Wednesday 23 January 2019

JSON Files in R Language

JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.

Install rjson Package

In the R console, you can issue the following command to install the rjson package.
install.packages("rjson")

Input Data

Create a JSON file by copying the below data into a text editor like notepad. Save the file with a .json extension and choosing the file type as all files(*.*).
{ 
   "ID":["1","2","3","4","5","6","7","8" ],
   "Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],
   "Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],
   



   "StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",
      "7/30/2013","6/17/2014"],
   "Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]
}

Read the JSON File

The JSON file is read by R using the function from JSON(). It is stored as a list in R.
# Load the package required to read JSON files.
library("rjson")

# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Print the result.
print(result)
When we execute the above code, it produces the following result −
$ID
[1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"

$Name
[1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"     "Nina"     "Simon"    "Guru"

$Salary
[1] "623.3"  "515.2"  "611"    "729"    "843.25" "578"    "632.8"  "722.5"

$StartDate
[1] "1/1/2012"   "9/23/2013"  "11/15/2014" "5/11/2014"  "3/27/2015"  "5/21/2013"
   "7/30/2013"  "6/17/2014"

$Dept
[1] "IT"         "Operations" "IT"         "HR"         "Finance"    "IT"
   "Operations" "Finance"

Convert JSON to a Data Frame

We can convert the extracted data above to a R data frame for further analysis using the as.data.frame()function.
# Load the package required to read JSON files.
library("rjson")




# Give the input file name to the function.
result <- fromJSON(file = "input.json")

# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)

print(json_data_frame)
When we execute the above code, it produces the following result −
      id,   name,    salary,   start_date,     dept
1      1    Rick     623.30    2012-01-01      IT
2      2    Dan      515.20    2013-09-23      Operations
3      3    Michelle 611.00    2014-11-15      IT
4      4    Ryan     729.00    2014-05-11      HR
5     NA    Gary     843.25    2015-03-27      Finance
6      6    Nina     578.00    2013-05-21      IT
7      7    Simon    632.80    2013-07-30      Operations
8      8    Guru     722.50    2014-06-17      Finance

Environment Setup in R Language

Try it Option Online

You really do not need to set up your own environment to start learning R programming language. Reason is very simple, we already have set up R Programming environment online, so that you can compile and execute all the available examples online at the same time when you are doing your theory work. This gives you confidence in what you are reading and to check the result with different options. Feel free to modify any example and execute it online.
Try the following example using Try it option at the website available at the top right corner of the below sample code box −
# Print Hello World. 
print("Hello World") 
 



# Add two numbers. 
print(23.9 + 11.6)
For most of the examples given in this tutorial, you will find Try it option at the website, so just make use of it and enjoy your learning.

Local Environment Setup

If you are still willing to set up your environment for R, you can follow the steps given below.

WINDOWS INSTALLATION

You can download the Windows installer version of R from R-3.2.2 for Windows (32/64 bit) and save it in a local directory.
As it is a Windows installer (.exe) with a name "R-version-win.exe". You can just double click and run the installer accepting the default settings. If your Windows is 32-bit version, it installs the 32-bit version. But if your windows is 64-bit, then it installs both the 32-bit and 64-bit versions.
After installation you can locate the icon to run the Program in a directory structure "R\R3.2.2\bin\i386\Rgui.exe" under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

LINUX INSTALLATION

R is available as a binary for many versions of Linux at the location R Binaries.
The instruction to install Linux varies from flavor to flavor. These steps are mentioned under each type of Linux version in the mentioned link. However, if you are in a hurry, then you can use yum command to install R as follows −
$ yum install R
Above command will install core functionality of R programming along with standard packages, still you need additional package, then you can launch R prompt as follows −
$ R

R version 3.2.0 (2015-04-16) -- "Full of  Ingredients"          
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)
        
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
            
R is a collaborative project with many  contributors.                    
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
       
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>  
Now you can use install command at R prompt to install the required package. For example, the following command will install plotrix package which is required for 3D charts.
> install.packages("plotrix")

