Bash Script to Create users in a DB

Many a times when running unit test on a clean build, I want to skip the user creation part, or may be reset the user details/profile for a particular user(s) in Development environment.
Of course there are unit test suite written to automate the flow, but sometimes when you are in the flow or designing the test cases itself, you need something to do the little rub-scrub-and-setup the little tiny jobs.
Here is my small script which deletes two very specific users from the my_dev_db db and creates them again. The details of the user are in file UserProd.txt which is tab delimited.  Enjoy.

[parag@paragcentosvm ~]# cat populateUsers.sh
#!/bin/sh

mysql -u parag_dev -pSOMEPASSWORD -e "DELETE FROM User_Table where UserId in (110,111);" -D my_dev_db;
mysql -u parag_dev -pSOMEPASSWORD -e "LOAD DATA LOCAL INFILE '/home/Parag/UserProd.txt' INTO TABLE User_Table;" -D my_dev_db;

Boost Mutex scoped_lock example

Here is a quick example showing how mutex helps in doing a synchronous access on shared resource. We are using boost::mutex::scoped_lock here, which ensures that the ownership of the mutex object is relinquished even in case the code following the lock throws an exception, thus preventing possible thread deadlocks.

#include <boost/thread/thread.hpp>
#include <boost/thread/mutex.hpp>
#include <boost/bind.hpp>
#include <iostream>

//Compile using : g++ program.cpp -lboost_thread

boost::mutex io_mutex;

void count(int id)
{
  for (int i = 0; i < 5; ++i)
  {
    boost::mutex::scoped_lock lock(io_mutex);
    std::cout << id << ": " << i << std::endl;
  }
}


void countWithoutMutex(int id)
{
  for (int i = 0; i < 5; ++i)
  {
    std::cout << id << ": " << i << std::endl;
  }
}


int main(int argc, char* argv[])
{
std::cout << "The wrong way\n";
  boost::thread thrd1(
    boost::bind(&countWithoutMutex, 1));
  boost::thread thrd2(
    boost::bind(&countWithoutMutex, 2));
  thrd1.join();
  thrd2.join();
std::cout << "Now the right way\n";
   boost::thread thrd3(
    boost::bind(&count, 3));
  boost::thread thrd4(
    boost::bind(&count, 4));
  thrd3.join();
  thrd4.join();
  

  return 0;
}

Output:

The wrong way
2: 01: 0
1: 1
1: 2
1: 3
1: 4

2: 1
2: 22
: 3
2: 4
Now the right way
3: 0
3: 1
3: 2
3: 3
3: 4
4: 0
4: 1
4: 2
4: 3
4: 4

R : Webcrawler Parser with Try-Catch

Well, I felt the need to do some analysis over content hosted on some initial set of web-sites and then aggregate it, plot. I created a very simple and easy parser which would crawl and parse the data read from these websites. I used the try-catch block for fault tolerance and resilience from error (website down, unavailable or trust error, etc). Here is a sample code, where I have used tryCatch and readLines methods :

  
>myUrlStats <- function(urlToCrawl) {
    statData <- tryCatch(
               {
   dataReadFromUrls <- readLines(con=urlToCrawl)
   myWebCrawlParser(dataReadFromUrls)
  },
        error=function(errorMessageStr) {
            message(paste("URL does not seem to exist:", urlToCrawl))
            message("Error message:")
            message(errorMessageStr)
            return(-1)
        },
        warning=function(warningMessageStr) {
            message(paste("URL caused a warning:", urlToCrawl))
            message("Warning message:")
            message(warningMessageStr)
            # Choose a return value in case of warning
            return(NULL)
        },
        finally={
   ##Clean up code
  }
 )
    return(statData)
}


> myWebCrawlParser <- function(dataReadFromURL){
# Do your analysis parsing here
# also like you can mine other outlinks from this data read for further traversing the web-links
return(1)
}


> urlToCrawl <- c(
  "http://superdevresources.com",
     "http://superbloggingresources.com"
     )
> finalReslt<- mapply(myUrlStats, urlToCrawl)

Happy programming!

