Cricpy takes guard for the Twenty20s

There are two ways to write error-free programs; only the third one works.”” Alan J. Perlis

Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to produce bigger and better idiots. So far, the universe is winning. ” Rick Cook

My software never has bugs. It just develops random features.” Anon

If you make an ass out of yourself, there will always be someone to ride you.” Bruce Lee

Introduction

This is the 3rd and final post on cricpy, and is a continuation to my 2 earlier posts

1. Introducing cricpy:A python package to analyze performances of cricketers
2.Cricpy takes a swing at the ODIs

Cricpy, is the python avatar of my R package ‘cricketr’. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

With this post  cricpy, like cricketr, now becomes omnipotent, and is now capable of handling Test, ODI and T20 matches.

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru.

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

Cricpy can now analyze performances of teams in Test, ODI and T20 cricket see Cricpy adds team analytics to its arsenal!!

This post is also hosted on Rpubs at Int

This post is also hosted on Rpubs at Cricpy takes guard for the Twenty 20s. You can also download the pdf version of this post at cricpy-TT.pdf

You can fork/clone the package at Github cricpy

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. The functions can be executed in RStudio or in a IPython notebook.

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!!

Untitled

The cricpy package

The data for a particular player in Twenty20s can be obtained with the getPlayerDataTT() function. To do this you will need to go to T20 Batting and T20 Bowling and click the player you are interested in This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence,this can be used to get the data for Virat Kohlias shown below

The cricpy package is a clone of my R package cricketr. The signature of all the python functions are identical with that of its clone ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familar with one of the languages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my own interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca 

2. Invoking functions with Python package cricpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataTT)
## Help on function getPlayerDataTT in module cricpy.analytics:
## 
## getPlayerDataTT(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the Twenty20 International player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory~
##     
##     Description
##     
##     Get the Twenty20 player data given the profile of the batsman/bowler. The allowed inputs are home,away, neutralboth and won,lost,tied or no result of matches. The data is stored in a <player>.csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataTT(profile, opposition="",host="",dir = "./data", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Virat Kohli this turns out to be 253802 http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition  
##     The numerical value of the opposition country e.g.Australia,India, England etc. The values are Afghanistan:40,Australia:2,Bangladesh:25,England:1,Hong Kong:19,India:6,Ireland:29, New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Note: If no value is entered for opposition then all teams are considered
##     host        
##     The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,New Zealand:5, South Africa:3,Sri Lanka:8,United States of America:11,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "./data". Default="./data"
##     file        
##     Name of the file to store the data into for e.g. kohli.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     bowlerWktRateTT getPlayerData
##     
##     Examples
##     
##     ## Not run: 
##     # Only away. Get data only for won and lost innings
##     kohli =getPlayerDataTT(253802,dir="../cricketr/data", file="kohli1.csv",
##     type="batting")
##     
##     # Get bowling data and store in file for future
##     ashwin = getPlayerDataTT(26421,dir="../cricketr/data",file="ashwin1.csv",
##     type="bowling")
##     
##     kohli =getPlayerDataTT(253802,opposition = 2,host=2,dir="../cricketr/data", 
##     file="kohli1.csv",type="batting")

The details below will introduce the different functions that are available in cricpy.

4. Get the Twenty20 player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerDataTT for all subsequent analyses

import cricpy.analytics as ca
#kohli=ca.getPlayerDataTT(253802,dir=".",file="kohli.csv",type="batting")
#guptill=ca.getPlayerDataTT(226492,dir=".",file="guptill.csv",type="batting")
#shahzad=ca.getPlayerDataTT(419873,dir=".",file="shahzad.csv",type="batting")
#mccullum=ca.getPlayerDataTT(37737,dir=".",file="mccullum.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test, ODI and Twenty20 records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli in T20s

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

ca.batsmanMeanStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanRunsRanges("./kohli.csv","Virat Kohli")

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

ca.batsmanDismissals("./kohli.csv","Virat Kohli")

ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")

7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Kohli’s Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

8. Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Kohli is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Kohli’s highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

11. A look at the Top 4 batsman – Kohli,  Guptill, Shahzad and McCullum

The following batsmen have been very prolific in Twenty20 cricket and will be used for the analyses

  1. Virat Kohli: Runs – 2167, Average:49.25 ,Strike rate-136.11
  2. MJ Guptill : Runs -2271, Average:34.4 ,Strike rate-132.88
  3. Mohammed Shahzad :Runs – 1936, Average:31.22 ,Strike rate-134.81
  4. BB McCullum : Runs – 2140, Average:35.66 ,Strike rate-136.21

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./guptill.csv","M J Guptill")

ca.batsmanPerfBoxHist("./shahzad.csv","M Shahzad")

ca.batsmanPerfBoxHist("./mccullum.csv","BB McCullum")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 Twenty20 batsmen.

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./guptill.csv","M J Guptill")
#ca.batsmanMovingAverage("./shahzad.csv","M Shahzad") # Gives error. Check!

ca.batsmanMovingAverage("./mccullum.csv","BB McCullum")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career.Kohli’s average tops around 45 runs around 43 innings, though there is a dip downwards

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./guptill.csv","M J Guptill")

ca.batsmanCumulativeAverageRuns("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeAverageRuns("./mccullum.csv","BB McCullum")

15 Cumulative Average strike rate of batsman in career

Kohli, Guptill and McCullum average a strike rate of 125+

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./guptill.csv","M J Guptill")

ca.batsmanCumulativeStrikeRate("./shahzad.csv","M Shahzad")

ca.batsmanCumulativeStrikeRate("./mccullum.csv","BB McCullum")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman. Kohli is way above all the other 3 batsmen. Behind Kohli is McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullumn"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

17. Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percetages for each 10 run bucket. The plot below show that Kohli tops the overall strike rate followed by McCullum and then Guptill

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./guptill.csv","M J Guptill")

ca.battingPerf3d("./shahzad.csv","M Shahzad")

ca.battingPerf3d("./mccullum.csv","BB McCullum")

19. 3D plot of Runs vs Balls Faced and Minutes at Crease

Guptill and McCullum have a large percentage of sixes in comparison to the 4s. Kohli has a relative lower number of 6s

import cricpy.analytics as ca
frames = ["./kohli.csv","./guptill.csv","./shahzad.csv","./mccullum.csv"]
names = ["Kohli","Guptill","Shahzad","McCullum"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")
print(kohli)
##             BF        Mins        Runs
## 0    10.000000   30.000000   14.753153
## 1    37.857143   70.714286   55.963333
## 2    65.714286  111.428571   97.173513
## 3    93.571429  152.142857  138.383693
## 4   121.428571  192.857143  179.593873
## 5   149.285714  233.571429  220.804053
## 6   177.142857  274.285714  262.014233
## 7   205.000000  315.000000  303.224414
## 8   232.857143  355.714286  344.434594
## 9   260.714286  396.428571  385.644774
## 10  288.571429  437.142857  426.854954
## 11  316.428571  477.857143  468.065134
## 12  344.285714  518.571429  509.275314
## 13  372.142857  559.285714  550.485494
## 14  400.000000  600.000000  591.695674

