Turning your R (or Python) models into APIs

More and more real-world systems are relying on data science and analytical models to deliver sophisticated functionality or improved user experiences. For example, Microsoft combined the power of advanced predictive models and web services to develop the real-time voice translation feature in Skype. Facebook and Google continuously improve their deep learning models for better face recognition features in their photo service.

Some have characterised this trend as a shift from Software-as-a-Service (SaaS) to an era of Models-as-a-Service (MaaS). These models are often written in statistical programming languages (e.g., R, Python), which are especially well suited to analytical tasks.

With analytical models playing an increasingly important role in real-world systems, and with more models being developed in R and Python, we need powerful ways of turning these models into APIs for others to consume.

But how?

It is actually a lot easier than you might think. I wrote a step-by-step guide about deploying analytical models as REST APIs. This guide will walk you through how to set up your own MaaS WITHOUT a team of full-stack developers/engineers. All you need are the R/Python models you develop and a Domino Data Lab account. You can find the full article on ProgrammableWeb here.

You can also find my slides for the related LondonR talk here:

Deploying your Predictive Models as a Service via Domino from Jo-fai Chow

When I created this blog back in 2013, my aim was simply to learn ggplot2. Thanks to the feedback and advice from the R community, I continued to learn new stuff and somehow found an opportunity to work for Domino and Virgin Media. I wouldn't say I have seen enough to make a fair comparison with other programming communities. But so far the support from the R community, for me, has been truly special! I believe blogging is one of the best ways to contribute so I better get back to the writing habit! For the next post, I would like to talk about using R with other Microsoft tools (SQL Server, PowerPoint) in a commercial environment.

London Kaggle Meetup

I met Alex Glaser and Wojtek Kostelecki after my LondonR talk. They have already set up a meetup for Kagglers. We are working on a collaborative project to build / test / stack models on the Domino platform. For more information, join the meetup first. Let's Kaggle together!

Slidify my R journey from @matlabulous to rCrimemap

My LondonR Talk

Thanks to Mango Solutions (LondonR organiser), I was given the opportunity last night to talk about my mini project ‘CrimeMap’. Instead of going through all the technical details behind the scenes, I chose to talk the audience through my R journey from a noob to a heavy user. CrimeMap was used as a case study to show how ones can benefit from learning R (or, in some ways, trying to justify the time I spent staring at RStudio IDE last year). The feedback was really great and the talk effectively expanded my network in the data science community so I am really grateful for that! You can find my presentation here.

Before the main event, there was an excellent R-Python workshop by Chris Musselle. The other two interesting presentations were "Dynamic Report Generation" by Kate Hanley and "Customer Clustering for Retail Marketing" by Jon Sedar. Their presentations will soon be made available here.

CrimeMap - A Wonderful Learning Experience

When I first started learning R for real, the goal was very simple - "let's plot something pretty with ggplot2". Well, a lot has changed since then. The more I learned, the more I discovered. It is really hard to summarise the 'R' awesomeness in a few slides due to its diversity. One thing I am absolutely certain is that I made the right move about a year ago to shift from MATLAB to R. Yet, I am keeping my twitter account name @matlabulous just to remind myself that ones should always keep an open mind for new and evolving technology (... and should avoid getting a tattoo of your potential ex-gf/bf's name. On that note, no, I don't have a tattoo.) For more information about the CrimeMap, please see my previous posts here, here and here.

Using Slidify for Professional Presentation

The talk was also the first time I presented something totally unrelated to water engineering. I thought, for a change, let’s try something different. Then I remembered looking at the Slidify slides from Jeff Leek’s Data Analysis course back in Jan-March last year. I thought that would fit perfectly for LondonR because the whole presentation would be coded completely in R. It would be a good reason to learn Slidify too. So I went through the Slidify examples, put some slides together, tweaked the CSS a little bit and then published it to GitHub – a streamline Slidify workflow well thought and designed by Ramnath Vaidyanathan. To me, the results are amazing! So amazing that I am confident to leave PowerPoint and use Slidify for professional presentations in the future.

rMaps + CrimeMap = rCrimemap

Two weeks before the presentation, I wrote an email to Ramnath as I wanted to thank him for Slidify. I told him how I enjoyed using Slidify for the LondonR slides. Out of the blue, Ramnath told me that he had seen my CrimeMap already and he kindly pointed me to this blog post about using Leaflet heat map in rMaps. I thought, OMG, why now? Then I thought, yeah, why not? So I created a new package called ‘rCrimemap’ based on Ramnath’s example and the codes from the CrimeMap project – just in time for the LondonR meeting. At first, I wanted to called the package something different but eventually I chose rCrimemap so it aligns well with Ramnath’s rCharts and rMaps.

