My travels through the realms of Data Science, Machine Learning, Deep Learning and (AI)

Then felt I like some watcher of the skies 
When a new planet swims into his ken; 
Or like stout Cortez when with eagle eyes 
He star’d at the Pacific—and all his men 
Look’d at each other with a wild surmise— 
Silent, upon a peak in Darien. 
On First Looking into Chapman’s Homer by John Keats

The above excerpt from John Keat’s poem captures the the exhilaration that one experiences, when discovering something for the first time. This also  summarizes to some extent my own as enjoyment while pursuing Data Science, Machine Learning and the like.

I decided to write this post, as occasionally youngsters approach me and ask me where they should start their adventure in Data Science & Machine Learning. There are other times, when the ‘not-so-youngsters’ want to know what their next step should be after having done some courses. This post includes my travels through the domains of Data Science, Machine Learning, Deep Learning and (soon to be done AI).

By no means, am I an authority in this field, which is ever-widening and almost bottomless, yet I would like to share some of my experiences in this fascinating field. I include a short review of the courses I have done below. I also include alternative routes through  courses which I did not do, but are probably equally good as well.  Feel free to pick and choose any course or set of courses. Alternatively, you may prefer to read books or attend bricks-n-mortar classes, In any case,  I hope the list below will provide you with some overall direction.

All my learning in the above domains have come from MOOCs and I restrict myself to the top 3 MOOCs, or in my opinion, ‘the original MOOCs’, namely Coursera, edX or Udacity, but may throw in some courses from other online sites if they are only available there. I would recommend these 3 MOOCs over the other numerous online courses and also over face-to-face classroom courses for the following reasons. These MOOCs

  • Are taken by world class colleges and the lectures are delivered by top class Professors who have a great depth of knowledge and a wealth of experience
  • The Professors, besides delivering quality content, also point out to important tips, tricks and traps
  • You can revisit lectures in online courses
  • Lectures are usually short between 8 -15 mins (Personally, my attention span is around 15-20 mins at a time!)

Here is a fair warning and something quite obvious. No amount of courses, lectures or books will help if you don’t put it to use through some language like Octave, R or Python.

The journey
My trip through Data Science, Machine Learning  started with an off-chance remark,about 3 years ago,  from an old friend of mine who spoke to me about having done a few  courses at Coursera, and really liked it.  He further suggested that I should try. This was the final push which set me sailing into this vast domain.

I have included the list of the courses I have done over the past 3 years (33 certifications completed and another 9 audited-listened only without doing the assignments). For each of the courses I have included a short review of the course, whether I think the course is mandatory, the language in which the course is based on, and finally whether I have done the course myself etc. I have also included alternative courses, which I may have not done, but which I think are equally good. Finally, I suggest some courses which I have heard of and which are very good and worth taking.

1. Machine Learning, Stanford, Prof Andrew Ng, Coursera
(Requirement: Mandatory, Language:Octave,Status:Completed)
This course provides an excellent foundation to build your Machine Learning citadel on. The course covers the mathematical details of linear, logistic and multivariate regression. There is also a good coverage of topics like Neural Networks, SVMs, Anamoly Detection, underfitting, overfitting, regularization etc. Prof Andrew Ng presents the material in a very lucid manner. It is a great course to start with. It would be a good idea to brush up  some basics of linear algebra, matrices and a little bit of calculus, specifically computing the local maxima/minima. You should be able to take this course even if you don’t know Octave as the Prof goes over the key aspects of the language.

2. Statistical Learning, Prof Trevor Hastie & Prof Robert Tibesherani, Online Stanford– (Requirement:Mandatory, Language:R, Status;Completed) –
The course includes linear and polynomial regression, logistic regression. Details also include cross-validation and the bootstrap methods, how to do model selection and regularization (ridge and lasso). It also touches on non-linear models, generalized additive models, boosting and SVMs. Some unsupervised learning methods are  also discussed. The 2 Professors take turns in delivering lectures with a slight touch of humor.

