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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# R Tutorial\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R is a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka).\n",
    "\n",
    "## Basic Syntax\n",
    "\n",
    "As a convention, we will start learning R programming by writing a \"Hello, World!\" program. Depending on the needs, you can program either at R command prompt or you can use an R script file to write your program. Let's check both one by one.\n",
    "\n",
    "### R Command Prompt\n",
    "\n",
    "Once you have R environment setup, then it’s easy to start your R command prompt by just typing the R command.\n",
    "This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Hello, Nancy!\"\n"
     ]
    }
   ],
   "source": [
    "myString <- \"Hello, World!\"\n",
    "print (myString)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here first statement defines a string variable myString, where we assign a string \"Hello, World!\" and then next statement print() is being used to print the value stored in variable myString.\n",
    "\n",
    "### R Script File\n",
    "\n",
    "Usually, you will do your programming by writing your programs in script files and then you execute those scripts at your command prompt with the help of R interpreter called Rscript. So let's start with writing following code in a text file called test.R as under −\n",
    "\n",
    "    myString <- \"Hello, World!\"\n",
    "    print ( myString)\n",
    "\n",
    "Save the above code in a file test.R and execute it at Linux command prompt as given below. Even if you are using Windows or other system, syntax will remain same.\n",
    "\n",
    "    $ Rscript test.R \n",
    "\n",
    "When we run the above program, it produces the following result.\n",
    "\n",
    "    [1] \"Hello, World!\"\n",
    "\n",
    "\n",
    "### Comments\n",
    "\n",
    "Comments are like helping text in your R program and they are ignored by the interpreter while executing your actual program. Single comment is written using # in the beginning of the statement as follows −"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My first program in R Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R does not support multi-line comments but you can perform a trick which is something as follows −"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "if(FALSE) {\n",
    "   \"This is a demo for multi-line comments and it should be put inside either a \n",
    "      single OR double quote\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Types\n",
    "\n",
    "Generally, while doing programming in any programming language, you need to use various variables to store various information. Variables are nothing but reserved memory locations to store values. This means that, when you create a variable you reserve some space in memory. The variables are assigned with R-Objects and the data type of the R-object becomes the data type of the variable. There are many types of R-objects. The frequently used ones are −\n",
    "\n",
    "    Vectors\n",
    "    Lists\n",
    "    Matrices\n",
    "    Arrays\n",
    "    Factors\n",
    "    Data Frames\n",
    "\n",
    "### Vectors\n",
    "\n",
    "When you want to create vector with more than one element, you should use c() function which means to combine the elements into a vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"red\"    \"green\"  \"yellow\"\n",
      "[1] \"character\"\n"
     ]
    }
   ],
   "source": [
    "# Create a vector.\n",
    "apple <- c('red','green',\"yellow\")\n",
    "print(apple)\n",
    "\n",
    "# Get the class of the vector.\n",
    "print(class(apple))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists\n",
    "\n",
    "A list is an R-object which can contain many different types of elements inside it like vectors, functions and even another list inside it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]]\n",
      "[1] 2 5 3\n",
      "\n",
      "[[2]]\n",
      "[1] 21.3\n",
      "\n",
      "[[3]]\n",
      "function (x)  .Primitive(\"sin\")\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create a list.\n",
    "list1 <- list(c(2,5,3),21.3,sin)\n",
    "\n",
    "# Print the list.\n",
    "print(list1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrices\n",
    "\n",
    "A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     [,1] [,2] [,3]\n",
      "[1,] \"a\"  \"a\"  \"b\" \n",
      "[2,] \"c\"  \"b\"  \"a\" \n"
     ]
    }
   ],
   "source": [
    "# Create a matrix.\n",
    "M = matrix( c('a','a','b','c','b','a'), nrow = 2, ncol = 3, byrow = TRUE)\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrays\n",
    "\n",
    "While matrices are confined to two dimensions, arrays can be of any number of dimensions. The array function takes a dim attribute which creates the required number of dimension. In the below example we create an array with two elements which are 3x3 matrices each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array.\n",
    "a <- array(c('green','yellow'),dim = c(3,3,2))\n",
    "print(a) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factors\n",
    "\n",
    "Factors are the r-objects which are created using a vector. It stores the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character or Boolean etc. in the input vector. They are useful in statistical modeling.\n",
    "\n",
    "Factors are created using the factor() function. The nlevels functions gives the count of levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a vector.\n",
    "apple_colors <- c('green','green','yellow','red','red','red','green')\n",
    "\n",
    "# Create a factor object.\n",
    "factor_apple <- factor(apple_colors)\n",
    "\n",
    "# Print the factor.\n",
    "print(factor_apple)\n",
    "print(nlevels(factor_apple))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Frames\n",
    "\n",
    "Data frames are tabular data objects. Unlike a matrix in data frame each column can contain different modes of data. The first column can be numeric while the second column can be character and third column can be logical. It is a list of vectors of equal length.\n",
    "\n",
    "Data Frames are created using the data.frame() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the data frame.\n",
    "BMI <- \tdata.frame(\n",
    "   gender = c(\"Male\", \"Male\",\"Female\"), \n",
    "   height = c(152, 171.5, 165), \n",
    "   weight = c(81,93, 78),\n",
    "   Age = c(42,38,26)\n",
    ")\n",
    "print(BMI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables\n",
    "\n",
    "A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, group of atomic vectors or a combination of many Robjects.\n",
    "\n",
    "### Variable Assignment\n",
    "\n",
    "The variables can be assigned values using leftward, rightward and equal to operator. The values of the variables can be printed using print() or cat() function. The cat() function combines multiple items into a continuous print output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assignment using equal operator.\n",
    "var.1 = c(0,1,2,3)           \n",
    "\n",
    "# Assignment using leftward operator.\n",
    "var.2 <- c(\"learn\",\"R\")   \n",
    "\n",
    "# Assignment using rightward operator.   \n",
    "c(TRUE,1) -> var.3           \n",
    "\n",
    "print(var.1)\n",
    "cat (\"var.1 is \", var.1 ,\"\\n\")\n",
    "cat (\"var.2 is \", var.2 ,\"\\n\")\n",
    "cat (\"var.3 is \", var.3 ,\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note − The vector c(TRUE,1) has a mix of logical and numeric class. So logical class is coerced to numeric class making TRUE as 1.\n",
    "\n",
    "### Data Type of a Variable\n",
    "\n",
    "In R, a variable itself is not declared of any data type, rather it gets the data type of the R - object assigned to it. So R is called a dynamically typed language, which means that we can change a variable’s data type of the same variable again and again when using it in a program."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_x <- \"Hello\"\n",
    "cat(\"The class of var_x is \",class(var_x),\"\\n\")\n",
    "\n",
    "var_x <- 34.5\n",
    "cat(\"  Now the class of var_x is \",class(var_x),\"\\n\")\n",
    "\n",
    "var_x <- 27L\n",
    "cat(\"   Next the class of var_x becomes \",class(var_x),\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting Variables\n",
    "\n",
    "Variables can be deleted by using the rm() function. Below we delete the variable var.3. On printing the value of the variable error is thrown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "echo": true
   },
   "outputs": [],
   "source": [
    "rm(var.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "## Operators\n",
    "\n",
    "An operator is a symbol that tells the compiler to perform specific mathematical or logical manipulations. R language is rich in built-in operators and provides following types of operators.\n",
    "### Types of Operators\n",
    "\n",
    "We have the following main types of operators in R programming −\n",
    "\n",
    "    Arithmetic Operators\n",
    "    Relational Operators\n",
    "    Logical Operators\n",
    "\n",
    "### Arithmetic Operators\n",
    "\n",
    "    +  Add\n",
    "\n",
    "    - Subtract\n",
    "\n",
    "    * Multiply\n",
    "\n",
    "    / Divide\n",
    "\n",
    "### Relational Operators\n",
    "\n",
    "    > greater than\n",
    "    < less than\n",
    "    == equal to\n",
    "    <= less than or equal to\n",
    "    >= greater than or equal to\n",
    "    != unequal to\n",
    "    \n",
    "### Logical Operators\n",
    "\n",
    "    & AND\n",
    "    | OR\n",
    "    ! NOT\n",
    "    \n",
    "\n",
    "## Decision making\n",
    "\n",
    "Decision making structures require the programmer to specify one or more conditions to be evaluated or tested by the program, along with a statement or statements to be executed if the condition is determined to be true, and optionally, other statements to be executed if the condition is determined to be false. R provides the following types of decision making statements.\n",
    "\n",
    "### If Statement\n",
    "\n",
    "The basic syntax for creating an if statement in R is −\n",
    "\n",
    "\n",
    "    if(boolean_expression) {\n",
    "        # statement(s) will execute if the boolean expression is true.\n",
    "    }\n",
    "\n",
    "\n",
    "If the Boolean expression evaluates to be true, then the block of code inside the if statement will be executed. If Boolean expression evaluates to be false, then the first set of code after the end of the if statement (after the closing curly brace) will be executed.\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- 30L\n",
    "if(is.integer(x)) {\n",
    "   print(\"X is an Integer\")\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If...Else Statement\n",
    "\n",
    "The basic syntax for creating an if...else statement in R is −\n",
    "\n",
    "    if(boolean_expression) {\n",
    "        # statement(s) will execute if the boolean expression is true.\n",
    "    } else {\n",
    "        # statement(s) will execute if the boolean expression is false.\n",
    "    }\n",
    "If the Boolean expression evaluates to be true, then the if block of code will be executed, otherwise else block of code will be executed.\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- c(\"what\",\"is\",\"truth\")\n",
    "\n",
    "if(\"Truth\" %in% x) {\n",
    "   print(\"Truth is found\")\n",
    "} else {\n",
    "   print(\"Truth is not found\")\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loops\n",
    "\n",
    "There may be a situation when you need to execute a block of code several number of times. In general, statements are executed sequentially. The first statement in a function is executed first, followed by the second, and so on.\n",
    "\n",
    "Programming languages provide various control structures that allow for more complicated execution paths.\n",
    "\n",
    "A loop statement allows us to execute a statement or group of statements multiple times.\n",
    "R programming language provides the following kinds of loop to handle looping requirements.\n",
    "\n",
    "### While Loop\n",
    "\n",
    "The basic syntax for creating a while loop in R is −\n",
    "\n",
    "    while (test_expression) {\n",
    "      statement\n",
    "    }\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v <- c(\"Hello\",\"while loop\")\n",
    "cnt <- 2\n",
    "\n",
    "while (cnt < 7) {\n",
    "   print(v)\n",
    "   cnt = cnt + 1\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "### For Loop\n",
    "\n",
    "The basic syntax for creating a for loop statement in R is −\n",
    "\n",
    "    for (value in vector) {\n",
    "      statements\n",
    "    }\n",
    "    \n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v <- LETTERS[1:4]\n",
    "for ( i in v) {\n",
    "   print(i)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "A function is a set of statements organized together to perform a specific task. R has a large number of in-built functions and the user can create their own functions.\n",
    "\n",
    "In R, a function is an object so the R interpreter is able to pass control to the function, along with arguments that may be necessary for the function to accomplish the actions.\n",
    "\n",
    "The function in turn performs its task and returns control to the interpreter as well as any result which may be stored in other objects.\n",
    "\n",
    "### Function Definition\n",
    "\n",
    "An R function is created by using the keyword function. The basic syntax of an R function definition is as follows −\n",
    "\n",
    "    function_name <-     function(arg_1, arg_2, ...) {\n",
    "      Function body \n",
    "    }\n",
    "    \n",
    "### Function Components \n",
    "\n",
    "The different parts of a function are −\n",
    "\n",
    "    Function Name − This is the actual name of the function. It is stored in R environment as an object with this name.\n",
    "\n",
    "    Arguments − An argument is a placeholder. When a function is invoked, you pass a value to the argument. Arguments are optional; that is, a function may contain no arguments. Also arguments can have default values.\n",
    "\n",
    "    Function Body − The function body contains a collection of statements that defines what the function does.\n",
    "\n",
    "    Return Value − The return value of a function is the last expression in the function body to be evaluated.\n",
    "\n",
    "R has many in-built functions which can be directly called in the program without defining them first. We can also create and use our own functions referred as user defined functions.\n",
    "\n",
    "### Built-in Function\n",
    "\n",
    "Simple examples of in-built functions are seq(), mean(), max(), sum(x) and paste(...) etc. They are directly called by user written programs. You can refer most widely used R functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [1] 32 33 34 35 36 37 38 39 40 41 42 43 44\n",
      "[1] 53.5\n",
      "[1] 1526\n"
     ]
    }
   ],
   "source": [
    "# Create a sequence of numbers from 32 to 44.\n",
    "print(seq(32,44))\n",
    "\n",
    "# Find mean of numbers from 25 to 82.\n",
    "print(mean(25:82))\n",
    "\n",
    "# Find sum of numbers frm 41 to 68.\n",
    "print(sum(41:68))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User-defined Function\n",
    "\n",
    "We can create user-defined functions in R. They are specific to what a user wants and once created they can be used like the built-in functions. Below is an example of how a function is created and used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "# Create a function to print squares of numbers in sequence.\n",
    "new.function <- function(a) {\n",
    "   for(i in 1:a) {\n",
    "      b <- i^2\n",
    "      print(b)\n",
    "   }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling a Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 1\n",
      "[1] 4\n",
