My first couple of months at Codurance

Some background

Five Characteristics of a Great Company CultureSome of you may know me from the various meetups in the city, especially my attendance at a number of LJC and LSCC meetup events. Attending these events I learnt about various conferences like Devoxx, SoCraTes, JAX LondonJava2Days, OpenFest, and I ended up attending and later presenting on various topic including Adopt OpenJDK.

During this time I met a lot of people with various levels of experience and my interest and urge to learn more about the Java/JVM platform, Code Quality, Software Design, XP Practices, Software Craftsmanship, etc…, were on the rise and saw no end. And whilst attending these events I came across Sandro and Mash, who were in those days hosting LSCC events. I went to many of LSCC events, especially liked the hands-on sessions (which are still my favourite).

I also noticed that many things I learnt at such events and conferences wouldn’t always be immediately recognised or accepted at the workplace. And moving to another work environment didn’t always solve this problem fully. I found that I wasn’t learning what I wanted from my peers and the things I learnt from the community I couldn’t apply at work. Besides very few were really in tuned with what the community was about. So one fine day I decided to take charge of my career and make a serious decision and take up the Apprenticeship program offered by Codurance and go through the process.

I was urged to go this way after being inspired by Sandro’s book: The Software Craftsman, attending all the SoCraTes UK conferences, and meeting with developers who valued and took pride of their work namely their craft.

I was urged to go this way after being inspired by Sandro’s book: The Software Craftsman, attending all the SoCraTes UK conferences, and meeting with developers who valued and took pride of their work namely their craft.

Where we are just now

It’s now been nearly two months since I have been working for Codurance, a formidable force. And so it’s also about time that I share my experiences with my fellow mates and the community around me.

During my first few weeks at Codurance, I have been busy learning various things that have been chalked out for becoming a craftsman.

When working on a kata or learning a concept, we paired or did what is known as ‘mob programming’ along with other apprentices and craftsmen. And most of the time used the pomodoro technique. Time boxing our work in intervals is something done both in groups and working individually. We would have a lot of discussions and retrospectives after working on a problem or writing some code from scratch.

Structure of my program

We used an internal tool based on the concept of Impact Mapping. I soon got interested in it when I saw my colleague Franzi (who is now a craftswoman) had used it to plan out her Apprenticeship route. Such a tool helps map out our goals and the tasks we need to perform to achieve it. And this can differ from person-to-person, depending on what they want to work on (driven by the Apprentice).

My mentor and other craftsmen reviewed them to get an idea of what I wanted to achieve for myself. And then its up to me to apply my own drive and perseverance to achieve the individual stories. My mentor and I meet and talk informally on a regular basis, many times pairing on a kata or a project or on the white board trying to get my head around a concept.

Days in the life of an Apprentice

I found the working hours quite flexible, remote working is also an option (when you are on the bench or if the client allows, if you are in a project). Our co-founders are understanding and compassionate about our individual situations.

Meetings are at their minimum, except for a weekly Apprentices meeting (run by an Apprentice and guided by at least one Craftsperson) and a bi-monthly company-wide catchup.

The Apprentices meetings are full of fun — we are accompanied by at least one Craftsperson, who disperses their knowledge and experience from a wide variety of topics designed to help us in the journey and fill the gaps in our knowledge and experience.

A bi-monthly catchup involves sharing of knowledge via lightning talks, discussions and pairing sessions on pet projects over pizzas and beer (and of course veggies and non-alcoholic beverages for the teetotalers).

Katas, code reviews, mob programming and projects make up a learning week – all of these done individually or when pairing with another.

Katas

On a daily basis I have worked on different katas or try to solve the same kata in various different ways (using different testing and refactoring approaches). This in turn gave me better insights into designing and refactoring techniques. Trying to solve the same problem in different ways has a positive impact on our problem solving skills especially when writing code. In my case I also learnt how to use the different libraries and methods to write tests. I would like to cite Samir, thanks to you, for the suggesting this approach during the first week of my Apprenticeship.

Code reviews

Just last week we did a group code review and time-boxed ourselves, performed a retrospective at the end of each interval and ensured we delivered a good chunk of the feedback before close of play. Such regular code review exercises are helping all of us learn about how to code better as we are not only learning from feedback from the tools we used, but also through exchange of feedback from our peers who were involved in the group code review session.