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

  1. Shakib Hasan:Wickets: 80, Average = 21.07, Economy Rate – 6.74
  2. Mohammed Nabi : Wickets: 67, Average = 24.25, Economy Rate – 7.13
  3. Rashid Khan: Wickets: 64, Average = 12.40, Economy Rate – 6.01
  4. Imran Tahir : Wickets:62, Average – 14.95, Economy Rate – 6.77

22. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#shakib=ca.getPlayerDataTT(56143,dir=".",file="shakib.csv",type="bowling")
#nabi=ca.getPlayerDataOD(25913,dir=".",file="nabi.csv",type="bowling")
#rashid=ca.getPlayerDataOD(793463,dir=".",file="rashid.csv",type="bowling")
#tahir=ca.getPlayerDataOD(40618,dir=".",file="tahir.csv",type="bowling")

23. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsFreqPercent("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsFreqPercent("./rashid.csv","Rashid Khan")

ca.bowlerWktsFreqPercent("./tahir.csv","Imran Tahir")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken.

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./shakib.csv","Shakib Al Hasan")

ca.bowlerWktsRunsPlot("./nabi.csv","Mohammad Nabi")

ca.bowlerWktsRunsPlot("./rashid.csv","Rashid Khan")

ca.bowlerWktsRunsPlot("./tahir.csv","Imran Tahir")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues.

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsGround("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsGround("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsGround("./tahir.csv","Imran Tahir")

26 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./shakib.csv","Shakib Al Hasan")

ca.bowlerAvgWktsOpposition("./nabi.csv","Mohammad Nabi")

ca.bowlerAvgWktsOpposition("./rashid.csv","Rashid Khan")

ca.bowlerAvgWktsOpposition("./tahir.csv","Imran Tahir")

27 Wickets taken moving average

From the plot below it can be see

import cricpy.analytics as ca
ca.bowlerMovingAverage("./shakib.csv","Shakib Al Hasan")

ca.bowlerMovingAverage("./nabi.csv","Mohammad Nabi")

ca.bowlerMovingAverage("./rashid.csv","Rashid Khan")

ca.bowlerMovingAverage("./tahir.csv","Imran Tahir")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Rashid Khan has been the most effective with almost 2.28 wickets per match

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgWickets("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgWickets("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgWickets("./tahir.csv","Imran Tahir")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Rashid Khan has the nest economy rate followed by Mohammed Nabi

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./shakib.csv","Shakib Al Hasan")

ca.bowlerCumulativeAvgEconRate("./nabi.csv","Mohammad Nabi")

ca.bowlerCumulativeAvgEconRate("./rashid.csv","Rashid Khan")

ca.bowlerCumulativeAvgEconRate("./tahir.csv","Imran Tahir")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate is given below. It can be seen that Rashid Khan has the best economy rate followed by Mohammed Nabi and then Imran Tahir

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Rashid Khan has the best figures for wickets between 2-3.5 wickets. Mohammed Nabi pips Rashid Khan when takes a haul of 4 wickets.

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

Rashid has the best performance with cumulative average wickets. He is followed by Imran Tahir in the wicket haul, followed by Shakib Al Hasan

import cricpy.analytics as ca
frames = ["./shakib.csv","./nabi.csv","./rashid.csv","tahir.csv"]
names = ["Shakib Al Hasan","Mohammad Nabi","Rashid Khan", "Imran Tahir"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Kohli, Guptill, Shahzad and McCullum
1.Kohli has the best overall cumulative average runs and towers over everybody else
2. Kohli, Guptill and McCullum has a very good strike rate of around 125+
3. Guptill and McCullum have a larger percentage of sixes as compared to Kohli
4. Rashid Khan has the best cumulative average wickets, followed by Imran Tahir and then Shakib Al Hasan
5. Rashid Khan is the most economical bowler, followed by Mohammed Nabi

You can fork/clone the package at Github cricpy

Conclusion

Cricpy now has almost all the functions and functionalities of my R package cricketr. There are still a few more features that need to be added to cricpy. I intend to do this as and when I find time.

Go ahead, take cricpy for a spin! Hope you enjoy the ride!

Watch this space!!!

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

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Cricpy takes a swing at the ODIs

No computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either.” Marvin Minksy

“The competent programmer is fully aware of the limited size of his own skull. He therefore approaches his task with full humility, and avoids clever tricks like the plague” Edgser Djikstra

Introduction

In this post, cricpy, the Python avatar of my R package cricketr, learns some new tricks to be able to handle ODI matches. To know more about my R package cricketr see Re-introducing cricketr! : An R package to analyze performances of cricketers

Cricpy uses the statistics info available in ESPN Cricinfo Statsguru. The current version of this package supports only Test cricket

You should be able to install the package using pip install cricpy and use the many functions available in the package. Please mindful of the ESPN Cricinfo Terms of Use

Cricpy can now analyze performances of teams in Test, ODI and T20 cricket see Cricpy adds team analytics to its arsenal!!

This post is also hosted on Rpubs at Int

To know how to use cricpy see Introducing cricpy:A python package to analyze performances of cricketers. To the original version of cricpy, I have added 3 new functions for ODI. The earlier functions work for Test and ODI.

This post is also hosted on Rpubs at Cricpy takes a swing at the ODIs. You can also down the pdf version of this post at cricpy-odi.pdf

You can fork/clone the package at Github cricpy

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton cricpy-template from Github (which is the R Markdown file I have used for the analysis below). You will only need to make appropriate changes for the players you are interested in. The functions can be executed in RStudio or in a IPython notebook.

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!!

Untitled

The cricpy package

The data for a particular player in ODI can be obtained with the getPlayerDataOD() function. To do you will need to go to ESPN CricInfo Player and type in the name of the player for e.g Virat Kohli, Virendar Sehwag, Chris Gayle etc. This will bring up a page which have the profile number for the player e.g. for Virat Kohli this would be http://www.espncricinfo.com/india/content/player/253802.html. Hence, Kohli’s profile is 253802. This can be used to get the data for Virat Kohlis shown below

The cricpy package is a clone of my R package cricketr. The signature of all the python functions are identical with that of its clone ‘cricketr’, with only the necessary variations between Python and R. It may be useful to look at my post R vs Python: Different similarities and similar differences. In fact if you are familar with one of the lanuguages you can look up the package in the other and you will notice the parallel constructs.

You can fork/clone the package at Github cricpy

Note: The charts are self-explanatory and I have not added much of my owy interpretation to it. Do look at the plots closely and check out the performances for yourself.

