ) #...