Software Design, Specification Gathering & Communication

Recently we had an interesting mob-programming session where we were trying to model and write a game. At the end of the session, we had a retrospective, discussing the things we did well and didn’t do well. Each of the apprentices and craftsmen were performing a specific role i.e. Developer, Domain Expert, etc… We learnt in retrospective, about areas where we could have done better and should focus on. That any test written gives immediate feedback about how well we have understood the domain and if we were taking the right approach. Why a certain approach when starting a project is more advantageous than another approach. What questions to ask and why it is important to ask the right questions to the domain expert or to give the right level of information to another developer and vice-versa. Sandro has described this process in detail in his blog post recently.

Fun, socialising and sharing

I found our office environment to be conducive to learning, sharing and collaboration. We even have a pairing rota that we use from time-to-time to record or suggest pairing sessions during the week.

We share links to events, conferences, tweets, interesting articles, videos, blog posts, etc… via slack, document discussions and brain dumps via Google doc, huddles during lunch- and tea- breaks to talk about anything we are working on. Thanks to the library of printed and digital books to our disposal, the huge collection of blog posts and videos on our site.

The apprentices and some craftsmen have collectively started a social event which of course happens every Friday, sometimes it’s dinner at a nearby restaurant, while at other times an indoor movie over snacks and drinks at our office premises.

It is worthwhile and that’s why we are here

It is a privilege to be able to work alongside very experienced craftsmen from our industry. We are very lucky and thankful to have the opportunity to be guided and mentored by talented and like minded developers.

This is my first job where the company has a completely flat hierarchy and where we share similar values.

greatCompanyCulture

Closing note

Work is fun and learning is enjoyable when we love what we do and are amongst friends with similar goals and aspirations.

Thank you for taking the time to read this post and I hope it was interesting. Looking forward to write more and share such experiences in future posts.

Many thanks to Sandro, Tomaz, Alex, Franzi and David for all the feedback provided for this blog post.

 

SoCrates UK 2013 – my experiences!

Refactoring TDD habits

It’s about 15:21 on 19th September, a group of us from the London Software Craftsmanship Community gathered to leave for SoCrates UK 2013. We all gathered in the same and luckily found empty seats next to each other.

Amongst a lot of jokes, we suggested lets do a small dojo in the two hours we will be travelling. Wifi is free but terrible, on this train – nevertheless we tethered our phones and enabled Wifi on our laptops to make the most of the bandwidth.

This was also about the time when others sitting around me took notice of the desktop wallpaper, it looked something like this:

13 habits of good TDD programming

13 habits of good TDD programming

One thing lead to another, and before we knew, we were going through the above list of habits – some found the habits difficult to remember, others found duplicates and overlaps. There was this tendency that, this is a big list to remember when doing TDD!

So like developers we said we can “refactor” these habits into something more meaningful, maybe even organise them as per our thinking process.

From this conversation the below list was born:

12 habits to good TDD programming:
  1) Write the assertion first and work backwards
  2) Test should test one thing only
  3) See the test fail
  4) Write the simplest code to pass the test
  5) Refactor to remove duplications
  6) Don’t refactor with failing test(s)
  7) Write meaningful tests
  8) Triangulate
  9) Keep your test and model code separate (except when practising TDD-as-if-you-meant-it)
10) Isolate your tests
11) Organise your tests to reflect model code
12) Maintain your tests

You can notice already that the 13…habits shrunk to 12 habits, and their order changed in comparison to the original.

Then @Frankie mentions the difference between Test-first development and Test driven development, no one claimed to know it, but suggested the below:

Test-first development
– you start the development with tests, and change the tests if goal is not achieved via model (domain) code. – No guidelines about design, flexible approach.

Test driven development
– test drives the model code, you never change the test, only model code to reflect any changes that does not satisfy the tests! Guided by design.
Model code = production code or implementation, domain (better term)

Soon we arrived at Moreton-in-Marsh and our focussed changed to getting a taxi to the venue.

I continued contemplating with the idea of collaborating with other developers and ironing our this list – so it can be helpful at the least.

I met @gonsalo and @sandromancuso and ran the idea by them and they thought it was certainly an idea to present and get others involved to see what the final outcome could be.

Next morning everyone was proposing their presentations at the “Open Session”, and I took the chance of presenting – Refactoring TDD habits… in the Cheltham Loft room in the house called Coach at the Cotwold estate!