1 Importing cricpy – Python

# Install the package
# Do a pip install cricpy
# Import cricpy
import cricpy.analytics as ca 

2. Invoking functions with Python package crlcpy

import cricpy.analytics as ca 
ca.batsman4s("./kohli.csv","Virat Kohli")

3. Getting help from cricpy – Python

import cricpy.analytics as ca 
help(ca.getPlayerDataOD)
## Help on function getPlayerDataOD in module cricpy.analytics:
## 
## getPlayerDataOD(profile, opposition='', host='', dir='./data', file='player001.csv', type='batting', homeOrAway=[1, 2, 3], result=[1, 2, 3, 5], create=True)
##     Get the One day player data from ESPN Cricinfo based on specific inputs and store in a file in a given directory
##     
##     Description
##     
##     Get the player data given the profile of the batsman. The allowed inputs are home,away or both and won,lost or draw of matches. The data is stored in a .csv file in a directory specified. This function also returns a data frame of the player
##     
##     Usage
##     
##     getPlayerDataOD(profile, opposition="",host="",dir = "../", file = "player001.csv", 
##     type = "batting", homeOrAway = c(1, 2, 3), result = c(1, 2, 3,5))
##     Arguments
##     
##     profile     
##     This is the profile number of the player to get data. This can be obtained from http://www.espncricinfo.com/ci/content/player/index.html. Type the name of the player and click search. This will display the details of the player. Make a note of the profile ID. For e.g For Virender Sehwag this turns out to be http://www.espncricinfo.com/india/content/player/35263.html. Hence the profile for Sehwag is 35263
##     opposition      The numerical value of the opposition country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,Bermuda:12, England:1,Hong Kong:19,India:6,Ireland:29, Netherlands:15,New Zealand:5,Pakistan:7,Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27, West Indies:4, Zimbabwe:9; Africa XI:405 Note: If no value is entered for opposition then all teams are considered
##     host            The numerical value of the host country e.g.Australia,India, England etc. The values are Australia:2,Bangladesh:25,England:1,India:6,Ireland:29,Malaysia:16,New Zealand:5,Pakistan:7, Scotland:30,South Africa:3,Sri Lanka:8,United Arab Emirates:27,West Indies:4, Zimbabwe:9 Note: If no value is entered for host then all host countries are considered
##     dir 
##     Name of the directory to store the player data into. If not specified the data is stored in a default directory "../data". Default="../data"
##     file        
##     Name of the file to store the data into for e.g. tendulkar.csv. This can be used for subsequent functions. Default="player001.csv"
##     type        
##     type of data required. This can be "batting" or "bowling"
##     homeOrAway  
##     This is vector with either or all 1,2, 3. 1 is for home 2 is for away, 3 is for neutral venue
##     result      
##     This is a vector that can take values 1,2,3,5. 1 - won match 2- lost match 3-tied 5- no result
##     Details
##     
##     More details can be found in my short video tutorial in Youtube https://www.youtube.com/watch?v=q9uMPFVsXsI
##     
##     Value
##     
##     Returns the player's dataframe
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com>
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://www.espncricinfo.com/ci/content/stats/index.html
##     https://gigadom.wordpress.com/
##     
##     See Also
##     
##     getPlayerDataSp getPlayerData
##     
##     Examples
##     
##     
##     ## Not run: 
##     # Both home and away. Result = won,lost and drawn
##     sehwag =getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag1.csv",
##     type="batting", homeOrAway=[1,2],result=[1,2,3,4])
##     
##     # Only away. Get data only for won and lost innings
##     sehwag = getPlayerDataOD(35263,dir="../cricketr/data", file="sehwag2.csv",
##     type="batting",homeOrAway=[2],result=[1,2])
##     
##     # Get bowling data and store in file for future
##     malinga = getPlayerData(49758,dir="../cricketr/data",file="malinga1.csv",
##     type="bowling")
##     
##     # Get Dhoni's ODI record in Australia against Australua
##     dhoni = getPlayerDataOD(28081,opposition = 2,host=2,dir=".",
##     file="dhoniVsAusinAusOD",type="batting")
##     
##     ## End(Not run)

The details below will introduce the different functions that are available in cricpy.

4. Get the ODI player data for a player using the function getPlayerDataOD()

Important Note This needs to be done only once for a player. This function stores the player’s data in the specified CSV file (for e.g. kohli.csv as above) which can then be reused for all other functions). Once we have the data for the players many analyses can be done. This post will use the stored CSV file obtained with a prior getPlayerDataOD for all subsequent analyses

import cricpy.analytics as ca
#sehwag=ca.getPlayerDataOD(35263,dir=".",file="sehwag.csv",type="batting")
#kohli=ca.getPlayerDataOD(253802,dir=".",file="kohli.csv",type="batting")
#jayasuriya=ca.getPlayerDataOD(49209,dir=".",file="jayasuriya.csv",type="batting")
#gayle=ca.getPlayerDataOD(51880,dir=".",file="gayle.csv",type="batting")

Included below are some of the functions that can be used for ODI batsmen and bowlers. For this I have chosen, Virat Kohli, ‘the run machine’ who is on-track for breaking many of the Test & ODI records

5 Virat Kohli’s performance – Basic Analyses

The 3 plots below provide the following for Virat Kohli

  1. Frequency percentage of runs in each run range over the whole career
  2. Mean Strike Rate for runs scored in the given range
  3. A histogram of runs frequency percentages in runs ranges
import cricpy.analytics as ca
import matplotlib.pyplot as plt
ca.batsmanRunsFreqPerf("./kohli.csv","Virat Kohli")

ca.batsmanMeanStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanRunsRanges("./kohli.csv","Virat Kohli")

6. More analyses

import cricpy.analytics as ca
ca.batsman4s("./kohli.csv","Virat Kohli")

ca.batsman6s("./kohli.csv","Virat Kohli")

ca.batsmanDismissals("./kohli.csv","Virat Kohli")

ca.batsmanScoringRateODTT("./kohli.csv","Virat Kohli")


7. 3D scatter plot and prediction plane

The plots below show the 3D scatter plot of Kohli’s Runs versus Balls Faced and Minutes at crease. A linear regression plane is then fitted between Runs and Balls Faced + Minutes at crease

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

Average runs at different venues

The plot below gives the average runs scored by Kohli at different grounds. The plot also the number of innings at each ground as a label at x-axis.

import cricpy.analytics as ca
ca.batsmanAvgRunsGround("./kohli.csv","Virat Kohli")

9. Average runs against different opposing teams

This plot computes the average runs scored by Kohli against different countries.

import cricpy.analytics as ca
ca.batsmanAvgRunsOpposition("./kohli.csv","Virat Kohli")

10 . Highest Runs Likelihood

The plot below shows the Runs Likelihood for a batsman. For this the performance of Kohli is plotted as a 3D scatter plot with Runs versus Balls Faced + Minutes at crease. K-Means. The centroids of 3 clusters are computed and plotted. In this plot Kohli’s highest tendencies are computed and plotted using K-Means

import cricpy.analytics as ca
ca.batsmanRunsLikelihood("./kohli.csv","Virat Kohli")

A look at the Top 4 batsman – Kohli, Jayasuriya, Sehwag and Gayle

The following batsmen have been very prolific in ODI cricket and will be used for the analyses

  1. Virat Kohli: Runs – 10232, Average:59.83 ,Strike rate-92.88
  2. Sanath Jayasuriya : Runs – 13430, Average:32.36 ,Strike rate-91.2
  3. Virendar Sehwag :Runs – 8273, Average:35.05 ,Strike rate-104.33
  4. Chris Gayle : Runs – 9727, Average:37.12 ,Strike rate-85.82