3a. Data Science Specialization: Prof Roger Peng, Prof Brian Caffo & Prof Jeff Leek, John Hopkins University (Requirement: Option A, Language: R Status: Completed)
This is a comprehensive 10 module specialization based on R. This Specialization gives a very broad overview of Data Science and Machine Learning. The modules cover R programming, Statistical Inference, Practical Machine Learning, how to build R products and R packages and finally has a very good Capstone project on NLP

3b. Applied Data Science with Python Specialization: University of Michigan (Requirement: Option B, Language: Python, Status: Not done)
In this specialization I only did  the Applied Machine Learning in Python (Prof Kevyn-Collin Thomson). This is a very good course that covers a lot of Machine Learning algorithms(linear, logistic, ridge, lasso regression, knn, SVMs etc. Also included are confusion matrices, ROC curves etc. This is based on Python’s Scikit Learn

3c. Machine Learning Specialization, University Of Washington (Requirement:Option C, Language:Python, Status : Not completed). This appears to be a very good Specialization in Python

4. Statistics with R Specialization, Duke University (Requirement: Useful and a must know, Language R, Status:Not Completed)
I audited (listened only) to the following 2 modules from this Specialization.
a.Inferential Statistics
b.Linear Regression and Modeling
Both these courses are taught by Prof Mine Cetikya-Rundel who delivers her lessons with extraordinary clarity.  Her lectures are filled with many examples which she walks you through in great detail

5.Bayesian Statistics: From Concept to Data Analysis: Univ of California, Santa Cruz (Requirement: Optional, Language : R, Status:Completed)
This is an interesting course and provides an alternative point of view to frequentist approach

6. Data Science and Engineering with Spark, University of California, Berkeley, Prof Antony Joseph, Prof Ameet Talwalkar, Prof Jon Bates
(Required: Mandatory for Big Data, Status:Completed, Language; pySpark)
This specialization contains 3 modules
a.Introduction to Apache Spark
b.Distributed Machine Learning with Apache Spark
c.Big Data Analysis with Apache Spark

This is an excellent course for those who want to make an entry into Distributed Machine Learning. The exercises are fairly challenging and your code will predominantly be made of map/reduce and lambda operations as you process data that is distributed across Spark RDDs. I really liked  the part where the Prof shows how a matrix multiplication on a single machine is of the order of O(nd^2+d^3) (which is the basis of Machine Learning) is reduced to O(nd^2) by taking outer products on data which is distributed.

7. Deep Learning Prof Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh : Requirement:Mandatory,Language:Python, Tensorflow Status:Completed)

This course had 5 Modules which start from the fundamentals of Neural Networks, their derivation and vectorized Python implementation. The specialization also covers regularization, optimization techniques, mini batch normalization, Convolutional Neural Networks, Recurrent Neural Networks, LSTMs applied to a wide variety of real world problems

The modules are
a. Neural Networks and Deep Learning
In this course Prof Andrew Ng explains differential calculus, linear algebra and vectorized Python implementations of Deep Learning algorithms. The derivation for back-propagation is done and then the Prof shows how to compute a multi-layered DL network
b.Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Deep Neural Networks can be very flexible, and come with a lots of knobs (hyper-parameters) to tune with. In this module, Prof Andrew Ng shows a systematic way to tune hyperparameters and by how much should one tune. The course also covers regularization(L1,L2,dropout), gradient descent optimization and batch normalization methods. The visualizations used to explain the momentum method, RMSprop, Adam,LR decay and batch normalization are really powerful and serve to clarify the concepts. As an added bonus,the module also includes a great introduction to Tensorflow.
c.Structuring Machine Learning Projects
A very good module with useful tips, tricks and traps that need to be considered while working on Machine Learning and Deep Learning projects
d. Convolutional Neural Networks
This domain has a lot of really cool ideas, where images represented as 3D volumes, are compressed and stretched longitudinally before applying a multi-layered deep learning neural network to this thin slice for performing classification,detection etc. The Prof provides a glimpse into this fascinating world of image classification, detection andl neural art transfer with frameworks like Keras and Tensorflow.
e. Sequence Models
In this module covers in good detail concepts like RNNs, GRUs, LSTMs, word embeddings, beam search and attention model.

8. Neural Networks for Machine Learning, Prof Geoffrey Hinton,University of Toronto
(Requirement: Mandatory, Language;Octave, Status:Completed)
This is a broad course which starts from the basic of Perceptrons, all the way to Boltzman Machines, RNNs, CNNS, LSTMs etc The course also covers regularization, learning rate decay, momentum method etc

9.Probabilistic Graphical Models, Stanford  Prof Daphne Koller(Language:Octave, Status: Partially completed)
This has 3 courses
a.Probabilistic Graphical Models 1: Representation – Done
b.Probabilistic Graphical Models 2: Inference – To do
c.Probabilistic Graphical Models 3: Learning – To do
This course discusses how a system, which can be represented as a complex interaction
of probability distributions, will behave. This is probably the toughest course I did.  I did manage to get through the 1st module, While I felt that grasped a few things, I did not wholly understand the import of this. However I feel this is an important domain and I will definitely revisit this in future

10. Mining Massive Data Sets Prof Jure Leskovec, Prof Anand Rajaraman and ProfJeff Ullman. Online Stanford, Status Partially done.
I did quickly audit this course, a year back, when it used to be in Coursera. It now seems to have moved to Stanford online. But this is a very good course that discusses key concepts of Mining Big Data of the order a few Petabytes

11. Introduction to Artificial Intelligence, Prof Sebastian Thrun & Prof Peter Norvig, Udacity
This is a really good course. I have started on this course a couple of times and somehow gave up. Will revisit to complete in future. Quite extensive in its coverage.Touches BFS,DFS, A-Star, PGM, Machine Learning etc.