30 minutes into the conversation and we already attracted discussions between @sleepyfox and @sandromancuso, we did try to persuade them to avoid tangenting from our core topic of discussion.

@sandromancuso also shared with us one of the rules of simple design – as these have evolved over time, these sorta look like this on the flip chart he scribbled on:

4 rules of simple design

4 rules of simple design (thanks @racheldavies for taking this pic at the event, I have cropped it to fit it in here)

– passes all tests
– minimises duplication
– maximises clarity (clear, expressive, consistent)
– has fewer elements

The idea is that all your actions and practises could use these as guiding light to keep on track.

An hour and few minutes later and we have refactored and cleansed the original 13 habits down to 12 habits – reshuffled and rejuvenated. They may not be perfect but close (Kent Beck: use ‘perfect’ as a verb, not a noun – its a journey not a destination!

12 habits of good TDD programming

I sincerely hope that these list of habits do cover the essence of the principles, values and practises of TDD programming.

Prepare yourself with things you should or should not do, and then perform the Red-Green-Refactor actions to satisfy the TDD process.

The things some of the attendees not like is the way of the first list of habits were worded:

  • definition of the word ‘model’ or ‘domain’
  • use of the term duplication
  • the habits being assigned with numbers
  • use of the term assert – one assert per test or groups of assert per logical test

I also brought up the topic of the difference between Test-first development and Test driven development – and there were disagreements about it amongst the attendees, on the meanings of the definition itself. Please share with me your input!

Back to the final list of habits, @CarlosBle brought up a very valid point that some of the habits on the list might be time-based rather than relevant all the time – they were not linear but appear and disappear from the list depending on the current task in hand. We agreed we would sit together and work it out, but @sleepyfox was ahead of us and kindly drew this flow / state diagram on the flip chart that illustrated the list of habits but in a diagrammatic format:

Flow diagram of TDD habits

State/flow diagram of TDD habits (my apologies if its not clear, sometime down the line one of us could come up with a digital version of the state/flow diagram)

@CarlosBle – please feel free to come up with your time-based / non-linear list and share it with us when you get a chance.

Although we got a lot out of the session with discussions on various topics, we haven’t covered everything and finished discussing everything!

I’m more of the idea, that we could try to look at the above habits as trigger points (practical and pragmatic use) to make us do the right things when we are writing code with the intention of writing good code…clean code…quality code…whatever the terminology be in your environment.

At the end of the day, good practises become habitual only when practised repeatedly, with focus and intent.

Please do provide constructive criticism, if any, such feedback to the above are very welcome as they help improve the quality of the post!

Thanks to @sandromancuso & @sleepfox for helping and participating during my session. Big thanks to Socrates UK – its host, facilitators, organisors and sponsors for making this event happen!

My experience of learning R – from basic graphs to performance tuning

Background

R as some of you may know is a statistical and graphics programming language (see Wikipedia [1]) used by academia and recently by IT professionals of our ever growing software industry. There is a sudden demand for Data Scientists, Data Analysts and Statisticians with a background in R among other things data and development related subjects.

I have been fortunate to work with such a programming language, even though I haven’t had any prior experience working with such a programming language and moreover with Data Scientists. My interest in Mathematics and affinity for numbers drew me to learning it, and with further help of Herve Schnegg our in-house Senior Data Scientist, I was able to pick a fair bit of the subject.

 

R is a mix of a object-oriented programming, Clojure-like functional programming, Javascript-like style of writing code and a Smalltalk-like programming interface. And it offers REPL like many functional programming environments. The fundamental units of the data we manipulate are usually objects like lists, vectors, data-frames, tables, etc…
 
Initial baby-steps
 
I went through a few hours of tutoring by getting an understanding of the R environment, how to install it, and an overview of RStudio and how amazing it is! What fascinates me, is that you can load objects into memory and play with it and when you shutdown your environment your data is not cleared! Rather you can save it (into the .Rdata file) and it retains such information per project!
You are able to remove individual objects from memory, view them, modify them, and reload them from the command-line or by just executing single lines of code in your R script file (they have the obvious extension of .r) in an IDE like RStudio.
R gives developers access to a REPL (stands for Read–eval–print loop [2]) environment and thats how you are able to do the above actions seamlessly! A number of other popular languages have a similar environment i.e. Clojure, Haskell, Python, Ruby, Scala, and Smalltalk, and so forth.