R: Alternative and easy approach to Data concatenation

Sometimes in R you have the requirement of concatenating/appending data from two sources to create one Super Set, for eg. You may have a list of countries and economic growth indicators, and you want to create a data set which is a super set of this data (AxB), here is how you can do it via simple apply operation:

> myCustColNames
  MyColNames
1          A
2          B
3          C
4          D
5          E
> growthIndicator
[1] "GDP"        "Inflation"  "Population"
> apply(myCustColNames,1, paste, growthIndicator, sep="_")
     [,1]           [,2]           [,3]           [,4]           [,5]          
[1,] "A_GDP"        "B_GDP"        "C_GDP"        "D_GDP"        "E_GDP"       
[2,] "A_Inflation"  "B_Inflation"  "C_Inflation"  "D_Inflation"  "E_Inflation" 
[3,] "A_Population" "B_Population" "C_Population" "D_Population" "E_Population"
> 

Another example can be:

> myCustColNames<-data.frame(MyColNames=LETTERS[1:5])
> myCustColNames
  MyColNames
1          A
2          B
3          C
4          D
5          E
 > apply(myCustColNames,1, paste,seq(1:6), sep="")
     [,1] [,2] [,3] [,4] [,5]
[1,] "A1" "B1" "C1" "D1" "E1"
[2,] "A2" "B2" "C2" "D2" "E2"
[3,] "A3" "B3" "C3" "D3" "E3"
[4,] "A4" "B4" "C4" "D4" "E4"
[5,] "A5" "B5" "C5" "D5" "E5"
[6,] "A6" "B6" "C6" "D6" "E6"
> 

R: Converting a list to a dataframe

Sometimes a function returns a list data type, which you often need to convert to a data.frame data type. To convert the list to data.frame ran the following statement:

 compositeRowOfDataFrame<-do.call(rbind.data.frame, listData)

R deleting rows in a dataframe

To remove all rows from a data frame in R, you can use any of the following ways:

> b
   x  y   Result
1  1  1   B
2  1  2   B
3  1  3   C
4  1  4   A
5  1  5   B
6  1  6   A
7  1  7   C
8  1  8   C
9  1  9   B
10 1 10   C
> deleteRowsVec=c(FALSE)
> b[deleteRowsVec,]
[1] x   y   Result
<0 rows> (or 0-length row.names)

R: How to get current working directory and change it

If you are working on a R project, and you wish to load/save your data in some file, it is important to know which directory you are currently working in, so that you can easily browse or save your data to that directory or may be change it to a specific location. Here are the list of commands to get you going :

> getwd()
[1] "C:/Users/parag/Documents"
> ?setwd
> setwd("X:/Share")
> getwd()
[1] "X:/Share"
> 

WhatsApp call feature is out

The much awaited call feature of WhatsApp is out now and available to all Android users.  After installing the update and opening WhatsApp, you see a new tab for Calls next to the Chats and Contacts tab. Tap on the Calls tab and select the name of the person that you want to call. 
I made some quick phone call in successions to some of my friends and family and found the voice quality at par with similar services like Skype. Currently its available only to Android users. IPhone and Windows phone users would need to wait for some time. 

Windows Server 2003 End of Support

Starting July 14, 2015, Microsoft will end all support and updates for Windows Server 2003. Microsoft hosting an exclusive series of webinars to help you with a smooth transition from Windows Server 2003.

Presenter(s):	Manpreet Madaan and Vishal Mitbawkar.
Language(s):	English.
Product(s):	Microsoft server product portfolio.
Audience(s):	IT Decision Maker, IT Manager, Tech Influencing BDM and Tech Support - Partners.

Webinar Details:

Date: April 02, 2015   Time: 3:00 pm to 4:00 pm

Register here.

Microsoft IOT DevCamp in Pune & Hyderabad

Microsoft DevCamps are no-fluff events for developers, by developers. You learn from experts in an interactive way and then get to apply what you’ve learned.

.

Our upcoming IoT DevCamp will show how to use Microsoft's Azure services (specifically Event Hub, Streaming Analytics, and Web Sites) in the context of a full end-to-end IoT solution. The lab currently includes a sensor/gateway approach with low-cost Arduino devices as the sensor end-point, and Raspberry Pi as the gateway. A dummy Windows console gateway is provided for those who wish to focus solely on the Azure solution.

.

Participants are also encouraged to bring their own devices to hack, and learn to connect to Azure. At the end of the session, you will have a good end-to-end understanding of a typical IoT solution.

.

Topics that will be covered:

.

IoT overview, architecture, Microsoft platform Event Hubs  Azure NRT Devices (Arduino, Raspberry Pi, etc.)

.

Instruction, demos, equipment and hands on labs will be provided in class.

When & Where

.

. City: . Pune . Date: . 29th April 2015 . Timings: . 9:00 am - 6:00 pm . Venue: . The O Hotel, North Main Road, Koregaon Park, Pune 411001 . . City: . Hyderabad . Date: . 24th April 2015 . Timings: . 9:00 am - 6:00 pm . Venue: . Radisson Hyderabad Hitec City, Gachibowli, Miyapur Road, Hyderabad 500032 .