12. Deep Learning (with TensorFlow), Vincent Vanhoucke, Principal Scientist at Google Brain.
Got started on this one and abandoned some time back. In my to do list though

My learning journey is based on Lao Tzu’s dictum of ‘A good traveler has no fixed plans and is not intent on arriving’. You could have a goal and try to plan your courses accordingly.
And so my journey continues…

I hope you find this list useful.
Have a great journey ahead!!!

My 3 video presentations on “Essential R”

In this post I include my  3 video presentations on the topic “Essential R”. In these 3 presentations I cover the entire landscape of R. I cover the following

  • R Language – The essentials
  • Key R Packages (dplyr, lubridate, ggplot2, etc.)
  • How to create R Markdown and share reports
  • A look at Shiny apps
  • How to create a simple R package

You can download the relevant slide deck and practice code at Essential R

Essential R – Part 1
This video cover basic R data types – character, numeric, vectors, matrices, lists and data frames. It also touches on how to subset these data types

Essential R – Part 2
This video continues on how to subset dataframes (the most important data type) and some important packages. It also presents one of the most important job of a Data Scientist – that of cleaning and shaping the data. This is done with an example unclean data frame. It also  touches on some  key operations of dplyr like select, filter, arrange, summarise and mutate. Other packages like lubridate, quantmod are also included. This presentation also shows how to use base plot and ggplot2

Essential R – Part 3
This final session covers R Markdown , and  touches on some of the key markdown elements. There is a brief overview of a simple Shiny app. Finally this presentation also shows the key steps to create an R package

These 3 R sessions cover most of the basic R topics that we tend to use in a our day-to-day R way of life. With this you should be able to hit the ground running!

Hope you enjoy these video presentation and also hope you have an even greater time with R!

Check out my 2 books on cricket, a) Cricket analytics with cricketr b) Beaten by sheer pace – Cricket analytics with yorkr, now available in both paperback & kindle versions on Amazon!!! Pick up your copies today!

Also see
1. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
2. Computer Vision: Ramblings on derivatives, histograms and contours
3. Designing a Social Web Portal
4. Revisiting Whats up, Watson – Using Watson’s Question and Answer with Bluemix – Part 2
5. Re-introducing cricketr! : An R package to analyze performances of cricketers

To see all my posts click – Index of posts

GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

In this post I introduce my new Shiny app,“GooglyPlus”, which is a  more evolved version of my earlier Shiny app “Googly”. My R package ‘yorkr’,  on which both these Shiny apps are based, has the ability to output either a dataframe or plot, depending on a parameter plot=TRUE or FALSE. My initial version of the app only included plots, and did not exercise the yorkr package fully. Moreover, I am certain, there may be a set of cricket aficionados who would prefer, numbers to charts. Hence I have created this enhanced version of the Googly app and appropriately renamed it as GooglyPlus. GooglyPlus is based on the yorkr package which uses data from Cricsheet. The app is based on IPL data from  all IPL matches from 2008 up to 2016. Feel free to clone/fork or download the code from Github at GooglyPlus.

If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr  and Beaten by sheer pace-Cricket analytics with yorkr  A must read for any cricket lover! Check it out!!

1

 

Click  GooglyPlus to access the Shiny app!

The changes for GooglyPlus over the earlier Googly app is only in the following 3 tab panels

  • IPL match
  • Head to head
  • Overall Performance

The analysis of IPL batsman and IPL bowler tabs are unchanged. These charts are as they were before.

The changes are only in  tabs i) IPL match ii) Head to head and  iii) Overall Performance. New functionality has been added and existing functions now have the dual option of either displaying a plot or a table.