The following plots take a closer at their performances. The box plots show the median the 1st and 3rd quartile of the runs

12. Box Histogram Plot

This plot shows a combined boxplot of the Runs ranges and a histogram of the Runs Frequency

import cricpy.analytics as ca
ca.batsmanPerfBoxHist("./kohli.csv","Virat Kohli")

ca.batsmanPerfBoxHist("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanPerfBoxHist("./gayle.csv","Chris Gayle")

ca.batsmanPerfBoxHist("./sehwag.csv","Virendar Sehwag")

13 Moving Average of runs in career

Take a look at the Moving Average across the career of the Top 4 (ignore the dip at the end of all plots. Need to check why this is so!). Kohli’s performance has been steadily improving over the years, so has Sehwag. Gayle seems to be on the way down

import cricpy.analytics as ca
ca.batsmanMovingAverage("./kohli.csv","Virat Kohli")

ca.batsmanMovingAverage("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanMovingAverage("./gayle.csv","Chris Gayle")

ca.batsmanMovingAverage("./sehwag.csv","Virendar Sehwag")

14 Cumulative Average runs of batsman in career

This function provides the cumulative average runs of the batsman over the career. Kohli seems to be getting better with time and reaches a cumulative average of 45+. Sehwag improves with time and reaches around 35+. Chris Gayle drops from 42 to 35

import cricpy.analytics as ca
ca.batsmanCumulativeAverageRuns("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeAverageRuns("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeAverageRuns("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeAverageRuns("./sehwag.csv","Virendar Sehwag")

15 Cumulative Average strike rate of batsman in career

Sehwag has the best strike rate of almost 90. Kohli and Jayasuriya have a cumulative strike rate of 75.

import cricpy.analytics as ca
ca.batsmanCumulativeStrikeRate("./kohli.csv","Virat Kohli")

ca.batsmanCumulativeStrikeRate("./jayasuriya.csv","Sanath jayasuriya")

ca.batsmanCumulativeStrikeRate("./gayle.csv","Chris Gayle")

ca.batsmanCumulativeStrikeRate("./sehwag.csv","Virendar Sehwag")

16 Relative Batsman Cumulative Average Runs

The plot below compares the Relative cumulative average runs of the batsman . It can be seen that Virat Kohli towers above all others in the runs. He is followed by Chris Gayle and then Sehwag

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeAvgRuns(frames,names)

Relative Batsman Strike Rate

The plot below gives the relative Runs Frequency Percentages for each 10 run bucket. The plot below show Sehwag has the best strike rate, followed by Jayasuriya

import cricpy.analytics as ca
frames = ["./sehwag.csv","./gayle.csv","./jayasuriya.csv","./kohli.csv"]
names = ["Sehwag","Gayle","Jayasuriya","Kohli"]
ca.relativeBatsmanCumulativeStrikeRate(frames,names)

18. 3D plot of Runs vs Balls Faced and Minutes at Crease

The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A 3D prediction plane is fitted

import cricpy.analytics as ca
ca.battingPerf3d("./kohli.csv","Virat Kohli")

ca.battingPerf3d("./jayasuriya.csv","Sanath jayasuriya")

ca.battingPerf3d("./gayle.csv","Chris Gayle")

ca.battingPerf3d("./sehwag.csv","Virendar Sehwag")

3D plot of Runs vs Balls Faced and Minutes at Crease

From the plot below it can be seen that Sehwag has more runs by way of 4s than 1’s,2’s or 3s. Gayle and Jayasuriya have large number of 6s

import cricpy.analytics as ca
frames = ["./sehwag.csv","./kohli.csv","./gayle.csv","./jayasuriya.csv"]
names = ["Sehwag","Kohli","Gayle","Jayasuriya"]
ca.batsman4s6s(frames,names)

20. Predicting Runs given Balls Faced and Minutes at Crease

A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease.

import cricpy.analytics as ca
import numpy as np
import pandas as pd
BF = np.linspace( 10, 400,15)
Mins = np.linspace( 30,600,15)
newDF= pd.DataFrame({'BF':BF,'Mins':Mins})
kohli= ca.batsmanRunsPredict("./kohli.csv",newDF,"Kohli")
print(kohli)
##             BF        Mins        Runs
## 0    10.000000   30.000000    6.807407
## 1    37.857143   70.714286   36.034833
## 2    65.714286  111.428571   65.262259
## 3    93.571429  152.142857   94.489686
## 4   121.428571  192.857143  123.717112
## 5   149.285714  233.571429  152.944538
## 6   177.142857  274.285714  182.171965
## 7   205.000000  315.000000  211.399391
## 8   232.857143  355.714286  240.626817
## 9   260.714286  396.428571  269.854244
## 10  288.571429  437.142857  299.081670
## 11  316.428571  477.857143  328.309096
## 12  344.285714  518.571429  357.536523
## 13  372.142857  559.285714  386.763949
## 14  400.000000  600.000000  415.991375

The fitted model is then used to predict the runs that the batsmen will score for a given Balls faced and Minutes at crease.

21 Analysis of Top Bowlers

The following 4 bowlers have had an excellent career and will be used for the analysis

  1. Muthiah Muralitharan:Wickets: 534, Average = 23.08, Economy Rate – 3.93
  2. Wasim Akram : Wickets: 502, Average = 23.52, Economy Rate – 3.89
  3. Shaun Pollock: Wickets: 393, Average = 24.50, Economy Rate – 3.67
  4. Javagal Srinath : Wickets:315, Average – 28.08, Economy Rate – 4.44

How do Muralitharan, Akram, Pollock and Srinath compare with one another with respect to wickets taken and the Economy Rate. The next set of plots compute and plot precisely these analyses.

22. Get the bowler’s data

This plot below computes the percentage frequency of number of wickets taken for e.g 1 wicket x%, 2 wickets y% etc and plots them as a continuous line

import cricpy.analytics as ca
#akram=ca.getPlayerDataOD(43547,dir=".",file="akram.csv",type="bowling")
#murali=ca.getPlayerDataOD(49636,dir=".",file="murali.csv",type="bowling")
#pollock=ca.getPlayerDataOD(46774,dir=".",file="pollock.csv",type="bowling")
#srinath=ca.getPlayerDataOD(34105,dir=".",file="srinath.csv",type="bowling")

23. Wicket Frequency Plot

This plot below plots the frequency of wickets taken for each of the bowlers

import cricpy.analytics as ca
ca.bowlerWktsFreqPercent("./murali.csv","M Muralitharan")

ca.bowlerWktsFreqPercent("./akram.csv","Wasim Akram")

ca.bowlerWktsFreqPercent("./pollock.csv","Shaun Pollock")

ca.bowlerWktsFreqPercent("./srinath.csv","J Srinath")