. Pre-requisites for pre-event online briefing: . You should be IoT Start-ups or IoT enthusiasts having a working knowledge of Embedded/IoT/System on Chips devices. . Details about the pre-event online briefing will be sent by email a few days after you register. Attendance is required. . . Pre-Requisites for the Event: . A Laptop with Visual Studio 2013 Update 4 . Microsoft Azure Subscription (MSDN, BizSpark, Trial, Pay-As-You-Go or Company/Individual Account)

Explore the world of Azure with IoT

Microsoft Office 2013 - Upto 15% off

Today is the last day to register for Microsoft Office 2013 and get discounts of up to 15%. This is valid only for Small and Medium Businesses. Find your cloud partner here.

Microsoft Azure App Service Now available

Microsoft recently announced the Azure App Service which helps developers deliver cross-platform, cloud-connected apps faster. This solution integrates the Microsoft Azure Websites, Mobile Services and Biztalk Services into a single service with a common app hosting, runtime and extensibility model, enabling simplified integration with popular consumer and commercial services and on-premises systems, all for one low price.
Read more here. 

Windows 10: Windows Insider Hub

I have been using Microsoft Windows 10 for over two month now. Cortana is a big help and right now I am more or less using it as my "Search" cum "Run-as" feature. The side notification center situated on the task bar is a big help. Quick look up and turning features and settings of my machine on-off. The outlook and calender apps are the most used one at the moment as I could not find much exciting otherwise there. The good news Microsoft will update this to the RTM version of Windows 10 for free. Anyways with VS2014 community edition now also free its good for developers. 

One great thing about the Windows 10 preview built is the Windows Insider Hub app, through which you can give feedback and suggestions directly to the Microsoft team, which is really really cool. I can up vote on features requested by others which they would like to see. There are different categories under which you can directly provide your views and must haves directly to the concerned teams. 

R: Selecting unique values out of dataset

Many a times you have a dataset or a column which has a range of values and you wish to know the domain or range of values the column assumes. To do so run the following command:

>  y<-c('A', 'A','A','A','B', 'B','B','B','B','B','B','B','B', 'C','C','D','D', 'E','E', 'E','E','E' ,'E','E','E' ,'E','F','F','F', 'F','F', 'F','F', 'F','F')
> levels(factor(y))
[1] "A" "B" "C" "D" "E" "F"
> unique(y)
[1] "A" "B" "C" "D" "E" "F"

Windows 10 preview build Updates

I must say that when I installed and ran the first publicly available build of windows 10 announced in January this year, I was a little upset with the performance of the system. It ran so many apps and processes in the background that the machine felt sluggish and slow and non-responsive.

You had to keep it running for few minutes after boot before you could start working on it. This was a total turn off. With the recent updates it started running a lot better. No more background CPU hogging processes. Hope the improvements keep on coming.

Renumbering rows after ordering in R

Many a times after data cleaning and reordering, the dataset row numbers get jumbled up. To solve the issue simple run the following command:

> row.names(myDataFrame) <- 1:nrow(myDataFrame)

Happy programming!!

R : Adding column names to data

Sometimes you have a huge data with multiple fields and the data stored in your file is not marked with header information (i.e. column names).
To add column names to your data frame or matrix after it is read into a variable run the following command:

> CustColName <- c("time", "Age", "Surname", "Standard", "RollNo", "Name")
> colnames(StudData) <- CustColName

R : Removing empty rows from Data

Often the data we have collected in R has empty rows or has some rows which have the interesting or mandatory columns missing or NA.

To remove such rows from your data run the following:

 myDF <- myDF[!is.na(studentData$RollNo),]

R : Subsetting data

To subset a data frame or matrix:

> x <- c(1,2,3,4,5,6)

> y <- c(3,5,2,4,1,4)

> z <- c(2,3,4,3,2,1)

> dataF<-data.frame(x,y,z)

> dataF

  x y z

1 1 3 2

2 2 5 3

3 3 2 4

4 4 4 3

5 5 1 2

6 6 4 1

> dataF[2,]

  x y

2 2 5

> dataF[2:5,]

  x y

2 2 5

3 3 2

4 4 4

5 5 1

> dataF$x <- NULL

> dataF

  y

1 3

2 5

3 2

4 4

5 1

6 4

> 

If you are beginning with the R language, and need tips and help feel free to put down a comment below. 

R: Renaming Row numbers (Identifiers)

Many a times in R you have a dataset for which R provides a default numerical representation for identifying the individual rows :

> myData <- cbind(x = 3, y = c(4:1, 2:5))
> myData
     x  y
[1,]  3  4
[2,]  3  3
[3,]  3  2
[4,]  3  1
[5,]  3  2
[6,]  3  3
[7,]  3  4
[8,]  3  5

If you want to rename the rows as per your needs, you can do so by running the following command:

> dimnames(myData)[[1]] <- letters[1:8]
> myData
   x y
a  3  4
b  3  3
c  3  2
d  3  1
e  3  2
f  3  3
g  3  4
h  3  5

Happy programming!