The changes are

A) IPL Match
The following additions/enhancements have been done

-Match Batting Scorecard – Table
-Batting Partnerships – Plot, Table (New)
-Batsmen vs Bowlers – Plot, Table(New)
-Match Bowling Scorecard   – Table (New)
-Bowling Wicket Kind – Plot, Table (New)
-Bowling Wicket Runs – Plot, Table (New)
-Bowling Wicket Match – Plot, Table (New)
-Bowler vs Batsmen – Plot, Table (New)
-Match Worm Graph – Plot

B) Head to head
The following functions have been added/enhanced

-Team Batsmen Batting Partnerships All Matches – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard All Matches – Table (New)
-Team Batsmen vs Bowlers all Matches – Plot, Table (New)
-Team Wickets Opposition All Matches – Plot, Table (New)
-Team Bowling Scorecard All Matches – Table (New)
-Team Bowler vs Batsmen All Matches – Plot, Table (New)
-Team Bowlers Wicket Kind All Matches – Plot, Table (New)
-Team Bowler Wicket Runs All Matches – Plot, Table (New)
-Win Loss All Matches – Plot

C) Overall Performance
The following additions/enhancements have been done in this tab

-Team Batsmen Partnerships Overall – Plot, Table {Summary (New) and Detailed (New)}
-Team Batting Scorecard Overall –Table (New)
-Team Batsmen vs Bowlers Overall – Plot, Table (New)
-Team Bowler vs Batsmen Overall – Plot, Table (New)
-Team Bowling Scorecard Overall – Table (New)
-Team Bowler Wicket Kind Overall – Plot, Table (New)

Included below are some random charts and tables. Feel free to explore the Shiny app further

1) IPL Match
a) Match Batting Scorecard (Table only)
This is the batting score card for the Chennai Super Kings & Deccan Chargers 2011-05-11

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b)  Match batting partnerships (Plot)
Delhi Daredevils vs Kings XI Punjab – 2011-04-23

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c) Match batting partnerships (Table)
The same batting partnership  Delhi Daredevils vs Kings XI Punjab – 2011-04-23 as a table

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d) Batsmen vs Bowlers (Plot)
Kolkata Knight Riders vs Mumbai Indians 2010-04-19

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e)  Match Bowling Scorecard (Table only)
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B) Head to head

a) Team Batsmen Partnership (Plot)
Deccan Chargers vs Kolkata Knight Riders all matches

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b)  Team Batsmen Partnership (Summary – Table)
In the following tables it can be seen that MS Dhoni has performed better that SK Raina  CSK against DD matches, whereas SK Raina performs better than Dhoni in CSK vs  KKR matches

i) Chennai Super Kings vs Delhi Daredevils (Summary – Table)

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ii) Chennai Super Kings vs Kolkata Knight Riders (Summary – Table)
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iii) Rising Pune Supergiants vs Gujarat Lions (Detailed – Table)
This table provides the detailed partnership for RPS vs GL all matches

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c) Team Bowling Scorecard (Table only)
This table gives the bowling scorecard of Pune Warriors vs Deccan Chargers in all matches

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C) Overall performances
a) Batting Scorecard All Matches  (Table only)

This is the batting scorecard of Royal Challengers Bangalore. The top 3 batsmen are V Kohli, C Gayle and AB Devilliers in that order

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b) Batsman vs Bowlers all Matches (Plot)
This gives the performance of Mumbai Indian’s batsman of Rank=1, which is Rohit Sharma, against bowlers of all other teams

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c)  Batsman vs Bowlers all Matches (Table)
The above plot as a table. It can be seen that Rohit Sharma has scored maximum runs against M Morkel, then Shakib Al Hasan and then UT Yadav.

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d) Bowling scorecard (Table only)
The table below gives the bowling scorecard of CSK. R Ashwin leads with a tally of 98 wickets followed by DJ Bravo who has 88 wickets and then JA Morkel who has 83 wickets in all matches against all teams

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This is just a random selection of functions. Do play around with the app and checkout how the different IPL batsmen, bowlers and teams stack against each other. Do read my earlier post Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr  for more details about the app and other functions available.

Click GooglyPlus to access the Shiny app!

You can clone/fork/download the code from Github at GooglyPlus

Hope you have fun playing around with the Shiny app!

Note: In the tabs, for some of the functions, not all controls  are required. It is possible to enable the controls selectively but this has not been done in this current version. I may make the changes some time in the future.

Take a look at my other Shiny apps
a.Revisiting crimes against women in India
b. Natural language processing: What would Shakespeare say?

Check out some of my other posts
1. Analyzing World Bank data with WDI, googleVis Motion Charts
2. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1
3. Singularity
4. Design principles of scalable, distributed systems
5. Simulating an Edge shape in Android
6. Dabbling with Wiener filter in OpenCV

To see all posts click Index of Posts

Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr

Presenting ‘Googly’, a cool Shiny app that I developed over the last couple of days. This interactive Shiny app was on my mind for quite some time, and I finally got down to implementing it. The Googly Shiny app is based on my R package ‘yorkr’ which is now available in CRAN. The R package and hence this Shiny app is based on data from Cricsheet.