More about R
The order of precedence with regards to declaring a function is important in R, you can’t just call a function unless it has been defined in the package/library you have loaded like:

library([name of library])

or included a resource using the source() function like:source(“./Utils.LoadAndVerify.r”)

or defined the function in the beginning of the script file before referring to it, at a later stage! I had to learn this by the trial-an-error-then-ask-the-experts-around-you method.

 

Contents of any object can be viewed by referring to the object at the REPL CLI, that’s kind of easy!

 

>  someObject <- “contents”
>  someObject [press enter]
[1] “contents” <==== output

 

I discovered another way to view the contents of an object especially when its a list, vector, data-frame, etc…, and is a bit cumbersome to read its output on the console. I learnt that the View() function displays the contents of the object in a tabular form in a separate floating window:

 

> View(table(someList))

 

(the object is displayed in a grid like table in a separate window, which could look like the below)

Plotting graphs from a set of numeric values contained in a list or vector in R is like doing 1..2..3…:

 > counts par(bg = "white");
 > barplot(counts, main="Car Distribution by Gears and VS",
   xlab="Number of Gears", col=c("darkblue","maroon"),
   legend = rownames(counts), beside=TRUE)

And voila, you get a nice simple looking bar graph!

Thanks to a helpful R blogger who has put together some resource for us: Using R to plot data [4].

We can do something more advance by running the below commands:

> x  y  f  z  par(bg = "white");
> persp(x,y,z,zlim=c(0,0.25), theta=50, phi=10);

…and we have the below nice looking 3D mesh (wireframe), from an angle:
Note: the par (bg=”white”) command sets the colour of the canvas for the entirety of your session.

 

Logging
I wrote my own suite of very simple logging functions that log messages to the console depending on the nature of the message, these messages can of course be piped into a text file at run-time.
log.INFO print(paste(date(), "[INFO]", message))
}

log.WARNING print(paste(date(), "[WARNING]", message))
}

log.DEBUG print(paste(date(), "[DEBUG]", message))
}

log.ERROR print(paste(date(), "[ERROR]", message))
}
Of course the above block of code could have been written like this:
MSG_TYPE_INFO <- "[INFO]"
MSG_TYPE_WARNING <- "[WARNING]"
MSG_TYPE_DEBUG <- "[DEBUG]"
MSG_TYPE_ERROR <- "[ERROR]"

log.ANY print(paste(date(), typeOfMessage, message))
}

log.INFO log.ANY(MSG_TYPE_INFO, message)
}

log.WARNING log.ANY(MSG_TYPE_WARNING, message)
}

log.DEBUG log.ANY(MSG_TYPE_DEBUG, message)
}

log.ERROR log.ANY(MSG_TYPE_ERROR, message)
}

As you will know, the way R is, it is wise to have logging functions to hand, to dump values of variables when running scripts. Just because sometimes the error messages thrown by R can be obscure, which has been my finding during my pursuits. Hence I resorted to the above functions and relieved myself from annoyances during exceptions.Later some passed me a link to an R Logging library (an implementation of log4j in R) [7].

What’s up!
At the moment I’m refactoring bits of code I wrote during the last two weeks and still have many blocks of code to go through to find suitable method functions to place them into – our purpose is to make the code more readable, scalable and maintainable.
Just now in the process of replacing the slow and verbose for-loop like constructs with their equivalent xapply() functions. By doing this we will gain in speed and compactness with regards to the lines of code.

 

R gives us a number of MapReduce like functions to play with, here’s a blog [3] that covers the topic on the xapply() functions.
 
Performance measurement and performance tuning
As R is an interpreted language, if you don’t write efficient functions, you could end up waiting a bit longer than expected, before any results are thrown back onto the console. It is not verbose and does not usually tell you what it is upto.
We spent most of our two weeks performing this action as we came across performance bottlenecks in our scripts and could do with using the xapply() like functions. Applying them improved the performance of certain tasks from several hours to a reasonable number of minutes per execution.

 

“Measure, don’t guess.” was the motto!

 

Thanks to the sequence of calls to the proc.time() function, which we used voraciously to measure performances of the different blocks of code we thought needed attention.