      "[1] 9\n",
      "[1] 16\n",
      "[1] 25\n",
      "[1] 36\n"
     ]
    }
   ],
   "source": [
    "# Create a function to print squares of numbers in sequence.\n",
    "new.function <- function(a) {\n",
    "   for(i in 1:a) {\n",
    "      b <- i^2\n",
    "      print(b)\n",
    "   }\n",
    "}\n",
    "\n",
    "# Call the function new.function supplying 6 as an argument.\n",
    "new.function(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strings\n",
    "\n",
    "Any value written within a pair of single quote or double quotes in R is treated as a string. Internally R stores every string within double quotes, even when you create them with single quote."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples of Valid Strings\n",
    "\n",
    "Following examples clarify the rules about creating a string in R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a <- 'Start and end with single quote'\n",
    "print(a)\n",
    "\n",
    "b <- \"Start and end with double quotes\"\n",
    "print(b)\n",
    "\n",
    "c <- \"single quote ' in between double quotes\"\n",
    "print(c)\n",
    "\n",
    "d <- 'Double quotes \" in between single quote'\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Manipulation\n",
    "\n",
    "#### Concatenating Strings - paste() function\n",
    "\n",
    "Many strings in R are combined using the paste() function. It can take any number of arguments to be combined together.\n",
    "The basic syntax for paste function is −\n",
    "\n",
    "    paste(..., sep = \" \", collapse = NULL)\n",
    "    \n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    ... represents any number of arguments to be combined.\n",
    "\n",
    "    sep represents any separator between the arguments. It is optional.\n",
    "\n",
    "    collapse is used to eliminate the space in between two strings. But not the space within two words of one string.\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a <- \"Hello\"\n",
    "b <- 'How'\n",
    "c <- \"are you? \"\n",
    "\n",
    "print(paste(a,b,c))\n",
    "\n",
    "print(paste(a,b,c, sep = \"-\"))\n",
    "\n",
    "print(paste(a,b,c, sep = \"\", collapse = \"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting number of characters in a string - nchar() function\n",
    "\n",
    "This function counts the number of characters including spaces in a string. \n",
    "\n",
    "The basic syntax for nchar() function is −\n",
    "\n",
    "    nchar(x)\n",
    "   \n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result <- nchar(\"Count the number of characters\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the case - toupper() & tolower() functions\n",
    "\n",
    "These functions change the case of characters of a string.\n",
    "\n",
    "The basic syntax for toupper() & tolower() function is −\n",
    "\n",
    "    toupper(x)\n",
    "    tolower(x)\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing to Upper case.\n",
    "result <- toupper(\"Changing To Upper\")\n",
    "print(result)\n",
    "\n",
    "# Changing to lower case.\n",
    "result <- tolower(\"Changing To Lower\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extracting parts of a string - substring() function\n",
    "\n",
    "This function extracts parts of a String.\n",
    "\n",
    "The basic syntax for substring() function is −\n",
    "\n",
    "substring(x,first,last)\n",
    "\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    x is the character vector input.\n",
    "\n",
    "    first is the position of the first character to be extracted.\n",
    "\n",
    "    last is the position of the last character to be extracted.\n",
    "\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract characters from 5th to 7th position.\n",
    "result <- substring(\"Extract\", 5, 7)\n",
    "print(result)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSV Files\n",
    "\n",
    "In R, we can read data from files stored outside the R environment. We can also write data into files which will be stored and accessed by the operating system. R can read and write into various file formats like csv, excel, xml etc.\n",
    "\n",
    "In this chapter we will learn to read data from a csv file and then write data into a csv file. The file should be present in current working directory so that R can read it. Of course we can also set our own directory and read files from there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input as CSV File\n",
    "\n",
    "The csv file is a text file in which the values in the columns are separated by a comma. Let's consider the following data present in the file named input.csv.\n",
    "\n",
    "You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.\n",
    "\n",
    "    id,name,salary,start_date,dept\n",
    "    1,Rick,623.3,2012-01-01,IT\n",
    "    2,Dan,515.2,2013-09-23,Operations\n",
    "    3,Michelle,611,2014-11-15,IT\n",
    "    4,Ryan,729,2014-05-11,HR\n",
    "    5,Gary,843.25,2015-03-27,Finance\n",
    "    6,Nina,578,2013-05-21,IT\n",
    "    7,Simon,632.8,2013-07-30,Operations\n",
    "    8,Guru,722.5,2014-06-17,Finance\n",
    "\n",
    "### Reading a CSV File\n",
    "\n",
    "Following is a simple example of read.csv() function to read a CSV file available in your current working directory −"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'/home/houxiang/R_tutorial'"
      ],
      "text/latex": [
       "'/home/houxiang/R\\_tutorial'"
      ],
      "text/markdown": [
       "'/home/houxiang/R_tutorial'"
      ],
      "text/plain": [
       "[1] \"/home/houxiang/R_tutorial\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  id     name salary start_date       dept\n",
      "1  1     Rick 623.30 2012-01-01         IT\n",
      "2  2      Dan 515.20 2013-09-23 Operations\n",
      "3  3 Michelle 611.00 2014-11-15         IT\n",
      "4  4     Ryan 729.00 2014-05-11         HR\n",
      "5  5     Gary 843.25 2015-03-27    Finance\n",
      "6  6     Nina 578.00 2013-05-21         IT\n",
      "7  7    Simon 632.80 2013-07-30 Operations\n",
      "8  8     Guru 722.50 2014-06-17    Finance\n"
     ]
    }
   ],
   "source": [
    "getwd()\n",
    "data <- read.csv(\"input.csv\")\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "### Analyzing the CSV File\n",
    "\n",
    "By default the read.csv() function gives the output as a data frame. This can be easily checked as follows. Also we can check the number of columns and rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] TRUE\n",
      "[1] 5\n",
      "[1] 8\n"
     ]
    }
   ],
   "source": [
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "print(is.data.frame(data))\n",
    "print(ncol(data))\n",
    "print(nrow(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we read data in a data frame, we can apply all the functions applicable to data frames as explained in subsequent section.\n",
    "\n",
    "#### Get the maximum salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "# Get the max salary from data frame.\n",
    "sal <- max(data$salary)\n",
    "print(sal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the details of the person with max salary\n",
    "\n",
    "We can fetch rows meeting specific filter criteria similar to a SQL where clause."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  id name salary start_date    dept\n",
      "5  5 Gary 843.25 2015-03-27 Finance\n"
     ]
    }
   ],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "# Get the max salary from data frame.\n",
    "sal <- max(data$salary)\n",
    "\n",
    "# Get the person detail having max salary.\n",
    "retval <- subset(data, salary == max(salary))\n",
    "print(retval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get all the people working in IT department"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "retval <- subset( data, dept == \"IT\")\n",
    "print(retval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the persons in IT department whose salary is greater than 600"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "info <- subset(data, salary > 600 & dept == \"IT\")\n",
    "print(info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the people who joined on or after 2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  id     name salary start_date    dept\n",
      "3  3 Michelle 611.00 2014-11-15      IT\n",
      "4  4     Ryan 729.00 2014-05-11      HR\n",
      "5  5     Gary 843.25 2015-03-27 Finance\n",
      "8  8     Guru 722.50 2014-06-17 Finance\n"
     ]
    }
   ],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "\n",
    "retval <- subset(data, as.Date(start_date) > as.Date(\"2014-01-01\"))\n",
    "print(retval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Writing into a CSV File\n",
    "R can create csv file form existing data frame. The write.csv() function is used to create the csv file. This file gets created in the working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "retval <- subset(data, as.Date(start_date) > as.Date(\"2014-01-01\"))\n",
    "\n",
    "# Write filtered data into a new file.\n",
    "write.csv(retval,\"output.csv\")\n",
    "newdata <- read.csv(\"output.csv\")\n",
    "print(newdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the column X comes from the data set newper. This can be dropped using additional parameters while writing the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a data frame.\n",
    "data <- read.csv(\"input.csv\")\n",
    "retval <- subset(data, as.Date(start_date) > as.Date(\"2014-01-01\"))\n",
    "\n",
    "# Write filtered data into a new file.\n",
    "write.csv(retval,\"output.csv\", row.names = FALSE)\n",
    "newdata <- read.csv(\"output.csv\")\n",
    "print(newdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Excel File\n",
    "\n",
    "Microsoft Excel is the most widely used spreadsheet program which stores data in the .xls or .xlsx format. R can read directly from these files using some excel specific packages. Few such packages are - XLConnect, xlsx, gdata etc. We will be using xlsx package. R can also write into excel file using this package.\n",
    "\n",
    "### Install xlsx Package\n",
    "\n",
    "You can use the following command in the R console to install the \"xlsx\" package. It may ask to install some additional packages on which this package is dependent. Follow the same command with required package name to install the additional packages.\n",
    "\n",
    "    install.packages(\"xlsx\")\n",
    "\n",
    "### Load the \"xlsx\" Package\n",
    "\n",
    "    library(\"xlsx\")\n",
    "\n",
    "### Input as xlsx File\n",
    "\n",
    "Open Microsoft excel. Copy and paste the following data in the work sheet named as sheet1.\n",
    "\n",
    "    id\tname      salary    start_date\tdept\n",
    "    1\tRick\t    623.3\t  1/1/2012\t   IT\n",
    "    2\tDan       515.2     9/23/2013    Operations\n",
    "    3\tMichelle  611\t     11/15/2014\tIT\n",
    "    4\tRyan\t    729\t     5/11/2014\t   HR\n",
    "    5\tGary\t    43.25     3/27/2015  \tFinance\n",
    "    6\tNina\t    578       5/21/2013\t   IT\n",
    "    7\tSimon\t    632.8\t  7/30/2013\t   Operations\n",
    "    8\tGuru\t    722.5\t  6/17/2014\t   Finance\n",
    "\n",
    "\n",
    "Save the Excel file as \"input.xlsx\". You should save it in the current working directory of the R workspace.\n",
    "\n",
    "### Reading the Excel File\n",
    "\n",
    "The input.xlsx is read by using the read.xlsx() function as shown below. The result is stored as a data frame in the R environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  id     name salary start_date       dept\n",
      "1  1     Rick 623.30 2012-01-01         IT\n",
      "2  2      Dan 515.20 2013-09-23 Operations\n",
      "3  3 Michelle 611.00 2014-11-15         IT\n",
      "4  4     Ryan 729.00 2014-05-11         HR\n",
      "5  5     Gary 843.25 2015-03-27    Finance\n",
      "6  6     Nina 578.00 2013-05-21         IT\n",
      "7  7    Simon 632.80 2013-07-30 Operations\n",
      "8  8     Guru 722.50 2014-06-17    Finance\n"
     ]
    }
   ],
   "source": [
    "# Read the first worksheet in the file input.xlsx.\n",
    "library(xlsx)\n",
    "data <- read.xlsx(\"input.xlsx\", sheetIndex = 1)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing the Excel File\n",
    "The write.xlsx function is used to write a data frame to an Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "write.xlsx(data, \"output.xlsx\", sheetName=\"Sheet1\", row.names=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie Charts\n",
    "\n",
    "R Programming language has numerous libraries to create charts and graphs. A pie-chart is a representation of values as slices of a circle with different colors. The slices are labeled and the numbers corresponding to each slice is also represented in the chart.\n",
    "\n",
    "In R the pie chart is created using the pie() function which takes positive numbers as a vector input. The additional parameters are used to control labels, color, title etc.\n",
    "\n",
    "The basic syntax for creating a pie-chart using the R is −\n",
    "\n",
    "    pie(x, labels, radius, main, col, clockwise)\n",
    "\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    x is a vector containing the numeric values used in the pie chart.\n",
    "\n",
    "    labels is used to give description to the slices.\n",
    "\n",
    "    radius indicates the radius of the circle of the pie chart.(value between −1 and +1).\n",
    "\n",
    "    main indicates the title of the chart.\n",
    "\n",
    "    col indicates the color palette.\n",
    "\n",
    "    clockwise is a logical value indicating if the slices are drawn clockwise or anti clockwise.\n",
    "\n",
    "\n",
    "Example\n",
    "\n",