If you are passionate about cricket, and love analyzing cricket performances, then check out my 2 racy books on cricket! In my books, I perform detailed yet compact analysis of performances of both batsmen, bowlers besides evaluating team & match performances in Tests , ODIs, T20s & IPL. You can buy my books on cricket from Amazon at $12.99 for the paperback and $4.99/$6.99 respectively for the kindle versions. The books can be accessed at Cricket analytics with cricketr  and Beaten by sheer pace-Cricket analytics with yorkr  A must read for any cricket lover! Check it out!!

1

Googly is based on R package yorkr, and uses the data of all IPL matches from 2008 up to 2016, available on Cricsheet.

Googly can do detailed analyses of a) Individual IPL batsman b) Individual IPL bowler c) Any IPL match d) Head to head confrontation between 2 IPL teams e) All matches of an IPL team against all other teams.

With respect to the individual IPL batsman and bowler performance, I was in a bit of a ‘bind’ literally (pun unintended), as any IPL player could have played in more than 1 IPL team. Fortunately ‘rbind’ came to my rescue. I just get all the batsman’s/bowler’s performance in each IPL team, and then consolidate it into a single large dataframe to do the analyses of.

The Shiny app can be accessed at Googly

The code for Googly is available at Github. Feel free to clone/download/fork  the code from Googly

Check out my 2 books on cricket, a) Cricket analytics with cricketr b) Beaten by sheer pace – Cricket analytics with yorkr, now available in both paperback & kindle versions on Amazon!!! Pick up your copies today!

Also see my post GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables

Based on the 5 detailed analysis domains there are 5 tabs

IPL Batsman: This tab can be used to perform analysis of all IPL batsman. If a batsman has played in more than 1 team, then the overall performance is considered. There are 10 functions for the IPL Batsman. They are shown below

  1. Batsman Runs vs. Deliveries
  2. Batsman’s Fours & Sixes
  3. Dismissals of batsman
  4. Batsman’s Runs vs Strike Rate
  5. Batsman’s Moving Average
  6. Batsman’s Cumulative Average Run
  7. Batsman’s Cumulative Strike Rate
  8. Batsman’s Runs against Opposition
  9. Batsman’s Runs at Venue
  10. Predict Runs of batsman

IPL Bowler: This tab can be used to analyze individual IPL bowlers. The functions handle IPL bowlers who have played in more than 1 IPL team.

  1. Mean Economy Rate of bowler
  2. Mean runs conceded by bowler
  3. Bowler’s Moving Average
  4. Bowler’s Cumulative Avg. Wickets
  5. Bowler’s Cumulative Avg. Economy Rate
  6. Bowler’s Wicket Plot
  7. Bowler’s Wickets against opposition
  8. Bowler’s Wickets at Venues
  9. Bowler’s wickets prediction

IPL match: This tab can be used for analyzing individual IPL matches. The available functions are

  1. Batting Partnerships
  2. Batsmen vs Bowlers
  3. Bowling Wicket Kind
  4. Bowling Wicket Runs
  5. Bowling Wicket Match
  6. Bowler vs Batsmen
  7. Match Worm Graph

Head to head : This tab can be used for analyzing head-to-head confrontations, between any 2 IPL teams for e.g. all matches between Chennai Super Kings vs. Deccan Chargers or Kolkata Knight Riders vs. Delhi Daredevils. The available functions are

  1. Team Batsmen Batting Partnerships All Matches
  2. Team Batsmen vs Bowlers all Matches
  3. Team Wickets Opposition All Matches
  4. Team Bowler vs Batsmen All Matches
  5. Team Bowlers Wicket Kind All Matches
  6. Team Bowler Wicket Runs All Matches
  7. Win Loss All Matches

Overall performance : this tab can be used analyze the overall performance of any IPL team. For this analysis all matches played by this team is considered. The available functions are

  1. Team Batsmen Partnerships Overall
  2. Team Batsmen vs Bowlers Overall
  3. Team Bowler vs Batsmen Overall
  4. Team Bowler Wicket Kind Overall

Below I include a random set of charts that are generated in each of the 5 tabs

A. IPL Batsman
a. A Symonds : Runs vs Deliveries
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b. AB Devilliers – Cumulative Strike Rate
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c.  Gautam Gambhir – Runs at venues
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d. CH Gayle – Predict runs 
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B. IPL Bowler
a. Ashish Nehra – Cumulative Average Wickets
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b.  DJ Bravo – Moving Average of wickets
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c. R Ashwin – Mean Economy rate vs Overs
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C.IPL Match
a. Chennai Super Kings vs Deccan Chargers   (2008 -05-06) – Batsmen Partnerships