24. Wickets Runs plot

The plot below create a box plot showing the 1st and 3rd quartile of runs conceded versus the number of wickets taken. Murali’s median runs for wickets ia around 40 while Akram, Pollock and Srinath it is around 32+ runs. The spread around the median is larger for these 3 bowlers in comparison to Murali

import cricpy.analytics as ca
ca.bowlerWktsRunsPlot("./murali.csv","M Muralitharan")

ca.bowlerWktsRunsPlot("./akram.csv","Wasim Akram")

ca.bowlerWktsRunsPlot("./pollock.csv","Shaun Pollock")

ca.bowlerWktsRunsPlot("./srinath.csv","J Srinath")

25 Average wickets at different venues

The plot gives the average wickets taken by Muralitharan at different venues. McGrath best performances are at Centurion, Lord’s and Port of Spain averaging about 4 wickets. Kapil Dev’s does good at Kingston and Wellington. Anderson averages 4 wickets at Dunedin and Nagpur

import cricpy.analytics as ca
ca.bowlerAvgWktsGround("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsGround("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsGround("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsGround("./srinath.csv","J Srinath")

26 Average wickets against different opposition

The plot gives the average wickets taken by Muralitharan against different countries. The x-axis also includes the number of innings against each team

import cricpy.analytics as ca
ca.bowlerAvgWktsOpposition("./murali.csv","M Muralitharan")

ca.bowlerAvgWktsOpposition("./akram.csv","Wasim Akram")

ca.bowlerAvgWktsOpposition("./pollock.csv","Shaun Pollock")

ca.bowlerAvgWktsOpposition("./srinath.csv","J Srinath")

27 Wickets taken moving average

From the plot below it can be see James Anderson has had a solid performance over the years averaging about wickets

import cricpy.analytics as ca
ca.bowlerMovingAverage("./murali.csv","M Muralitharan")

ca.bowlerMovingAverage("./akram.csv","Wasim Akram")

ca.bowlerMovingAverage("./pollock.csv","Shaun Pollock")

ca.bowlerMovingAverage("./srinath.csv","J Srinath")

28 Cumulative average wickets taken

The plots below give the cumulative average wickets taken by the bowlers. Muralitharan has consistently taken wickets at an average of 1.6 wickets per game. Shaun Pollock has an average of 1.5

import cricpy.analytics as ca
ca.bowlerCumulativeAvgWickets("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgWickets("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgWickets("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgWickets("./srinath.csv","J Srinath")

29 Cumulative average economy rate

The plots below give the cumulative average economy rate of the bowlers. Pollock is the most economical, followed by Akram and then Murali

import cricpy.analytics as ca
ca.bowlerCumulativeAvgEconRate("./murali.csv","M Muralitharan")

ca.bowlerCumulativeAvgEconRate("./akram.csv","Wasim Akram")

ca.bowlerCumulativeAvgEconRate("./pollock.csv","Shaun Pollock")

ca.bowlerCumulativeAvgEconRate("./srinath.csv","J Srinath")

30 Relative cumulative average economy rate of bowlers

The Relative cumulative economy rate shows that Pollock is the most economical of the 4 bowlers. He is followed by Akram and then Murali

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgEconRate(frames,names)

31 Relative Economy Rate against wickets taken

Pollock is most economical vs number of wickets taken. Murali has the best figures for 4 wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlingER(frames,names)

32 Relative cumulative average wickets of bowlers in career

The plot below shows that McGrath has the best overall cumulative average wickets. While the bowlers are neck to neck around 130 innings, you can see Muralitharan is most consistent and leads the pack after 150 innings in the number of wickets taken.

import cricpy.analytics as ca
frames = ["./srinath.csv","./akram.csv","./murali.csv","pollock.csv"]
names = ["J Srinath","Wasim Akram","M Muralitharan", "S Pollock"]
ca.relativeBowlerCumulativeAvgWickets(frames,names)

33. Key Findings

The plots above capture some of the capabilities and features of my cricpy package. Feel free to install the package and try it out. Please do keep in mind ESPN Cricinfo’s Terms of Use.

Here are the main findings from the analysis above

Analysis of Top 4 batsman

The analysis of the Top 4 test batsman Tendulkar, Kallis, Ponting and Sangakkara show the folliwing

  1. Kohli is a mean run machine and has been consistently piling on runs. Clearly records will lay shattered in days to come for Kohli
  2. Virendar Sehwag has the best strike rate of the 4, followed by Jayasuriya and then Kohli
  3. Shaun Pollock is the most economical of the bowlers followed by Wasim Akram
  4. Muralitharan is the most consistent wicket of the lot.

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

Also see
1. Architecting a cloud based IP Multimedia System (IMS)
2. Exploring Quantum Gate operations with QCSimulator
3. Dabbling with Wiener filter using OpenCV
4. Deep Learning from first principles in Python, R and Octave – Part 5
5. Big Data-2: Move into the big league:Graduate from R to SparkR
6. Singularity
7. Practical Machine Learning with R and Python – Part 4
8. Literacy in India – A deepR dive
9. Modeling a Car in Android

To see all posts click Index of Posts

 

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

A video tutorial on R programming – The essentials

Here is a my video tutorial on R programming – The essentials. This tutorial is meant for those who would like to learn R,  for R beginners or for those who would like to get a quick start on R. This tutorial  tries to focus on those  main functions that any R programmer is likely to use often rather than trying to cover every aspect of R with all its subtleties. You can clone the R tutorial used in this video along with the powerpoint presentation from Github. For this you will have to install Git on your laptop  After you have installed Git you should be able to clone this repository and then try the functions out yourself in RStudio. Make your own variations to the functions, as you familiarize yourself  with R.

git clone https://github.com/tvganesh/R-Programming-.git
Take a look at the video tutorial R programming – The essentials

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

 

You could supplement the video by reading  on these topics. This tutorial will give you enough momentum for a relatively short take-off into the R. So good luck on your R journey

You may also like
1. Introducing cricketr! : An R package to analyze performances of cricketers
2. Literacy in India : A deepR dive.
3. Natural Language Processing: What would Shakespeare say?
4. Revisiting crimes against women in India
5. Sixer – R package cricketr’s new Shiny Avatar

Also see
1. Designing a Social Web Portal
2. Design principles of scalable, distributed systems
3. A Cloud Medley with IBM’s Bluemix, Cloudant and Node.js
4. Programming Zen and now – Some essential tips -2 
5. Fun simulation of a Chain in Android

The common alphabet of programming languages

                                                                   a                                                                                    

                                    “All animals are equal, but some animals are more equal than other.”                                     “Four legs good, two legs bad.”

from Animal Farm by George Orwell

Note: This post is largely intended for those who are embarking on their journey into the world of programming. The article below highlights a set of constructs that recur in many imperative, dynamic and object-oriented languages.  While these constructs cannot be applied directly to functional programming languages like Lisp,Haskell or Clojure, it may help. To some extent the programming language domain has been intentionally oversimplified to show that languages are not as daunting as they seem. Clearly there are a  lot more subtle and complex differences among languages. Hope you have fun programming!