Tuesday 22 January 2019

Students Records App with Source Code

 MainActivity.Java :-
 
package com.irawen.attendance;

import android.app.Activity;
import android.app.AlertDialog.Builder;
import android.content.Context;
import android.database.Cursor;
import android.database.sqlite.SQLiteDatabase;
import android.os.Bundle;
import android.view.Menu;
import android.view.View;
import android.view.View.OnClickListener;
import android.widget.Button;
import android.widget.EditText;

public class MainActivity extends Activity {
    EditText ename,eroll_no,emarks;
    Button add,view,viewall,Show1,delete,modify;
    SQLiteDatabase db;

    @Override    protected void onCreate(Bundle savedInstanceState) {
        super.onCreate(savedInstanceState);
        setContentView(R.layout.activity_main);

        ename=(EditText)findViewById(R.id.name);
        eroll_no=(EditText)findViewById(R.id.roll_no);
        emarks=(EditText)findViewById(R.id.marks);
        add=(Button)findViewById(R.id.addbtn);
        view=(Button)findViewById(R.id.viewbtn);
        viewall=(Button)findViewById(R.id.viewallbtn);
        delete=(Button)findViewById(R.id.deletebtn);
        Show1=(Button)findViewById(R.id.showbtn);
        modify=(Button)findViewById(R.id.modifybtn);



db=openOrCreateDatabase("Student_manage", Context.MODE_PRIVATE, null);
db.execSQL("CREATE TABLE IF NOT EXISTS student(rollno INTEGER,name VARCHAR,marks INTEGER);");


        add.setOnClickListener(new OnClickListener() {

            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 if(eroll_no.getText().toString().trim().length()==0||
                        ename.getText().toString().trim().length()==0||
                        emarks.getText().toString().trim().length()==0)
                {
                    showMessage("Error", "Please enter all values");
                    return;
                }
                db.execSQL("INSERT INTO student VALUES('"+eroll_no.getText()+"','"+ename.getText()+
                        "','"+emarks.getText()+"');");
                showMessage("Success", "Record added successfully");
                clearText();
            }
        });
        delete.setOnClickListener(new OnClickListener() {

            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 if(eroll_no.getText().toString().trim().length()==0)
                {
                    showMessage("Error", "Please enter Rollno");
                    return;
                }
                Cursor c=db.rawQuery("SELECT * FROM student WHERE rollno='"+eroll_no.getText()+"'", null);
                if(c.moveToFirst())
                {
                    db.execSQL("DELETE FROM student WHERE rollno='"+eroll_no.getText()+"'");
                    showMessage("Success", "Record Deleted");
                }
                else                {
                    showMessage("Error", "Invalid Rollno");
                }
                clearText();
            }
        });
        modify.setOnClickListener(new OnClickListener() {

            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 if(eroll_no.getText().toString().trim().length()==0)
                {
                    showMessage("Error", "Please enter Rollno");
                    return;
                }
 Cursor c=db.rawQuery("SELECT * FROM student WHERE rollno='"+eroll_no.getText()+"'", null);
                if(c.moveToFirst())
                {
     db.execSQL("UPDATE student SET name='"+ename.getText()+"',marks='"+emarks.getText()+
                            "' WHERE rollno='"+eroll_no.getText()+"'");
                    showMessage("Success", "Record Modified");
                }
                else                {
                    showMessage("Error", "Invalid Rollno");
                }
                clearText();
            }
        });
        view.setOnClickListener(new OnClickListener() {




            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 if(eroll_no.getText().toString().trim().length()==0)
                {
                    showMessage("Error", "Please enter Rollno");
                    return;
                }
 Cursor c=db.rawQuery("SELECT * FROM student WHERE rollno='"+eroll_no.getText()+"'", null);
                if(c.moveToFirst())
                {
                    ename.setText(c.getString(1));
                    emarks.setText(c.getString(2));
                }
                else                {
                    showMessage("Error", "Invalid Rollno");
                    clearText();
                }
            }
        });
        viewall.setOnClickListener(new OnClickListener() {