    "A very simple pie-chart is created using just the input vector and labels. The below script will create and save the pie chart in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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VWPHj0ePXqU+VJ8fLyBgUG9evU+7ZyU\nlGRkZKRQKHbt2vVp47p16xQKxdatW/9y/ObNmw8bNuz27dtTp07N3HL8+PHNmzfXrVt38uTJ\nmVv27dvn4OBgbm5ubGxcqVKlhQsXpqSkfBrh9u3bCoWid+/ewcHBXbt2LViwoI6OTkBAwJ+P\npVQqR4wYoVAo2rdvn5yc/I//79nt2rVr9evX79K1a/n6Ddaevdq231DukIAQhsYmHYe4rTvn\nX7buzy7du9euXfvy5cuiQwFyQ7FDNnJ1dS1RosSaNWtevHjxld02bdpUv379S5cutWjRYvTo\n0Q4ODgcPHqxevfq1a9ckSTI1Na1Zs+b169fj4uIy979y5Upm5fL29v40iI+PjyRJDRs2/Luj\nLFmyxNbW1t3d3dfX9+3bt/379zczM9u1a5eurq4kSePHj3dxcXny5EmPHj2GDx+ekZExadKk\npk2bpqWlfT5IWFhYrVq1bt++3axZs19++cXIyOiLoyQnJ3fu3Hn16tXDhg07dOjQn3fISSEh\nIV27dq1bt67KPP8qz4u9J8wwzc3V6xDMLE/e3hNmrDl92cjS2tHJycXF5eXLl6JDAfJBsUM2\nMjQ0nD9/fkpKyqdZsT8LCgoaNmxY48aNQ0NDt27dunDhwv379wcGBioUioEDB2bu06BBg/T0\n9IsXL2Z+6O3traur6+zs/KnYKZVKX19fGxub4sWL/92BjI2N9+zZo6en9+uvv/76669RUVEr\nV660sbGRJOnSpUtLliwpWbLk/fv3161bt2TJkrt37zZv3tzPz2/JkiWfD+Lj4+Pi4vLgwYOd\nO3cePny4atWqn7/6/v37xo0be3h4LFy4cM2aNTo6wn6+EhISpkyZYmtre/tJ8Ly9x8Ys31DI\n+m//ZoCcZ1G4yMjFq+fvPXYz6Imtre3MmTMTExNFhwLkgGKH7NW1a9fq1avv27fvxo0bf7nD\nunXr0tLSJk+enJCQ8O4/rKysGjZsePfu3dDQUOk/83Cfapy3t7e9vX2HDh1evXr15MkTSZJu\n374dHR39lem6TFWrVp0zZ05YWNiZM2fat2/fp0+fzO2ZJ3CnT59eoECBzC16enru7u4KhWLz\n5s2fj2BhYbFo0aLMSb4vhIaG1qtX79q1a7t27ZowYcI3/w1lvUOHDlWoUOG3bduHL1o1b8+x\nslXsBYYBvqJsFfsF+070n7Fw9foN5cqV27NnDxfeAf8SxQ7ZS6FQLF26VKVSjR079i938Pf3\nlyTJycmpwP86duyYJEmRkZGSJNWpU8fY2Diz2H348OGPP/5o2LBhgwYNpP+0vczzsJlbvm7s\n2LGWlpaSJC1duvTTxj/++EOSJGdn58/3tLW1LVy4cEhISGxs7KeNVatWNTEx+fOwjx8/rlOn\nTkREhJeXV/fu3f8xRjZ59OhRkyZNXLp1s2/WZpXnxbrNWufwyjLA91IoFE5tO645c6VWqw69\n+/Z1dnYOCgoSHQrQYBQ7ZDsnJ6c2bdr4+fmdOHHiz69GR0dLknT8+PFzf8XW1laSJAMDg/r1\n69+7d+/t27e+vr4ZGRkNGza0tbW1srLKLHbe3t4KheJbip2Ojo6hoaEkScbG/72B4MOHD5Ik\nZRa+zxUuXPjTq5msrKz+ctgnT55ERkba2Nj89NNP/5ghO8THx0+YMKFKlSpRSWnLT1zoMXqy\nofFfFFBAPRkam3Rzm7DypG+cSrdq1apTp05lxTvgx+iJDgCtsGjRIk9PzwkTJrRo0eKLlzKf\nNWRpaVmjRo2vjNCgQYNz5875+PhcvXrV0NAw8yZZZ2dnLy+vlJSUS5cuVaxYsWDBgj8WLzPD\n69evv7hEL3O+8POnIf3dBFjr1q3LlSs3efLkhg0bnj171sLC4seS/BgvL68hQ4YkpmW4ua+v\n1bh5Th4ayEKFrItP+W331dMn1s+fvm/fvrVr1zZt2lR0KEDDMGOHnFC+fPn+/fsHBQV9ccma\nJEm1a9eWJGnfvn1fH+HTZXY+Pj716tXLvNu0YcOG79+/X79+fUJCwj9eYPcV1apVkyTJ19f3\n842PHz+OjIwsWbLkNz4HadKkScuXL79165azs/ObN29+OMx3iYqK6tGjR6vWrSs7N115ypdW\nBxmo26z1Ss+L5eo4tWjZsmvXrpnvrwB8I4odcsisWbPMzMxmzJjx+eJwkiQNHz5cT09v9erV\nmdfJfRIfH79///5PH9rb2+fJk+fYsWMPHjz41OEy/7BgwQLp2y6w+zt9+/aVJGnOnDmZ54Ul\nSUpPTx8zZoxKperXr9+3j+Pm5rZ+/foHDx44OTlFRET8cJ5vtHv37goVKly+8cf8vcd7T5zJ\nuVfIhompWb+pcxceOHXjflDFihV37twpOhGgMSh2yCEFCxYcP378mzdv4uPjP99uZ2e3ceNG\nlUrVqFGjZs2aTZo0afz48W3atLG0tJwzZ86n3XR0dJycnKKioqTPFqsrVqxYqVKl3r59q6ur\n6+Tk9MPZHB0dR48eHRwcXLFixeHDh48fP75KlSqnTp1ycHAYN27cdw01ePDgrVu3Pn361NHR\nMftW5woNDW3evHnf/v0bd++75PDpMpWrZdOBAIFKVay84MDJln2G9B84sHXr1jnwZgmQAYod\ncs7o0aOLFCny5+19+/YNDAzs0aPHw4cPly1btnnz5ufPn/fs2fOL58xm9jlzc/Pq1at/sdHe\n3v7zK+F+gLu7++7du0uVKrVjx45Vq1YpFIq5c+eePXvWwMDge4fq3bv37t27Q0NDHR0dnz9/\n/m9S/aWtW7dWrlw5NOq9+5FzHYe46erxsFfIlq6u3i8Dhy89fObxy3A7O7sdO3aITgSoOwWL\nBgE57PHjx2/evHF0dPzeT3zz5s3AgQO9zpzp7jaxVa8BCnELIAM5TJmRcXzbxn2rlzRp1Gjj\nxo1/+RYRgMSMHZDzAgICmjdvfufOne/6LA8Pj0qVKt1/FrLk8OnWfQbR6qBVdHR12/UfutTj\n7NNXrytXrnzo0CHRiQA1xYwdIECvXr38/Pxu3LjxLQujfPz4cdy4cVu2bG3Td1DXEeP19Dn3\nCu2lzMg4tnXD3pWLu7l0XbdunampqehEgHqh2AECJCUl1atXz8LCwsvL6y8fUPaJv7+/i4tL\nmo7eiIUry1QRs/oxoG6e3Lm5YuxwcyOD3bt316xZU3QcQI1wNgcQwNjY+MCBA4GBgbNmzfq7\nfZRK5aJFixwdHUvXqLv0yFlaHfBJ2Sr27kfPFalYrX79+vPnz8/IyBCdCFAXzNgBwpw8ebJt\n27YHDx5s3779Fy+9efPm119/vXTVf/CsRfVbthMSD1B/VzyPb5w5vsZPP+3du/fPTwUEtBAz\ndoAwrVq1mjp1au/evb946vn58+erVq0aHP56qccZWh3wFfVatFl2zDs85mO1atUuXLggOg4g\nHsUOEGnGjBmOjo7t27f/+PGjJElKpXL69OnNmjWr3rT1/H0nLIuVEB0QUHcWhYvM3X3EvnHL\nJk2azJ8/n9NQ0HKcigUEi4mJqVGjRuXKlTdt2tSjR49LV6+6LlxVo0ET0bkADeN/5uTaKWOc\nHR127tyZP39+0XEAMSh2gHh37typXbt2rly5TPMXHL9mCxN1wI+JDA1ZOnJAQnTUmjVrOnfu\nLDoOIACnYgHx7t69q1QqzQpaLtjP6VfgxxUuXrL76MlRUVE9e/bcuXOn6DiAABQ7QKS0tDRX\nV9c+ffp2Gz3Z/cg5Q2MT0YkADRb9OnLNJLeqVauuXr26f//+gwYNSk9PFx0KyFF6ogMA2uv9\n+/cdO3a8dff+jO37K9aoIzoOoNnSUlOXjBygSku9fPlyrly5ypcv36lTpxcvXuzbty9v3ryi\n0wE5hBk7QIxnz57Vq1fvefjrhQdP0eqAf2/L3CnB9277+vrmypVLkiRHR0d/f/+IiIiaNWs+\nePBAdDogh1DsAAEuX75cp04d4wKF5+45WsCqqOg4gMbzO3bo3IHf16xZU61atU8bbWxsrl69\namdnV7duXS8vL4HxgBxDsQNy2ubNmxs0aFCredvJG3aamJqJjgNovJCgBxtmjO/cufOQIUO+\neMnMzMzDw8PV1bVNmzYbN24UEg/ISSx3AuQcpVI5ZsyYNWvXDpyxsGFHF9FxADn4GPN+XPum\n5saGwcHBX9lt+/btAwcOHDJkyPLly3V0mNSAbHHzBJBDUlJSevTocfrsuelb9lasWVd0HEAO\nlBkZy0YPToh9/+he2Nf37N27d9GiRTt27BgeHr5r1y5jY+OcSQjkMN61ADkhNja2adOmPhcv\nzdp5mFYHZJXdyxbcD7ji5eX1LY+aaNSo0eXLlwMDAxs2bBgVFZUD8YCcR7EDsl1kZKSzs3Nw\neOSC/SdLlK8gOg4gE9fPnz62Zd3s2bOdnJy+8VPs7OyuXLmSkJDg6OgYGhqarfEAIbjGDshe\nQUFBzZo1M8pbYNKGHeZ584mOA8hEREjw+E4t6tepffbs2e/93I8fP/7yyy9Pnz49e/Zs+fLl\nsyMeIAozdkA28vf3r1+/fuFyFWfvPESrA7JKYnzcwmF9cpvmOn369A98urm5uZeXV82aNevV\nq3ft2rUsjwcIRLEDssuFCxeaNGli36jF2BW/6Rsaio4DyIRKpVozyS3q1cvr16//8P2tBgYG\n+/fvb9euXaNGjc6fP5+1CQGBKHZAtvDy8mrZsqVD206DZi3S0dUVHQeQD4/fVl8/f/rAgQPW\n1tb/ZhxdXd3NmzcPHDiwVatWR44cyap4gFgsdwJkvRMnTnTu3Ll5z349xkwRnQWQlXv+l/ev\nWurm5ta2bdt/P5pCoXB3dzczM+vSpcvOnTu7du3678cExOLmCSCL7du3r2fPnh0Gj+w8fIzo\nLICsvIsMH9e+abnSpa5fv561I69YsWLcuHHbt2/v3r171o4M5DBm7ICstHXr1oEDB/aaOLNl\nz36iswCykpqcvGhYX11JdfHixSwf3M3NzcTEpHfv3hkZGb/++muWjw/kGIodkGUyn1k0YMbC\nxp150w9ksd9mTQp9/PD+/ftGRkbZMf7AgQMVCkW/fv0kSaLbQXNR7ICsceDAgQEDBvSdModW\nB2S503u2Xziyf/v27dm67NyAAQMkSaLbQaNR7IAscOjQoR49evSeNKtZt96iswBy8+TOze0L\nZ/7666+9evXK7mN96nYqlSoHDgdkOYod8G95eHh069at68gJzbv3EZ0FkJvY6KglIwaUsrHZ\nsWNHzhxxwIABaWlp/fv3NzY27ty5c84cFMgqFDvgXzl27FjXrl1d3Ca07TdUdBZAbjLS09zd\nBiXHffR/FJSTxx06dGhGRkaPHj1y5crVsmXLnDw08C9R7IAf5+Pj06VLl07DRtPqgOywc8nc\nRzevX758OU+ePDl8aFdX13fv3nXq1MnLy8vJySmHjw78MIod8IPu3LnToUOHhp26dxg8UnQW\nQIYunzp6csem+fPn16lTR0iAWbNmJSQktGnTxtvbu3r16kIyAN+LBYqBH/Hs2bP69euXq1V/\nxKJVCoVCdBxAbkKfBE3q0rpxwwYnTpwQGEOlUg0YMODYsWN+fn4VKlQQmAT4RhQ74LuFh4fX\nr1/fomSZ8Wu26Ooy7Q1ksYSPH8d3bKabkfby5UsdHcHPNE9PT+/UqdONGzeuXLlSrFgxsWGA\nf0SxA75PbGzszz//nKzQm7H9gKGRseg4gNyolMoFQ3vfu3rxxYsXhQsXFh1HkiQpJSWlRYsW\nr1+/vnLlSs5f7Qd8F8HvhADNkpyc3LJly4+p6VM3/U6rA7LDwXXL//DzPnLkiJq0OkmSDA0N\njxw5oq+v37Zt25SUFNFxgK+h2AHfKnPB0qcvXk75bbeJmbnoOIAM3fQ9f3D9inHjxrVo0UJ0\nlv9hbm7u6ekZEhLSq1cvznRBnXEqFvhWEydOXLVm7bw9x4qXsxWdBZCht+Fh4zs0q2JX8dKl\nS6Kz/LX79+87ODgMGTJk/vz5orMAf43rvoFvsnXr1qXuy6Zs3EmrA7JDanLyYtd++joKb29v\n0Vn+lp2dnYeHR7NmzYoUKTJs2DDRcYC/wKlY4J/5+voOGTKk35TZVeqxTimQLTbOnBj25JG/\nv7+BgYHoLF/j7Oy8ceNGNzc3Ly8v0VmAv8CMHfAP7t+/365du5a9BzZ14YngQFocVfcAACAA\nSURBVLY4tXOz79EDe/bsKVu2rOgs/6x3795Pnz51cXEJCAgoX7686DjA/+AaO+BroqOja9as\nWahMhTErNrIQMZAdHgYGzOzTpU/vXps2bRKd5VupVKouXbrcuXMnICAgb968ouMA/0WxA/5W\nRkZGq1atgkJCF+w7YWhsIjoOIEOx796Oa9+0qGWhO3fuiM7yfeLj4+vWrWtpaenp6amnx+kv\nqAuusQP+1rhx4y77B4xfvYVWB2SHjPS0pW6DUhMTrly5IjrLdzM1NT1+/Pjt27cnTJggOgvw\nX7zJAP7a77//vnLVqskbdloWKyE6CyBPW+ZNf/xH4PXr101NTUVn+RElSpQ4fPhwo0aNbG1t\n+/fvLzoOIEnM2AF/6fbt2wMHDuw1fno1B2fRWQB5unj88Jm9O1auXGlvby86y49zcHBwd3cf\nPnx4YGCg6CyAJHGNHfBnUVFR9vb2Nj/VGrFolegsgDw9f3hvSre2LZs39/DwEJ0lC/Tp08fX\n1/fmzZv58uUTnQXajmIH/A+lUtmiRYsnYeHz9xw3MDISHQeQobjYmPEdmpno64aEhIjOkjWS\nk5Pr1q1buHDhEydO6OhwKgwi8f0H/I/58+dfvHxltPt6Wh2QHVRK5arxrh+jo65duyY6S5Yx\nMjLav3//lStXFi1aJDoLtB03TwD/5efnN3PmTNdFq6xKlhKdBZCnPSsW3bp0wdvbu2DBgqKz\nZKUyZcrs2LGjQ4cO1atXb9y4seg40F7M2AH/7+3bt926dWvq0suh1S+iswDyFOhz1uO31TNm\nzHB2luFtSW3bth0+fHjPnj0jIiJEZ4H24ho7QJIkSalUNmvWLOR11Nzfj+qr96MqAQ0V8eL5\nhE4talW39/HxEZ0lu6Smpjo5ORkZGXl7e3OxHYTg2w6QJEmaO3eu/7XrY5ZvoNUB2SEpIX7R\n8L5mJsZnz54VnSUbGRgY7Nu37/bt21xsB1GYsQOkgIAABweHUcvW127SUnQWQIZUKpW726Ab\nPmeePn1avHhx0XGy3aFDh1xcXC5fvlyrVi3RWaB1mLGDtktISOjVq5djmw60OiCbHNuy3v/M\nyR07dmhDq5MkqWPHji4uLt27d4+LixOdBVqHYgdtN2LEiNjE5D6TZ4sOAsjTvYDLe5YvHDhw\noIuLi+gsOWfdunU6OjqjR48WHQRah1Ox0GpHjx7t2LHTnN+PlKuqwQ81AtTWu8iIcR2alilZ\n4saNG6Kz5LTAwMB69ert2rWrS5cuorNAi1DsoL0iIiIqV67c0KV3l+FjRGcBZCgtNXVq93Zv\nQ5+/fv3a2NhYdBwBZs2atWrVqnv37llZWYnOAm1BsYOWUqlUzZs3f/46at6eo7q6rNQNZL31\n08Ze8Nh/+/ZtOzs70VnESE9Pd3BwsLCwOHHihOgs0BZcYwcttW3btgt+fiMWraLVAdnh3IHf\nzx/cs3btWq1tdZIk6enpbd++3dvbe+fOnaKzQFswYwdtFBERYWdn12aga5s+g0VnAWTo6d1b\n03r80rlTp927d4vOIt6iRYsWLFhw//79okWLis4C+aPYQRu1a9fu4Yuw+XuO6ejqis4CyM2H\n6HfjOjTNbWIcHBwsOotaUCqVjo6OefPm5YQscgCnYqF1du3a5enlNWyeO60OyHLKjIyV44Yn\nfogNDAwUnUVd6OjobN68+fz588xfIgdQ7KBdXr9+7ebm1nn4GOvS5URnAWRo19J5d/0vnT59\nOl++fKKzqJHy5ctPnz7d1dU1PDxcdBbIHKdioV06dux4K+jJwoOnuGcCyHJXvY67jxq8cOHC\nCRMmiM6idtLT02vXrl2qVKn9+/eLzgI5o9hBi3h6erZp03bRIa+SthVFZwHkJuzZ44mdWzk5\n1D99+rToLGrq5s2btWrVOnbsWMuWPMAQ2YViB22RlJRkZ2dX0bFR74kzRWcB5CYpIX5i55bp\nCXHh4eE6Olzk87dGjBjh6el579497VyxGTmAHz9oi3nz5r3/GN95GI9uBLKYSqVaM9HtbVjo\n9evXaXVfN3fu3OTk5AULFogOAtniJxBa4enTp0uXLu03dY6JmbnoLIDcHFq/4tp5rwMHDlhb\nW4vOou7Mzc3d3d0XLVoUFBQkOgvkiVOx0AqNGjV6l5w+fcte0UEAubl79dLcAd1HjHBdtmyZ\n6Cwao1WrVvHx8RcuXFAoFKKzQG4odpC/PXv29O7bd8WJC5bFSojOAsjK21cvx3VoZmdb3t/f\nX3QWTRIcHFypUqVNmzZ1795ddJbsYmFhYWpq+uLFC9FBtA6nYiFzCQkJ48ePb9d/GK0OyFqp\nycmLXfsZ6Or4+fmJzqJhSpUqNW7cuIkTJyYkJGTH+MnJyQqFIk+ePNkxONQcxQ4yt2jRoqT0\njF/6DxUdBJCbTbMnhT19fOXKFQMDA9FZNM/EiRN1dHSWLFkiOgjkhmIHOXv16pW7u3vPsVMN\njU1EZwFkxXP3Vh+P/Zs3by5fvrzoLBrJ2Nh47ty5ixcvDg0NFZ0FskKxg5yNHz++SJnyDq1+\nER0EkJXHt2/uWDSrV69evXr1Ep1Fg/Xo0aNSpUpTpkwRFWDfvn0ODg7m5ubGxsaVKlVauHBh\nSkrKp1dv376tUCh69+4dFhbWrVs3CwsLY2PjGjVqeHp6fjGOUqlcsWKFra2tkZGRtbX1qFGj\n4uPjs/WI+ApunoBsBQQE1KtXb/YuD1v7mqKzAPIRGx01rn1Ty/z5Hjx4IDqLxsv8Z8rPz69+\n/fpZOGxycrKxsXHu3LljY2P/bp/x48cvWbKkYMGCHTp0yJUr16lTp4KCgpycnM6dO6evry9J\n0u3bt6tVq9agQYMHDx4UKVKkVq1ab9++PXr0qEql8vX1dXBw+DTUoEGDfvvtt+LFi3fs2FGh\nUHh4eFhZWd2/fz937tyf3zyRhUfEV1DsIE8qlcrBwUEnb0G3pWtFZwHkIyM9bWafLi+D7oeH\nh5ubsypkFujWrdvz58/9/f2zcOmTfyx2ly5dcnR0LFmy5LVr1woUKCBJUnp6eps2bby8vObN\nmzd58mTpPzVLkqSpU6fOnj07M97u3bt79uzZunXr48ePZw7l6+vr7OxcpUqVK1eu5MqVS5Kk\nxMTE+vXr37p1q3jx4p+KXRYeEV/HqVjI04EDBwJv3uwxZrLoIICsbF8469HN6z4+PrS6rLJw\n4cK7d+8eOHAgJw+6detWSZKmT5+e2bEkSdLT03N3d1coFJs3b/58z2LFis2YMeNT6ezevXvu\n3LmvX7/+aYft27dLkjRz5szMVidJkomJydy5c7PviPg6ih1kKCMjY+bMmS169LUoXER0FkA+\nLp084rl7q7u7e40aNURnkY9ixYoNHTp02rRpaWlpOXbQP/74Q5IkZ2fnzzfa2toWLlw4JCTk\n83m+atWq6enpffpQoVAULVo0Jibm05Zbt25JkuTo6Pj5UF98mLVHxNdR7CBDW7ZseRke0a7/\nMNFBAPkIfRy0furYtm3burm5ic4iN5MmTXrz5k3m1FfO+PDhgyRJlpaWX2wvXLjwp1cz/Xkx\nPD09vYyMjM+H0tPTy5cv3+f7mJqafprAy/Ij4usodpCb5OTkuXPntus3xCxPXtFZAJmI//hh\nsWu/QgULHD16VHQWGcqfP/+YMWNmzpyZmJiYM0fMnTu3JEmvX7/+YntkZOSnV799qPT09Pfv\n33++MT4+/ou1l7PwiPg6ih3kZvXq1R8SElv07Cc6CCATKqVy5bjh719HBAQEiM4iW6NHj87I\nyFi/fn3OHC7zHgVfX9/PNz5+/DgyMrJkyZLf9ciKzKEuXrz4+cYvPszaI+LrKHaQlQ8fPixa\ntKjzsNHGuUxFZwFkYv8a91sXfTw8PDLPmiE7mJqaTpw4ccGCBR8/fvy7fZRKpYeHh729/aBB\ng/7l4fr27StJ0pw5c6KjozO3pKenjxkzRqVS9ev3fe+KM9cynDlz5qcpusTExGnTpmXfEfF1\nev+8C6A5li1bpmtk0qiTbJ+rDeSwGxfOHd64asqUKS1bthSdReaGDBmyfPlyd3f3WbNmffFS\nRkbG/v3758+fHxwc3Ldv38zFQf5RYmJi7969/7x906ZNjo6Oo0ePXrZsWcWKFTt27GhiYnLq\n1KmHDx86ODiMGzfuu2I7OzsPGDBg06ZNdnZ2HTp0+LSO3ReTcFl4RHwdxQ7yERsbu3Llyu7j\npuvz5EogK0SGhqya4Fqvbt05c+aIziJ/hoaG06dPHzNmzKhRoz61orS0tL17986fP//Vq1f9\n+vU7c+ZMkSLferN/Wlrajh07/rx9w4YN+vr67u7uP/3007p163bs2JGWlla6dOm5c+eOGTPm\nB578u2HDBltb2w0bNqxevbpAgQKdOnWaM2dOiRIlvtgtC4+Ir2CBYsjH7NmzV2/4be3Zq3r6\n+qKzABovJSlxYudWCe+jIiIi+NWbM9LS0sqWLdu3b99p06alpqbu27dvzpw5r1+/7tu378SJ\nEzkVjm/BjB1kIj4+fvXq1e2Hj6XVAVli7ZTRESHPHj58SKvLMfr6+uPGjZs2bVru3LmXLFny\n8ePHIUOGjB8//ovFRICvYMYOMrFw4cLFy1asPx+gb2goOgug8Y5v27Bj0ezff/+9W7duorNo\nl5SUlDx58ujp6U2dOnXo0KFmZmaiE0HDcFcs5CAxMXH58uW/DBhGqwP+vUd/BO52X9CvXz9a\nXc4zNDScNGmSiYmJq6srrQ4/gGIHOdi4cWNqhqpRZ26GBf6tmKg3S0cOtKtY4YsneCLHjB07\nVpIk/v7xYyh20Hipqanu7u6t+wwyNDIWnQXQbGmpqYuH989ISbp69aroLNrLxMRk1KhRixcv\nTklJEZ0FmodiB423d+/e97EfmnTpIToIoPG2zJ367N4tPz8/ExMT0Vm02tChQ+Pj4/ft2yc6\nCDQPxQ4ab8WKFY079zAxMxcdBNBsfscOnTuwe9WqVVWrVhWdRduZm5sPGDBg6dKl3OCI78Vd\nsdBs58+fb9as+brz/haFv3XRTgB/FhL0YLJL63Zt2uzfv190FkiSJL169crGxsbLy6thw4ai\ns0CTUOyg2Zo3bx6vazTKfZ3oIIAGi/8QO65DMxM9nZCQENFZ8F8uLi5xcXEnT54UHQSahFOx\n0GCPHj06c+ZMq14DRAcBNJhKqVw+ZmhcdFRgYKDoLPgfY8aM8fT0DAoKEh0EmoRiBw22dOlS\n2+q1ylSuJjoIoMF+X77gzhU/Ly8vCwsL0VnwP6pXr163bt2VK1eKDgJNQrGDpoqOjt6zZ0+r\nX5muA35cwDnPI5vWzp4928nJSXQW/IXRo0fv3Lnz3bt3ooNAY1DsoKm2b99ukjtP9QaNRQcB\nNFVESPCaSaMaNWo0depU0Vnw19q2bVuwYMFt27aJDgKNQbGDRlKpVL/99lvjzj10dfVEZwE0\nUnJiwiLXfua5TLy8vERnwd/S1dXt37//xo0blUql6CzQDBQ7aKTz588HBz9v2MFFdBBAI6lU\nqjWTRr0JDQkMDNTT492RWuvfv//Lly99fHxEB4FmoNhBI61fv75mo2b5ClmKDgJopCOb1gSc\nPXXw4EFra2vRWfAPLC0t27Rps3HjRtFBoBkodtA8ERERJ0+ebOryq+gggEa6539538olI0eO\nbNu2regs+CaDBg06evRoRESE6CDQABQ7aJ5NmzYVKFrMrlY90UEAzRMV8WrZ6MHVqlVdvny5\n6Cz4Vo0aNSpZsuT27dtFB4EGoNhBwyiVyu3btzfq1E2hUIjOAmiYtJSUJa79dSXVpUuXRGfB\nd1AoFAMGDNi0aRO3UOAfUeygYfz8/MLCXjm2bi86CKB5fps96cWjB5cvXzYyMhKdBd+nV69e\n4eHhFy5cEB0E6o5iBw2zY8eOqg4/5y1QSHQQQMOc2bvD5/C+NWvWVKhQQXQWfLeCBQs2a9Zs\n165dooNA3VHsoEkSEhIOHz78c7tOooMAGubJnZvbFszo2bPn4MGDRWfBD+rZs+ehQ4fi4+NF\nB4Fao9hBkxw8eFDS0a3RoKnoIIAmiY2OWjpyoHXRojt37hSdBT+udevWBgYGR44cER0Eao1i\nB02yY8eO+q1+0TcwEB0E0BgZGenL3AYnfogNDAwUnQX/ipGRUefOnTkbi6+j2EFjhIaGXrx4\n8ee2HUUHATTJriVzH94IOHfuXL58+URnwb/Vs2dPb2/vsLAw0UGgvih20Bj79+8vWLRYmSo/\niQ4CaIzLp46d2P7bggUL6tVj3Uc5qFevXunSpffs2SM6CNQXxQ4aY//+/fVasFA+8K1CnwSt\nmzqmRYsWEyZMEJ0FWcbFxWXv3r2iU0B9KVQqlegMwD97/vx5qVKllh3zLl7OVnQWQAMkJcRP\n6NRCmZTw6tUrHR3ew8vH/fv3K1Wq9OjRo3LlyonOAnXETzs0w759+6xKlqLVAd9CpVKtnjgy\n6tXLwMBAWp3M2NnZ2draenh4iA4CNcUPPDTDgQMHOA8LfKODa5ddP3/60KFDRYoUEZ0FWa9D\nhw6HDh0SnQJqimIHDfDkyZM7d+7Ua95adBBAA9y5evHQ+pWjR49u3ZofGXnq2LHjH3/8ERwc\nLDoI1BHFDhrg4MGDRUuXtS7NBSXAP3j98sUyt0G1atVcunSp6CzILlWqVClXrtzhw4dFB4E6\nothBAxw7dqxOk5aiUwDqLjU52d1tkIGeLo+Kl7327dtT7PCXKHZQd5GRkTdu3Kju3Fh0EEDd\nbZw58eWTIH9/fwOeziJ3HTp0CAwMZKVi/BnFDuru5MmTufNZlKpYWXQQQK2d2rnZ9+iBbdu2\nlS1bVnQWZLuffvrJysrK09NTdBCoHYod1N2JEyeqN2isYMkG4O89vnVj55I5ffr06dGjh+gs\nyAkKhaJZs2YUO/wZvyyh1pKSkry9vas7NxEdBFBfse/eLh05oHy5clu3bhWdBTmnZcuW3t7e\nycnJooNAvVDsoNa8vb3TlcrKdeqLDgKoqYz0tKVug1ITE65cuSI6C3JU48aN09PT/fz8RAeB\neqHYQa2dPHmyUu36hsYmooMAamrbgpmP/wj09fU1NzcXnQU5ytTU1MHB4dSpU6KDQL1Q7KDW\nzp49W7X+z6JTAGrq4gkPr9+3LV++3N7eXnQWCNCyZcuTJ0+KTgH1QrGD+goODg4JCalS11F0\nEEAdvXj0cMO0cb/88suIESNEZ4EYLVu2DAkJefz4seggUCMUO6ivc+fO5StkWcSmtOgggNqJ\n//hhsWs/y0IFeRi8NitTpkyJEiW8vb1FB4EaodhBfXl7e1ep6yQ6BaB2VErlyrHDPkS9uX79\nuugsEKxBgwY8aASfo9hBTSmVSl9fX+6HBf5s78rFty5dOHnyZMGCBUVngWDOzs4XLlxQKpWi\ng0BdUOygpv7444/o6OhKFDvgfwX6nD28cdX06dMbNmwoOgvEa9CgQXR09L1790QHgbqg2EFN\n+fj4WJcul7dAIdFBADUS8eL5qgkjfv7555kzZ4rOArVgZWVVtmxZHx8f0UGgLih2UFN+fn52\nteqKTgGokeTEhMXD+5mZGJ87d050FqgRLrPD5yh2UEdKpTIgIKD8TzVFBwHUhUqlWjtlTOSL\n4EuXLunp6YmOAzXi7Ox88eLFjIwM0UGgFih2UEcPHjx4//59+Z9qiA4CqIvjW9df9Tr++++/\nlylTRnQWqBcnJ6cPHz7cvXtXdBCoBYod1NHly5cLFrHOb1lYdBBALdy/duX3ZQsHDhzYuXNn\n0VmgdgoVKmRjY+Pv7y86CNQCxQ7q6MqVK+XtOQ8LSJIkvYuMcB81uEqVyhs3bhSdBWqqTp06\nAQEBolNALVDsoI6uXLnCeVhAkqS01NQlI/qr0lL9/PxEZ4H6ql27NsUOmSh2UDvh4eEvXrwo\nX41iB0hb5k4JeXjv4sWLpqamorNAfdWuXfvZs2dRUVGig0A8ih3UTmBgoHEu02JlyokOAgh2\n/uCecwd+X7NmTdWqVUVngVqrUqWKsbHxtWvXRAeBeBQ7qJ0bN26UrGCn0OGbE1ot5OH9LfOm\ndu3adfDgwaKzQN3p6+vb29tzNhYSxQ5q6ObNm6UqVhadAhApLjZmsWu/olZWe/fuFZ0FmqF2\n7drM2EGi2EEN/fHHHzYUO2gxZUbG8jFD496/4/c0vt1PP/1069Yt0SkgHsUO6iUsLOzt27fM\n2EGb7Xaff/fqRS8vLwsLC9FZoDGqVq0aHR396tUr0UEgGMUO6uXmzZtGJrmsStiIDgKIcf38\n6WNb18+ePdvJyUl0FmiSMmXKmJiY3L59W3QQCEaxg3q5efMmd05Aa0WEBK+aOLJJkyZTp04V\nnQUaRldXt1KlShQ78OsT6uXu3bslylUQnQIQIDE+bsHQ3nnMTL28vERngUaqWrXqnTt3RKeA\nYBQ7qJcHDx5Ys4IdtI9KpVo7efS78LBr167pMGONH1KlShWKHfjnA2okOTn5xYsXxUpT7KB1\nDm9Yee2c58GDB62trUVngaaqWrVqcHBwXFyc6CAQiWIHNRIUFJSRkVGkVBnRQYAcdffqpQNr\nlo0aNapNmzais0CDVaxYUaVSBQUFiQ4CkSh2UCMPHz7Mk7+Aed58ooMAOScq4tXyMUNq1Kju\n7u4uOgs0m7m5uaWl5ZMnT0QHgUgUO6iRhw8fFi1dVnQKIOekpaQsce2vK6kuXLggOgvkoFy5\nco8fPxadAiLpiQ4A/NeDBw+sKXbQJr/Nmvji0YP79+8bGRmJzgI5KFu2LDN2Wo4ZO6iRx48f\nF7EpLToFkENO79nu47F/y5Yt5cuXF50FMsGMHSh2UBdKpTIkJMSyWAnRQYCc8OTOzW0LZvTq\n1atXr16is0A+ypUr9+TJE6VSKToIhKHYQV2Eh4enpKQUsi4uOgiQ7WKjo5aMGFC2TJnt27eL\nzgJZKVu2bFJSEk+M1WYUO6iL58+fK3R0ClgVFR0EyF4ZGenuboNS4uP8/f1FZ4HclCxZ0sDA\ngMvstBk3T0BdPH/+PH+hwvoGBqKDANlrx8JZj25eDwgIMDc3F50FcqOnp1ekSJEXL16IDgJh\nKHZQFyEhIZyHhexdOnnk1K4tCxcurFGjhugskKdixYqFhYWJTgFhOBULdfH8+fNC1sVEpwCy\nUejjoPVTx7Zp02bChAmis0C2ihUr9vLlS9EpIAzFDuoiJCSkUFGKHWQr/uOHxa79LPLnO3Lk\niOgskLPixYtT7LQZxQ7q4tWrV/ktC4tOAWQLlVK5ctzw968jrl27pqPDP7zIRtbW1hQ7bcY1\ndlALKpXqzZs3+Qpaig4CZIv9a5fduuhz4sSJIkWKiM4Cmcu8xk6lUikUCtFZIABvHKEW3r17\nl5KSkrdgIdFBgKx3w/fcofUrxo4d27JlS9FZIH/FihVLSUl58+aN6CAQg2IHtRAZGSlJUt4C\nFDvIzeuXL1aNd61fr97ixYtFZ4FWKFq0qCRJ4eHhooNADIod1EJERIS+gYFp7jyigwBZKTU5\neanbQCN9/fPnz4vOAm1hbm5ubGwcFRUlOgjE4Bo7qIWIiIi8BQpxRQhkZuPMCWFPHj148MCA\nlbeRgwoUKPD27VvRKSAGxQ5q4fXr13kLFBSdAshKJ7b/5nv04J49e8qWLSs6C7RLgQIFmLHT\nWpyKhVp4+/Zt7vwWolMAWebxrRu7ls7r16+fi4uL6CzQOhQ7bUaxg1qIiYnJZc4FdpCJmKg3\nS0YMsKtYYfPmzaKzQBsVLFiQU7Fai2IHtfD+/XvTPBQ7yEFGepq726D05MTLly+LzgItxYyd\nNqPYQS3ExMSYmucWnQLIApvnTH1864afn5+pqanoLNBSFDttRrGDWnj//n0uih00n9+xQ2f3\n71qxYkW1atVEZ4H2yp07d2xsrOgUEINiB7UQExNjliev6BTAvxIS9GDDjPGdOnVydXUVnQVa\nzczMLC4uTnQKiEGxg1qIiYlhxg4aLf5D7GLXflaWlgcOHBCdBdqOYqfNWMcO4qWkpKSkpJiY\nmYsOAvwgZUbGslGDP757+yAsTHQWQDIzM0tISFCpVKz6roWYsYN4SUlJkiQZGhmLDgL8oD0r\nFt71v+Tp6WlhwXKMEM/U1FSpVCYmJooOAgEodhAvs9gZGBmJDgL8iOveZ45sWjtjxgxnZ2fR\nWQBJkiQzMzNJkjgbq50odhAv822lgSHFDpon4sXz1RNHNmzYcMaMGaKzAP+PYqfNKHYQjxk7\naKjkxIRFw/uamRifOXNGdBbgvzLXUIyPjxcdBAJw8wTE+881dhQ7aBKVSrV28ug3oSFPnz7V\n1dUVHQf4Lz09PUmS0tPTRQeBABQ7iJdZ7PQNDEUHAb7D0U1rr54+ceTIkeLFi4vOAvwPfX19\nSZLS0tJEB4EAnIqFeMnJyXr6+jrMeUBz3L16ae/KxSNHjmzXrp3oLMCXmLHTZhQ7iKdUKhU6\nfCtCY7yLDF8+dmi1alVXrFghOgvwF3R0dHR0dCh22onfphCPVTShQdJSUpaMGKCrUl66dEl0\nFuBv6enpcSpWO3GNHcRTqVQKiWIHzbBpzuTnD+7evHnTiNt9oMb09fWZsdNOFDsA+CbRbyLn\nDegR+iQoX7583bp1Ex0H+JqkpKTIyEjRKSAAxQ7iqVQqiVOxUGPPH97/beaEZ/duq1Sq1l27\nmpnzXGOou2fBwaIjQAyKHcTjGjuorVuXLuxYOCss+ImRkZFKpRoyceKEBQtEhwL+2dHffy9c\nuLDoFBCAmyegFih2UDdev2/r71Bt7oDuxfKZr5w1MyM9vUXHjuPmzROdC/gm6enpmYueQNvw\nVYd4enp66encvQW1oExP37922amdm1KSklo0cJ7suqFcKZviteraVq3qvmOHDuvyQENkUOy0\nFV91iGdoaJiemio6BbRdYnz8zsWzLxw5oKuQOrduNdl1ePnSpdLT04vXqpunQIGtJ08am5iI\nzgh8E5VKlZGRQbHTTnzVIZ6hoaFKpUpPS9PT1xedBdroXWT41vnTA73PS+5PnQAAIABJREFU\nmOXKNbRn94nDhxYuWDDzpTptfvmYlHT4/HmLQoXEhgS+XUZ6uvSf509A2/BVh3iGhoaSJKWl\nplDskMOCbl7fPGfKi0cPSlgXXTZjWn+Xrrk+m5br5Tb6dlDQDi+vcnZ2AkMC3yudYqfF+KpD\nvMyFXtNSU41ziY4CrXH19Ind7vPfhIVWrVhhx4pl3dq1/eK34NyVq3YeOrx027Z6DRuKCgn8\nmLTUVEmSDAwMRAeBABQ7iJc5Y8dldsgZp/fsOLB22cf37+pWt980e3qrRg3/fFP2sbNnpy9d\nNmLatI69e4vICPwrCfHxkiSZmpqKDgIBKHYQL7PYpaYkiw4COcu83fXE9t+UaantmjUdN2RQ\njSpV/nLP2w8fdhwwuGWnTqNmzcrhkECWSIiLkyh22opiB/FMTEwkSUpJShIdBPIU+y7qt5kT\nAy+cNTEyGuTSZcyggcWKWP3dzu/ex9Rv16FanTruO3awvCI0VOaMnZmZmeggEIBiB/Fy586t\nUCgS4z6KDgK5CXv2ZNPsSQ8DAwrkzzdtpOuIvn3y5cnzlf3T09MrNWqS39Jyw+HDhkZGOZYT\nyFqJ8fEKhcKEBXq0EsUO4unp6eXKlSuBYoescz/gyraFM188elCqePHlM6cP7N7N+BuKWvUW\nrZPT0/d4eub/z3IngCaKj4vLlSsX62lrJ4od1ELu3LkTPn4QnQJy4Hf88P7VS9+EhdpXrjRr\nxbLuv7TT1dX9lk/sOcLtwdOnO0+ftilXLrtDAtkqMT6eC+y0FsUOaiFPnjyJ8XGiU0CDKZXK\n/auXnt6zPTHuY4sGzhNXLatXo/q3f/qsZSt2exxZvmtX3QYNsi8kkDMS4uO5wE5rUeygFvLk\nycOMHX5McmLi9oUzfY8eUKhUXdq0njhsaIWyZb5rhL3Hjs9avsJt5sxfevTIppBATop5987C\nwkJ0CohBsYNayJ07d2IcM3b4PtFvIrfMnRrofdY0l/GQHt3HDx1cxNLyewe5df9+zxFurbp0\nGTl9enaEBHLee4qdFqPYQS3kzZv39cdY0SmgMZ4/uLdpzuSnd/4oZGExzc3VrX+/PObmPzBO\n5Ju39dp1sK9b1337dhY3gWy8j4oqUKCA6BQQg2IHtVCoUKHHt+6JTgENEHDOc+/yRa+eP61s\na7t9ubtLu7b6P/pAzNTU1J+atShYpMiGw4cNDA2zNicg0Pt370pXqiQ6BcSg2EEtFCpUKPad\nj+gUUGuZjwL7EB1Vr0b1ddO2/OWjwL5LjZatU5TKvV5e+ZjbgLwwY6fNKHZQC5aWlrHv3opO\nAXWU+Siwkzs2pSYntWjgPHWka61q1f79sG379n8c8mKPt3fJMt93pwWg/rjGTptR7KAWLC0t\nY99FqVQqrnPCJ4nx8TsXz75wZL+uQtG5daupI13L2thkycjTFi89fvbcmn377OvWzZIBAbXC\nXbHajGIHtVCoUKH0tLSEjx9Mc3/tiU/QEhEhwRtmjH8YGGCRL98U12GufXrnz5s3qwbf7XFk\n7qrV4+bNa9WlS1aNCaiPuA8fEuLjraz+9oHIkDeKHdSCpaWlJEmxUW8pdlru4Y2ALXOnvXj0\nwKZYseUzpw/o5mJibJyF49+8e6/3qDGd+vQZNnlyFg4LqI+IsDBJkqytrUUHgRgUO6gFCwsL\nXV3dmHdvi5YuKzoLxLh4/PCeFYuiIl5Vs6s4a8Wybu3a6v3o7a5/J/LN2/q/dKhev/78DRuy\ndmRAfUSGhRkaGnIqVmtR7KAWdHV1rays3kWGiw4CATJvd42LiW5Qr+6IebNaN26UHUdJTk6p\n1KhJURub344c0TcwyI5DAOogIizM2tqa65W1FsUO6qJEiRJvw1+JToGck5qctHX+DN+jBxUq\nZZc2rScMG1KxbDbO11Zv2Uqlq7v5+PHcWXe5HqCGIsPCOA+rzSh2UBclS5Z8+SpMdArkhJio\nN5tmTw70OZvL2GhIj25jBw+0zuYLvVv07PUs9OVeH58SpUtn64EA4SIodtqNYgd1UaJEiZte\n50SnQPZ6+fTR5jlTHgYGFLTIP22k68h+ffPmzp3dBx03d/7Zi5eW79r1U5062X0sQLjIsDBn\nvtW1GMUO6qJ48eJvw5mxk617AZe3L5z14tGDMiVLLp85fVCP7kY58hSvbQcOLt2wceKiRW1c\nXHLgcIBw4aGhxVjKR4tR7KAuSpYsGf0mMiM9TVdPX3QW/F97dx5QU/74f/ym0DYkWULZ0tgT\nKluaqChKJlv27Pu+jT2DsUzZ15Ky3MrWRsiSfSlU1hSRIuvY2rTd3x/9Pn198JkxpHPvuc/H\nX93Tuee8JHr1fp9z3sXpiNTvwNZ1r54+aWvWcu32YlgK7Oudi4oeNm1G76FDR82YUTJnBISV\nl5v7ODnZ+Edergo5R7GDvKhVq1ZBfv7LtCdVDGoKnQXFoCAvz3/tivDd23Oyshw6WM/esqF1\ni+YlGeBx2lNb137m7dsv3rixJM8LCOhRUlJeXl49FspTYhQ7yAsDA4OyZcumJT+g2Cm6zPfv\nfZbOO3swqHApsNnjx9U3qlvSGbKzm9p2Mqhbl4ebQKk8SEzU1NRk2QllRrGDvFBTUzMyMnqc\ndK9Zu1+EzoJv9OpZ2rbFc6NPHP1JS2vMgP4zx46uVqWKIEnM7Luqqqv7hoeX02EtEyiRh4mJ\nRkZGPMROmVHsIEfq16//OOme0CnwLe5cjdqxYlHi9ZiaNap7Lpg3zLWPlqamUGFsXfslpaYG\nREZWr8noL5TLg8RE5mGVHMUOcqRBgwZBESeFToF/58KRsF0eS5+lJDdr1NB3lcePWArsX5ni\n/vupi5c27NnTzMJCwBiAIB4mJlqamQmdAkIqJXQA4P/Ur1//8f1EoVPgax2R+g1pa+I5eZRR\n1Uqh27ddOxI+sIeLsK3OS+q/ysv7txUrOnXvLmAMQCj3795lxE7JMWIHOVK/fv03r16kv32j\nXZ7rouRXQV5e4AbPMN+tuR+yHTpYz5880czEROhQEolEcuZS1KhZs12HDx86ebLQWQABvH39\nOi0lpUmTJkIHgZAodpAj9evXV1FRefzg/s/NWgidBV/w5uWLre6zrpyM0FBXH+nae+rIEYbV\n5eXmu/vJybaufS3t7H7n4SZQVvHXr6uqqjZs2FDoIBASxQ5yREtLq1atWo8S4il28ubRvbtb\nF8yKvxZVqaLu3InjJwxx05Wnu00zs7PNHBxrGRuvDwgQdi4YENCd69eNjIw0hbtvCfKA/wEh\nX5o1a/bgzk2hU+D/3Lx0fvuyhQ/jb9WtWXPVwvkj+vXVUFcXOtR/KSgoaNrRrrSmpt/hwz/9\n+JVnAbl198YN5mFBsYN8MTU13bU/ROgUkEgkktOh+wPX/fksJblF0ybuqz37dXdWVVUVOtQX\ndOjt+uTFi4DISH0DA6GzAEKKv3HD2d5e6BQQGHfFQr6Ympom371dkJ8vdBDlVVBQ4L9mxSCL\nhutnTTT72ehc0P4r4QcH9nCRz1Y3aYH7+egra6VSE3NzobMAQiooKEi4ebNp06ZCB4HAGLGD\nfDE1Nf2QnfXkYVKNutyxX9KyMzN9ly08FbxHRSbr7eQ4a+yYhsZy/bewfrvvmm0+C9eute3W\nTegsgMAeJSVlpKczFQuKHeRL9erVK1eu/ODOTYpdSXr94pnXotnRJyO0NTVG9+83Y8yo6lWr\nCh3qH0ReuDBxgfuQiRMHjx8vdBZAeHFRUbq6unXq1BE6CARGsYPcMTExeXD7pmVXHjBbEpIT\nbm9bPO929KUqenrzJo6fNGyoTrlyQof6Z4kPHnTuN9Cqc+c5Hh5CZwHkQlxUlLm5OavEgmIH\nudO8efNDp84JnUL8Lh0L3+WxNO1hUtMGDXxXebg6dyutIA8KSc/MtOjazbhJk/WBgfJ55R9Q\n8mKjohzt7IROAeFx8wTkTqtWrRKvx+Tn5wkdRLQKlwJbOX5YnUq6odu3xUYcHtjDRVFaXUFB\nQVMbuzJaWl4hIVra2kLHAeRCXm7u7dhYM1aJBSN2kENt2rT5kJX56G587YaNhc4iKoVLgR30\n88rJznLoYD134ngLU1OhQ/1r7V16PX/9Zt/Zs/o1agidBZAXd65fz87KothBQrGDHKpcuXLd\nunXjY6IpdsUlMz19x4pFkUGBqioqvRy7zp043lgxr7AeNn3GpWvXtgQFNZCP1WkBORF7+XKd\nOnUqV64sdBAIj2IHedSmTZuE2Kv2/dyEDqLwnjy477N0Qdz5UxUrVJgzfux4t8EVK1QQOtQ3\nWu29bZt/4JJNm2wcHYXOAsiXuOhohutQiGvsII9at24dH3NF6BSK7faVS1OdbcfbW75JTvRc\nMO/hpfMLp0xW3FYXfjJyivvvI6ZN6zdqlNBZALkTffZsu3bthE4BucCIHeRRmzZtnqc+ev3i\nWYVKVYTOonjOHQqWrl7+LCXZtHEj99WefZ27qSnIjRH/S0JSkvPQ4R26dJm5bJnQWQC58zwt\nLfn+fSsrK6GDQC4o9n/3EKvGjRv/9NNPd2OutLLrInQWhSGTyUJ9Ngd5rX//5nVbs5Zei+Y7\n2toIHaoYvHn3rqV91/pNm64LCODhJsDnLkZG6urqNmrUSOggkAsUO8gjVVVVS0vLm5cvUOy+\nRk52ls/SBaeC90oK8vt0c5oxZlTjn38WOlTxKCgoMLWzL6en53PwoKaWltBxAHl0+cyZ9u3b\nlyrFtVWQSCh2kFsdOnRYvWmL0CnkXdFSYFoa6qP79502aoRBtWpChypObbv9+vLdu31nz1aS\n+yXOAKFcPn167IgRQqeAvKDYQU516NBh2rRpfz17qluFn+hf8Cgx3vv3ObejL1XWqzhv4viJ\nQ4dUKF9e6FDFbMiU6Vdu3tx+6FD9pk2FzgLIqVfPnyfdvcsFdihCsYOcMjEx0dPTu3n5fHsn\nF6GzyJcbl875LnN/GH/LqFatVQvnj+zfT71sWaFDFb8/t2zdvmfPMi8vS1tbobMA8uvS6dPl\nypUz4cmO+A+KHeRUqVKlrKysblDsPnJE6ndg67pXT5+0NWu5dvu2rjYdxbrgd2jEsRmLl46e\nNavPsGFCZwHk2rljx3755RfuK0IRrrWE/OrQocP1C2eFTiG8grw8/zUr+rcw3rZ4TuvGDS6E\nBJ0L2u9oayPWVnc9Pt5l+Eh7F5fpS5YInQWQd6ePHu3UqZPQKSBHVGQymdAZgC9LSEj4+eef\n1x89r1+zttBZhJH5/r3P0nlnDwYVLgU2e/y4+kZ1hQ71Y/315k1NizZ1GzYMOHVKQ1NT6DiA\nXEu4dcuuceP79+/XUcxFAvEjMBUL+WVsbFy3bt1rp090Gah083GvnqVtWzw3+sTRn7S0xgzo\nP3Ps6GpVxP+s5ry8vCYd7SpUrrwtLIxWB/yjM0ePGhsb0+rwMYod5Jq9vf3ZU8eVqtjduRq1\nY8WixOsxNWtU91wwb5hrHy2lqTitnJzfZWXtP35cTwlaLPD9Th892rlzZ6FTQL5wjR3kWpcu\nXW5FX8zKSBc6SEm4cCRsjG3ruf2cNXKzfFd5JJ49PXHoEOVpdYMmTYm7E7/lwIGfGzcWOgug\nALKzsqLPnuUCO3yCETvINWtra/WyZW9cPGduI+bfSo9I/fZs8Hz318s2LVt4LZov4ttd/5fF\na9bu2Lf/z+3b23bsKHQWQDFcOnVKIpP98ssvQgeBfKHYQa6VLVvW2tr66unjoix2BXl5gRs8\nD/ptzcnOduhgPX/yRDOlfBhV0JEj8//0HD93bo/Bg4XOAiiMY6Ghv/zyi6bSDOrjK1HsIO+6\ndOny27wFMplMTINYb16+8Fux6EJ4qHrZMiP69J46coRhdVEtBfb1Ym/f7jVyTJeePacsWiR0\nFkBhFBQUHAsJ+X3hQqGDQO7wuBPIu9TUVENDw+X7DtdtJIZ1pR7du7t14az4q1GVKuqOHjhg\nwhA3XR0doUMJ5uVfr2u1atOwefNdx46VVVcXOg6gMK5euNDT0jI1NVVfX1/oLJAvjNhB3tWo\nUaNly5aXI8IVvdjdvHR++7KFD+Nv1a1Zc9XC+SP69dVQ7iqTl5fXxMauYtWqm/fvp9UB/8rR\noKDWrVvT6vA57oqFAujRo8eFI2FCp/h2Zw8GjbFtvWBwz4plSvmt9rx7JnLi0CFK3uokEklL\nB8fsvDzf8PCKlSsLnQVQMMdCQ52dnYVOAXnEVCwUwIMHD+rWrftn0LFa9RsKneVfKCgoCFz3\n5xGpb+b7dw4drCcOHWJj2U7oUPJiwIRJAWEHdxw50qZDB6GzAArm7s2bnZo0uXv3rrGxsdBZ\nIHeYioUCqF27tqmp6aWIQ4pS7LIzM32XLTwVvEdFJuvt5Dhr7JiGxvWEDiVH3D1X7zoQtHrX\nLlod8A0igoMbN25Mq8MXMRULxdCjR4/zh0OFTvHPXr94tmL80AFmP18+HDy6f7+ki+d2rFlF\nq/uYf0io+6rVkxYudO7XT+gsgEI6GBjYo0cPoVNATjEVC8WQmJhobGy8KuykYb36Qmf5suSE\n29sWz7sdfamKnt7IAf0mDRuqU66c0KHkTszNm2ZdnBx69lwrlYrp+TVAibkTF2ffrBnzsPhf\nmIqFYqhXr56JicmFIwflsNhdOha+22Ppk4dJTerX913l4ercrbQa/7K+IO3Z87bOLi3atPHw\n9aXVAd8m1N/fzMyMVof/halYKAxXV9fTwXvlaoz5iNRvSFuTleOH1a6kG7p9W9yxIwN7uNDq\nvignJ8e0s0Pl6tU3HzhQpmxZoeMACkkmk4UFBrq6ugodBPKLqVgojCdPnhgaGv6+K+hn05bC\nJvnPUmBeOdlZDh2s504cb2FqKmwk+dfUxi71xcsDFy/WrscVh8A3ij53rreV1aNHj6pXry50\nFsgphhagMKpVq2ZtbX06ZJ+AxS4zPX3HikWRQYGqKiq9HLvOnTjeuE4docIoECe3oQkPk6Un\nTtDqgO8R6u9vbW1Nq8PfYCoWimTAgAHnw0Nyc3JK/tRPHtxfPLz/IPP6sScOzxk/NvXK5R1r\nVtHqvsa8FX+GHTvu4evbok0bobMACiwvN/fQ3r19+vQROgjkGsUOisTFxUVSkH/19PGSPOnt\nK5emOtuOt7d8k5zouWDew0vnF06ZrKerW5IZFNeuA0GL166bvmRJ1969hc4CKLYTBw9mpafz\noBP8Pa6xg4IZMGBAwtOXM9f7lMC5zh0Klq5e/iwl2bRxo0nDhvZ17qbGjRH/xtXrNywcu/06\ncOBKn5L4+wLEbUjXrtV1dXfs2CF0EMg1fkpBwQwcONChS5e3r16Wr6j3g04hk8lCt28O2rrh\n/Zu/2pq19Fo039HW5gedS8TSnj1v192lZbt2SzdvFjoLoPCePn58+siREydOCB0E8o4ROyiY\ngoICIyOjdi79nIeNKfaD52RnSVcvjwjcVZCb06eb04wxoxr//HOxn0UZZGd/qGFmUaFq1f3n\nz5evUEHoOIDCW79kyQEfn3v37vEMSPw9RuygYEqVKjVs2LC1m7d2GzJKpVSxXST65uWLre6z\nok9GaGmoj3TtPW3UCINq1Yrr4EqoZZeuMlVV79BQWh3w/WQy2T5f35HDh9Pq8I8YsYPiefr0\nqaGh4ewtu5q2sfz+oz1KjPf+fc7t6EuV9SqOGtB/4tAhFcqX//7DKjOHAYNOXrzkf/Jk89at\nhc4CiMHFyMgBdnbJycnV+IUT/4QROyieqlWrOjk5RQTu/M5id+PSOd9l7g/jbxnVqrVq4fyR\n/fupsyLCd5v2+5KIM2fXBwbS6oDiEuDtbW9vT6vD16DYQSGNGDHCwaHLX8+f6Vau8g1vPyL1\nO7B13aunT9qatVyxeeOv9p1VVVWLPaQS2rl/v8eWrb+tWGHv4iJ0FkAkXjx9Gr5vX2hIiNBB\noBiYioVCkslkxsbGFk49fx0x/uvfVbgU2KEd3h+yMh06WP82bmybli1+XEhlcy4q2qpHr55u\nbsu9vYXOAojHanf3g7t23b17t1TxXVUMEeO7BApJRUVl+PDhEQE78/Pzvmb/rPT0DbMnu5rW\nDdu2sUdnu1snj4f5+tDqitHjtKe2rv3a2dou4eEmQPHJy80N8PIaN24crQ5fiRE7KKrXr18b\nGBiMWuLZprPj3+z26lnatsVzo08c1dbSHNyz58yxo6tV+ZbZW/yNzOxsg5YWFatV23/+fDkd\nHaHjAOIRFhDw2/Dhqamp5bmpC1+HYgcFNmbMmJOXopf6h37xs3djon2XuSdej6lZo/qkYUOH\nufbR0tQs4YRKopG1zYv374MvXapes6bQWQBRcWnb1qJZsw0bNggdBAqDYgcFlpiYWL9+/aX+\nofVMmn+8/cKRsF0eS5+lJJs0bDBlxHCWAvuhbPr0O3/1akBkZDMLC6GzAKJyKyama4sWcXFx\nTZo0EToLFAbFDoqtS5cu71TKTPbYWPjyiNRvzwbPt69etDVrOXPM6K42HXme5w81aaH7+u1+\nm/bts3N2FjoLIDZTBw168/jx8ePHhQ4CRcIwBhTbpEmT7O0d+k2ZdWJfwEG/rTnZ2Q4drOdP\nnmhmYiJ0NPHbvHPXGm+feZ6etDqg2KWlpoYGBISFfvlSE+B/YcQOik0mk9WpUyc5OVlLU3OY\na59Jw4bWrFFd6FBK4fSlSx16ufYeOvSPrVuFzgKI0KLJk6+cPBkbG8u0A/4VRuyg2FRUVHr3\n7v3nn39eP3a0tqGB0HGUxf3kZDvX/pZ2dr9v3Ch0FkCE3vz1V6C3t9fWrbQ6/Fs8FwcK7/ff\nf69Ro4Y0OFjoIMoiMzvbzMGxlrHx+oAA7koBfoQdGzZU0tPr2bOn0EGgeCh2UHilS5eeNm3a\nKq9t6RkZQmcRv4KCgqYd7UpravodPvwTD9YCfoAP2dk7N26cOnUqvzjhG1DsIAbDhg0rq6Hh\nJfUXOoj4dezt+vTVq+2HDukbMPEN/BB7t2+X5eW5ubkJHQQKiWIHMVBXV580adKKjZuzP3wQ\nOouYjZs7/1z0lbX+/o1MTYXOAohTbk7OxmXLJkyYoKWlJXQWKCSKHURizJgxeTKZ3959QgcR\nrQ2+fht8/eZ6enbs2lXoLIBoBW7blvnu3fjx44UOAkVFsYNIaGlpjRs3bum69bl5eUJnEaHI\nCxcmzF84ZNKkwfy8AX6Y3JycTcuXT506VYc1l/GtKHYQjwkTJrx5n86gXbFLSHrQqd9Aq86d\n5/z5p9BZADEL8PbOePt23LhxQgeBAqPYQTwqVKgwZcqUhR6rsrKzhc4iHumZmRZdnX5u0mR9\nYKCqqqrQcQDRyvnwYeMff0ybNo3hOnwPih1EZerUqbkFBVt27RY6iEgUFBQ0tbErq63tFRKi\npa0tdBxAzPy9vHKysri6Dt+JYgdR0dbWnjlz5pK169+9Txc6ixi0d+n14s1bv8OH9WvUEDoL\nIGZZmZmbli2bMmVKuXLlhM4CxUaxg9iMGTNGQ0trtfc2oYMovGHTZ1y6dm31rl31mzYVOgsg\ncttWrVIpKJg4caLQQaDwKHYQG3V19Xnz5v25Zcvzl6+EzqLA1m333eYfuGDNGhtHR6GzACL3\n18uXW1auXLhwIc+uw/dTkclkQmcAill+fn7jxo27/tJ+5dw5QmdRSOEnI7sOchs+derslSuF\nzgKIn/vEiReOHr158yZriOH7UewgToGBgW6DB8efjjSsXk3oLAom/t79pjZ27Tt33hoczG2w\nwI+W8uBBxwYNAgMCnJ2dhc4CMaDYQZxkMpmlpaWBXkX/DeuEzqJI3rx7Z2jWqlb9+oGnT2sy\nKwT8eONdXV88fHjhwgUVFRWhs0AMKHYQrcuXL7dp0+bU3kBLC3OhsyiG/Pz8Om3aFZQuE3Tx\nYqWqVYWOA4jf9StXnC0szpw507ZtW6GzQCQodhCz/v37346LvRJ+sFQp7hP6Z60dnW8lJe0/\nf964USOhswDiJ5PJelpa1qxadd8+1stBseGnHcRs+fLlCQ8e7ti3X+ggCmDIlOlXbt7cuHcv\nrQ4oGUG7dt26du1PVupDsaLYQcyqV68+ffr0WX8s43nFf2+1t8/2PXvc1661tLUVOgugFDLS\n05fPmjVjxoxatWoJnQWiQrGDyM2YMUNdU2vFps1CB5FfYceOT3FfNOa33/qNGiV0FkBZrF+8\nuIyq6vTp04UOArHhGjuIn1QqHTpkyK3I43UMDYXOIneux8e36ORg1737+sBArkQESkby/fu2\njRrt3rWrR48eQmeB2FDsIH4ymczGxqZ0fv6R3TuEziJfXr1+XcuijbGJifTECXUNDaHjAMpi\nqKNjQUbGyZMnhQ4CEeIXdIifiorKpk2bTl++vCfsoNBZ5EheXl5Tm066Vat6BQfT6oASExEc\nfObo0bVr1wodBOJEsYNSMDY2nj59+sT5C9+8eyd0FnnRysn5XVaWV0hIxcqVhc4CKIuM9+8X\njB8/derUxo0bC50F4kSxg7KYM2dOeV3duStY/FQikUgGTZoSdyd+y4EDP/PTBShBK+fMKaum\nNnfuXKGDQLQodlAWZcuW3bx58+aduy9evSZ0FoEtXrtux779y7282nbsKHQWQInERUfv3Lhx\n69atWqzXhx+GmyegXPr163czJubK4YOl1dSEziKMkIiI7kNHjJ87d8qiRUJnAZRIXl5eN3Nz\n00aNdu7cKXQWiBkjdlAunp6eKU+frvbaJnQQYcTeuuUyfFSXnj0nu7sLnQVQLl4eHmnJyR4e\nHkIHgcgxYgel4+vrO2rkyKtHDjUyNhY6S4l6/upVnVZtG7Zosfv48TJlywodB1AiSXfvOpia\nbtywwc3NTegsEDmKHZTRr7/+mnwv8VJYiPJMyObl5RmYty6jrR108SK3wQIlqaCgoLeVVXl1\n9YiICBUVFaHjQOSYioUy2rBhw8PHT/7cvEXoICWnpYNjdl6e3+HDtDqghG1ZsSLhxg0fHx9a\nHUoAxQ7KSF9ff82aNe6eq2/ExwudpSQMmDDp9r17m/btq61ks8+IyNDDAAAgAElEQVSA4O7d\nubPa3X3t2rUGBgZCZ4FSYCoWyqtHjx734+9EHQoT94Ss+6o1Cz08PXfs+HXAAKGzAMolLy/P\npU0bw6pVQ0NDhc4CZcGIHZTXpk2bnrx4+ce6DUIH+YH8Q0LdPVdNdnen1QElb/2SJalJSVu3\nbhU6CJQII3ZQaoGBgQP69z8XvN+8WTOhsxS/6Li41o7Ozv37/+nry8U9QAmLi452adt2544d\nffr0EToLlAjFDspu4MCB50+fjjl6uNxP2kJnKU5pz57XbWvZxMxs17FjPNwEKGEZ6eldW7Ro\nZ2GxY8cOobNAuVDsoOzS09NbtmxpYlwvcJN45mRzcnIMLdqolysXdOmSrp6e0HEApTNl4MDY\n8+djYmLKlSsndBYoF66xg7LT1tbevXt38NGInfsPCJ2l2LR06JpTUOB7+DCtDih5B/fsCQsI\n2L17N60OJY9iB0hatGixePHiMbPn3r2fJHSWYuDkNjThYbJ3aGjtevWEzgIonUdJSbOGD1+8\neHGrVq2EzgJlxFQsIJFIJDKZzNHR8WnKowshQWVKlxY6zrdb6LnK3XP16l27nPv1EzoLoHTy\n8vJ6tW//U5kyJ06cUFVVFToOlBEjdoBEIpGoqKh4e3vfS37kPGSY0Fm+nd/efe6eq6cvWUKr\nAwSxfNaslHv3/P39aXUQCsUOkEgkkg8fPixdujQ9Pf3o6TNBR44KHedbXI6JHTp1ek83t7Gz\nZwudBVBGx0JCfFav3rlzp76+vtBZoLyYigUkjx496t27d1JS0q5duy5evLhyxYrLYSENjRXp\nArW0Z8/rtGln0qrVroiI0mXKCB0HUDoPEhOdzMxmTp8+Z84cobNAqVHsoOxCQ0MHDx5sYmIi\nlUr19fVlMpmLi8vt63FRB8MU5cl22dkfaphZVKhadf/58+UrVBA6DqB0MjMynC0s6tWqFRoa\nWqoUU2EQEt9/UF55eXmzZs3q3r37iBEjjh8/Xjh7oqKi4uPjkydRGTx5iqL82tOyS1eZqqp3\naCitDhDEvDFjPqSn+/n50eogOL4FRSg1NVVFRcXZ2VnoIHItJSXFysrKx8cnPDx82bJlH1/p\nrKOjc+DAgaNnzq723iZgwq/kMGDQ/UcpPgcP1jIyEjoLoIx2bNhwaM+eAwcOVKxYUegsAMVO\n0eTm5q5fv75t27Y6OjplypTR19c3MzObOHHi6dOnhY6mSMLCwpo1a1a6dOnY2NhOnTp9vkPT\npk29vLxmLPnj9KVLJR/v6037fXHEmbOrdu5sZmEhdBZAGV0+fXrR5MkbNmxo3ry50FkAiYRr\n7BTLhw8fbGxszp07p6mpaW1tra+v/+LFi4SEhDt37nTp0uXgwYOFu+Xk5ERFRVWsWLFBgwbC\nBpZP3t7eo0aNmj179oIFC/7+kQRjx47dv3fP5bDQmjWql1i8r7d9z94hU6b9tmLFyOnThc4C\nKKPUhw+dzM0H9O27evVqobMA/x/FTpGsW7duwoQJLVq0iIiI0NXVLdp+7969O3fuODo6CphN\ngcTFxb19+7Z9+/b/uGdubq69vX1ayqMLIUHlf/qpBLJ9vXNR0VY9evV0c1vu7S10FkAZZaSn\nu7RpY1C1anh4uJqamtBxgP+PqVhFcuHCBYlEMn78+I9bnUQiMTIy+rjVfX6NXWxsrIqKyuDB\ng1NSUvr27aunp6ehoWFmZhYeHv7JKfLz8z08POrXr6+urm5gYDBp0qT09HQ9Pb1atWp9vJuX\nl5ezs3Pt2rU1NDR0dHSsrKz27t378Q5FZ7x9+7aTk5Ourq6Wllb79u0jIyM//3MFBARYWlqW\nK1dOQ0OjSZMmy5Yt+/Dhw+eHun//fp8+fSpXrlyqVKlL/5khvXjxoouLS9WqVcuUKVOtWrX+\n/fvHx8f//ZfRxMTka1qdRCIpXbr03r178yQqrmPG5efnf81bSsbjtKe2rv3a2dou2bxZ6CyA\nMiooKJjUr1/Bhw+BgYG0OsgVip0iqVy5skQiSUlJ+ba3p6SkmJmZ3b17t1evXl26dImJiXF0\ndDx79uzH+4wYMWLatGkfPnwYN26cq6vrwYMH7e3tP+80I0eOfPr0qbW19aRJk1xcXOLj43v1\n6rVixYpPdrt//36bNm3S09PHjh3r6up65coVW1vb4ODgj/eZMWOGq6trQkJC//79x40bl5+f\n/9tvv3Xq1Ck3N/eT8BYWFrGxsZ07d+7evbu6urpEIvHy8mrXrt3Zs2cdHBymTJliaWm5d+/e\nli1bXr58+du+RJ+rUKHC4cOHo2/cnLHkj+I65nfKzM5uatvJoG7d9QEB/EQBBLFy9uzLp04d\nOHCgAreiQ84wFatILl68aGlpqaqqOmbMGEdHx+bNm+vo6Hy+W2pqqoGBQbdu3YoqVGxsrKmp\nqUQimTt37qJFi1RUVCQSya5duwYMGODo6BgaGlq424kTJ2xsbExMTM6fP6+lpSWRSLKystq3\nb3/lypWaNWs+fPiw6BQpKSkGBgZFLzMzM62srG7duvX48ePC/+aKzjhz5sxly5YV7hYTE2Nh\nYVG+fPnk5GRNTU2JRHL27Nn27dvXrl378uXLlSpVkkgkeXl5Tk5Ohw8fXrJkyezZsz8+1Lhx\n41avXl10VdydO3dMTEw6dOgQFBSkoaFRuPH69ett27atU6dOXFxc8XzRJZLCnDY2NmvcF4wa\n0L8YD/tt6lt1eJ2ZGXzpUjVDQ6GzAMpo/44dM4cOPXTokJ2dndBZgE8xYqdIWrduvXv37kqV\nKq1evbpjx44VKlSoXbu2m5vbuXPnvubthoaGCxYsKGx1EomkX79+5cuXj4qKKtphx44dEonE\n3d29sNVJJBINDY3Fixd/fqjCVieTyd6+ffvs2bN379517949Kyvrk/E/HR2duXPnFr00NTXt\n27fvy5cvw8LCCrf4+PhIJJL58+cXtjqJRKKmpubh4VG4cuvHh9LT01u+fPnH9zps3LgxNzd3\n9uzZGRkZL/+jWrVqHTt2vH79enJy8td8Tb6SpaXl5s2bJ8xbcOLc+WI87Dewde2X8vSpd2go\nrQ4QxLnjx2cNH75q1SpaHeQTxU7B9O7dOzk5+dSpU4sXL+7Ro0dGRoavr6+lpeWMGTP+8b2m\npqYfz9ypqKjUqFHj9evXRVtiYmIkEomlpeXH72rXrt3nh4qJienWrVv58uV1dHSqVq2qr69f\nuIrO48ePPzmjtvZ/Ld5QePDCE0kkkmvXrkkkEmtr64/3adCggb6+/oMHD968eVO0sVmzZoWD\nfEUuXrwokUisrKwq/beQkBCJRJKWlvaPX5B/xc3NbcLEiT1Hjr6VkFC8R/56kxa4n7p4adXO\nnU1bthQqA6DM7t68OaZnz8mTJo0bN07oLMCXcYGO4lFVVbWysrKyspJIJDKZzN/f383NbeXK\nlQ4ODr/88svfvPHzeVs1NbWPr5979+6dmpraJ3dmaGlpFQ3gFbp27Vq7du3U1dVHjx5tYmJS\nvnx5VVXV48ePe3h4fHzTg0QiqVKlyidnLNzy9u3bwpeFH1StWvWT3fT19Z88efL27duizNWq\nVftkn1evXkkkktDQ0KJ52I/9iEe9LF++/OHDh/b9B50P3m/wWZ4fbfPOXWu2+czz9OzUvXsJ\nnxqARCJ5+vjxYAcH+06d/vhDXq64BT5HsVNsKioqffv2PXXqlJeX17Fjx/6+2P2jcuXKJScn\n//XXXx93u4yMjIyMDD09vaItnp6eWVlZoaGhNjY2RRuvXr36+QGfPXv2xS3ly5cvfFn4wdOn\nT2vWrPnxboXjbUW7SSSSohnkIoWfrVq1qpmZ2df/Gb+HqqqqVCrt2rVr5/4Dzx7Yp/ulCxx/\nkDOXosbOmec6fPjQyZNL7KQAiqS/ezfYwaFe7dqsGwY5x3enGJQuXVoikXz/8ziaNWsmkUg+\nuWLv8wv4Cu+iaNWq1ccbT548+fkBY2Ji0tPTP95SeBFe4c0QRR+cOnXq433u3r2blpZWu3bt\nL94aUqQwQEBAwN/sU+zKlCmzb9++slraDgMGZ2RmlsxJ7ycn27r2tercefGmTSVzRgAfy/nw\nYVi3bqr5+SEhIWXLlhU6DvB3KHaKZMOGDUFBQTk5OR9vvHLlilQqlXx2bdw3GDhwoEQiWbhw\nYeZ/Kkt2dvb8+fM/2a1OnToSieTYsWNFW6RS6ReL3Zs3bz6+9yImJkYqlerp6RU9dW/IkCES\nieT3338vnFeVSCR5eXlTp06VyWRDhw79+7Tjxo1TU1Nbt27dJ6dOT08PDAz85z/ttypXrtyh\nQ4eev3nTe/TYvLy8H3eiQumZmS3tuxo1arQ+IODv18kA8CPk5+dP6Ns3JTExPDz873/bBOQB\nU7GKJDo62s/P76effjI3N69Vq1Zubu69e/cuXrwok8kKH033nce3sbEZNGiQn59f48aNXVxc\nVFRUgoKCqlatqqOj8/HUw7hx46RSqaura+/evWvWrBkbGxseHt6zZ89PnlEskUjatWu3efPm\nqKiotm3bpqWlSaXSgoKCrVu3Ft0G0b59+ylTpnh6ejZq1KhHjx6ampqHDh26ffu2paXl9H9a\nJqtx48ZbtmwZOXKkjY2NnZ2dqalpfn5+fHz8yZMna9Wq1bt37+/8avwNfX39Y8eOtW3b1m3K\ntB1rVn0+TVxcCgoKmtl2LqutvS00VEvOlr4AlIFMJps9cuSVM2dOnTplyK3oUASM2CmSpUuX\nbtmyxdbW9vHjx3v27PH393/48KG9vb1UKi2uGclt27atWLFCTU1t7dq1UqnUwcEhNDQ0PT29\nXLlyRfuYm5sfP37c3Nw8ODh4zZo1GRkZERERTk5Onx+tbt26Fy5c0NbWXr9+vVQqLVwMrft/\nX/vv4eGxa9euunXr+vn5rV27VkVFZfHixREREWXKlPnHtEOGDImOju7fv//t27c9PT29vb2T\nkpIGDBiwfv367/9S/L26deuGhoYGRxybufQHXkbdobfrkxcvtgYH63/01EAAJWbJtGmH9+49\nfPhwo0aNhM4CfBUeUIx/EBcX16xZsz59+vj7+3/9uwqfKjxo0CBfX98fFk14x44dc3Jymj5q\nxKJpU4v94OPmztuyS7pp3z7bbt2K/eAA/pHHvHneHh6HDx8ufAoBoBCYisV/efny5cc3wGZm\nZhZOiXbnERtfYmtrGxIS4uTkVLZMmTkTxhfjkTf4+m3w3bFw7VpaHSCI7WvXbl6+PCgoiFYH\nxUKxw39ZuHDhqVOnfvnll6pVqz558iQ8PDw5Odne3r5nz55CR5NTdnZ2AQEBvXr1UlVVnTV2\nTLEc8/i5c+PnLRgyceLg8cVZFgF8pV2bNi2eMkUqlX7/tctACaPY4b907tw5ISFh3759r1+/\nVlNT+/nnn8eNGzdx4sQfd3+ACDg7O/v7+/fp00dNVW3aqBHfebSEpAcOAwbbODrO8fAolngA\n/pWdGze6T5y4ffv2Xr16CZ0F+Ne4xg4oHnv27OnXr9/aRQtHDxzwzQdJz8w0aGlRw8go8PRp\nrf9ejQ1ACZBu3Tp/7Nht27YVPv4JUDiM2AHFo1evXunp6SNGjChVqtTI/v2+4QgFBQVNOtiW\n1db2Cgmh1QElj1YHEaDYAcVmyJAhKioqw4cPz8zKmjx82L99e3uXXi/evt139qx+jRo/Ih6A\nvyHdsmX+uHE+Pj4DBnz7oDsgOIodUJzc3Nw0NTUHDBjw7MXLZbNnff0bh02bcTkmZltYWAMT\nkx8XD8AXbVu16o8ZM3x9ffv1+5bhdkB+UOyAYta