Using ‘rCrimemap’

rCrimemap is still raw and experimental. It depends on some new packages such as dplyr, dev version of rCharts and rMaps etc. I have only developed and tested it recently on Linux. Please give it a try if you have a chance. All feedback and suggestions are welcome. Codes are here.

To install it, you will need the RStudio IDE version 0.98.501 or newer and the following packages ...

require(devtools)
install.packages(c("base64enc", "ggmap", "rjson", "dplyr"))
install_github('ramnathv/rCharts@dev')
install_github('ramnathv/rMaps')

After that, install rCrimemap package via ... 

install_github('woobe/rCrimemap')

rCrimemap is basically a big wrapper function. In fact, there is only one function 'rcmap( )' in the package at the moment. (OK, it is obviously an overkill ... but I really wanted to try developing a package.) The function is very similar to the first one I did for CrimeMap prior to the Shiny development. In terms of graphical functionality, it is not as flexible as the CrimeMap yet (for example, CrimeMap can do all these colours and facet). However, it is much more powerful than CrimeMap in the sense that users can move around, zoom in and out like using a real digital map. The colour of the heat map also changes when you zoom in/out. This gives users a much better visibility of where the local crime hot spots are when they zoom in. OK, enough said, let’s go through some example usage …

The arguments of the function 'rcmap( )' are:

1. location: point of interest within England, Wales and Northern Ireland
2. period: a month between Dec 2010 and Jan 2014 (in the format of yyyy-mm)
3. type: category of crime (e.g. "All", "Anti-social behaviour")
4. map_size: the resolution of the map in pixel (e.g. Full HD = c(1920, 1080))
5. provider: the base map provider (e.g. "Nokia.normalDay", "MapQuestOpen.OSM")
6. zoom: zoom level of the map (e.g. I recommend starting with 10 to show all the crimes)

Example 1: “Ball Brothers EC3R 7PP” (LondonR venue since March 2013) during the London riot (Aug 2011). The map can be viewed within RStudio IDE or be exported to a browser. The animation was created outside R (Oh ... what if rCrimemap + animation package? ... I will leave that for later.)

rcmap("Ball Brothers EC3R 7PP", "2011-08", "All", c(1000,1000),"Nokia.normalDay")

Example 2: Manchester in Jan 2014 - using "MapQuestOpen.OSM" as base map instead.

rcmap("Manchester", "2014-01", "All", c(1000,1000), "MapQuestOpen.OSM")

Credits

There you go, enjoy :)

CrimeMap, LondonR and a Book Review

In preparation for my LondonR talk in March, I am polishing up my CrimeMap (see previous blog post here and here) in my spare time.

Thanks to Chris Beeley and Packt, I won a free e-copy of Chris Beeley’s book following his great talk about Shiny web app during the last LondonR meeting. I find this book really useful as I am trying to implement new functionality and ideas into my CrimeMap. It illustrates very well what you can do with Shiny using lots of practical examples. So here is a quick book review for those who are also interested in developing Shiny web apps.

The book begins with a short but essential introduction to some key R functions for handling data and graphics. Chapter 2 is a walk-through of key Shiny components nicely demonstrated by an example of Google Analytics API integration. It then discusses how Shiny can be further extended with the use of HTML, CSS, JavaScript and jQuery. I find chapter 4 most useful as it goes deep into the practical aspects of handling reactivity and taking full control of inputs and outputs. The book ends with some tips on code sharing and browser compatibility.

I hope you will find this short review useful. Reviews from others can be found here, here and here. 

BTW, LondonR is great (thank you very much Mango Solutions for sponsoring it since 2009)!!! You can find the presentations from previous meetings here.