            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 Cursor c=db.rawQuery("SELECT * FROM student", null);
                if(c.getCount()==0)
                {
                    showMessage("Error", "No records found");
                    return;
                }
                StringBuffer buffer=new StringBuffer();
                while(c.moveToNext())
                {

                    buffer.append("Rollno: "+c.getString(0)+"\n");
                    buffer.append("Name: "+c.getString(1)+"\n");
                    buffer.append("Marks: "+c.getString(2)+"\n\n");
                }
                showMessage("Student Details", buffer.toString());
            }
        });
        Show1.setOnClickListener(new OnClickListener() {

            @Override            public void onClick(View v) {
                // TODO Auto-generated method stub 
 showMessage("Student Management Application", "Irawen Education");
            }
        });

    }
    public void showMessage(String title,String message)
    {
        Builder builder=new Builder(this);
        builder.setCancelable(true);
        builder.setTitle(title);
        builder.setMessage(message);
        builder.show();
    }
    public void clearText()
    {


        eroll_no.setText("");
        ename.setText("");
        emarks.setText("");
        eroll_no.requestFocus();
    }
    @Override    public boolean onCreateOptionsMenu(Menu menu) {
        // Inflate the menu; this adds items to the action bar if it is present. 
 getMenuInflater().inflate(R.menu.student_main, menu);
        return true;
    }

}


MainActivity.xml :- 

<LinearLayout xmlns: 
android="http://schemas.android.com/apk/res/android"
 xmlns:tools="http://schemas.android.com/tools" 
android:id="@+id/LinearLayout1" 
android:layout_width="match_parent" 
android:layout_height="match_parent"
 android:background="#707777"
 android:orientation="vertical" 
android:paddingBottom="@dimen/activity_vertical_margin" 
android:paddingLeft="@dimen/activity_horizontal_margin"
 android:paddingRight="@dimen/activity_horizontal_margin"
 android:paddingTop="@dimen/activity_vertical_margin"
 tools:context=".MainActivity" >

    <EditText    android:id="@+id/roll_no" 
 android:layout_width="match_parent" 
 android:layout_height="wrap_content" 
 android:ems="10" 
 android:hint="Enter Roll No." 
 android:inputType="number" >
    <requestFocus />
</EditText>



<EditText 
 android:id="@+id/name" 
 android:layout_width="match_parent" 
 android:layout_height="wrap_content" 
 android:ems="10"    android:hint="Enter Your Name" />

<EditText 
 android:id="@+id/marks" 
 android:layout_width="match_parent" 
 android:layout_height="wrap_content" 
 android:ems="10" 
 android:hint="Enter Marks" 
 android:inputType="number" />



<LinearLayout 
 android:layout_width="match_parent" 
 android:layout_height="98dp" >

    <Button 
 android:id="@+id/addbtn" 
 android:layout_width="140dp" 
 android:layout_height="90dp"
        android:text="Add" />



    <Button         
android:id="@+id/deletebtn" 
 android:layout_width="140dp" 
 android:layout_height="90dp"         
android:layout_marginLeft="50dp" 
 android:text="Delete" />
</LinearLayout>

<LinearLayout 
 android:layout_width="match_parent" 
 android:layout_height="98dp" >

    <Button         
android:id="@+id/modifybtn" 
 android:layout_width="140dp" 
 android:layout_height="90dp" 
 android:text="Modify" />

    <Button 
 android:id="@+id/viewbtn" 
 android:layout_width="140dp" 
 android:layout_height="90dp" 
 android:layout_marginLeft="50dp" 
 android:text="View" />
</LinearLayout>



<LinearLayout 
 android:layout_width="match_parent" 
 android:layout_height="wrap_content" 
 android:layout_weight="0.74" >

    <Button 
 android:id="@+id/viewallbtn" 
 android:layout_width="140dp" 
 android:layout_height="90dp" 
 android:text="View all" />




    <Button 
 android:id="@+id/showbtn"         
android:layout_width="140dp" 
 android:layout_height="90dp" 
 android:layout_marginLeft="50dp" 
 android:text="Show" />

</LinearLayout>

</LinearLayout> 

Wednesday 16 January 2019

Chi-Square test in R Language

Chi-Square test is a statistical method to determine if two categorical variables have a significant correlation between them. Both those variables should be from same population and they should be categorical like − Yes/No, Male/Female, Red/Green etc.
For example, we can build a data set with observations on people's ice-cream buying pattern and try to correlate the gender of a person with the flavor of the ice-cream they prefer. If a correlation is found we can plan for appropriate stock of flavors by knowing the number of gender of people visiting.