7d28tLa3C+4i/stut9t62LSBwyaZNVp07\n/+B0AD61asGCjX/8sWvXrh+6Yg1QMrh5AvghIiMjnZycBvzaff3iRR8vyPa58JORXQe5DZ86\ndfbKlSUWD4BEIpHJZEumTdu5YUNAQICzs7PQcYBiQLEDfpRTp045OTn1cey6ednS/9Xt4u/d\nb2pj175z563BwaqqqiWcEFBm+fn5v40YEb5nT3BwcMeOHYWOAxQPih3wA126dKlLly7WrSx2\nrVujXrbsJ599/fZtTfPWterXDzx9WlNLS5CEgHLKzcmZ2K/fpZMnw8PDLSwshI4DFBuKHfBj\n3blzx97eXr+ibpivj56ubtH2/Pz8Oq3bFZQpE3zpkl6VKgImBJRN+rt3I3/9Nfnu3WPHjtWv\nX1/oOEBx+rtLfwB8vwYNGly8ePGDTNLepeejx0+Ktrd1dnmdkeFz8CCtDihJaampPSwtX6el\nnTt3jlYH8aHYAT+cvr7+mTNnDOvUbeXYLfbWbYlEMnjy1Ks3b27cu9e4USOh0wFK5O7Nmy5t\n2+pqa58+fbpmzZpCxwGKH1OxQAnJyckZPHhw+MGDPbt28fYPWLJpU79Ro4QOBSiR8ydOjHJx\n6WRru3PnTnV1daHjAD8ExQ4oOTKZbNq0aZ6enh27dt0WFiZ0HECJ7Pfzmzl8+NQpU/744w8V\nFRWh4wA/iurChQuFzgAoCxUVlU6dOn348MF/x46M9PS2HTvyAwb40QoKCjzmzVs2c+a6detm\nzZrFPzqIGyN2gACOHz/eq1evFu3ardm9W+unn4SOA4hWxvv3kwYMiDp1yt/f397eXug4wA9H\nsQOEce/ePScnpwJVVe/QUIPatYWOA4jQo6Sk4d265Wdnh4SENGzYUOg4QEngrlhAGEZGRufP\nnzeoWrWbhUX0uXNCxwHE5mJkZDdz85rVqkVFRdHqoDwodoBgKlSocPjwYddevVw7dPBdt07o\nOIB47NiwYUCnToMHDgwPD69QoYLQcYCSw1QsIDw/P7/Ro0fbde/+x9atrC0GfI+szMw5o0Yd\n2rNn06ZNbm5uQscBShrFDpALcXFxLi4uktKlN+/fX49pI+CbPEhMHN2jR8br13v27GnVqpXQ\ncQABMBULyAUTE5Nr1641bdDAuVWr8H37hI4DKJ5jISHdzM3r1KgRGxtLq4PSotgB8qJcuXL7\n9++fOX36+D59lk6fnpebK3QiQDHk5eYumjx5lIvLjGnTDh48qKurK3QiQDBMxQJyJyIiYuDA\ngVVr1lwrldasW1foOIBcS334cELfvqn370ul0o4dOwodBxAYI3aA3LGzs4uLi6uio9OlefMQ\nqVToOID8CvX3t2/WTEdD49q1a7Q6QEKxA+RTlSpVjhw5snjRoulublMGDsxITxc6ESBfMt6/\nnz1y5JSBA6dMmhQREVG9enWhEwFygalYQK5FRUX17dtXpqa2ZvfuJi1aCB0HkAtxUVET+vYt\nraIilUrNzMyEjgPIEUbsALlmbm5+7dq11i1bdm/derW7O3dUQMnl5eWtX7LEpV07a0vLa9eu\n0eqATzBiByiGffv2jR49Wr9mTQ8/P+NGjYSOAwgg8fbtaYMHp9y/v3Hjxt69ewsdB5BHjNgB\niqFHjx63bt0yMjR0bNly0/Ll+fn5QicCSk5BQYHPmjVdmjevpqd3/fp1Wh3wvzBiBygYPz+/\niRMnGjdpsnL79lpGRkLHAX64+/Hx09zcHsTHr1mzZuDAgULHAeQaI3aAghk0aNCNGzd0NDQ6\nN2268Y8/uOoOIpaXl7dl5UoHU1N9Xd2bN2/S6oB/xIgdoJBkMtmOHTumTp1aqXr15V5eJubm\nQicCillcVNRvI0emJSd7eHi4ubkJHQdQDIzYAQpJRUVl0KBBCQkJ7Vu16t669eyRIzPevxc6\nFFA8MjMyls+a9WubNnVq1Lhx4watDvh6jNgBCu/o0aOjR4/OzstbtG6dbbduQscBvsvh/fsX\nTpigpa6+cePGTp06CR0HUDCM2AEKr1OnTjdv3uzXu/eYnj0HOzg8SEgQOhHwLR4lJQ11dJzg\n6jpk0KCbN2/S6oBvQLEDxEBTU3PlypWxsbGqOTl2TZosnzWLVcigQDLS01fMnm3bqFHe+/fX\nrl1bunSphoaG0KEAhcRULCA2YWFhEyZMyPzwYeayZb8OGKCioiJ0IuB/kslk4fv2LZk2TSU/\nf+nSpQP4jgW+DyN2gNg4OjreunVr5LBhc0aO7G1lFRcdLXQi4MuuX7ni0rbt9MGDRwwZkpiY\nOHDgQFod8J0odoAIaWpqLlq06Pbt24ZVqnRv1Wpcnz6PkpKEDgX8n9SHDycPGOBsYWFkYHDn\nzp0FCxYw9woUC4odIFq1a9feu3fv+fPn3zx+3LFBg0WTJ79+9UroUFB2f718uWjyZOuff067\nd+/UqVOBgYGGhoZChwLEg2IHiFyrVq3Onj27JzDw/OHDVkZGm5Yvz87KEjoUlFFmRsb6JUva\n16174ciRAH//CxcuWFpaCh0KEBtungCURV5enpeXl7u7u0RVdfSsWX1HjChTtqzQoaAUcnNy\n9vj4rFm0SE1FZcGCBUOGDFFTUxM6FCBOFDtAuWRkZKxfv37lypVlNDTGzp7de+jQ0mXKCB0K\nolVY6Tb88UfW+/fTpk2bNGmSpqam0KEAMaPYAcqosN6tWLGirJbW8KlT+48axegdildebm6I\nv//aRYv+ev58zJgxs2bN0tHREToUIH4UO0B5vX37dvXq1atXr9bW0RkxbVpPNzcNRlPw3XI+\nfCgcpfuQkTF58uQJEyaUK1dO6FCAsqDYAcru9evX69atW7duXYGKituECQPGjNHR1RU6FBTS\n29evd2/evH3t2vycHCodIAiKHQCJRCL58OFDYGDg4sWLn6Sl9RoyZMS0afoGBkKHgsJ48fTp\ngvHjI8PDdcqXHzFixOTJk8uXLy90KEAZUewA/J+8vLyAgIAVK1bE373r1KfP4AkTmrRoIXQo\nyLU7cXFb//wzLDCwSuXK6enpqamp2traQocClBfPsQPwf9TU1Pr37x8XFxccFPQuLc3JzMyl\nbduwgIC83Fyho0G+5OXmHtyzp5eVlX2zZu+fPj108GBCQoKamppUKhU6GqDUGLED8D8lJiau\nX7/ex8dHQ1u7x6BBg8aPr1q9utChILCXz57t9fXduXHjX8+f9+zZc+rUqSYmJoWfmjdv3p49\ne+7cuVOqFKMGgDAodgD+wdu3b7dt27Zx48aU1NTOv/7aZ9iw1tbWLNaubGQyWfS5czs3bjxy\n4EDtWrVGjx7t5ub2yYV0z58/r1Wr1u7du7t37y5UTkDJUewAfJWCgoLDhw97eXkdOnSoes2a\nvYYM6TF4cJVq1YTOhR/u2ZMn+/389mzfnpKU5ODgMHbsWDs7u//V7EeNGhUTE3P58uUSDgmg\nEMUOwL+Tlpbm6+vr4+Pz8OHDX+ztew8bZm1vr1a6tNC5UMxyc3JOHDy4x8fn9JEjtWvXdnNz\nGzRoUPV/motPSkoyNjY+depUu3btSiYngI9R7AB8C5lMdurUKW9v7wMHDmhoaXXp1atb374t\n27ZlilbRyWSymEuXwgICQvz9P2Rm9ujRY8iQIZaWll//N+vi4pKXlxcSEvJDcwL4IoodgO/y\n9u3b/fv3S6XSyMhIfQODbn37duvb9+fGjYXOhX8t/vr1EH//sICAJ48eWVpa9u/fv1evXt/w\nhOHo6GgLC4ubN282bNjwR+QE8DcodgCKx5MnTwICAqRS6dWrV+s3bWrv4tKpe/f6TZoInQv/\n4N6dO+H79oUFBCTevm1mZubq6tqrV69/nHL9e+3btzc2Nvb29i6ukAC+EsUOQDGLj48PDAwM\nCgqKi4urZWTU+ddfO3Xv3szCglla+VFQUBB7+XJEcPDR4OAHCQmNGjXq06dPnz59jIyMiuX4\nYWFhPXv2TEpKqsbtNUDJotgB+FGSkpIOHDgQFBR06dKlyvr6Nk5O1g4Ora2tNbW0hI6mpD5k\nZ184eTIiOPh4WNir588tLCycnZ2dnZ2NjY2L90QymczOzm7BggXcQgGUMIodgB8uLS0tJCQk\nODj4zJkz+QUFFu3bW3XubNW5cz2uwSoRibdvnzl69PTRo1FnzsgKCjp27NitWzcnJ6eqVasK\nHQ1AMaPYASg5WVlZp06dOnLkyOHDhxMTE6vXrGnVqVNra+tWv/xSiZJRrP568eJCZOTZiIgz\nR4+mpabWq1evU6dOdnZ21tbWrOUKiBjFDoAw7t+/f+TIkWPHjp05c+b169d169e3sLJqZWXV\n6pdfKuvrC51OIaWlpl4+fTrq7NmoM2fux8eXK1euQ4cOhX2udu3aQqeTR7GxsaampoMGDfL1\n9f3OQ6WmphoYGHTr1i04OLg4ogHfSE3oAACUVN26dceOHTt27NiCgoK4uLjTp09HRkYuGDv2\n9evXdX7+2bRVK1MLi2YWFvWbNlVT43+qL8vLy7t740ZcdPTV8+ejzp5NefBAV1e3Xbt2o4YO\ntbS0bN68uVx96bKzszU0NCQSiaqq6sOHD2vUqPHJDg0bNrxz545EIgkLC+vatasAEQHFJ0f/\n5gEop1KlSpmampqamk6aNKmgoOD69etnzpy5fPnydg+P+/fvq2toNGnRwsTcvJmFRePmzQ3r\n1FHmBeZlMtmDhIS46Ojr0dFx0dG3Y2Ozs7IMDQ1bt249c+rU9u3bN2rUSM6/Pmpqanl5edu3\nb583b97H28+fP3/nzp3CzwqV7XtUrlz57NmzFStWFDoIlB3FDoAcKVWqVLNmzZo1a1b48sWL\nF1FRUZcvX46Kitrn4/PmzRtNLS3jxo0bmpg0MDGp37Rp/SZNfvrvdehF5vWrV3dv3Lh78+bd\nGzfib9xIuHUr/d07PT09MzOzrra27rNnm5mZValSReiY/0L16tV1dHR8fHzmzp378RNwvL29\nS5cubWNjc/jwYQHjfbMyZcpwCzDkgVz/YgdAyVWqVKlLly6LFi06cuTI69evHzx44C+VunTt\nmvv69c41a3pbWTXR0WltYNC3Y8fZo0Z5e3qePHToQWKigg755ObkJN29e/LQIZ/Vq+ePGzfA\nzs6ienVTPb1+Nja7163L+esvp06dfH18kpKSXrx4ER4e7u7u3rVrV8VqdYWGDRv28OHD48eP\nF2159+7d3r17nZycKleu/PGeBw8eVFFRWbhw4SdH0NHR+Y8p19sAAAtNSURBVPiRe7GxsSoq\nKoMHD753796vv/6qq6tbrlw5BweHhIQEiUSSlpY2ePDgKlWqaGhotGvX7urVq59Hun37tpOT\nk66urpaWVvv27SMjIz/ZwcvLy9nZuXbt2hoaGjo6OlZWVnv37v14h9TUVBUVFWdn52/5igDF\nhxE7AAqjVq1atWrVcnJyKnyZmZl58+bNO3fuJCQkJCYmhu3YkZCQkJWVpVa6dI2aNfUNDKoZ\nGFT/zwfVDA2rGRho/fSTsH8EiUTy7s2btNTUtJSUp48fP01NfZKSkpaS8vDevSePHuXn52tq\nahoZGRkZGbU2NR3ar1/jxo0bNmxYeGmaaPTv33/69One3t62traFW6RSaUZGxrBhwwICAr75\nsI8ePWrdurWRkVHfvn3j4+MPHz4cGxt75swZa2trPT09FxeXR48eHTp0yNbWNikpSUdHp+iN\n9+/fb9OmTfPmzceOHZuWliaVSm1tbfft2/dxSxs5cqS5ubm1tXWVKlWeP39+8ODBXr16LV++\nfMaMGd8cGPgRKHYAFJWmpqa5ubm5uXnRFplMlpKSkpCQ8ODBg5SUlOTk5Otnzx5KSUlNTf3w\n4YNEIlHX0Kigp1epSpWKlSvr6unpVqqkW6lS+QoVtLS1NbW1tbS1fypfXrtcOS1t7bLq6hKJ\npHyFCv8YIy8vL+P9+4z377OzsjLS09PfvfuQnZ2Rnv7m1avXr1799fJl0QevX7588fRpVmam\nRCJRV1evXr169erVDQ0N27ZoMbBXLyMjo3r16n3nWl4KQUdHp0ePHnv27Hn16lXhRWne3t6G\nhoZ2dnbfU+wiIyPd3d3nz59f+HL48OHe3t7m5uYDBw5ctWpV4bTvvHnzFi9evGXLlpkzZxa9\n8dy5czNnzly2bFnhy7Fjx1pYWAwfPtzOzk5TU7NwY3JysoGBQdFbMjMzraysFi5cOHz48Apf\n8U0ClBiKHQDxUFFRMTQ0NDQ0/PxTaWlpT548efbs2cuXL1+8ePHs2bMXL148iY+/fu7c69ev\n09PT09PT371797+OrKGpWaZs2aKX79++LSgo+F87a2lpqaurV6xYUU9Pr2LFihUrVqzdoEGl\nSpX09PQqV65sYGBQrVq1T+Yclc2wYcN27dq1Y8eOyZMnx8bGXr16dcGCBd9520fNmjXnzJlT\n9HLw4MGFi9X+8ccfRRfzDR48ePHixbGxsR+/UUdHZ+7cuUUvTU1N+/bt6+fnFxYW1rt378KN\nha1OJpO9e/cuOztbJpN17979ypUrZ8+eLRpCBuQBxQ6AUtDX19f/isfjvX37Nv0/JBJJfn5+\nUdt79+5dfn6+qqpquXLlPn6Ljo5OqVKldHR0NDU1NTQ0yov6Zo7iYmVlVa9evW3btk2ePNnL\ny6tUqVJDhgz5zmOampqqqqoWvSwc+2zUqNHHE9mFG1NTUz954ycPbba0tPTz84uJiSkqdjEx\nMQsXLoyMjHz//v3Hez5+/Pg7YwPFi2IHAP+nfPnyNLOSMWzYsJkzZ0ZGRhZe0/bFcdZ/5ZO/\nuMJn+H1xY25u7scbP78BpXDL27dvC19eu3atXbt26urqo0ePNjExKV++vKqq6vHjxz08PAqn\n+AH5QbEDAAhg0KBBc+fOHThw4Js3b4YOHfrFfQonZz+5zTk3NzcjI0NPT6+4kjx79uyLW4pK\noaenZ1ZWVmhoqI2NTdE+X7y7FhAcjzsBAAigSpUqXbt2TU1N1dPT69at2xf3KbwvISUl5eON\nMTExxftEm5iYmMKZ9yJnz56VSCSmpqaFLx8+fCiRSFq1avXxPidPnizGDEBxodgBAITh4eER\nFBR06NChMmXKfHGHJk2aqKurh4SEPH36tHDL27dvp0yZUrwx3rx5s3jx4qKXMTExUqlUT0/P\n0dGxcEudOnUkEsmxY8eK9pFKpRQ7yCemYgEAwqhdu3bt2rX/Zgdtbe3Ro0evWrWqWbNmjo6O\nOTk5x44da9GixSf3r3yndu3abd68OSoqqm3btoXPsSsoKNi6dWvRs07GjRsnlUpdXV179+5d\ns2bN2NjY8PDwnj17fvKMYkAeMGIHAJBfK1euXLBggbq6up+f3+nTp4cOHbp///6P1yL7fnXr\n1r1w4YK2tvb69eulUmmLFi0iIiK6d+9etIO5ufnx48fNzc2Dg4PXrFmTkZERERHBU04gn1Rk\nMpnQGQAAAFAMGLEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAk\nKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYA\nAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAi\nQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbED\nAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQ\nCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYod\nAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACA\nSFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDs\nAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAA\nRIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJi\nBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAA\nIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIU\nOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAA\nAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg\n2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEA\nAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgE\nxQ4AAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4A\nAEAkKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAk\nKHYAAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYA\nAAAiQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAi\nQbEDAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbED\nAAAQCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQ\nCYodAACASFDsAAAARIJiBwAAIBIUOwAAAJGg2AEAAIgExQ4AAEAkKHYAAAAiQbEDAAAQCYod\nAACASFDsAAAARIJiBwAAIBL/Dy4wiqCclf1cAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data for the graph.\n",
    "x <- c(21, 62, 10, 53)\n",
    "labels <- c(\"London\", \"New York\", \"Singapore\", \"Mumbai\")\n",
    "\n",
    "# Plot the chart.\n",
    "pie(x,labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Chart Title and Colors\n",
    "\n",
    "We can expand the features of the chart by adding more parameters to the function. We will use parameter main to add a title to the chart and another parameter is col which will make use of rainbow colour pallet while drawing the chart. The length of the pallet should be same as the number of values we have for the chart. Hence we use length(x).\n",
    "Example\n",
    "\n",
    "The below script will create and save the pie chart in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KS0a9fO19e3XLly/v7+zz77rL+/v81my0qxc3Nz8/LykpR+dzExMZJSC196ZcuW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",
      "text/plain": [
       "Plot with title “City pie chart”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data for the graph.\n",
    "x <- c(21, 62, 10, 53)\n",
    "labels <- c(\"London\", \"New York\", \"Singapore\", \"Mumbai\")\n",
    "\n",
    "\n",
    "\n",
    "# Plot the chart with title and rainbow color pallet.\n",
    "pie(x, labels, main = \"City pie chart\", col = rainbow(length(x)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slice Percentages and Chart Legend\n",
    "\n",
    "We can add slice percentage and a chart legend by creating additional chart variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTo0EFOTs7U1DQoKKisrEy0Kj09PSYmxsrK6qtJ5OTkrK2tr1+/rq+vb/hvVf84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SLhvBQnQNkcbxx9i8uhdu3rxpqmZ/YiTJ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\nYII=",
      "text/plain": [
       "Plot with title “City pie chart”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data for the graph.\n",
    "x <-  c(21, 62, 10,53)\n",
    "labels <-  c(\"London\",\"New York\",\"Singapore\",\"Mumbai\")\n",
    "\n",
    "piepercent<- round(100*x/sum(x), 1)\n",
    "\n",
    "\n",
    "\n",
    "# Plot the chart.\n",
    "pie(x, labels = piepercent, main = \"City pie chart\",col = rainbow(length(x)))\n",
    "legend(\"topright\", c(\"London\",\"New York\",\"Singapore\",\"Mumbai\"), cex = 0.8,\n",
    "   fill = rainbow(length(x)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D Pie Chart\n",
    "\n",
    "A pie chart with 3 dimensions can be drawn using additional packages. The package plotrix has a function called pie3D() that is used for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABAbsAVOwAAAIMg2AEAABgEwQ4AAMAg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wnk0a8gAL7PUVnjut3GjpRivZPE+ISskl\nJZ4zCZxNKP732TzSATUW1rg506w5w33pId9ZBusqfxzi8xVLvpWlddVNCjshqpIlTi0Y3oLh\n+j8WobnJ0RscTuTceTadYGkRGsAOTzdautNaX/A54y9Hb4UoVUBWEuf1BZy+nsvkJmCC2gon\nexo0Y3B9OtSnnZy+WiskceFX3njrnTfHjh2rdBbjJ98SQlQjE9T6jfFKr8kn4xoHbnI0magb\nHMknU0cRYIeXC82daeaCvwvNnfF3pKEKE+WyC1ETdBSlczWJC8lcSOJ8MlFJXMjgOqDCxAI7\nO+rXp70XwT50tUB2x6h9Uon5mRmjxj/61ltvKZ2lTpDCTogaZYF92X1VgCzib3AkgbMpXExg\nYx5pWgoANRbO+OtLPWf8XWhejyZWOCmXXYgHlUd6MlHJRCURWXLhgn7FginmljjY4ulFaFue\nqE97WzyVziseVArRP/Jc/+E9lixZolKplI5TJ0hhJ4TCbHH3Z7A/g0uvKUJzi1O3OJlEZBzH\nL/FLAdn6xp4VTk40uuPLgQZyQEoYlCI06VxLJSaNy6nElH7lkAyoMDHHxhoXe3zaMMmDNh60\nkf+HjU8S53/iuSEj+61atUqtlv++NUT+ooUwOCaovQjxIqTsldkk6DdxSOPyTY5eYkcB2foV\neyaoHWhwe7XnZ4+3TMsQNSCbhHSu3VHApXNVSyFggqkZNlbUs8OzMQ8709yLUDlZtS5IIGI7\n00eMe2T58uWmpqZKx6lDpLATonawwe2OY7iAhoJEIhI4m0J0BtfiOZNPhoY80AFqLOzxscfb\nAR8HGtjjbY+PIw3t8bbEUaH3IWqlfDLSuZZObAbXM7ieVnIhnav6A6mgUmNhgYMNLvVo1oQB\nbgS60VIGrdZNtzj1MzPGPTly0aJFJiayVrhGSWEnRC2mxtyTtp60veP6HJISiUgmOp2rWdy6\nzJ58MgrJ0Xf4AHNsHWjgQAN76ttR3xZ3O+rb4GaHly3u8sO4DtKQn01CJjeyScjkZha3Mrmp\nL93SuZZPhv5uJqjNsLLAwRoXJxr70kO/DFTWw4lSMez6jXemPPvEN998I+vqap4UdkIYIWtc\nGtK9Id3/flMaV5I4n8rlNK5kcC2e0wVkasjTotH3+QArnGzxtMXdDi8b3PSVny2eNrhZ42KN\ns6nMYqpttBTmkpxDUhbxWcRlk5BRpobL4lYuKSX3VZmiNsXCAjsr6tng7kX7evg54+9EYyXf\ng6gFdMdYdNJ0yYcffvjqq68qHaaOksKuRhV3pI8epUMHpbOIOsoR34omYRSQlcLFVGL0rZpc\nUlKJySezkFwt+fqzN/QssLPG1QZXK5ytcbbGxQpnG1ytcdFftsbZCmdTzGroXdVhRWhySM4h\nKZfkHJJzSMwhKYekHJL1lVwOydkklLbcABUmppibYa0v3ezw8iLUjvqO+DrTVLYUEfdHQ/5e\n3k2wOxi+Onzw4MH//ABRPaSwq1FjxozZtm3bj9On8+ijzJiBlZXSiYT4izm2+vMTK7i9KI2r\nGVzPIi6LhBwSc0lO43I8ZwrJ1pCnpaAIbWnbDzDD2hIHCxwscbDE0QIHSxwtcSy9svQmc2wt\nsNd3iWrmzRqmfDK15OeTkU9mPul5pJf9dx5peaTdcX0B2WWeQGWCqSnmaiz1dZsFDi74N6Cr\nDW62eNjj7YQvsj+iqGo5JP7CLBvfnP1b9rdq1UrpOHWaFHY1yt7eftu2bfv37580adLFkSN5\n+21CQv75YUIYBJO7dPtKaSjI5FoGN7KJzyU1j9Q8MgrIyOBGIVGF5GrI15JfhKYITdkuYClz\nbNRYWuBghpUaS0scS8qU4spPjZV+FaAlDipMTDAzx1b/QFPMQaU/NUSNpRl//e5kiSPcudzH\nFHNzbCp6LwVk6/cU/Nt7zC05YwANeYXkAvlk6NAWodX3xgrJ0ZIP5JGmQ1dEYQFZBWRpyNOv\nd9SQn0eq/uH5pGvILyDrby+lUqEyQW2C2hQLNRZqrMyxMcfWHh9XAq1wtKSeLe72eNvho5ZD\n5EIJCZzdyattujTduHGPm5ub0nHqOinsFNClS5dTp069/vrrXz37LEOH8uKL0roTRkONuRON\n730xVhGaLOKzuFVIdiFZOaQUkltAVgGZGvILyMwlRUtBATlFFGop1JKnQ6elEHQ6dDq0gI4i\nXXGnUHf3l6t+Kv0/KkwBFSb68SGmmKtQmWJpgtoUc3OsTTFXY2WFsxpLc+zMsVVjaYOzGbZm\n2NjhYYO7bO0mDJyOolOsOMqCSU9OmDdvnoWFhdKJhHxqKMTa2vrLL78cOHDg5MmTr40ezTvv\n0Kai419CGDMT1PbUt6d+FT5nAdn5pAE5JGlLTgQGQJdDwt/vX0jeHd0yc+zMKOdHlL7qKv2j\nCaY2uOrvL0vTRF2TQ+Ie3s60P79y/rdjxoxROo4oJoWdkh5++OHTp0/PmDFj5dNP89hjPPMM\ndnV6gZEQVcIcG/0BVrsqrReFEKWuc+g33g5s12jNmmNNmjRROo74iyyhVZijo+OKFSt+3LKl\nybFjPPooW7ZQVM6qIyGEEMIQaCk4wCc/m8yYPGPsvn37pKozNFLYGYQBAwacO3fui7fesvv8\ncyZO5MwZpRMJIYQQd0rgbDgTUrx+37Fjx5dffmluLufrGBwjKezOnj2rUqlUKtV//vMfpbPc\nJzMzs5kzZ0ZGRo4PDeWpp3jrLVJS/vlhQgghRPUrJOcAn27myQHjOp46dap3795KJxLlM9DC\n7sCBA9OmTQsODnZ2djYzM7OxsfHx8enRo8eLL764YcOG9PR0pQNWl/r1669YsWLXjh0tLl/m\nscdYvx6tVulQQggh6rRrHFjPyCzfgz9t/3HlypUuLi5KJxIVMriTJ1JSUiZNmrR169ayV2o0\nmpycnOvXr+/du/eLL74wMzNLSkqytzfac9B69+596tSpr7766r333kv/7jueeYY+fZA5ykII\nIWpWPhlHmHvBZNPkyZM//fRTW1tbpROJf2BYhV1+fn6fPn1OnDgBqFSqzp07d+7c2cPDQ6vV\nxsfHnzhx4uDBg7m5uYWFhUW3n2Hg6en58ccfA506dVImelUzMzObNWvWE088MWfOnK/eey93\n8WKmTqVPH6VzCSGEqBN0FEWx7RBfNA/yO7z4cGhoqNKJxD0xrMLuiy++0Fd1Xl5eGzdu7PC3\ngao5OTnbt29fuHChSnXbDvLOzs4vv/xyzQWtKfXq1Zs9e/ZLL7302WefffH22/mrV/Pcc8h3\nlxBCiOoUx/GDfJ5rc/XdN9+cNWuWWm1Y1YK4C8M6urdmzRr9hSVLlvy9qgOsra0fffTRHTt2\nODg41Gw0Jbm5uc2ePfvChQtTO3Uyfe45pk0jMlLpUEIIIYxQFrf28NaPJs/0HNEyIiLi3//+\nt1R1tYthFXZRUVGAqalp3759K/XAis6K/fnnn/XXf/HFF8CVK1defPHFZs2aWVlZOTk5devW\nbfHixdq7np2wefPmgQMHuru7W1pa+vr6jhs37tChQ8Dy5cv1z7xp06Y7HpKZmbl27dopU6aE\nhIQ4OTmZmZk5OTm1adPmhRde0L/Bv7sj59GjRydNmuTn52dpaenu7j5gwIDw8PCGDRsuXLjw\n+PHjg93dmTCBV1/l3Lm/niI2lk8/ZeRIevSgc2cGDeK119i9u/x3deAAoaGEhqKvpGNimDOH\n4cN56CFCQ/ntt9vunJfH2rU8/zz9+9OpE716MWEC8+eTmnqXvzchhBC1SyE5xwhbx/B6HZL3\n7dv3/fffN2zYUOlQotIMqwzX11harTY7O7vKz43YsmXL+PHjMzIy9H/My8v7448//vjjj82b\nN2/cuNHMzOyO+2s0mokTJ5Y2EYHY2NjY2Njvvvtu9uzZrq6uFb2Qm5tbXl5e2WvS0tLS0tJO\nnTo1d+7cOXPmvPTSS3fJ+fXXX7/00ksaTfEcpISEhO3bt2/fvn3UqFErV65s3br1li1bDh48\n+MEHH/w0caKuXTsmTeLMGcLCbjt/9tYtbt1i1y6Cg/n4Y+7S4Ny2jQ8/pKDMpHNdmWmbR47w\n5pskJ/91TWEh585x7hzffcf779Ot213eixBCCMOnoyia7Yf50sXHZun7i8aPH3/HeidRixhW\nYdesWbOIiAhg+fLlM2bMqMJnPnLkyGuvvQZMnDixffv25ubmR48eXb58eX5+/rZt2z7++OP/\n+7//u+Mh06dP11d1NjY2U6ZM6dSpk4mJyZ9//rlw4cJXX3116NChFb1WXl6eu7t73759g4KC\nPD09TU1Nr1+/fvTo0R9++EGj0cyaNcvLy2vUqFHlPnbnzp3bt2+3srJ67rnnOnfurFKpDh8+\nvHDhwqysrLVr19rY2CxevBjo1KnTtm3bzpw58/HHH69+/vniU0lMTOjblw4dsLDg4kU2byYl\nhePHeeYZli+n3NnMp0+zZw86Hd260bIlajUxMX/d848/ePlltFpUKjp1olMnXF3JyeHIEXbs\nICeHl1/mm29o167S/z2EEEIYAB1Fl/jlGItwSH/3jTdmzJhhUe4PC1F7qHRl2zNKmz179uuv\nvw6o1eqpU6dOmjQpJCTE5B62+Th79myrVq2AN9544/333y+9/ueff+7fv7/+sp+f3y+//NK0\nadPSW/fv39+jRw+NRuPm5nb9+vWyTbv9+/d37doV8PT03Lt3b9lHxcbGdu/ePTY2Vv/H8PDw\nO4q8H3/8sX///n+PffHixX79+l26dMnb2/vy5ctlVy2Uzenm5rZ3797mzZuX3hodHd29e/e4\nuDhg586dfcqcG3v8+PH27dsXH012cWHiRIYPL67M0tN57jnOnwcYP56ZM/+KcuAApXWzoyNf\nfklg4J1/p0lJPP44GRnY2vLZZwQH33bryZNMn05uLh4ebCrGjpgAACAASURBVNrEHSswrlzh\nscdYu5aaGTXTv3/LxN6dMcKzZ4QQoproKLrEzuMsKrRJfO6551555RXZnc44GNYauxdffLF7\n9+6ARqOZN29e+/btHR0de/To8fLLL2/YsCE+Pv5BnnzdunVl6zOgS5cuI0eOBBISEk6ePFn2\nps8//1x/YeHChXc8qmHDhsuWLbvLCw0cOLDcYrRJkyZfffUVcP369d/uWMdWxrx588pWdUDT\npk0XLVqkv/zpp5+WvemTTz7RV3Xvv//+G08/7bR0KUOHsmIFGRk4ODBnDvp5Lxs2kJlZ/uu9\n/XY5VR2wZg36w9ZvvXVnVQe0acP06QC3blW4kk8IIYQh0sXy+0bG7bf47+NTH46Ojv7f//4n\nVZ3RMKzCzsLCYseOHW+//XbpArvMzMy9e/d++umnI0aM8PLy6tOnz+77KiO6devWrrwjhqXd\nr3NlTkTQaDQ//fQT4OfnN3jw4L8/qmfPnq1bt76PGF26dNFfOHz4cLl3aNCgwfDhw/9+/cCB\nA/39/YGdO3dmZ2frr9RqtVu2bAEcHBxmzZr1/vvvx8bGfvzqq94bN9K/P++9R0YGDz8MkJvL\noUPlvF7Dhjz0UPlZf/wRwNubXr3Kv8OgQcWNugreixBCCAOjL+nG7zF/7dGpPWJiYhYuXOjp\n6al0KlGVDKuwA8zNzd955524uLi1a9dOnjw5MDDQ1NRUf1NRUdGvv/7au3fvmWWPKt6bzp07\nl3u9t7e3/kJqmXM8IyIicnNzAX37sFw9evS4y8vdvHnzo48+6tOnT/369a2trVUlHB0d9Xe4\nfv16RU9b0ZLVnj17Alqt9vjx46U59UXeQw89ZGlpCdjZ2b388suxsbFb1q/vk52tGj+eEyeK\nH3/2bDlPGhJS/hu4dq34hIlmzUhIKP8rKwtnZ4DLl+/yV1G9cnL48UfS0hQLIIQQtUERmmh+\n2sCYvZb/N3p638uXLy9cuNDLy0vpXKLqGdbJE6Wsra1HjhypP06am5t7/PjxnTt3LlmyRF8P\nffXVVw0aNJg1a9a9P2FFTWZ9PQSUPY/15s2b+guNGjWq6AkbN25c0U1LliyZOXNmaV+tXKUn\n596hScWL0kpvKo2nX3UH3HGw2MTEZPDgwYMHD46Kivrwww+//fZbgAMHGDmSO76NPTzKf7GS\nZ2b37n8+0lrRQd5qFRlJeDg//+xkYVFgbk6hAhGEEMLwFZB9no1n+M7ELnfy5MmzZs2qX7++\n0qFENTK4jt3fWVlZdenS5Z133omKihoyZIj+yvfeey8/P//en6S07XcvSmsya2vriu5T0U1b\ntmyZPHlydna2tbX1lClTVq9efeDAgbNnz0ZHR0dHR58taZtVtHneXV7RxsZGfyErK0t/IbOk\noiq96Q7NmjXTnwsM2CUlMXQor7zCkSN/bWhSUtfeqeQl7klhDVZVGRls3Mjo0SYTJ/bJyPh2\n/vwbN27Uqd2qhRDiHuWQfIyw7xh82X3ty28/e+XKlc8++0yqOqNnoB27cllZWS1btszb2zsn\nJyc9Pf3IkSMPVbQ+7MGU1kk5OTkV3aeim95880191IMHD/59HV5iYuLdX/our1habpbOYLaz\ns7vjpr8rrQIHDRo0efLkefPmbZ4xQ/OPlZCVVfGFSZN4/vl/uHMNKCri6FE2buS33+q7u48b\nN+7pp5/28/NTOpYQQhiiZKJOs+oSOwJbtfjq+Y8nTJhgWdGv8cLo1KbCDnBycmrZsuWRI0eA\nGzduVNOrlC47iImJqeg+ly5d+vuVCQkJp0+fBoYPH17u2RXlPqqsixcv/uNNpfFKV7xWNNCi\n7E1eXl69evXq1avXrVu33nnnnYULFwKsXo2lJf36cUen0M2tNPHdA1e7+Hh+/pkNGyxSUh55\n5JHxGzcOGDCgUv1XIYSoI4rQxrL3HBtucKRPnz5fzdr6r3/9S7YarmtqWWEHFG/Ge9ejlg8o\nMDDQysoqNzd37969Fd2n3P1KSjdkKT0n4w76k23vYs+ePTqdrtzvQ/3pwKampsElm48EBgba\n2NhkZ2fv27cvLy+v3F/IduzYob/Qvn17/QUPD4+hQ4fqC7veLVueXrQo8ZNP6NaN4cNp1w79\nSzdqhKMjaWn8+SeZmZS0BmtOQQG//87Gjfz5Z4vmzSfOnPnkk0/eZdqHEELUZdkkRBJ+nk06\nq6xRo0bNmLGwTZs2SocSyjCsNXaR/zTb/v/bu/OwKOv94eOfYYAZdgQBEcIdckVNZdFccjku\ntGhKXR4xO/rQk9WprI7PVfmc6td16te5TvVEV7aYmZ1yTT2iaaJZpgIn00Q0QURZRTZh2GGG\nef64j/MjFrUSB7++X9f8cXvf98x8xz/s3b187zNnzhy7fJvnoEGDOmkYjo6OM2fOFJFz587t\n1Gb9+KXvvvtOOzLXiq012z2EVlJSsnLlyit/dV5e3ubNm9uuT0xM1D5z2rRptjPFer1eu+iw\nsrLy/fffb/uuc+fObdy4UUTc3NymafOe/FJMTExeXt7Gf/5zlsGgf+IJufde+fBDKSgQnU60\nCZPr6+XyFHo3SHa2JCTIzJmef/tbXFhY0p49p06dWr58OVUHAK1YpblA/r1X/s86ubtqwLf/\n9/Vn8vLyVq9eTdXdyrpW2EVHR8fExGzfvr2x5aNLL0tLS7v77ru12w4iIiKucAPp7/fUU09p\nC/Hx8a3On+bl5T388MPtvqtPnz6+vr4ikpiYePjw4ZabysvLZ8+eXVpaetWvXrp0actJ9UQk\nMzMzPj5eW251L/AzzzyjnZd8/vnn9+3b13JTaWnp3LlztXlbHn30UdtMK60YDIZ58+bt2LEj\nLy/vjaeeGnTwoNx7ryxcKK6u/zlQ98UX8tFHcvlA6S+UlcmHH0rHJ4J/hepq2bJF4uIkNvaO\n9PQP/vu/CwoK1q5d2/IxGwAATaNUpcu6DTJnt/6JQTHGr5N2Z2RkLF++XPtvEG5lXetUrNVq\n3blz586dOz09PSMiIoYOHern5+fg4FBcXJyamnr48GHtPKy7u/tVD339TuPGjXvkkUc++OCD\nwsLC4cOHx8fHR0ZG2p4VW1FRcd99923btk1EWj5kwsHB4bHHHnvllVfMZvOkSZMWL14cERFh\nNBqPHTu2evXqkpKShQsXrl279grfGxMT89VXX40aNWrJkiVRUVE6nS4lJWXVqlXa7RGLFy+e\nPHlyy/1Hjhy5YsWKl156qa6ubtq0afPmzZs8ebKLi0t6err2jSIybNiwV1555ao/OTAw8Lnn\nnnvuuedOnjy5adOmdevWZVZViU4nVqt88IEkJsqkSdKvnxiNUl0teXly4oSkpUlzs1w+yfsb\naROX7NrVw8srNjZ2yYYN2tPhAACtWKU5T5LPyM7z8m1wr8C//K//vXjx4h4dTVyFW1LXCrtx\n48bt3r3bYrGYTKakpKSkpKS2+wwePPiTTz4ZMWJEZw/m3XffNZlM69atq66ufvPNN23rHRwc\nXn/99e7du2th5/HL689efPHFH3/8cefOnY2NjStXrmwZoLGxsQkJCVcOu8mTJ8+YMePJJ59M\nSEhISEhouemBBx5oN2f/+te/Ojg4aDW5YcOGDRs2tNw6fvz4LVu2uNjucr0GgwcPHjx48Esv\nvXTy5Mm33377008/bWpqksJC+fzzdvZ2dZXLd+n+OqWlsnOnbNvmUFBw1113xX/66X333dfy\ncb0AAJtyOZMpO7Jkt8VQHRMT8/8e3jJ9+nTuJENbXSvsduzYUVxc/PXXXx84cODEiRPnzp2r\nqKiwWq2enp69evUaPnz4vffeO3PmTEfHGzFsR0fHL774IjY29sMPPzxy5IjJZAoICIiOjn78\n8cfHjh37xhtvaLv5+Pi0fJeTk9P27dvXrFmzZs2atLS0urq6gICAkSNHLlq06L777ms5DXJH\nli5dOmbMmHfeeefAgQNFRUUeHh6jRo2Kj4+fPXt2R29ZsWJFbGzse++9t2/fvry8vIaGBj8/\nv4iIiPnz57f7gLJrNHjw4I8++uj9999/7bXXPv/886ysLLPZLDqdGAxy220yZIiMGSPjxsmv\nqcb/mbhk//4BffvO/9Of/vSnP4WEhPzmQQKAwhrElC17z8jOIjl+xx13/C1uxfz587nmGFeg\ns9rmqsWvERsbu2nTJicnp6qqKoPB8Ds/bffu3TNmzBCRt956y3Z5X1djtVpTU1M3btz45Zdf\n5ubmSmio3HmnTJggAwdKq9t4z5+XuXNl/XppeR1kbq7861+SmGisrb377rvj4+MnT558Xe7D\nDwoK8ikcHy3P/v6PAoCuwCKNOfJdpuzMl+TgkKCFCxfGxcWFhobae1y4CXStI3Y3i8LCwsTE\nRBGJjIz8/VV3s9DpdJGRkZGRkW+++ebJkyd37NiRmJiY/MknzV5eEhUl48dLdHTr+fDkFxOX\nDBo4cOFf/rJkyRIu7wWAtizSmCfJ2bI3Vw4YPBzuv//+hQtfmTBhQsuLuYErI+w6lJOT4+7u\n3jZBysrK5s2bp51UfeSRR+wxNPvTrsNbvnz5hQsXEhMTExMT9738cp3VKmPGyPjxctttIiIZ\nGbJxo3z9dTdn5wULFiz+8MPw8HB7DxwAuhyLNObJ4WzZmyMHHF2sM2fOfHXexzExMR09LhK4\nAsKuQ99//318fPz06dMnTJjQr18/o9FYVlaWkpKydu3a8vJyEbnrrrvmz59v72HaWWBgYHx8\nfHx8fG1t7d69exMTE3esWlVUVCQiDi+/PGnSpMUffTR79myeZgMArVikMV9SsmVvjnznYLRM\nmTJlxbyVs2fP9rjxc8JDIYTdldTV1W3dunXr1q1tN02fPn3Dhg08qsXG1dX1nnvuueeee5qb\nm1NSUt57772XXnqpU+caBICbUZPU5kvKOfnGdnzuvzg+h+uHsOtQTEzM2rVr9+zZc+zYsdLS\n0vLycoPB0KNHj6ioqPnz50+fPt3eA+yiHBwcoqOjo6Oj7T0QAOhCqqQwVw7myIELctTF3Xn6\n9Omvzl1Nz+G6465Y3PS4KxZA12SV5jLJyJHvc+T7Ujndq1fIH/7wh5iYmGnTpt06N97hBuOI\nHQAA15N2sjVXDubKwQaHitGjRz99z4KYmJhhw4bZe2hQH0fscNMbMGBAdtY5fxkWLBHBEukn\ng3Rd7CHIAJRnleZSOZ0vKQWSWiRpLm7O06ZNi4mJmTVrVkBAgL1Hh1sIYYebXm1t7eHDh/ft\n27dnz56ffvrJqdm9p4wKlshgifSQnvYeHQCVVcuFfEnNl9RC+XeDzhQeHj516tSpU6feeeed\nzAYAuyDsoJTS0tL9+/fv3bt39+7dubm5nhIUJBFBMiZYIpyFGQQAXAdmqbsoJwokNV/+XSo/\n+/v7T5gwYcqUKbNmzQoKCrL36HCrI+ygrFOnTiUlJe3Zs+e7776rq6nvLoMCZUSgjOwhw53F\n3d6jA3AzaZLaIvnpghy9IEdL5JTR1Xn8+PHTpk2bOnXqkCFD7D064H8QdlBfY2NjcnLy/v37\nDxw4kJKSUl/X4CuhgTIyUO7oIcON4mXvAQLoihrEZIu5MslwNjqNGTNmwoQJkyZNio6O5rZW\ndE2EHW4tZrP5+PHje/fuPXjw4MGDBysqKrTTtQES3lNGuQvXOAO3tHq5dFFOXJTj+fLvMslw\ncTWOGDFi3LhxU6ZMGTduHJfNoesj7HDrMpvNR44cOXDgwIEDBw4ePFhZWeklIQEy1F+GBsiw\nbtLPQfT2HiOAzmWV5kty9qKcuCgniiW9Qs57enqMGzfuzjvvHD9+/OjRo52cnOw9RuBXIOwA\nERGLxXL8+PFDhw6lpqYmJydnZ2c7ioufDAyQof4yzF+GuIqvvccI4Pqok/JiSS+W9IuSViKn\nmqS2V69eUVFRkZGRY8eOHTFihF7P/9ThZkXYAe0oLi5OSUlJSUlJTk4+cuRIdXW1h/TUDub5\nyxBfCdWLs73HCOBaNUtTqWQWS3qxnCiWEyYpcHNzGzVqVORlPXr0sPcYgeuDsAOuwmKxpKen\na5GXmpqakZGhs+q7Sd/ucrv28pVQR+HKG6ALMUtDuWSWymntVS5nrTpLaGhoREREZGRkVFTU\nkCFDHB159hIURNgBv05FRcWPP/549LIzZ86IVectvW2d111udxJXew8TuLU0SW2ZZNhKrkLO\nW3XN/fr1G3nZHXfc4ePjY+9hAp2OsAN+F5PJdOzYMVvnZWRkNFusnhKsHcnzkX7dpB8PwACu\nu2opuiTZ5ZKl9Vyl5OkcJCwszFZyI0aM8PJiMiPccgg74Hqqqak5fvy4FnlpaWmnTp2qq6tz\nFrdu0s9H+vtIf22ByfOAX6VBTOWSpb0uydlyOdso1UajceDAgeHh4VrJhYeHu7sz9zhudYQd\n0IksFsvZs2dPnDiRnp6enp6elpZ29uxZi8XiKt19pJ+PDNAO6XlLb87eAjZNUlsh57WAK5es\ncjlbKyV6vb5v377Dhg0bMmTIkCFDhg4d2r9/f25fBVoh7IAbqq6u7ueff9ZSLy0t7eTJkwUF\nBSLiKn7e0ttbQrykl7f09pbe7tJDJw72Hi/QuazSXC1FlZJTITkVcr5ScivkfI0Ui0hQUNDg\nwYNtJTdo0CAXFxd7jxfo6gg7wM4qKyszMjIyMjJOnz6dmZmZkZGRmZnZ0NCgF2cv6dUy9Twl\n2CCe9h4v8Ns1SnWl5FZKziU5Vym5WsZZpNFgMAwYMCAsLCw0NDQsLOz2228PCwvz9va293iB\nmw9hB3Q5FoslJycnMzPz9OnTGZcVFhaKiEE8PSTIU4I8JchDgjwl2EOC3KUHD8lAl9Islhq5\naJL8Kik0SUGVFJgkv0oK6qVSRAIDA8PCwrSMGzhwYGhoaO/evTmpClwXhB1wczCZTFlZWdm/\nlJub29TU5CB6d+lh6zxPCfKQnm4SwNMycAPUSXmNXDRJoa3eTFJQLUXNYnZ0dLztttv6/tKA\nAQO4WRXoPIQdcBMzm815eXnZbZSXl4uIXpzdJMBd/N0kwF16uEmAuwS4S6C7+DuLh73HjptJ\no9TUyMUquVAjF2ukuFouVEtxjVysliKLNIpIt27dtG7r06ePreFCQkJ40CpwgxF2gIKqqqpy\nc3Nzc3Pz8/Pz8/NzcnLy8vLy8/Nzc3Pr6+tFxElc3aWHu/RwEz9X8XcVH1fxd5FubuLnIr48\nMO0WZJHGOrlUK8V1cqlGSuqkrEZKtHSrkYuNUiMiRqMxODg4ODg4JCQkJCREW+7Vq1dwcDDX\nwwFdBGEH3FpKSkq0yMvJycnPzy8oKCgoKLh48eKFCxcqKiq0fQzi6SrdXcTHVfxcpJub+LuI\nj7bGKN5G8XIQDsPcZJrFXC+V9VJRJ2W1Utqq4WqltEFM2p4eHh5BQUH+/v5BQUEt0y04ODgg\nIMC+v+L3271794wZM0Tkrbfeeuqppzr769LT04cOHSoiL7zwwquvvtrZXweICE/KA24tfn5+\nfn5+I0eObLupvr6+qKiosLCwuLi4oKCguLi4sLCwqKioqCgz88KFixcvms1mbU8ncXWRbkbp\nZhQvg3gZxbvVyyBeRvFy4F+YztcslgaprJeKeqlokMo6uWRbrv/P+sp6KdcOuYmIo6Ojv79/\nYGBgSGBgQEBgz553+Pv79+zZMyAgIDAwMDAwsKGhoVu3bqdPn9b2f/bZZ//85z9fYQALFiz4\n/PPPtWU3N7fq6upO/b0Arox/dgH8h9Fo7N27d+/evdvdarVaS0tLS0tLy8rKysrKSktLS0pK\ntD+WlpaWlZ0rKi0tLS29dOmS7S2OYnQW98svD2dxN4hHizX/eRnEw1FcncVNL863+ETNTVJr\nkcZGqWmSmkapbvtqkKpWa8xSZ3u7t7e3n5+fr69vr+7dfX27+/qGde/e3c/Pr3v37r6+vr6+\nvtofdTrdFcbQ0NDQ8o///Oc/X3/99Y5uWTWZTFu3br0uvx3AdUHYAbgmOp1OO9p35d3MZrNW\nfuXl5ZUtVFRUVFRUVFZWVlaWVFZmXfrPcmXbAzyO4uIozs7irheDXgwG8dCLs6MYncXNQZyd\nxVVbLyLO4q4TnYM4ajnoKC56cRIRg3iIiG03jaG9+0X04uQoHc55a5Y6izS1XW+RBrM02pYt\n0iAijVJjlWarWLRjY2ap1+4qaJRqq1ibxWyW2iaps0hDo9RoWxukSnt7o1RbpKlJalt9kYeH\nh9dl3b28PD09vbz6dOvWzeuXfHx8tHRzdLye/6Q7OjqazeaioqI9e/Zopy/b2rhxY21trU6n\nc3BwsFgs1/Hb1RAYGPj3v/9dRKKiouw9FtwqCDsA15Ojo2NAQMC1X4xlsVi07Kuvr6+rq6us\nrKyvr6+pqamqqmpoaDCZTDU1NQ0NDbYdKioKrVZrZWVlc3OzqalJ68KamprGxkar1Wq7TNBe\nPD099Xq9Xq/39PQUERcXF6PRKCJeXl56vd7b29to9HRxCfD29jYYDG5ubh4eHkaj0cPDw83N\nzWAweHt7a2/R6s3BwZ6PHhk0aFBlZWVOTs6aNWs6Crs1a9aIyLhx49LS0iorK2/o+G4Gvr6+\nzz77rL1HgVsLYQfAnvR6vY+Pj4+Pz3X8zIaGhtraWhExmUwtDyN1VH62/W3c3Nycndu5NdgW\nahoHBwdtSjaj0aje0650Ol1cXNyrr776r3/9q6Kiou19r1lZWYcOHRKRRYsWLVu2zB5jBNCG\nFQCAy2xXSYaHh2dlZWnL77//fts9X3zxRRFxdXU1mUxa4Lq5ubXaRzsRKSL79+9v9+tWrlyp\n7ZCYmNhy/a5du7T1b731ltVqPXnyZHx8fN++fbUpV+bMmXPkyBHbzs3NzZs2bZo6dWrPnj0N\nBkPfvn2XLVtWVlbW9utafewPP/zw0EMP9e7d22Aw+Pv7z5gxY8uWLR39zZhMpnXr1i1ZsmTk\nyJHe3t6Ojo7e3t7h4eFPPvlkRkZGu285ceKE9nUvvPBCRx8LXF8csQMAtK9fv35jx449dOjQ\nmjVrHnnkkZabrFbr2rVrRWTOnDkeHp073/UXX3yxePFibQpGEdFmZ9y+ffv69evvv//+2tra\nuLi4LVu22PbPzs5+8803t23b9v333/fs2bOjj01ISFi2bJntXu/i4uJdu3bt2rXrwQcf/Oyz\nz9pesOjv728bg0a7cvT48ePvvvvuG2+8wWFLdAWEHQCgQ4sWLTp06FBKSkpmZmZoaKht/Tff\nfJObm6vt0KkDSE5O3rp1q9FofPjhh4cPH97U1LRz585du3aZzeYFCxaMHTv26aef3rJly+DB\ng2NjY4ODgwsKCj7++OOcnJzs7OylS5du27at3Y9NSkratWuXi4vLY489Fh0drdPpUlNTP/jg\ng+rq6vXr17u5ua1atarVW+rr6wMCAqZOnRoeHh4YGKjX6/Pz848cOfLll1+azeZnnnmmZ8+e\nDz74YKf+bQBXZ+9DhgCALqTlqVir1VpZWaldPvj888+33G3BggUiEhIS0tzcbLVaO+9UrIiE\nhYXl5eW13GqbW1ibkfGJJ54wm822rWVlZbZZe7Kysjr6WH9//59//rnl1szMzMDAQG1rUlJS\nq6Hu2LHDYrG0/Qlnzpzp16+fiAQHBzc1NbXcxKlY3Hj2vOUKANDFeXp6zp49W0Q+++yz5uZm\nbWVVVZV26jMuLu7Ks+L9fjqdbuPGjcHBwS1XvvzyywaDQUSOHj06evTot99+u+VMez4+PsuX\nL9eW9+zZ09Env/fee7fffnvLNQMGDPjoo4+05X/84x+t9p81a1a79yn379//nXfeEZH8/Pxv\nv/32mn8Z0CkIOwDAlWgnW/Py8r755httjTZ9nYg89NBDnf3tEydOHDZsWKuVnp6ew4cP15Yf\nf/zxtr01YcIEbeHUqVPtfmxISMicOXParp81a1ZYWJiIJCUl1dTUXOMgx44dqy2kpqZe41uA\nTkLYAQCuZPLkydoBs08//VRboy2MHTt2wIABnf3tkZGR7a63nTNtdwfbPRMtH4XS0sSJEzs6\n1jhp0iQRsVgsR48ebbWpsLDwtddemzJlSlBQkKurq+4y21ww+fn5V/k9QCcj7AAAV+Lg4BAX\nFyciW7Zsqaqqys7OPnjwoNyQw3Ui4uvr2+567VRsRzvYtra6j9Wmf//+HX2jbVNhYWHL9R9/\n/HFoaOjzzz+/b9++wsLCurq6tu81mUwdfSxwY3BXLADgKhYtWvTaa6/V1tZu2rTp/PnzVqvV\nxcXlgQceuAFf3dFjaq99h3a5unb4VGI3NzdtoeXz7rZv375kyRLtjX/84x8nTpzYp08fT09P\nrSAbGhqGDBkiIjxXDXZH2AEAriI0NDQqKio5OfmTTz7Jy8sTkdmzZ2uPTfud7FVCrZ410pLt\n0jp3d3fbyhUrVoiIi4tLcnJy22v+SkpKOmGMwG/BqVgAwNVpJ14PHjyYk5Mj13we1nZKtLGx\nsd0diouLr9MAfx3bQzWusMl2oV5xcXFaWpqIzJkzp23VicjZs2c7YYzAb0HYAQCu7sEHH7Q9\nJzc4OHjKlCnX8q5u3bppC9psxm0dPnz4ugzv19Lm1Wt3k3bzr16v1ybJE5GLFy9qC61mXbH5\n6quvOmGMwG9B2AEArs7Ly+uJJ56IiIiIiIhYtmxZuzO6tTVo0CBtISkpqe3Wn376ad++fddz\nlNcsLy9v8+bNbdcnJiZmZmaKyLRp02wX29kuyNM2tVJSUmKbZhmwO66xAwBckzfeeOPXvkV7\n+taFCxc2b9787bffTpw40bYpKytr7ty5HR02uwGWLl06ePBgW3qKSGZmZnx8vLb8zDPP2Nb3\n6dPH19e3rKwsMTHx8OHD0dHRtk3l5eWzZ88uLS29YcMGroywAwB0Fr1ev2zZsueee665uXna\ntGlxcXGjR49uamr64YcfNm7caLVa4+LiPvvssxs/sJiYmK+++mrUqFFLliyJiorS6XQpKSmr\nVq3S7pxYvHjx5MmTbTs7ODg89thjr7zyitlsnjRpAtXrNgAABypJREFU0uLFiyMiIoxG47Fj\nx1avXl1SUrJw4cK1a9fe+F8BtEXYAQA60dNPP3348OGtW7c2NTWtXr169erV2no3N7c1a9aU\nlpbaJewmT548Y8aMJ598MiEhISEhoeWmBx54oO2p1RdffPHHH3/cuXNnY2PjypUrW+4QGxub\nkJBA2KGL4Bo7AEAn0uv1mzdvXrNmzYQJE7y9vQ0GQ79+/R599NGjR4/OnTvXjgNbunRpcnJy\nXFxcr169DAZD9+7dp0+fvmXLlvXr1zs5ObXa2cnJafv27R9//PGdd97p5eXl7Ox822233Xvv\nvVu3bt2wYYOzs7NdfgLQls6O1zcAAADgOuKIHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEA\nACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrAD\nAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARh\nBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAI\nwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABA\nEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAA\ngCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsA\nAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2\nAAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog\n7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAU\nQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAA\nKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMA\nAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEH\nAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjC\nDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEAR\nhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACA\nIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAA\nAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYA\nAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDs\nAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB\n2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAo\ngrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAA\nUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcA\nAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIO\nAABAEYQdAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGE\nHQAAgCIIOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAi\nCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIgg7AAAA\nRRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCIIOwAAAEUQdgAA\nAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOwA\nAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQdAACAIv4/d/X1sFdPLhkAAAAASUVORK5C\nYII=",
      "text/plain": [
       "Plot with title “Pie Chart of Countries ”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the library.\n",
    "library(plotrix)\n",
    "\n",
    "# Create data for the graph.\n",
    "x <-  c(21, 62, 10,53)\n",
    "lbl <-  c(\"London\",\"New York\",\"Singapore\",\"Mumbai\")\n",
    "\n",
    "# Plot the chart.\n",
    "pie3D(x,labels = lbl,explode = 0.1, main = \"Pie Chart of Countries \")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts\n",
    "\n",
    "A bar chart represents data in rectangular bars with length of the bar proportional to the value of the variable. R uses the function barplot() to create bar charts. R can draw both vertical and Horizontal bars in the bar chart. In bar chart each of the bars can be given different colors.\n",
    "\n",
    "The basic syntax to create a bar-chart in R is −\n",
    "\n",
    "    barplot(H,xlab,ylab,main, names.arg,col)\n",
    "    \n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    H is a vector or matrix containing numeric values used in bar chart.\n",
    "    xlab is the label for x axis.\n",
    "    ylab is the label for y axis.\n",
    "    main is the title of the bar chart.\n",
    "    names.arg is a vector of names appearing under each bar.\n",
    "    col is used to give colors to the bars in the graph.\n",
    "\n",
    "Example\n",
    "\n",
    "A simple bar chart is created using just the input vector and the name of each bar.\n",
    "\n",
    "The below script will create and save the bar chart in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data for the chart\n",
    "H <- c(7,12,28,3,41)\n",
    "\n",
    "# Plot the bar chart \n",
    "barplot(H)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart Labels, Title and Colors\n",
    "\n",
    "The features of the bar chart can be expanded by adding more parameters. The main parameter is used to add title. The col parameter is used to add colors to the bars. The args.name is a vector having same number of values as the input vector to describe the meaning of each bar.\n",
    "Example\n",
    "\n",
    "The below script will create and save the bar chart in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wo1bty4iRMnpqWlJWQ2AICEKz/sOnfuXA1zlGv//v19+vTJzs5OSkrq0qVL27Zt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\nAIiEsAMAiMSftIFNLTtxYtgAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Revenue chart”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data for the chart\n",