 

startTimer <- proc.time()
and
proc.time() – startTimer
 
This paid off at the end of the process as we were able to determine how much time it would take for the script to transform and validate the heaps of data we have been playing with.
At the end of each such iteration we saw the stats in the below format. It got us excited if it was a low number and dejected if it wasn’t to our liking:

 

   user  system elapsed
 87.085   0.694  87.877
 
We tried various methods to bring down the total elapsed time. Some of the things we did even before we came to a final resolution:
 – used for-loop to iterate through a list or vector and perform the same action repeatedly and accumulate results
  – we noticed the for-loop slowed down after a number of iterations and this was a standard pattern. To relieve that we split the for-loop into an inner and outer loop. The outer loop split the inner loop into batches of 40-50 iterations followed by a call to gc() at the end of the iteration. This wasn’t a decent solution from an algorithms or language point of view
finally we settled to refactoring the for-loop into a mapply() which looked like:
result &lt;- unlist(mapply(FUN=transposeColumnAsRow, rangeOfIndices, SIMPLIFY=TRUE))
 
The last action gave us a better grip over the performance and we were confident that if we had to run all the data we had through the script, we would be able to finish transposing it within several hours as opposed to a few days, previously.
Here’s the equation we used to benchmark our functions each time we improved it. It was more to find out for us if we would be able to meet our goals. If the action was acceptable, otherwise we needed to investigate further to find a better method:

 

nm = (ns / nr) * tnr / nsm

 

 nm – no. of minutes it would take to process the whole raw file
 ns – no. of seconds taken to process the batch of records
 nr – total number of records process in the batch
tnr – grand total of the number of records in raw file
nsm – number of seconds in a minute

 

The method we settled for gave us the below results, which was a great benchmark based on processing a sample of 100 records, and when extrapolated on 11200+* records gave the below – which was pretty acceptable at the time:

 

 9.315 / 100 * 11250 / 60 = 17.465 minutes per raw data file

* – each row was made up of 1300+ columns which added to the processing time


We had about 24 files in total to process, which compute to

17.465 * 24 / 60 = 6.986 hours if run one file per session

The tasks of processing each file was split into 3 to 4 sessions processing 1000 records per session.

But it wasn’t as easy as said, we had a number of sessions running doing the above on different pieces of raw data, but never got to committing the data into the database and wondered why? We thought it was hardware/software limitations on our systems. But after further investigations and experimentations found out that no system can handle writing mega-tons of data from memory into the database system without creating giga-tons of swap files. And these swap files are a catch-22, because now the OS needs resources to manage its own resources so our resource requirements would take a back seat!

After a couple discussions, and trials we finally decided to write data back into the database system, in smaller blocks at a time, which means we can still have multiple sessions running in the background and have each one of them write smaller blocks of data into the database.
Everyone is happy as processes can handle smaller blocks much better than bigger blocks – didn’t we already know this, maybe we re-learnt it by facing a bottleneck?   Our script learnt from it as well and got modified to be able to accept and handle processing smaller blocks of data by splitting the processes into smaller batches of records per execution.$ RScript IncrementalLoad.r [filename] [starting record no.] [ending record no.]

The verification script also imitated the same and elected to be run in batches:$ RScript VerifyData.r [filename] [starting record no.] [ending record no.]

Once data had been transformed and written to a database, we wrote a script to validate the data written into the database, we chose Postgres as a trial, and found it was a pretty good database system with an intuitive SQL language.

The verification process was run in the same manner in parallel which took similar amount of time, so at the end of the 7th hour we had both the data written into the database and verified.

 

R can write to such a database system easily. We were further helped with the primary, simple and compound indices that we created to facilitate the searching and selecting processes that our SQL statements would make it do. Postgres also has an efficient caching mechanism, which helps further speed things up.

 

Tweaking the R environment to get efficiency out of it
What I didn’t mention was that before we returned to the R script to tweak it and improve its performance, we thought it was the environment and the way R was, that made it slow – so we wanted to speed up our scripts using the below methods to get the maximum out of the R environment:
  • JIT compiling R scripts – thinking its not slow when interpreted anymore
  • Converting R scripts into C/C++ code and compiling and running it instead
  • Running R scripts using parallel processing (need some library for it)
  • Learning how to use GPUs via R to get that extra performance (need some library for it)
  • Investigating other methods of High Performance Computing in R
We have parked these ideas for now, but it will be a great experience to be able to explore them at a later date.
But once again it was techniques over technology that made our day. Rory Gibson, rightly said “Its not surprising to know how game developers produce some of the best pieces of work under restricted environments”. Such situations are a good nudge to everyone especially developers when faced with performance bottleneck – look at your code not your machine first!

 

At the end of it all, it feels we did what Hadoop or Cloudera would do to our jobs – split, slice, execute, verify, put together and bring back the results at an efficient speed.

 

Hurdles
 
The time I spend learning and applying R, I had to get familiar with its unique or rather say different from other programming language syntax. Like the use of the <- (arrow sign or indirection operator) instead of the usual = (equal to sign). How you point the arrow makes a difference in R, instead of assigning a value to a variable or function you might end up doing something else if you are not careful.

 

You need to define your functions at the top first, otherwise you can’t refer to it. And all entities are case-sensitive, please pay careful attention or else you will only be notified when you least expect it and in the middle of an execution of a block – remember its an interpreted language, no compile time warnings / error messages are available.
There was one more hurdle which put the spanners at work for us – we bumped into an encoding/decoding issue with reading data from the raw file. The ESS plugin [5] in Emacs was reading the data literally at a stage and not evaluating the escape codes. When we switched to RStudio or even the R repl, we immediately became free from the issue – this was also because both I and Herve were using different development environments. He used Emacs to develop in R while I used RStudio. Why this was happening is not known to us, we think there might be a bug in the plugin – at this stage its still a speculation, but more importantly we don’t have to investigate the issue anymore.Our raw file was written using the application called SPSS which writes data in a proprietary format. Such files can be read via a few ways, and using R is one way to achieve that. There is also a Java library [6] that facilitates reading such files, but remains unexplored at this time.
Test driven development in R
 
This is where I have still been hovering around with regards to R, I came across two libraries that enables writing unit tests in R, i.e. RUnit and svUnit. See below in the External Resources section for a number of links I have put together while searching for TDD methodologies in R.
It still needs to be investigated further but a promising start – since test-first driven development is a great way to start working on any piece of problem in any programming language of choice.

 

 

Refactoring
Another action which has been a continuous process since the start. We have applied it to generalise, and make the code base more compact and manageable.

 

Move away common function calls into another .r file and called it into our main script using the source() function. Make our work more maintainable and re-usable – basically keep our code-base clean and tidy.

 

I learnt that refactoring is a continuous effort – its a journey not a destination.

 

What we took away…
I and Herve both took away a lot of learning both technical and non-technical from the whole process  – pair-programming and pair-investigation of a problem space, as the old adage goes “Two minds, are better than one.” Also reveals another reason why pair-programming is encouraged as part of a development process.

 

One important point: we learnt that when we started working on this project, slicing it into simple smaller atomic chunks when solving a problem was effective and efficient, and learnt the hard way. Also when we had a solution to apply to a dataset,  we had already decided to only apply any experimental solution to a smaller subset of the dataset first, verify the results and then scale it incrementally till the dataset was exhausted. Both these working methods came to our rescue and reduced the combination and permutations of trial-and-error!
We both exchanged ideas that we were new to and very well incorporated many of them into our work methods and was able come up with a fine, and a re-usable solution.
I have taken this project further by documenting the work, continuing with refactoring the script files, writing this blog post, and tidying up the project space as a whole.This blog comes about as a documentation of our trial to check the viability of different platforms that could serve us as an ETL (Extract, Transform, Load) – of which we have made good use of RWhether is brilliant at it, or another tools serves betters is debatable. R stands good at what it does, and it does it well – but can be used to do light/medium weight ETL work.So what would be the next tool or platform of choice for our next ETL project. We can’t tell which one is better till we have tried a few and benchmarked them against their pro-s and con-s.


Thanks

Herve Schnegg – for a good partnership during our R session the last couple of weeks, and all the input and learning.

Rory Gibson – for lending his reviewer eyes, for reviewing our R work I and Herve did and also for reviewing this post.

External resources
This blog has also been published on the web’s popular R blogging site: http://www.R-bloggers.com.
During my quest, while learning and applying R, I came across the below links that could come useful to anyone who is interested in furthering their knowledge.
JIT for R
R to CPP
Parallels in R
High Performance Computing using R
GPU programming with R
Test driven development in R
Other useful topics
PSPP

Read more….