Syntax

The function used for performing chi-Square test is chisq.test().
The basic syntax for creating a chi-square test in R is −
chisq.test(data)
Following is the description of the parameters used −
  • data is the data in form of a table containing the count value of the variables in the observation.

Example

We will take the Cars93 data in the "MASS" library which represents the sales of different models of car in the year 1993.
library("MASS")
print(str(Cars93))
When we execute the above code, it produces the following result −
'data.frame':   93 obs. of  27 variables: 
 $ Manufacturer      : Factor w/ 32 levels "Acura","Audi",..: 1 1 2 2 3 4 4 4 4 5 ... 
 $ Model             : Factor w/ 93 levels "100","190E","240",..: 49 56 9 1 6 24 54 74 73 35 ... 
 $ Type              : Factor w/ 6 levels "Compact","Large",..: 4 3 1 3 3 3 2 2 3 2 ... 
 $ Min.Price         : num  12.9 29.2 25.9 30.8 23.7 14.2 19.9 22.6 26.3 33 ... 
 $ Price             : num  15.9 33.9 29.1 37.7 30 15.7 20.8 23.7 26.3 34.7 ... 
 $ Max.Price         : num  18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ... 
 $ MPG.city          : int  25 18 20 19 22 22 19 16 19 16 ... 
 $ MPG.highway       : int  31 25 26 26 30 31 28 25 27 25 ... 
 $ AirBags           : Factor w/ 3 levels "Driver & Passenger",..: 3 1 2 1 2 2 2 2 2 2 ... 
 $ DriveTrain        : Factor w/ 3 levels "4WD","Front",..: 2 2 2 2 3 2 2 3 2 2 ... 
 $ Cylinders         : Factor w/ 6 levels "3","4","5","6",..: 2 4 4 4 2 2 4 4 4 5 ... 
 $ EngineSize        : num  1.8 3.2 2.8 2.8 3.5 2.2 3.8 5.7 3.8 4.9 ... 
 $ Horsepower        : int  140 200 172 172 208 110 170 180 170 200 ... 
 $ RPM               : int  6300 5500 5500 5500 5700 5200 4800 4000 4800 4100 ... 
 $ Rev.per.mile      : int  2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ... 
 $ Man.trans.avail   : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ... 
 $ Fuel.tank.capacity: num  13.2 18 16.9 21.1 21.1 16.4 18 23 18.8 18 ... 
 $ Passengers        : int  5 5 5 6 4 6 6 6 5 6 ... 
 $ Length            : int  177 195 180 193 186 189 200 216 198 206 ... 
 $ Wheelbase         : int  102 115 102 106 109 105 111 116 108 114 ... 
 $ Width             : int  68 71 67 70 69 69 74 78 73 73 ... 
 $ Turn.circle       : int  37 38 37 37 39 41 42 45 41 43 ... 
 $ Rear.seat.room    : num  26.5 30 28 31 27 28 30.5 30.5 26.5 35 ... 
 $ Luggage.room      : int  11 15 14 17 13 16 17 21 14 18 ... 
 $ Weight            : int  2705 3560 3375 3405 3640 2880 3470 4105 3495 3620 ... 
 $ Origin            : Factor w/ 2 levels "USA","non-USA": 2 2 2 2 2 1 1 1 1 1 ... 
 $ Make              : Factor w/ 93 levels "Acura Integra",..: 1 2 4 3 5 6 7 9 8 10 ... 
The above result shows the dataset has many Factor variables which can be considered as categorical variables. For our model we will consider the variables "AirBags" and "Type". Here we aim to find out any significant correlation between the types of car sold and the type of Air bags it has. If correlation is observed we can estimate which types of cars can sell better with what types of air bags.
# Load the library.
library("MASS")