Introduction: Anybody who is about to venture into the deep waters of programming will be bewildered and awed by the almost limitless number of programming languages and the associated paradigms on which they are based on. It is easy to feel apprehensive of programming, when faced with this  this array of languages, not to mention the seemingly quirky syntax of each language.  Many opinions abound, about what is the best programming language. In my opinion each language is best suited for a particular class of problems and is usually clunky if used outside of this. As an aside here is an interesting link provided by reader AKS to Rosetta Code, which is stated to be a a programming chrestomathy (present solutions to the same task in as many different languages as possible, to demonstrate how languages are similar and different, and to aid a person with a grounding in one approach to a problem in learning another. Rosetta Code currently has 772 tasks, 165 draft tasks, and is aware of 582 languages)

You are likely to hear  “All programming languages are equal, but some languages are more equal than others” from seasoned programmers who have their own pet language. There may also be others who swear that “procedural languages good, object oriented languages bad” or maybe “object oriented languages good, aspect oriented languages bad”. Unity in diversity Regardless of the language this post discusses a thread that is common to all programming languages. In fact any programming language can be expressed as

Lx = C + Sx

Where Lx is any programming language ‘x’. All programming languages have a set of core, common constructs which I have denoted as ‘C’ and a set of Specialized constructs, unique to each language ‘x’ which I have denoted as Sx. I would like to look at these constructs that are common to most programming languages like C,C++,Perl, Python, Ruby, C#, R, Octave etc. In my opinion knowing these core, common constructs and a few of the more specialized constructs should allow you to get started off in the language of your choice. You can pick up the more unique constructs as you go along.   Here are the common constructs (C mentioned above) that you must familiarize yourself with when embarking on a new language

  1. Reading user input and printing to screen
  2. Reading and writing from a file
  3. Conditional statement if-then-else if-else
  4. Loops – For, while, repeat, do while etc.

Knowing these constructs and some of the basic concepts unique to each language for e.g.
– Structure, Pointers in C,
– Classes, inheritance in C++
– Subsetting in Octave, R
– car, cdr in Lisp will enable you to get started off in your chosen language.
I show the examples of these core constructs in many languages. Note the similarity between these constructs
1. C
Read from and write to console

scanf(x,”%d); printf(“The value of x is %d”, x);
Read from and write to file
fread(buffer, strlen(c)+1, 1, fp);
fwrite(c, strlen(c) + 1, 1, fp);

Conditional
if(x > 5) {
printf(“x is greater than 5”);
}
else if (x < 5)
{ printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”);
}

Loops I will only consider for loops, though one could use while, repeat etC.
for(i =0; i <100; i++)
{ money = money++)
}

2. C++
Read from and write to console
cin >> age;
Cout << “The value is “ << value

Read from and write to a file // open a file in read mode.
ifstream infile;
infile.open("afile.dat");
cout << "Reading from the file" <<
endl;
infile >> data;
ofstream outfile;
outfile.open("afile.dat");
// write inputted data into the file.
outfile << data <<
endl;

Conditional same as C
if(x > 5) {
printf(“x is greater than 5”);
}

else if (x < 5) {
printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”);
}

Loops
for(i =0; i <100; i++)
{ money = money++)

}

2. C++ Read from and write to console
cin >> age;
Cout << “The value is “ << value
Read from and write to a file // open a file in read mode.
ifstream infile;
infile.open("afile.dat");
cout << "Reading from the file" << endl;
infile >> data; ofstream outfile;
outfile.open("afile.dat");
// write inputted data into the file.
outfile << data << endl;
Conditional same as C
if(x > 5) {
printf(“x is greater than 5”);
}
else if (x < 5) {
printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”);
}
Loops
for(i =0; i <100; i++){
money = money++)
}
3. Java
Reading from  and writing to standard input
Console c = System.console();
int val = c.readLine("Enter a value: ");
System.out.println("Value is "+ val);
Reading and writing from file
try {
in = new FileInputStream("input.txt");
out = new FileOutputStream("output.txt");
int c;
while ((c = in.read()) != -1) {
out.write(c); } } ...
Conditional (same as C)
if(x > 5) {
printf(“x is greater than 5”);
}
else if (x < 5) {
printf(“x is less than 5”);
}
else{ printf(“x is equal to 5”); }
Loops (same as C)
for(i =0; i <100; i++){
money = money++)
}

4. Perl Read from console
#!/usr/bin/perl
$userinput =  ;
chomp ($userinput);
Write to console
print "User typed $userinput\n";
Reading and write to a file
open(IN,"infile") || die "cannot open input file";
open(OUT,"outfile") || die "cannot open output file";
while() {
print OUT $_;
# echo line read
}
close(IN);
close(OUT)
Conditional
if( $a  ==  20 ){
# if condition is true then print the following
printf "a has a value which is 20\n";
}
elsif( $a ==  30 ){
# if condition is true then print the following
printf "a has a value which is 30\n";
}else{
# if none of the above conditions is true
printf "a has a value which is $a\n";
}
Loops
for (my $i=0; $i <= 9; $i++) {
print "$i\n";
}

5. Lisp
The syntax for Lisp will be different from the others as it is a functional language. You need to familiarize yourself with these constructs to move ahead
Read and write to console
To read from standard input use
(let ((temp 0))
(print ‘(Enter temp))
(setf temp (read))
(print (append ‘(the temp is) (list temp))))
Read from and write to file
(with-open-file (stream “C:\\acl82express\\lisp\\count.cl”)
(do ((line (read-line stream nil) (read-line stream nil)))
(with-open-file (stream “C:\\acl82express\\lisp\\test.txt” :direction :output :if-exists :supersede)
(write-line “test” stream) nil)
Conditional
$ (cond ((< x 5)
(setf x (+ x 8))
(setf y (* 2 y)))
((= x 10) (setf x (* x 2)))
(t (setf x 8)))
Loops
$  (setf x 5)
$ (let ((i 0))
(loop (setf y (* x i))
(when (> i 10) (return))
(print i) (prin1 y) (incf i )))

6. Python
Reading and writing from console
var = raw_input("Please enter something: ")
print “You entered: ”  value
Reading and writing from files
f = open(filename, 'r')
a = f.readline().strip()
target = open(filename, 'w')
target.write(line1)
Conditionals
if x > 5:
print "x is greater than 5”
elif
x < 5:
print "x is less than 5"
else:
print "x is equal to 5"
Loops
for i in range(0, 6):
print "Value is :" % i 7.

R
x=5
paste('The value of x is =',x)
Reading and writing to a file
infile = read.csv(“file”)
write(x, file = "data", sep = " ")
Conditional
if(x > 5){
print(“x is greater than 5”) 
}else if(x < 5){
print(“x is less than 5”) 
}else {
print(“x is equal to 5”)
}
Loops
for (i in 1:10) print(i)

Conclusion
As can be seen the core constructs are very similar in different languages save for some minor variations. It is generally useful to get started with just knowing these constructs and few other important other features  of the language that you are trying to learn. It is possible to code most programs with these Core constructs and a few of the Specialized constructs in the language. These Core constructs are the glue that hold your code together.

You can learn more compact and more powerful features of the language as you go along The above core constructs are like the letters of the programming language alphabet. You need to construct words by stringing together these constructs and form sensible sentences which will be your program. Good luck with your adventure in your next new programming language!!!

Also see
1.Programming languages in layman’s language
2. The mind of the programmer
3. How to program – Some essential tips
4. Programming Zen and now – Some essential tips -2 

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R incantations for the uninitiated

Here are some basic R incantations that will get you started with R

A) Scalars & Vectors:
Chant 1 – Now repeat after me, with your right hand forward at shoulder height “In R there are no scalars. There are only vectors of length 1”.
Just kidding:-)

To create an integer variable x with a value 5 we write
x <- 5 or
x = 5

While the former notation may seem odd, it is actually more logical considering that the RHS is assigned to LHS. Anyway both seem to work
Vectors can be created as follows
a <- c( 2:10)
b <- c("This", "is", 'R","language")

B) Sequences:
There are several ways of creating sequences of numbers which you intend to use for your computation
<- seq(5:25) # Sequence from 5 to 25

Other ways to create sequences
Increment by 2
> seq(5, 25, by=2)
[1]  5  7  9 11 13 15 17 19 21 23 25

>seq(5,25,length=18) # Create sequence from 5 to 25 with a total length of 18
[1]  5.000000  6.176471  7.352941  8.529412  9.705882 10.882353 12.058824 13.235294
[9] 14.411765 15.588235 16.764706 17.941176 19.117647 20.294118 21.470588 22.647059
[17] 23.823529 25.000000

C) Conditions and loops
An if-else if-else construct goes like this
if(condition) {
do something
} else if (condition) {
do something
} else {
do something
}

Note: Make sure the statements appear as above with the else if and else appearing on the same line as the closing braces, otherwise R complains about ‘unexpected else’ in else statement

D) Loops
I would like to mention 2 ways of doing ‘for’ loops  in R.
a) for (i in 1:10) {
statement
}

> a <- seq(5,25,length=10)
> a
[1]  5.000000  7.222222  9.444444 11.666667 13.888889 16.111111 18.333333
[8] 20.555556 22.777778 25.000000

b) Sequence along the vector sequence. Note: This is useful as we don’t have to know  the length of the vector/sequence
for (i in seq_along(a)){
+   print(a[i])
+ }

[1] 5
[1] 7.222222
[1] 9.444444
[1] 11.66667

There are others ways of looping with ‘while’ and ‘repeat’ which I have not included in this post.

R makes manipulation of matrices and data frames really easy. All the elements in a matrix are numeric while data frames can have different types for each of the element

E) Matrix
> rnorm(12,5,2)
[1] 2.699961 3.160208 5.087478 3.969129 3.317840 4.551565 2.585758 2.397780
[9] 5.297535 6.574757 7.468268 2.440835

a) Create a vector of 12 random numbers with a mean of 5 and SD of 2
> a <-rnorm(12,5,2)
b) Convert the vector to a matrix with 4 rows and 3 columns
> mat <- matrix(a,4,3)
> mat[,1]     [,2]     [,3]
[1,] 5.197010 3.839281 9.022818
[2,] 4.053590 5.321399 5.587495
[3,] 4.225763 4.873768 6.648151
[4,] 4.709784 4.129093 2.575523

c) Subset rows 1 & 2 from the matrix
> mat[1:2,]
[,1]     [,2]     [,3]
[1,] 5.19701 3.839281 9.022818
[2,] 4.05359 5.321399 5.587495

d) Subset matrix a rows 1& 2 and with columns 2 & 3
> mat[1:2,2:3]
[,1]     [,2]
[1,] 3.839281 9.022818
[2,] 5.321399 5.587495

e) Subset matrix a for all row elements for the column 3
> mat[,3]
[1] 9.022818 5.587495 6.648151 2.575523

e) Add row names and column names for the matrix as follows
> names <- c(“tim”,”pat”,”joe”,”jim”)
> v <- data.frame(names,mat)
> v
names       X1       X2       X3
1   tim 5.197010 3.839281 9.022818
2   pat 4.053590 5.321399 5.587495
3   joe 4.225763 4.873768 6.648151
4   jim 4.709784 4.129093 2.575523

> colnames(v) <- c("names","a","b","c")
> v
names        a        b        c
1   tim 5.197010 3.839281 9.022818
2   pat 4.053590 5.321399 5.587495
3   joe 4.225763 4.873768 6.648151
4   jim 4.709784 4.129093 2.575523

F) Data Frames
In R data frames are the most important method to manipulate large amounts of data. One can read data in .csv format into data frame using
df <- read.csv(“mydata.csv”)
To get a feel of data frames it is useful to play around with the numerous data sets that are available with the installation of R
To check the available dataframes do
>data()
AirPassengers                    Monthly Airline Passenger Numbers 1949-1960
BJsales                          Sales Data with Leading Indicator
BJsales.lead (BJsales)           Sales Data with Leading Indicator
BOD                              Biochemical Oxygen Demand
CO2                              Carbon Dioxide Uptake in Grass Plants
ChickWeight                      Weight versus age of chicks on different diets
...

I will be using the mtcars data frame. Here are some of the most important commands on data frames
a) load data from mtcars
data(mtcars)
b) > head(mtcars,3) # Display the top 3 rows of the data frame
mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

c) > tail(mtcars,4) # Display the boittom 4 rows of the data frame
mpg cyl disp  hp drat   wt qsec vs am gear carb
Ford Pantera L 15.8   8  351 264 4.22 3.17 14.5  0  1    5    4
Ferrari Dino   19.7   6  145 175 3.62 2.77 15.5  0  1    5    6
Maserati Bora  15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
Volvo 142E     21.4   4  121 109 4.11 2.78 18.6  1  1    4    2

d) > names(mtcars)  # Display the names of the columns of the data frame
[1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"

e) > summary(mtcars) # Display the summary of the data frame
mpg             cyl             disp             hp             drat             wt
Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0   Min.   :2.760   Min.   :1.513
1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581
Median :19.20   Median :6.000   Median :196.3   Median :123.0   Median :3.695   Median :3.325
Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7   Mean   :3.597   Mean   :3.217
3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610
Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0   Max.   :4.930   Max.   :5.424
qsec             vs               am              gear            carb
Min.   :14.50   Min.   :0.0000   Min.   :0.0000   Min.   :3.000   Min.   :1.000
1st Qu.:16.89   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000
Median :17.71   Median :0.0000   Median :0.0000   Median :4.000   Median :2.000
Mean   :17.85   Mean   :0.4375   Mean   :0.4062   Mean   :3.688   Mean   :2.812
3rd Qu.:18.90   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000
Max.   :22.90   Max.   :1.0000   Max.   :1.0000   Max.   :5.000   Max.   :8.000

f) > str(mtcars) # Generate a concise description of the data frame - values in each column, factors
'data.frame':   32 obs. of  11 variables:
$ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
$ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
$ disp: num  160 160 108 258 360 ...
$ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
$ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
$ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
$ qsec: num  16.5 17 18.6 19.4 17 ...
$ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
$ am  : num  1 1 1 0 0 0 0 0 0 0 ...
$ gear: num  4 4 4 3 3 3 3 4 4 4 ...
$ carb: num  4 4 1 1 2 1 4 2 2 4 ...