Note: You can choose either team in the match from the drop down ‘Choose team’

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b. Kolkata Knight Riders vs Delhi Daredevils (2013-04-02) – Bowling wicket runs
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c. Mumbai Indians vs Kings XI Punjab (2010-03-30) – Match worm graph
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D. Head to head confrontation
a. Rising Pune Supergiants vs Mumbai Indians in all matches – Team batsmen partnerships

Note: You can choose the partnership of either team in the drop down ‘Choose team’
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b.  Gujarat Lions – Royal Challengers Bangalore all matches – Bowlers performance against batsmen
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E. Overall Performance
a.  Royal Challengers Bangalore overall performance – Batsman Partnership (Rank=1)
This is Virat Kohli for RCB. Try out other ranks
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b.  Rajashthan Royals overall Performance – Bowler vs batsman (Rank =2)
This is Vinay Kumar.
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The Shiny app Googly can be accessed at Googly. Feel free to clone/fork the code from Github at Googly

For details on my R package yorkr, please see my blog Giga thoughts. There are more than 15 posts detailing the functions and their usage.

Do bowl a Googly!!!

You may like my other Shiny apps

Also see my other posts

  1. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
  2. Deblurring with OpenCV: Weiner filter reloaded
  3. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
  4. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
  5. Fun simulation of a Chain in Android
  6. Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
  7. Introducing cricketr! : An R package to analyze performances of cricketers
  8. Cricket analytics with cricketr!!!

For more posts see Index of posts

Sixer – R package cricketr’s new Shiny avatar

Published in R-bloggers: Sixer – R package cricketr’s new Shiny app

In this post I create a Shiny App, Sixer, based on my R package cricketr. I had developed the R package cricketr, a few months back for analyzing the performances of batsman and bowlers in all formats of the game (Test, ODI and Twenty 20). This package uses the statistics info available in ESPN Cricinfo Statsguru. I had written a series of posts using the cricketr package where I chose a few batsmen, bowlers and compared their performances of these players. Here I have created a complete Shiny app with a lot more players and with almost all the features of the cricketr package. The motivation for creating the Shiny app was to

  • To show case the  ‘cricketr’ package and to highlight its functionalities
  • Perform analysis of more batsman and bowlers
  • Allow users to interact with the package and to allow them to try out the different features and functions of the package and to also check performances of some of their favorite crickets

If you are passionate about cricket, and love analyzing cricket performances, then check out my racy book on cricket ‘Cricket analytics with cricketr and cricpy – Analytics harmony with R & Python’! This book discusses and shows how to use my R package ‘cricketr’ and my Python package ‘cricpy’ to analyze batsmen and bowlers in all formats of the game (Test, ODI and T20). The paperback is available on Amazon at $21.99 and  the kindle version at $9.99/Rs 449/-. A must read for any cricket lover! Check it out!!

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$4.99/Rs 320 and $6.99/Rs448 respectively

Important note*: The latest release of ‘cricketr’ now includes the ability to analyze performances of teams now!!  See Cricketr adds team analytics to its repertoire!!!

Important note**: Do check out the python avatar of cricketr, ‘cricpy’ in my post ‘Introducing cricpy:A python package to analyze performances of cricketers

a) You can try out the interactive  Shiny app Sixer at – Sixer
b) The code for this Shiny app project can be cloned/forked from GitHub – Sixer
Note: Please be mindful of  ESPN Cricinfo Terms of Use.
(Take a look at my short video tutorial on my R package cricketr on Youtube – R package cricketr – A short tutorial)

Important note: Do check out my other posts using cricketr at cricketr-posts

In this Shiny app I have 5 tabs which perform the following function
1.  Analyze Batsman
This tab analyzes batsmen based on different functions and plots the performances of the selected batsman. There are functions that compute and display batsman’s run-frequency ranges, Mean Strike rate, No of 4’s, dismissals, 3-D plot of Runs scored vs Balls Faced and Minutes at crease, Contribution to wins & losses, Home-Away record etc. The analyses can be done for Test cricketers, ODI and Twenty 20 batsman. I have included most of the Test batting giants including Tendulkar, Dravid, Sir Don Bradman, Viv Richards, Lara, Ponting etc. Similarly the ODI list includes Sehwag, Devilliers, Afridi, Maxwell etc. The Twenty20 list includes the Top 10 Twenty20 batsman based on their ICC rankings