# Create a data frame from the main data set.
car.data <- data.frame(Cars93$AirBags, Cars93$Type)

# Create a table with the needed variables.
car.data = table(Cars93$AirBags, Cars93$Type) 
print(car.data)

# Perform the Chi-Square test.
print(chisq.test(car.data))
When we execute the above code, it produces the following result −
                     Compact Large Midsize Small Sporty Van
  Driver & Passenger       2     4       7     0      3   0
  Driver only              9     7      11     5      8   3
  None                     5     0       4    16      3   6

        Pearson's Chi-squared test

data:  car.data
X-squared = 33.001, df = 10, p-value = 0.0002723




Warning message:
In chisq.test(car.data) : Chi-squared approximation may be incorrect

CONCLUSION

The result shows the p-value of less than 0.05 which indicates a string correlation.



Monday 14 January 2019

Survival analysis in R Language

Survival analysis deals with predicting the time when a specific event is going to occur. It is also known as failure time analysis or analysis of time to death. For example predicting the number of days a person with cancer will survive or predicting the time when a mechanical system is going to fail.
The R package named survival is used to carry out survival analysis. This package contains the function Surv() which takes the input data as a R formula and creates a survival object among the chosen variables for analysis. Then we use the function survfit() to create a plot for the analysis.

Install Package

install.packages("survival")

 

SYNTAX

 

The basic syntax for creating survival analysis in R is −
Surv(time,event)
survfit(formula)
Following is the description of the parameters used −
  • time is the follow up time until the event occurs.
  • event indicates the status of occurrence of the expected event.
  • formula is the relationship between the predictor variables.

EXAMPLE

We will consider the data set named "pbc" present in the survival packages installed above. It describes the survival data points about people affected with primary biliary cirrhosis (PBC) of the liver. Among the many columns present in the data set we are primarily concerned with the fields "time" and "status". Time represents the number of days between registration of the patient and earlier of the event between the patient receiving a liver transplant or death of the patient.
# Load the library.
library("survival")

# Print first few rows.
print(head(pbc))
When we execute the above code, it produces the following result and chart −
  id time status trt      age sex ascites hepato spiders edema bili chol
1  1  400      2   1 58.76523   f       1      1       1   1.0 14.5  261
2  2 4500      0   1 56.44627   f       0      1       1   0.0  1.1  302
3  3 1012      2   1 70.07255   m       0      0       0   0.5  1.4  176
4  4 1925      2   1 54.74059   f       0      1       1   0.5  1.8  244
5  5 1504      1   2 38.10541   f       0      1       1   0.0  3.4  279
6  6 2503      2   2 66.25873   f       0      1       0   0.0  0.8  248
  albumin copper alk.phos    ast trig platelet protime stage
1    2.60    156   1718.0 137.95  172      190    12.2     4
2    4.14     54   7394.8 113.52   88      221    10.6     3
3    3.48    210    516.0  96.10   55      151    12.0     4
4    2.54     64   6121.8  60.63   92      183    10.3     4
5    3.53    143    671.0 113.15   72      136    10.9     3
6    3.98     50    944.0  93.00   63       NA    11.0     3
From the above data we are considering time and status for our analysis.

APPLYING SURV() AND SURVFIT() FUNCTION

 

Now we proceed to apply the Surv() function to the above data set and create a plot that will show the trend.
# Load the library.
library("survival")

# Create the survival object. 
survfit(Surv(pbc$time,pbc$status == 2)~1)

# Give the chart file a name.
png(file = "survival.png")

# Plot the graph. 
plot(survfit(Surv(pbc$time,pbc$status == 2)~1))

# Save the file.
dev.off()
When we execute the above code, it produces the following result and chart −
Call: survfit(formula = Surv(pbc$time, pbc$status == 2) ~ 1)

      n  events  median 0.95LCL 0.95UCL 
    418     161    3395    3090    3853 
SUrvival analysis using R
 The trend in the above graph helps us predicting the probability of survival at the end of a certain number of days.

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