g) > mtcars[mtcars$mpg == 10.4,] #Select all rows in mtcars where the mpg column has a value 10.4
mpg cyl disp  hp drat    wt  qsec vs am gear carb
Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4

h) > mtcars[(mtcars$mpg >20) & (mtcars$mpg <24),] # Select all rows in mtcars where the mpg > 20 and mpg < 24
mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

i) > myset <- mtcars[(mtcars$cyl == 6) | (mtcars$cyl == 4),] # Get all calls which are either 4 or 6 cylinder
> myset
mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2…

j) > mean(myset$mpg) # Determine the mean of the set created above
[1] 23.97222

k) > table(mtcars$cyl) #Create a table of cars which have 4,6, or 8 cylinders

4  6  8
11  7 14

G) lapply,sapply,tapply
I use the iris data set for these commands
a) > data(iris) #Load iris data set

b) > names(iris)  #Show the column names of the data set
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
c) > lapply(iris,class) #Show the class of all the columns in iris
$Sepal.Length
[1] "numeric"
$Sepal.Width
[1] "numeric"
$Petal.Length
[1] "numeric"
$Petal.Width
[1] "numeric"
$Species
[1] "factor"

d) > sapply(iris,class) # Display a summary of the class of the iris data set
Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species
"numeric"    "numeric"    "numeric"    "numeric"     "factor"

e) tapply: Instead of getting the mean for each of the species as below we can use tapply
> a <-iris[iris$Species == "setosa",]
> mean(a$Sepal.Length)
[1] 5.006
> b <-iris[iris$Species == "versicolor",]
> mean(b$Sepal.Length)
[1] 5.936
> c <-iris[iris$Species == "virginica",]
> mean(c$Sepal.Length)
[1] 6.588

> tapply(iris$Sepal.Length,iris$Species,mean)
setosa versicolor  virginica
5.006      5.936      6.588

Hopefully this highly condensed version of R will set you on a R-oll.

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How to program – Some essential tips

If one follows the arrow of time from the early 1980s to the present day, the number of programming problems have not only proliferated but have also become more difficult. Fortunately  programming in itself has  become more manageable with massive increases in computing horsepower, smarter tools and instant availability of information on the internet, typically with the click of a mouse.

Learning to program is no easy task, but can be done with the right mix of attitude, curiosity and interest. Becoming adept at programming, however, is something else. An interesting essay in this context is Peter Norvig’s ‘Teach yourself programming in 10 years’

Back in the 1980s when I wrote my first Fortran program on my college Mainframe, programming was a lengthy exercise, spanning several days.

1

My first program was to plot a sine wave of characters on a computer printout. Running this program required the following several steps

  1. Enter the program on a teletype terminal and create a stack of Hollerith (punched) cards
  2. Submit the stack of cards to the computer center
  3. The computer center would do a batch execute in the evening on the Mainframe
  4. God forbid, if your program has a syntax error. If you did find an error, go back to step 1, the next day.
  5. Assuming everything is fine, the computer center would run your program and your output (printout) would be placed in the appropriate pigeon hole which you would need to pick up the next day.

The whole exercise to write a small-sized program could take anywhere between a couple of days to a whole week.

In the early 1990s things got a little better where one could code, compile, link and execute sitting at one’s desk. However while the programming itself got much simpler than before, certain tasks were still difficult.  Till the late 90s programs of any sort had to be written using a regular text editor (vi , emacs etc.)  You would then have to go through the process of compiling, linking and executing.

An angry compiler would typically spew forth venom at missing semi-colons, undeclared variables, and uninitialized values. This would happen till you are able to iron out all syntax errors.  Then you would link, get undefined symbols and have to include appropriate libraries etc. And then finally you would execute your code, only to have it crash. The process of debugging would then start.

Luckily technology has made life a whole lot easier except for the last step where you could still  run into an execution errors . In these days an IDE (Interactive Development Environment) like Eclipse will flag syntax errors, missing definitions/declarations etc. as you write your code. Moreover Eclipse can also indicate which libraries (imports) you would need to include in your package for it to build. The only missing step in IDEs of these days is the ability to predict possible execution errors in your program.  I wouldn’t be surprised, if in future, like Microsoft Word,  the IDE is able to tell you if a programming construct does not make sense.

So things have gotten a lot easier for the programmer. The following tips for are particularly useful as you progress along in programming

  1. These days when you are learning a new programming language it is not necessary to know the language from cover to cover by reading a book. In those days when we learnt C it was necessary to know everything from bit structures, macros, pragma etc. The reason being that every syntax or execution error one had to rush to get the textbook and thumb through it for the answer. Not so, in these days of Google. You have the world’s library at your fingertips.
  2. To get started it is necessary to learn just the most important programming constructs of the language say structure, class, car, cdr besides the usual suspects like loops, conditions and case constructs
  3. Download and install an IDE for the language. In most case Eclipse will work
  4. Try to write a simple program and test out your code.
  5. To do any sort of programming these days you will necessarily need to make 3 friends
    1. Google
    2. Stackoverflow
    3. Git & GitHub
  6. Honing your Googling skills is very important. There are answers to almost any sort of programming problems out there. You would be surprised to know that there are many others who did exactly the same stupid mistake that you did out there. Also googling will take you to interesting tutorials, blogs, articles that discuss different aspects of the programming language and the problem you are trying to solve
  7. Stackoverflow is really a God send to all programmers. There are so many questions on so many aspects of every programming language on earth there. If you spend time searching Stackoverflow you are bound to find answers, code snippets that you can readily use in your code
  8. Post your questions in stackoverflow when you don’t find the answers there. You are bound to get quick answers. Thanks to the gamification of Stackoverflow (points, upvotes,badges  etc) that has been created on Stackoverflow.
  9. Git & GitHub: I would suggest that you download and install GitHub for Windows. This will provide you with version control on your desktop. You can modify code while being to switch back to an earlier version with Git. Read up a good tutorial on Git for Windows
  10. Once you have working code you push it onto GitHub and share with other programmers

Now that you have the basic setup here are few other extremely important tips

  1. The most important criteria for programming is ‘attitude’. Initially you are bound to get frustrated, angry, irritated etc. But it is necessary to look at the errors that you get with the right attitude. Know that an error is telling you something. Usually the answers to your mistake are in the ‘error message’ itself. Look at it closely and try to understand it. You will learn a lot more when you learn from errors than from copy-pasting from somebody else’s code, even if works right the first time around!
  2. Make sure you do something different each time. As Einstein said “ If you keep doing the same thing, you will keep getting the same result’
  3. There are different ways to debug your code. You could use the debugger and single step through the code and keep checking the values of the variables. I personally prefer print statements to localize where things are going wrong. I then try to narrow down the problem to a few lines of code and try to take it apart.

Hopefully the above tips are useful. Programming can be creative activity and will be indispensable in our future.

Above all have fun coding, there are so many possibilities these days!

Also see

1. Programming languages in layman’s language
2. The common alphabet of programming languages
3. The mind of the programmer
4. Programming Zen and now – Some essential tips -2 

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