2. Analyze bowler
This tab analyzes the bowling performances of bowlers, Wickets percentages, Mean Economy Rate, Wickets at different venues, Moving average of wickets etc. As earlier I have all the Top bowlers including Warne, Muralidharan, Kumble- the famed Indian spin quartet of Bedi, Chandrasekhar, Prasanna, Venkatraghavan, the deadly West Indies trio of Marshal, Roberts and Holding and the lethal combination of Imran Khan, Wasim Akram and Waqar Younis besides the dangerous Dennis Lillee and Jeff Thomson. Do give the functions a try and see for yourself the performances of these individual bowlers

3. Relative performances of batsman
This tab allows the selection of multiple batsmen (Test, ODI and Twenty 20) for comparisons. There are 2 main functions Relative Runs Frequency performance and Relative Mean Strike Rate

4. Relative performances of bowlers
Here we can compare bowling performances of multiple bowlers, which include functions Relative Bowling Performance and Relative Economy Rate. This can be done for Test, ODI and Twenty20 formats

5. Check for In-Form status of players
This tab checks the form status of batsman or bowler selected for all of the different formats of the game. The below computation uses Null Hypothesis testing and p-value to determine if the batsman is in-form or out-of-form. For this 90% of the career runs is chosen as the population and the mean computed. The last 10% is chosen to be the sample set and the sample Mean and the sample Standard Deviation are calculated. Note: The accuracy of the p-value test depends on the size of the population and the size of the sample set. It  may not be very significant for players with a few innings played.

Some of my earlier posts based my R package cricketr are listed below
1. Introducing cricketr!: An R package for analyzing performances of cricketers
2. Taking cricketr for a spin – Part 1
3. cricketr plays the ODIs
4. cricketr adapts to the Twenty20 International
5. cricketr digs the Ashes

Do try out the interactive Sixer Shiny app – Sixer
You can clone the code from Github – Sixer

There is not much in way of explanation. The Shiny app’s use is self-explanatory. You can choose a match type ( Test,ODI or Twenty20), choose a batsman/bowler  from the drop down list and select the plot you would like to seeHere a few sample plots
A. Analyze batsman tab
i) Batsman – Brian Lara , Match Type – Test, Function – Mean Strike Rate
sxr-1ii) Batsman – Shahid Afridi, Match Type –  ODI, Function – Runs vs Balls faced
The plot below shows that if Afridi faces around 50 balls he is likely to score around 60 runs in ODIs.
sxr-2iii)   Batsman – Chris Gayle, Match Type – Twenty20  Function – Moving Average
sxr-3B. Analyze bowler tab

i. Bowler – B S Chandrasekhar, Match Type – Test, Function – Wickets vs Runs
sxr-4ii)  Bowler – Malcolm Marshall, Match Type – Test, Function – Mean Economy Ratesxr-5iii)  Bowler – Sunil Narine, Match Type – Twenty 20, Function – Bowler Wicket Rate
sxr-6
C. Relative performance of batsman (you can select more than 1)
The below plot gives the Mean Strike Rate of batsman. Viv Richards, Brian Lara, Sanath Jayasuriya and David Warner are best strikers of the ball.
sxr-7

Here are some of the great strikers of the ball in ODIs
sxr-8D. Relative performance of bowlers (you can select more than 1)
Finally a look at the famed Indian spin quartet.  From the plot below it can be seen that  B S Bedi  & Venkatraghavan were more economical than Chandrasekhar and Prasanna.
sxr-9

But the latter have a better 4-5 wicket haul than the former two as seen in the plot below

sxr-11Finally a look at the average number of balls to take a wicket by the Top 4 Twenty 20 bowlers.
sxr-10

E. Check for In-form status of players
Note: The accuracy of the p-value depends on the size of the population and the size of the sample set. It  may not be very significant for smaller data sizes

i. Match Type – Test,  Player Type – Batsman  Name – Wickets v
In this plot the in-form status of Viv Richards is checked. This shows that Viv Richards was out-of-form
sxr-12In the plot below the form status of S. Venkataraghavan is shown. According to this at the time of  his retirement S Venkat was still in-form
sxr-13

Do give the Shiny app Sixer a try.

Also see
1. Literacy in India : A deepR dive.
2.  Natural Language Processing: What would Shakespeare say?
3. Revisiting crimes against women in India
4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
5. Experiments with deblurring using OpenCV
6.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
7.  Working with Node.js and PostgreSQL
8. A method for optimal bandwidth usage by auctioning available bandwidth using the OpenFlow Protocol
9.  Latency, throughput implications for the cloud
10.  A closer look at “Robot horse on a Trot! in Android”

Literacy in India – A deepR dive

Published in R-bloggers: Literacy in India – A deepR dive
You can do magic!
You can have anything,
That you desire
Magic…
You can do magic – song by America (1982)

That is exactly how I feel when I write code in R. A few lines of R, lo behold, hundreds of rows and columns are magically transformed into  easily understandable graphs, regression curves or choropleth maps. (By the way, the song is a really cool! Listen to it if you have not heard it before). You really can do magic with R

In this post I do a deep dive into literacy in India The dataset is taken from Open Government Data (OGD) platform India was used for this purpose. This data is based on the 2001 census. Though the data is a little dated, it is extremely rich with literacy details across different age groups, and over all Indian States. The data includes the total number of persons/males/females who are in the primary, middle.matric, college,technical diploma, non-technical diploma and so on. In fact the data also includes the educational background of people in the districts in each state. I slice and dice the data across multiple parameters. I have created an interactive Shiny App which will provide very detailed visualization based on the parameters chosen

Do try out my interactive Shiny app : IndiaLiteracy

The entire code for this app is on GitHub. Feel free to download/clone/fork/modify or enhance the code – literacyInIndia

For analyzing   such a rich data set as the Census data of 2001, I create 4 tabs
1) State Literacy
2) Educational Levels vs Age
3) India Literacy and
4) District Literacy

Here are the details of these 4 tabs in my Shiny app

A) State Literacy
This tab provides the age wise distribution of people (Persons/Males/Females) who attend educational institutions. This is shown as a barplot. The plot also includes the national average. In the plot below which is for entire India we see that the national average

1

The distribution of females attending primary school in the state of Haryana is shown. Also included is the national average. As can be seen there are options for (Total/Urban/Rural) against (Persons/Males/Females) and whether these people attend educational institutions are illiterate of literate.

2

I also have another option under “Who’ which is “All” This will plot the age wise distribution of males/females/persons in urban/rural or entire state.

3

B. Educational Institutions vs Age plot

This plot displays the the educational institutions attended by people in a particular age group. So for example in the state of Orissa for the 18 year age group we can see that there persons who are in (Primary, Matric, Higher Secondary, Non-Technical Diploma and Technical Diploma). The bar length for each color is the percentage of the total persons at that level of education

4

C. Literacy across India
This tab plots a chorpleth map for a region(Urban+Rural, Urban, Rural), Who(Persons, Males, Females) and the literacy level (attending educational institutions, primary, higher secondary, Matric etc) across the whole of India.

6

D. Literacy within a state
This tab plots a chorpleth map of literacy in the districts of a state. A sample plot for Karnataka is shown below

7

E. Key observations

There is a wealth of insights you can glean by looking at the various charts. Here a few insights from my initial observations
1) The literacy in Kerala across ages is higher than the national average while in Bihar it is less than the national average

a) Kerala

8b) Bihar

9
2) In Rajasthan The Males Attending education instituions is higher than the national average while for females it less than the national average. However the situation is reverse in Chandigarh where there are the percentage of females attending education instiuons is higher than the national average and the males

a) Rajasthan

10b) Chandigarh

11
3) When we look at the number of persons attending educational institution across India the north-eastern states lead with Manipur, Nagaland and Sikkim in the top 3.

12

We have heard that Kerala is the most literate state. But  it looks like Manipur, Nagaland, Sikkim actually edge Kerala out. If we look at the State literacy chart for Kerala and Manipur this becomes more clear

a) Kerala

13

b) Manipur

14

It can be seen that in Manipur the number of persons attending educational instition in the age range 13-24 years it is much higher than the national average and much higher than Kerala

4) If we take a look at the District wise literacy for the state of Bihar we see that the literacy is lower in the north eastern districts.,

15

5) Here is another interesting observation I made. The top 3 states which are most ‘literate with no education’ are i) Rajasthan ii) Madhya Pradesh iii) Chhattisgarh

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While I have included several charts with accompanying explanation, this is largely unnecessary as  most of the charts are self-explanatory.

Do try out the Shiny app and see for yourself the literacy in each state/district/age group educational  level etc – IndiaLiteracy

Feel free to clone/fork my code and make your own enhancements –literacyInIndia

You may also like
1.  Natural Language Processing: What would Shakespeare say?
2. Introducing cricketr! : An R package to analyze performances of cricketers
3. Revisiting crimes against women in India
4. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
5. Re-working the Lucy-Richardson Algorithm in OpenCV
6.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
7.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
8. TWS-4: Gossip protocol: Epidemics and rumors to the rescue
9. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
10.  Simulating an Edge Shape in Android