    "H <- c(7,12,28,3,41)\n",
    "M <- c(\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\")\n",
    "\n",
    "\n",
    "# Plot the bar chart \n",
    "barplot(H,names.arg=M,xlab=\"Month\",ylab=\"Revenue\",col=\"blue\",\n",
    "main=\"Revenue chart\",border=\"red\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boxplots\n",
    "\n",
    "Boxplots are a measure of how well distributed is the data in a data set. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.\n",
    "\n",
    "Boxplots are created in R by using the boxplot() function.\n",
    "\n",
    "The basic syntax to create a boxplot in R is −\n",
    "\n",
    "    boxplot(x, data, notch, varwidth, names, main)\n",
    "\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    x is a vector or a formula.\n",
    "\n",
    "    data is the data frame.\n",
    "\n",
    "    notch is a logical value. Set as TRUE to draw a notch.\n",
    "\n",
    "    varwidth is a logical value. Set as true to draw width of the box proportionate to the sample size.\n",
    "\n",
    "    names are the group labels which will be printed under each boxplot.\n",
    "\n",
    "    main is used to give a title to the graph.\n",
    "\n",
    "\n",
    "Example\n",
    "\n",
    "We use the data set \"mtcars\" available in the R environment to create a basic boxplot. Let's look at the columns \"mpg\" and \"cyl\" in mtcars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   mpg cyl\n",
      "Mazda RX4         21.0   6\n",
      "Mazda RX4 Wag     21.0   6\n",
      "Datsun 710        22.8   4\n",
      "Hornet 4 Drive    21.4   6\n",
      "Hornet Sportabout 18.7   8\n",
      "Valiant           18.1   6\n"
     ]
    }
   ],
   "source": [
    "input <- mtcars[,c('mpg','cyl')]\n",
    "print(head(input))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Boxplot\n",
    "The below script will create a boxplot graph for the relation between mpg (miles per gallon) and cyl (number of cylinders)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c6q6GKKkuW5/MMICTpNGb/SFB0fzOUfRkjQiZACCAn5IqQAQkK+CCmAkJAvQgog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",
      "text/plain": [
       "Plot with title “Mileage Data”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Plot the chart.\n",
    "boxplot(mpg ~ cyl, data = mtcars, xlab = \"Number of Cylinders\",\n",
    "   ylab = \"Miles Per Gallon\", main = \"Mileage Data\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "\n",
    "A histogram represents the frequencies of values of a variable bucketed into ranges. Histogram is similar to bar chat but the difference is it groups the values into continuous ranges. Each bar in histogram represents the height of the number of values present in that range.\n",
    "\n",
    "R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms.\n",
    "\n",
    "The basic syntax for creating a histogram using R is −\n",
    "\n",
    "    hist(v,main,xlab,xlim,ylim,breaks,col,border)\n",
    "\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    v is a vector containing numeric values used in histogram.\n",
    "\n",
    "    main indicates title of the chart.\n",
    "\n",
    "    col is used to set color of the bars.\n",
    "\n",
    "    border is used to set border color of each bar.\n",
    "\n",
    "    xlab is used to give description of x-axis.\n",
    "\n",
    "    xlim is used to specify the range of values on the x-axis.\n",
    "\n",
    "    ylim is used to specify the range of values on the y-axis.\n",
    "\n",
    "    breaks is used to mention the width of each bar.\n",
    "\n",
    "\n",
    "Example\n",
    "\n",
    "A simple histogram is created using input vector, label, col and border parameters.\n",
    "\n",
    "The script given below will create and save the histogram in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xr16oKCgng8npWV1a5du3bt2sVisVSP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\nTkSuQmCC",
      "text/plain": [
       "Plot with title “Histogram of v”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data for the graph.\n",
    "v <-  c(9,13,21,8,36,22,12,41,31,33,19)\n",
    "\n",
    "\n",
    "\n",
    "# Create the histogram.\n",
    "hist(v,xlab = \"Weight\",col = \"yellow\",border = \"blue\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range of X and Y values\n",
    "\n",
    "To specify the range of values allowed in X axis and Y axis, we can use the xlim and ylim parameters.\n",
    "\n",
    "The width of each of the bar can be decided by using breaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CAQwg4AIBDCDgAgEMIOACAQwg4AIBBp\nHHavvvrqt7/97YYNGx5zzDGdO3eeOHFiXl5eqocCAEiZWGFhYapnKJEmTZoMGzbst7/9bfzp\n448/ftlll+Xn5ydvM3jw4L/97W+xWCwVAwIApFjanLH74osvtm/fHn+8ZcuWa665prCw8NZb\nb/3oo4+2bt06c+bMpk2bPv3003/9619TOycAQKpkpXqAspgxY8auXbvGjh3785//PL5k6NCh\nzZo169Gjx9SpU0eOHFnyXRUUFLzyyiuHfw+3sLBw06ZNpdotAEDlS8uwe+utt6Iouvrqq5MX\nnnHGGZ07d37jjTdKtav169dffPHFhw+7vLy8nTt3XnzxxdWqVSvDtAAAlSMtw27v3r1RFLVp\n06bI8uOPP/7tt98u1a7atGmzadOmw2+zaNGiXr16pcvFiABAlZU219glO/HEE6Mo2rFjR5Hl\n27Zty8nJScVEAACpl05n7P70pz898cQTURQVFBREUbRq1apjjz02eYO1a9e2bNkyNcMBAKRa\n2oRdu3btiix5/fXX+/fvn3j6r3/9a926dQMHDqzcuQAAjhZpE3bvvvvu4TfIz8+/6667klMP\nAKBKSZuwK1b37t27d++e6ikAAFImLW+eAADgQMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDC\nDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQ\nwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAg\nEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4A\nIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIO\nACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDC\nDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQ\nwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAg\nEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4A\nIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgECGE\n3VVXXTV16tRUTwEAkGIhhN1DDz306quvpnoKAIAUy0r1ACV16623Hmbt8uXLExvccccdlTIR\nAMDRJVZYWJjqGUokFouVcMty/4kWLVrUq1evffv2ZWdnl++eAQDKUdqcsYuiqE6dOjfddFP9\n+vWLLL/pppt69OgxbNiwMuxz27Ztt956a15e3mG2+eKLL8qwZwCASpY2YTd79uyrrrpqypQp\nkydPPv/885NX3XTTTe3btx87dmyqZgMAOBqkzVuxURR9+eWX11xzzaxZs6644op77723bt26\n8eWxWOzKK6+cMmVKBb2ut2IBgLSQTnfFNmrU6G9/+9vDDz88Y8aMDh06vPjii6meCADgKJJO\nYRd3xRVXvPXWW23atDn33HO///3v79q1K9UTAQAcFdIv7KIoat269fz583/9618/8sgjp556\naqrHAQA4KqRl2EVRlJGRccsttyxdurROnTqpngUA4KiQNnfFHlTHjh3feOON/Pz8jIx0LVQA\ngPKS3mEXRVEsFsvKSvufAgDgyDnRBQAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcA\nEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEH\nABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcA\nEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEH\nABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABCIEMJu27ZtO3fuTPUUAAAp\nlk5ht27dumuuuaZv37433XTT5s2boyhatmxZp06d6tevn5OT07t37/feey/VMwIApEyssLAw\n1TOUyObNmzt27Pj555/Hn3bp0uW555479dRTv/jii6ZNm27atCk/P79Zs2Zvv/12bm5u+b70\nokWLevXqtW/fvuzs7PLdMwBAOUqbM3b33Xff559/fumll7788ss33HDDihUrRo0aVbNmzVWr\nVm3cuHHbtm1DhgzZuHHj/fffn+pJAQBSI23O2HXu3Pmzzz779NNPs7KyCgsLTzzxxI8++mja\ntGkXX3xxfIMtW7a0bNmyU6dOS5YsKd+XdsYOAEgLWakeoKTWr1/fs2fPrKysKIpisdhpp532\n0Ucf9e7dO7FBgwYNunXrtmrVqlLtdu3atWeccUZeXt5htomvTZcCBgCqrLQJu6+//rp27dqJ\np/Xq1Yui6Nhjj03epkmTJqU9XdeqVavp06cfPuzefvvtsWPHxmKxUu0ZAKCSpU3YNW7ceMuW\nLYmnNWrUSO68uK1btzZo0KBUu83IyOjTp8/ht6lVq1ap9gkAkBJpc/PEKaecsmbNmsTTe++9\nd9euXUW2WbduXevWrSt1LACAo0bahN2ZZ565YcOGTz755FAbvPHGG0WuugMAqFLSJuxuvfXW\nvXv3tmjR4lAbfP3117/4xS9GjRpViUMBABxFir/Gbtu2bfE7FVIrMzMzMzPzMBv06NGjR48e\nlTYPAMDRpvgzds2bNx81atTixYsrYRoAAMqs+LBr0aLF1KlTe/bseeqpp/7+97/fsWNHJYwF\nAEBpFR9277333ty5cy+++OJ33333+uuvb9as2VVXXbV06dJKGA4AgJIrxVeKbdq06ZFHHpk8\nefKHH34YRVHXrl1Hjx59ySWX1KlTpyInTD1fKQYApIVSf1dsYWHhSy+99OCDD86ePXv//v3H\nHHPMyJEjr7/++g4dOlTQiCkn7ACAtFDqjzuJxWInnXTSKaecEr9VdufOnZMmTerUqdOIESO2\nb99eARMCAFAipQi7/Pz82bNnn3/++ccff/wdd9xRvXr1n/3sZxs2bPj73//eu3fvJ5544vrr\nr6+4QQEAOLwSfVfsJ5988tBDD02ZMuXTTz+NxWIDBgy47rrrBg0aFP9guebNmw8cOHDw4MF/\n//vfK3haAAAOqfiwGzRo0HPPPZefn1+/fv2bb775+9///oknnlhkm1gs1qNHj2eeeaZihgQA\noHjFh92zzz7bvXv36667bvjw4TVq1DjUZgMHDqxbt265zgYAQCkUH3bLli3r1q1bsZt17dq1\na9eu5TESAABlUfzNEyWpOgAAUq74sJs+fXrfvn03bNhQZPmGDRv69Onz1FNPVcxgAACUTvFh\nN3ny5J07d7Zo0aLI8hYtWnz11VeTJ0+umMEAACid4sNu5cqVp5122kFXnXbaaStXrizvkQAA\nKIviw27r1q0NGjQ46KrGjRtv3ry5vEcCAKAsig+7Bg0avP/++wdd9cEHH+Tm5pb3SAAAlEXx\nYXfWWWfNnj373XffLbJ89erVs2fP7tWrV8UMBgBA6RQfdjfffPP+/ft79ep1//33f/DBB3v3\n7v3ggw/uv//+s846a//+/ePHj6+EKQEAKFbxH1B85plnPvDAAzfccMMPfvCD5OWZmZkPPPBA\nz549K2w2AABKofiwi6Lo2muv7dmz5+9///vXXnvtq6++ys3N7dGjx3XXXdexY8eKng8AgBKK\nFRYWpnqGo92iRYt69eq1b9++7OzsVM8CAHBIxV9jBwBAWhB2AACBKFHYLViw4MILL2zSpEn1\n6tWzDlDRIwIAUBLFZ9mzzz47ePDggoKCnJyctm3bKjkAgKNT8ZV2++23x2Kxv/zlLyNGjIjF\nYpUwEwAAZVB82K1atWro0KGXXHJJJUwDAECZFX+NXe3atRs3blwJowAAcCSKD7sBAwa89tpr\nlTAKAABHoviw+/Wvf71hw4YJEybk5+dXwkAAAJRN8d88MWrUqE8++WTevHmtWrXq3Llzbm5u\nkQ0effTRipru6OCbJwCAtFB82BV7J2zwX0om7ACAtFD8XbErVqyohDkAADhCxYdd586dK2EO\nAACOUCm+K3b9+vWLFy/evn17xU0DAECZlSjslixZcuqpp7Zu3bpnz55Lly6NL3ziiSc6dOiw\nYMGCihwPAICSKj7sVq9ePWDAgI8++mjw4MHJyy+44IJ169Y9+eSTFTYbAAClUPw1dnfcccf+\n/fuXLVvWtGnTp59+OrG8Tp06ffv2XbhwYUWOBwBASRV/xm7u3LlDhw7t2LHjgatOPvnkDRs2\nVMBUAACUWvFht2XLltatWx90VWZm5s6dO8t5IgAAyqT4sKtXr96XX3550FUrVqxo2rRpeY8E\nAEBZFB92vXr1mjNnzr59+4osnzdv3osvvtinT58KmQsAgFIqPuzGjx//5ZdfDh069J133omi\naO/evUuXLh03btzAgQOzsrJuvvnmih8SAIDiFf9dsVEUTZo0acyYMXl5eckLq1WrNmXKlMsv\nv7zCZjta+K5YACAtFP9xJ1EUXXvttWefffakSZMWL168ZcuWnJycHj16jBkzpn379hU9HwAA\nJVSiM3ZVnDN2AEBaKMV3xQIAcDQTdgAAgSj+GrsTTzzx8Bt88MEH5TQMAABlV3zYbd68uciS\n3bt3x++QrVu3biwWq5C5AAAopeLD7quvviqyZP/+/StWrBg7dmzDhg2feuqpihkMAIDSKcs1\ndtWqVTv99NPnzJmzbNmyO++8s9xnAgCgDMp+80S9evUGDBgwderUcpwGAIAyO6K7YqtXr/7p\np5+W1ygAAByJsofd559//swzzzRv3rwcpwEAoMyKv3ni9ttvL7IkLy/vk08+mTVr1o4dO372\ns59VyFwAAJRS8V8pdqgPNKlZs+b111//q1/9KiMj8E859pViAEBaKP6M3TPPPFNkSUZGRr16\n9Tp27FinTp2KmQoAgFIrPuwuuOCCSpgDAIAjFPi7qAAAVYewAwAIRPFvxbZu3brku1u3bl2Z\nRwEA4EgUH3a7du3Kz89PfGNs7dq1d+/eHX+cm5ubmZlZgdMBAFBixb8Vu27dug4dOnTt2nXO\nnDk7d+7ctWvXzp0758yZ06VLlw4dOqxbt25zkkqYGACAgyo+7G677baNGze++uqr3/72t+Of\nb1KnTp1vf/vbCxcu3Lhx42233VbxQwIAULziw+7JJ5+86KKLatWqVWR5rVq1LrroohkzZlTM\nYAAAlE7xYffll18e6tspCgsLv/zyy/IeCQCAsig+7Fq3bv3UU08lbphI2L1794wZM9q0aVMx\ngwEAUDrFh9211167bt26Xr16zZo1a+vWrVEUbd26ddasWb169Vq/fv3o0aMrfkgAAIpX/Med\n3HjjjatXr548efLQoUOjKMrKysrLy4uvuuaaa37wgx9U7IAAAJRM8WGXkZHxxz/+ccSIEVOn\nTl2xYsX27dtzcnK6dOkyatSoPn36VPyEAACUSPFhF9e3b9++fftW6CgAAByJUnxX7Pr16xcv\nXrx9+/aKmwYAgDIrUdgtWbLk1FNPbd26dc+ePZcuXRpf+MQTT3To0GHBggUVOdvvQu4AABxQ\nSURBVB4AACVVfNitXr16wIABH3300eDBg5OXX3DBBevWrXvyyScrbDYAAEqh+Gvs7rjjjv37\n9y9btqxp06ZPP/10YnmdOnX69u27cOHCihwPAICSKv6M3dy5c4cOHdqxY8cDV5188skbNmyo\ngKkAACi14sNuy5YtrVu3PuiqzMzMnTt3lvNEAACUSfFhV69evUN9IeyKFSuaNm1a3iMBAFAW\nxYddr1695syZs2/fviLL582b9+KLL/qMYgCAo0TxYTd+/Pgvv/xy6NCh77zzThRFe/fuXbp0\n6bhx4wYOHJiVlXXzzTdX/JAAABQvVlhYWOxGkyZNGjNmTOIrYuOqVas2ZcqUyy+/vMJmO1os\nWrSoV69e+/bty87OTvUsAACHVKKvFLv22mvPPvvsSZMmLV68eMuWLTk5OT169BgzZkz79u0r\nej4AAEqo+LBbsmRJjRo1OnfufP/991fCQAAAlE3x19j17NnzjjvuqIRRAAA4EsWHXYMGDWrV\nqlUJowAAcCSKD7s+ffq8/vrr+fn5lTANAABlVnzY3XnnnZs3bx47duyePXsqYSAAAMqm+I87\nGTVq1Mcffzx//vyGDRt27ty5WbNmsVgseYNHH320Agc8Cvi4EwAgLRQfdkUy7kAl+SS8tCbs\nAIC0UPzHnaxYsaIS5gAA4AgdMuyeeOKJNm3anHHGGZ07d67MgQAAKJtD3jwxYsSIP/zhD4mn\nEydOHDhwYKWMBABAWRR/V2zcypUrn3/++QodBQCAI1HSsAMA4Cgn7AAAAiHsAAACIewAAAJx\nyA8ojsVi1apVq1WrVvzpnj179u/fn5OTc+CWX331VQUOeBTwAcUAQFo43AcU79+/f/v27clL\nijwFAODocciw27t3b2XOAQDAETpk2NWoUaMy5wAA4Ai5eQIAIBCHu8buaFNQUDBt2rQFCxZU\nr1590KBBAwYMKLLBxIkTX3zxxX/84x8pGQ8AILXSJuzy8/MHDx48Z86c+NP77rvvoosueuSR\nR+rWrZvYxveeAQBVWdqE3eTJk+fMmXPsscfedNNNdevWffTRR2fOnLl+/fqXXnopNzc31dMB\nAKRe2oTdY489lpWVtWDBgnbt2kVRNHr06AkTJvzsZz8777zzXnzxxeTzdqVSUFDwyiuv5OXl\nHWabt99+u2w7J4qi6LPPoir1C9y6NYqiqH79VM9Ridq3j5o2TfUQlaWq/X2OHN/QVanjWzWk\nTditWrWqV69e8aqLoigjI2PChAmNGjUaM2bMt7/97eeff7527dpl2O369esvvvjiw4ddfO2h\nPsmZYtx22zd/emh3WQ5OWsrZHuVlRVXn5629O8q+7MpoypRUD1JZqtjfZ8c3bFXu+FYNaRN2\n33zzTePGjYssvOGGG77++utbbrll0KBBicvvSqVNmzabNm06/Dbxb56IxWJl2D9Rfv5fL4mu\neCTVY1SW99tGC8+qQj/vI1dEo/LzUz1FJapif58d37BVueNbNaRN2LVs2XLDhg0HLh8/fvyu\nXbsmTJhw0UUX1atXr/IHAwA4SqRN2HXu3Hn27Nnbt28/8Ptqb7/99h07dtxzzz2ZmZkpmQ0A\n4GiQNh9QPHTo0G+++ebxxx8/6Nrf/OY3V199db5TygBAFZY2Z+wGDRp0zz33HHiZXcKkSZPa\ntm27ZcuWypwKAODokTZhd8wxx4wdO/YwG2RkZNxyyy2VNg8AwNEmbd6KBQDg8IQdAEAghB0A\nQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQd\nAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCE\nHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAg\nhB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBA\nIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0A\nQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQd\nAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCE\nHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAg\nhB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBA\nIIQdAEAghB0AQCCEHQBAIIQdAEAgggq78ePHt27dOtVTAACkRlBht3nz5vXr16d6CgCA1Agq\n7AAAqrKsVA9QUsOHDy92m9dee620u922bdutt96al5d3mG2++OKL0u4WqogWG6Lo40XR6NGp\nHqSyLFoU9Uz1DFBOqtz/fz/+OIqi6Ljjymdv55wTjRxZPrsqV2kTdtOmTUv1CEBRrddFazLW\nvBytSfUglWT456meAMpPlfv/76Lo8ybRy+XUdVnRB/8ZCbsjULt27ebNm0+cOPEw29x7771z\n584t1W7r1av3wAMPHH6bRYsWPf3006XaLVQdi3pGox9M9RCVpd+8VE8A5aqq/f+3HH/e0VHb\n/yyfPZWztAm7Tp06vf322+eff34sFjvUNjNmzKjMkQAAjippc/NE165dd+zY8dFHH6V6EACA\no1TanLHr16/fkiVLNmzYcMIJJxxqmwsvvLBFixaVORUAwNEjbcLuoosuuuiii458GwCAUKXN\nW7EAAByesAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMA\nCISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLAD\nAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISw\nAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiE\nsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAI\nhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMA\nCISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLAD\nAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISw\nAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiE\nsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAhEVqoHKLXCwsI1a9asWbNm+/bthYWFubm5\nJ5100kknnRSLxVI9GgBAKqVT2O3du3fixImTJk369NNPi6xq0aLF6NGjx40bV7NmzZTMBgCQ\ncmkTdrt37+7fv/9rr72WkZHRpUuXtm3b5uTkxGKxr776as2aNW+99dZtt902Z86cuXPn1qpV\nK9XDAgCkQNqE3Z133vnaa6+NHDny17/+dbNmzYqs/fTTT2+55ZbHH3/8zjvvvOOOO1IyIQBA\naqXNzRNPPPFEt27dHnvssQOrLoqi5s2b//nPf+7ateu0adMqfzYAgKNBrLCwMNUzlEj16tWv\nu+66e+655zDbjB07dtKkSV9//XXJd7t27dozzjgjLy/vMNvk5eXt3Lnzm2++qVatWsn3zP9z\n1VXf/Omh3bVTPUZlydke5WVFft5QVbWft+6OKDOrelR1rm/Zs+ebwn1V5/hWtb/P5fvz/mtU\np/6/ebN89lWu0uat2JycnLVr1x5+m48++ig3N7dUu23VqtX06dMPH3aFhYWbNm1SdWX0859n\nDx+eneopKs/WrdlRlF2/fqrnqCx+3rBt3RpFUVSVft6qdnz9vGXWv3nzctlPuUubsBswYMC0\nadMee+yxyy+//KAbPProo88+++yIESNKtduMjIw+ffqUw3wcStOmUdOmqR4CAKqEtHkr9sMP\nP+zWrdv27du7dOkycODAdu3a5eTkRFG0ffv2995777nnnnvjjTdyc3OXLVt2wgknpHpYAIAU\nSJuwi6Jo1apVV1555euvv37QtaeffvpDDz3UoUOHSp4KAOAokU5hF/evf/1r3rx577333vbt\n26MoysnJadeuXb9+/bp27Zrq0QAAUin9wg4AgINKm8+xAwDg8IQdAEAghB0AQCCEHQBAIIQd\nAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCEHQBAIIQdAEAghB0AQCCE\nHQBAILJSPUCVc+aZZy5ZsiTVUwBAldOjR4/FixeneoqKJewq2/HHH9+oUaOf/vSnqR6ECjFh\nwoQoihzfUDm+YXN8wzZhwoRjjjkm1VNUOGFX2bKzsxs0aNCtW7dUD0KFaNCgQRRFjm+oHN+w\nOb5hix/f4LnGDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBC+\neaKyZWdnp3oEKpDjGzbHN2yOb9iqyPGNFRYWpnqGqmXbtm1RFNWrVy/Vg1AhHN+wOb5hc3zD\nVkWOr7ADAAiEa+wAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAAC\nIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewqz4cffjhy5MgmTZrUqFGjbdu2t956\n6549e1I9FKU2c+bMMWPG9OrVq06dOrFYbPjw4Yfa0hFPO7t27Zo2bdqIESNOOeWUWrVq5eTk\nnHXWWVOmTCkoKDhwY8c37eTn5//sZz/71re+1apVq1q1atWvX79Lly4TJkzYunXrgRs7vunu\nmWeeicVisVjs1ltvPXBtyMe3kEqxcuXK3NzcWCw2aNCgG2+8sWvXrlEU9ejRY8+ePakejdLp\n1q1bFEV169Y96aSToigaNmzYQTdzxNPRPffcE0VRdnZ2jx49/vf//t/nnHNOVlZWFEUXXnhh\nfn5+8paObzrau3dvFEVNmjQ555xzvvvd7w4cOLBRo0ZRFDVr1mzdunXJWzq+6W7Tpk3HHnts\nnTp1oij68Y9/XGRt2MdX2FWS008/PYqiRx55JP40Pz9/xIgRURT9/Oc/T+lclNr8+fPff//9\ngoKCZ5555jBh54inoxkzZvz+97//6quvEkvefvvtxo0bR1H017/+NXlLxzcdFRQUFAm4ffv2\njRw5Moqiq6++Onm545vuhgwZ0rRp09tuu+2gYRf28RV2lWH58uVRFHXu3Dl54YYNGzIyMlq0\naFFQUJCqwTgShwk7Rzwkv/jFL6IoGj16dGKJ4xuSl19+OYqiPn36JJY4vunu4YcfjqLo2Wef\njZ+DLxJ2wR9f19hVhnnz5kVR9K1vfSt5YfPmzTt16rRhw4Y1a9akaC4qiiMekpycnCiKqlev\nnlji+IbkqaeeiqLo1FNPTSxxfNPaunXrbrzxxiuuuOL8888/6AbBH19hVxnee++9KIratWtX\nZHn8Iq0A/hpRhCMejMLCwsceeyyKokGDBiUWOr7pbuzYsddee+2IESPatm17//33d+rU6cc/\n/nFireObvgoKCr73ve/l5ubGz9UdVPDHNyvVA1QJ27dvj/793/3JcnNzoyj66quvUjATFckR\nD8aECROWLFly0UUXDRgwILHQ8U13U6ZM2b17d/zxwIEDH3300fhdFHGOb/qaOHHiK6+88sIL\nLxx4+BKCP77O2KVSYWFhFEWxWCzVg1BJHPH08rvf/W7ChAldu3Z95JFHSrK945sudu3aVVBQ\n8Nlnnz3xxBOrV6/u3Lnzv/71r2L/lON7lFu5cuVtt9127bXX/sd//EcZ/ngwx1fYVYb4fxnE\n/ysh2aH+u4F054gHYOLEiWPGjOnWrdtLL71Ut27d5FWObwBisViTJk2GDRs2Z86czz///Ior\nrkiscnzTUWFh4WWXXdasWbO77rrr8FsGf3yFXWWIv5cff18/2fvvvx/9+319QuKIp7vbb799\n/PjxZ5555ty5c+vVq1dkreMbkvbt2zdt2vStt97atm1bfInjm47y8/PffPPNtWvXHnPMMbF/\nu+mmm6Io+u///u9YLHbVVVfFtwz++Aq7ytCvX78oiv7xj38kL9y4ceObb77ZvHnzAP4aUYQj\nntZuvvnmCRMm9OnT51BX6ji+Idm5c+emTZuiKIp/GHXk+KanjIyMKw/Qo0ePKIo6d+585ZVX\nnn322fEtwz++KfyolSol/nGIU6dOjT/Nz8+PfypmGB+HWDWV5AOKHfH0kp+ff/XVV0dRdN55\n5x3+M+gd33S0ePHiN954I3nJ5s2bhwwZEkXROeeck7zc8Q3DQT/HrjD04xsrLCxMRU9WOatW\nrTrrrLN27tw5aNCgNm3avPrqq8uXLz/jjDPmz59fs2bNVE9HKcycOXP27NlRFG3YsGHu3Lmt\nW7fu3bt3FEUNGza8++67E5s54unorrvu+j//5/9kZGQMGzYsOzs7eVXHjh3HjRuXeOr4pqNf\n/vKXP/zhD48//vg2bdrUq1fv888/X758+d69e5s2bTpv3ryTTz45saXjG4Z77733pptu+vGP\nf3zHHXckLw/8+Ka6LKuQDz74YMSIEY0aNcrOzj7++ON/9KMf7dq1K9VDUWrJn3eVrFWrVkW2\ndMTTzv/9v//3UP9UnnfeeUU2dnzTzjvvvDNu3Lhu3bo1bNgwMzMzJyfn9NNPv/3227du3Xrg\nxo5vAA51xq4w6OPrjB0AQCDcPAEAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHULwNGzbEYrEhQ4aU9g++8cYbsVhs1KhRFTAUQFHCDgjB8uXL\nY7FYjx49iiz/61//GovFYrHY2rVrk5fv3bu3Ro0atWrV2rdvXyWOeUgffPBBLBYbPnx4qgcB\n0puwA0LQpUuXevXqLVu2bMeOHcnL582bF4vF4g+Sl//zn//ct2/fWWedVb169ZLsv3Hjxq++\n+uovfvGLcpwZoNwJOyAEGRkZffr0yc/PX7BgQfLyefPm9enTp0GDBkXCLv60f//+Jdx/dnb2\nWWeddcopp5TXwAAVQdgBgYhXWnLArVu3bu3atf379+/du/f8+fOTNz4w7BYvXvyd73ynSZMm\n2dnZzZo1u/TSS999993E2oNeY5efnz9x4sSTTz65Ro0aLVu2HDt27K5duxo2bNi6desDx/vk\nk08uueSShg0b1qxZs3v37n//+98Tq375y1+2bds2iqJp06bF/u3Pf/7zkf0+gKooK9UDAJSP\nfv36RVE0d+7cxJL44379+uXk5MycOfOdd975X//rf0VRtGPHjmXLluXm5nbt2jW+5eTJk6+9\n9toGDRpccMEFjRs3Xrt27ZNPPjlr1qy5c+eeccYZh3rFa6655uGHH27duvUNN9yQkZExc+bM\n5cuX5+fnH7jlJ5980r179+bNm1988cWbNm2aNWvWoEGDXn755bPPPjuKokGDBlWrVm38+PE9\nevS4/vrr43+kV69e5farAaqOQoBQNGvWLBaLbdq0Kf70kksuqVOnzv79+1etWhVF0f333x9f\nPnv27CiKhg4dGn/6zjvvVKtW7bzzztuzZ09iV2+++WadOnU6deoUf/rJJ59EUTR48ODEBi+9\n9FIURaeeeuquXbviS/bs2XPaaadFUdSqVavEZitWrIj/Y3vrrbcWFBTEF/7pT3+KomjQoEGJ\nzd5///0oioYNG1auvw+gyvFWLBCOvn37FhYWJt51nT9//tlnn52VldW+ffvGjRsn3qUt8j7s\n73//+/379//oRz/avXv35n9r1qxZ//7933rrrfXr1x/0tR577LEoiiZMmFC7du34kpo1a95x\nxx0H3fi444776U9/Gr+NI4qikSNH5uTkvP766+XzYwP8m7ADwpF8md3q1as/++yzvn37xlf1\n6dPn5ZdfLigoSGwwYMCA+KrFixdHUdS7d+9G/9PTTz8dRdFnn3120NeKn4qLv5eacNZZZx10\n4y5dumRl/f+XvsRisRYtWmzbtu1IfliAA7nGDghHPOzil9YlLrCLr+rTp8/06dNXrFjRqlWr\nlStXNm/evF27dvFVW7ZsiaJo9uzZNWvWPHCfh7oTdseOHVlZWfXr109eWLt27cQJvGS5ublF\nlmRlZR30ajyAIyHsgHAcd9xxJ5xwwgcffPDJJ5/MmzcvNze3S5cu8VXxU3fz5s2LXwCXfD9s\nTk5OFEVNmjTp3r17yV+rbt2669ev37p1a3Lb7d69e/fu3Q0bNiyfnweglLwVCwQlXmwvvfTS\nggULevfunZHx//6VO/nkk5s2bTpv3rwDP+gk/n0VTzzxRKleqHPnzlEULVy4MHlhkacll5mZ\nGUWRc3jAERJ2QFDi773ec889W7duTVxgF9enT59XX331hRdeiP5n2N1www1ZWVn3339/kQ8x\n3rVr17Rp0w71QpdffnkURbfffvuePXviS77++uuf/OQnZRu7QYMGURR9/PHHZfvjAHHeigWC\n0q9fv1gstnLlyijpAru4vn37Pv7442vXrm3Xrl3z5s0Tyzt06PDggw+OHj16wIAB5557bpcu\nXfLz899999158+a1bt162LBhB32hAQMGfO9735s6dWqHDh2+853vxGKxv/3tb02aNMnNzU2c\nJiy5unXrnnHGGa+99tqIESNOPvnkzMzMIUOGdOjQobT7Aao4YQcEpVGjRh07dnzrrbcaNmxY\nJIwSJ/AO/Cax//zP/+zatetvfvObl19+ef78+bVr127WrNlll112qKqLe+ihh9q3bz958uT7\n7ruvUaNG3/nOd26//fbGjRu3atWqDJP/+c9/vummm55//vlp06YVFha2bt1a2AGlFSssLEz1\nDACBePPNNzt37jx8+PDHH3881bMAVZFr7ADKaPPmzclP9+zZc8stt0RRNHTo0BRNBFR1ztgB\nlNENN9zw8ssv9+nTp0mTJhs3bvz73/++fv36b33rW3PmzEl8yQRAZXKNHUAZDRw4cM2aNTNm\nzNi2bVtWVla7du1uuOGGG2+8UdUBqeKMHQBAIFxjBwAQCGEHABAIYQcAEAhhBwAQCGEHABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh9/+1WwcyAAAAAIP8re/xFUUAABNiBwAwIXYA\nABNiBwAwIXYAABMBO9UHiaOZ9ZsAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Histogram of v”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create data for the graph.\n",
    "v <- c(9,13,21,8,36,22,12,41,31,33,19)\n",
    "\n",
    "\n",
    "\n",
    "# Create the histogram.\n",
    "hist(v,xlab = \"Weight\",col = \"green\",border = \"red\", xlim = c(0,40), ylim = c(0,5),\n",
    "   breaks = 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Graphs\n",
    "\n",
    "A line chart is a graph that connects a series of points by drawing line segments between them. These points are ordered in one of their coordinate (usually the x-coordinate) value. Line charts are usually used in identifying the trends in data.\n",
    "\n",
    "The plot() function in R is used to create the line graph.\n",
    "\n",
    "The basic syntax to create a line chart in R is −\n",
    "\n",
    "    plot(v,type,col,xlab,ylab)\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    v is a vector containing the numeric values.\n",
    "\n",
    "    type takes the value \"p\" to draw only the points, \"l\" to draw only the lines and \"o\" to draw both points and lines.\n",
    "\n",
    "    xlab is the label for x axis.\n",
    "\n",
    "    ylab is the label for y axis.\n",
    "\n",
    "    main is the Title of the chart.\n",
    "\n",
    "    col is used to give colors to both the points and lines.\n",
    "\n",
    "\n",
    "Example\n",
    "\n",
    "A simple line chart is created using the input vector and the type parameter as \"O\". The below script will create and save a line chart in the current R working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bEF2o6lM+0dhiBpJa+ptm/sKunfdmyb2MyzMIKkk/XRy7hLMKR/27G5LZgWRpA0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\n9B2FgQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data for the chart.\n",
    "v <- c(7,12,28,3,41)\n",
    "\n",
    "\n",
    "\n",
    "# Plot the bar chart. \n",
    "plot(v,type = \"o\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart Title, Color and Labels\n",
    "\n",
    "The features of the line chart can be expanded by using additional parameters. We add color to the points and lines, give a title to the chart and add labels to the axes.\n",
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x47duzo0aMF7bB79+7vv/8+76w7AABgBncSM8Q2xe6VV165ePFi7dq1C9ohLS1t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\nhM2bdfq0eIg5XMhjil3Dhg2//PLL7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x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\nFDsAAACLoNgBAABYxP8HR5wM/7ZdkT0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Rain fall chart”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the data for the chart.\n",
    "v <- c(7,12,28,3,41)\n",
    "\n",
    "\n",
    "\n",
    "# Plot the bar chart.\n",
    "plot(v,type = \"o\", col = \"red\", xlab = \"Month\", ylab = \"Rain fall\",\n",
    "   main = \"Rain fall chart\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplots\n",
    "\n",
    "Scatterplots show many points plotted in the Cartesian plane. Each point represents the values of two variables. One variable is chosen in the horizontal axis and another in the vertical axis.\n",
    "\n",
    "The simple scatterplot is created using the plot() function.\n",
    "\n",
    "The basic syntax for creating scatterplot in R is −\n",
    "\n",
    "    plot(x, y, main, xlab, ylab, xlim, ylim, axes)\n",
    "\n",
    "Following is the description of the parameters used −\n",
    "\n",
    "    x is the data set whose values are the horizontal coordinates.\n",
    "\n",
    "    y is the data set whose values are the vertical coordinates.\n",
    "\n",
    "    main is the tile of the graph.\n",
    "\n",
    "    xlab is the label in the horizontal axis.\n",
    "\n",
    "    ylab is the label in the vertical axis.\n",
    "\n",
    "    xlim is the limits of the values of x used for plotting.\n",
    "\n",
    "    ylim is the limits of the values of y used for plotting.\n",
    "\n",
    "    axes indicates whether both axes should be drawn on the plot.\n",
    "    \n",
    "Example\n",
    "\n",
    "We use the data set \"mtcars\" available in the R environment to create a basic scatterplot. Let's use the columns \"wt\" and \"mpg\" in mtcars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     wt  mpg\n",
      "Mazda RX4         2.620 21.0\n",
      "Mazda RX4 Wag     2.875 21.0\n",
      "Datsun 710        2.320 22.8\n",
      "Hornet 4 Drive    3.215 21.4\n",
      "Hornet Sportabout 3.440 18.7\n",
      "Valiant           3.460 18.1\n"
     ]
    }
   ],
   "source": [
    "input <- mtcars[,c('wt','mpg')]\n",
    "print(head(input))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Scatterplot\n",
    "\n",
    "The below script will create a scatterplot graph for the relation between wt(weight) and mpg(miles per gallon)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJ\nEEBIgABCAgR4ENJHPXKrtp64J9ouhISAMRpS3gjrw3NpynZOUZQdCQkBYzQkNVjr9VVTb1mx\n8ZU6anqUHQkJAWM8pEfVNfbwE9U1yo6EhIAxHtJV6mtn3KZWlB0JCQFjPKRLVL4z7p0RZUdC\nQsAYD+kutcYZdz40yo6EhIAxG1JqVlaGmuWMG7WNsiMhIWCMhtTCcbc9XKiuiLIjISFgvHpn\nw7wJn0X5LCEhYHz0FqHFC0rcREgIFv+EtDxFhdkalzmAOPFPSHrrxhL38xUJwWI2pMLnho2Y\n6Q4ndouyH8+REDBGQyroaT9q673FHg+OdhRCQsAYDekRlXfvlPaq3SZNSEgsRkPqmL7Uenj3\nZ9V+CyEhsRgNqdopzmayOnE7ISGhGA0pq4+7naA65xMSEonRkJp1DA3Gqe79CAkJxGhIF2Ru\nDo2uVWmEhARiNKRn1SPFw6GKkJBAjIa0ddLLxcPC8WOi7EhICBgfvUUoDCEhYAgJEEBIgABC\nAgQQEiCAkAABhBRPRW+O6nX549u9Xgbij5DiaFu3rF7XXZjXeJHXC0HcEVIcXdh8ufVxR5/D\nE+JsEA0hxc9SNd/Z5td/0OOVIO4IKX4eaxwaXNHb03XAAEKKn/HtQ4Nbu3i6DhhASPEz/bBC\ndzBgoLcLQfwRUvz8VuV5Z/tT1Rc8XgnijpDi6I6qz1lfkxYe1anQ65Ug3ggpjorurFLj+Prq\ngk1eLwRxR0hxtfa1CdOXer0IGEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQ\nEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQ\nEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQIhLRqzmahxZQgJARMzCHNbaXUTK3/efQHYmsi\nJAROrCEtyal6jh3Stpyr5RZFSAiaWEPqn/nVOjsk3au12JoICYETa0h5f9RuSKNzxdZESAic\nWENKHxMKaUym2JoICYETa0i1Lg2FdEZDqSVpQkLgxBrSuXm/OyG9lzJYbE2EhMCJNaSPU3t8\nqGbMuy4j40u5RRESgibm7yM9kq5sGc+ILUkTEgIn9nc2LB7erlHrYYulFuQgJAQM77UDBBAS\nIICQAAGxhtSwWOPWF75YJLUqQkLAxBpSbg2lVI71T410pXrtEVoVISFgYg1p60nt39mut79z\n3Embvuiq7hNaVcBDmjNp9KPfeL0IGBVrSCOa7XS2+U1H6h0N2gmtKtAhrT09rU2v5inDdnu9\nEBgUa0j1rg8Nrq+v9RVVRdYU7JAKTjh2mbX5sPZVXq8EBsUaUubo0GBUlta3Z4msKdghvVj1\nV2c7O3W5xyuBQbGGdETjHc52e6MW1lekekKrCnJIl/4xNGgyxdN1wKhYQxqv2r6+QW94rbWa\nqPUfugutKsgh9Sr+Gn3qbZ6uA0bFGlLBYKWU/b7VSwv0uqvfFFpVkEMaPCA0aD7Z03XAqNjf\n2TBzYMuGLQfNklqQI8ghTauxwdl+msIr4EmEtwhJ292y02pr82WjgV6vBAYRkrifjsvuMuiE\n1D75Xi8EBsUe0obXp0xySC1JBzwkXfjmrZfeM8frVcComEO6u4oKEVtT0ENCEoo1pH+q4+9S\no+7sovpMk1sUISFoYg3ppLz81eptraenvSe3KEJC0MQaUrXL9Br1ljXo1UVsTYSEwIk1pKyx\neqN6zhrcUl1sTYSEwIn5b8gO1UVVx1qDiwgJSSzWkM4+0XpUd8isbS9nniy3KEJC0MQa0mMp\nP+n59ivgabPF1kRICByRdzYsGHDiwHkiywkhJAQMbxECBBASIICQAAGxhHROWYKrIiQETCwh\nqbIEV0VICJhYQvqpLMFVERIChudIgABCAgQQEiAgtlftlmhetQN0rK/a/Vfzqh2gY33Vbpfm\nVTtA8xwJEEFIgICYQnqjDMFVERICJqaQeIsQ4IotpPQzB5cSXBUhIWBiCukolXH+24Wi63ER\nEgImthcbPr44RzUY94PkghyEhICJ9VW7LY+0U6ndXtwltyIbISFgBF7+/vyqGqrWp0LrcRES\nAkbk+0jv11eviqymGCEhYGIPad39R6uM81ZILchBSAiYGEMqerdvpmr+198EV2QjJARMTCH9\ndHsjlX3RB6ILchASAiamkFJVm8mbRJcTQkgImBjf2dAwjOCqCAkBw3vtAAExhbSzDMFVERIC\nhr+PBAgIREjvXdS21YWSf98JEBaEkEan9500eWDWpfF4nzkgIgAhTc+ebW8WVH8w7vMClRSA\nkI4d424nNCySm2GH3KH2xx7ht8fDb/wf0q6Uj9zB12q10OF/urShqt75P0JH26eCSa0zM466\n63dT88ED/g9pi/rMHfygvpc5+uJDOzw997VhaRNkDrcvu3vk3jlr9oQ6Jxr+KgiT/B+Szp3m\nbv9dReZbVYVtehfY2xfTvhA53r6Mr+X8D2B1wzFGpoMnAhDSla2dgPZ06iNz8LmpP7uDLsNl\nDrgPzUJf+Z46tMDIfPBCAEL6reHJn+wu+LzHYStlDv74EaHBbafIHDC6fPWJO1imJH+qM/wl\nACHpH3ulZGapzt9JHbxFaHD7yUJHjGq7mucOVqhVJuaDJ4IQktZr33v3V7GDf5S+zh30vFzs\nmNE0mOxu/1l9t5H54IVghCSqoPklznZm6pz4TRJmXP019mbzkVcbmQ6eSMKQ9NycM9/6fu6t\nVQy9irajY4PHv1ryTPNj4vJ3IOEPyRiSXnLWQSr1mOnxnCJc/k11lKo1coup+eCBpAxJ68If\n8+M7wV7WS/94GPhMkoYEyCIkQAAhAQIICRCQSCEVvX5ll75/XRvDvF+OOfPMMV/GcAAkqwQK\nKb9nlfP/fEXz3PcrPe09aZ2uv75T2j2VPgCSVgKFNKTxMutjwciDf6nkrP/KfMXevJL5UiUP\ngOSVOCH9mjrL2Ra2GlvJWduOdrejj63kAZC8Eiekl2qGfqTDuEq+qXubmusO5qRsr9wRkLwS\nJ6SnGocGD7au3KS/qm/dwVIl91ZzJInECendKqGfiTC8Z+Um3V3lLXfwZhX+vgMOUOKEtDN3\norP97ZC/V3LW889wHhwWdj2/kgdA8kqckPTUjPt2aj2/ZfvKfj1ZWr3/aq1X96/+bSUPgOSV\nQCHppw/J+MMhKRdsqPS0C49RjRqpYxZW+gBIWokUkt4x+5GXYvqt0IULp05dwI8Yx4FLqJAA\nrxASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEIC\nBBASIICQAAGEBAggJECA6ZCKls6Y9o8ZS4ui70VICBizIeXfUU856t8R9beKExICxmhI2zuo\n1LZ9h17et02qOmFHlB0JCQFjNKSb1IDQLwH7uZ+6OcqOhISAMRpSk3YlP8W08NhmUXYkJASM\n0ZAyrykdj8yKsiMhIWCMhlTrnNLxWXlRdiQkBIzRkPqlPlM8fDqlf5QdCQkBYzSk5dVV27FT\nX3116tg2qsbyKDsSEgLG7PeRFrVXIe0XRduPkBAwpt/ZsHDCkD59hkyI9Lu8tm4scT8hIVj8\n81675SkqzNa4zAHEiX9C0osXlLiJr0gIFh+FFIbnSAgYsyEVPjdsxEx3OLFblP0ICQFjNKSC\nnvbTn95b7PHgaEchJASM0ZAeUXn3Tmmv2m3ShITEYjSkjulLrYd3f1btt3gb0u6ofxkKOHBG\nQ6p2irOZrE7c7l1IhVNaZ6Y0uZ7X1yHJaEhZfdztBNU536uQCvtWv/29Tx9udtT6eM2AZGQ0\npGYdQ4Nxqns/j0J6stpie7Ol1eB4zYBkZDSkCzI3h0bXqjSPQupwo7t9K4sHd5BjNKRn1SPF\nw6HKo5By3nS3W1Sk9/sBlWM0pK2TXi4eFo4fE2XHOIZ00FuhtagF8ZoCSSjp3iJ03K3u9t2S\nh5lA7JIupCk1ltmbHcf1i9cMSEZJF9KeXofeP3/RP45pujpeMyAZJV1Ies/4pimq1pUb4jYB\nklHyhWTZtjaeR0cySsqQAGmEBAggJEAAIQECCAkQQEiAAEICBBASIICQ/GzdDe0ObnHRl3Gf\nZ0G/Iw4+/qZNcZ8ngRGSj31X76jxMx7tmfnPOM/zdPp5j824t3mjlXGeJ5ERkn8Vtuv5u72d\nkL0qrvN8mzHF3uSfdlJcp0lshORfH6f97GyL2twa13mu+z93uzzl87jOk9AIyb/+dkxoMKpn\nXOfpXNxp4yfjOk9CIyT/uq9daHBT17jOc+KdocGRj0TdD1EQkn/NqBr6ibA9rorrPBdf4G43\nZ86K6zwJjZD8Kz/vFmf7UeqB/zc6EDPT5znb6xrsjus8CY2QfOz19GGf5S+fVO1PcZ7nshoP\nf79jwcUZ78Z5nkRGSH42+1ilVO3JRXGepnDiYfbvx47v170ER0j+tunTH43Ms2oeP50sJoQE\nCCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYTkZ/l/O++o08f+7PUysG+E5GOrW+b96aGb2lR/\nz+uFYJ8Iyce6dNxofSy8tga/PcP3CMm/FqZ862wLmt/t8UqwT4TkXw+3CA1GnO3pOrAfCMm/\nJhwXGtxymqfrwH4gJP/6V43Q31jtPcTbhWDfCMm/tlR/0NkuynzH45VgnwjJx57MuHOdzn+p\nTl+vF4J9IiQ/e66uyk2rcsMur9eBfSIkX9v95ctztnq9COwHQgIEEBIggJAAAYQECCAkQAAh\nAQIICRBASIAAQgIEEBIggJAAAYQECCAk3/v9/tPrtR60wOtlICpC8ruN7fJumPbg2emPer0Q\nRENIfvfHluvszdNpn3m9EkRBSD73S+p/3UGPS71dCKIiJJ+bUTX0q5gfaOXtQhAVIfncv2qF\nBo8393QdiI6QfO6zlF/cwfAe3i4EURGSzxUdebmzXVHtGY9XgmgIye8+qjJ40Z4NL9TvVuj1\nShAFIfne3HYqXWWP3un1OhANIQXAmg++5Efb+Rwh+dqGfz/w4vdeLwL7gZD8bHxOTqtaKf23\neL0O7BMh+di9OVMLtP6kRZcir1eCfSEk/1qbPd3Zrsp5yeOVYJ8Iyb+erRV6xbvfIG8Xgn0j\nJP8af3xocGsXT9eB/UBI/vVY09Dgyt6ergP7gZD86xu10NnuPPwBj1eCfSIkH7vgD6usjzv7\n1eX1b98jJB/b0iW799jBdQ//3OuFYJ8Iyc8KXx3e7eKH+JV9AUBIgABCAgQQEiCAkAABhAQI\nICRAACEBAggJEEBISaJo+YyZa7xeRAIjpOQwv62qnpVyHinFCyElhc+qXvSd3jP3+CN5+2uc\nEFJSOLmP82Mftja92euVJCpCSga/prh/s0lPauHtQhIXISWDT1To57S+k+XtQhIXISWDr9Ra\nd/Cvmt4uJHERUjLYXfMpdzCY3w0TJ4SUFMYdttjevJA2y+uVJCpCSgp7+mYPuv/u7un3eb2Q\nhEVISeKV/m06Dlvg9SoSFyEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABC\nAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIICRAACEhBoVPnFGv8dkz\nvF6GDxASKm/XmdWvefapIZnDvV6I9wgJlXdLneX25uPs6V6vxHOEhErbk/ukOxjdwduF+AAh\nodKWqR/dwbsZRd6uxHuEhEpbXPyLAD9K3e3tSrxHSKi0rZkz3cHkxt4uxAcICZV3fifnK9Gm\nxjd7vRLPERIqb2Xtzh9u2/jG0a22er0SzxESYrDqrBSlMi7d6PU6vEdIiMm2eV/87vUa/ICQ\nAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCClOvhvcNK3JgCVeLwOG\nEFJ8zM7p/Pf3nzwj+x2vFwIzCCkuttUe4fw4kDG5/FWd5EBIcfGP3J3OdnfdxzxeCcwgpLgY\n1Ss06HOlp+uAKYQUFyPOCw36D/V0HTCFkOJiSsNCZ1vUYqLHK4EZhBQXa6pOdrZPVPnR45XA\nDEKKj6fSRsxbO39U+sNeLwRmEFKcvN02RalWr3u9DBhCSHGzbRE/NjF5EFLg7P73veNe2OT1\nKlAWIQXN/KYHte9yyMHTvF4HyiCkgFlVc+Bmrffcn84vbvUVQgqYof/nfofq+iM9XgjKIKSA\nqf+Eu/1WrfB2ISiDkAIm4113+7ua4+1CUAYhBUzt0KsMq9S33i4EZRBSwAzo5m7vapD0v//Y\nVwgpYL7OHrPH2ryYNdXrlSAcIQXNO7l1zx9wdPpfvV4HyiCkwNn0+PDL7vve61WgLELytaJ4\nvV1vK8+wZBGSj/3n1Goqr7/8F59Vg+qonJPfFD9uMiMk/3ogbdibC5879eD5wsf98pATpy/4\n9/D0e4SPm9Q8COmjHrlVW0/cE20XQrJ8nf6svSka1CLqv6sDVtiqj/Muo1dSPxM9bnIzGlLe\nCOvDc2nKdk60B+mEZBl9ortdn/Ge6HHnpP3qDk6/WvS4yc1oSGqwdVlUTb1lxcZX6qjpUXYk\nJEv3MaFB6wdEj/v4EaHBbaeIHje5GQ/pUXWNPfxEdY2yIyFZut0YGrSZJHrcx1qEBrefLHrc\n5GY8pKvU1864Ta0oOxKS5dpT3e3mrP+IHve/6b+5gx5XiB43uRkP6RKV74x7Z0TZkZAsX6a9\n4myHNdlV8U4Fnz4x/YsDO27BHwY5z0/fTv200mvD3oyHdJda44w7HxplR0Ky3ZUxeva3M3oe\n9HHFu3zUNLVJfdX+mwM67l7X4DQAAAj9SURBVLxqZ7y29MMbM2+JcX0IYzak1KysDDXLGTdq\nG2VHQnK8clyGqnbW1xXvMD/7yvVarzqr9i8HdNxvzztYpbd5PsbVIZzRkFo47raHC1W0B+iE\nFLL756hv5Tmln7vX8cMO9MA/R3m4iErw6p0N8yZE+24gIe2X9amfuIOped4uBD56i9CKWjVL\nHKS2x2WOBPOV2uAOPlZ8gfGYf0IqnD2zxANcGPtjlVrmDmZke7sQ+CikcP8jpP1R1CD01/su\nPsPbhcC7kEY1jPJJQto/j+Q4b8N7Kv19r1eS9DwLaXC0oxDSfro+teuN13XIfNTrdYCQgu3T\nUWecfTM/mMt7RkP6Y5hGhIQEYvadDWVE2ZGQEDBGQ8pp/kaJ0wgJCcRoSB0PLn3DC8+RkEiM\nhnS1Wl4yJiQkEqMhvdzug9LxzVF2JCQEDO9sAAQQEiCAkAABhAQIICRAACEBAggJEEBIgABC\nAgQQEiCAkIJt+V8HD/3bb16vAj4Nab4CAubAf7Fi/EPSXyyoQPdO0zzVifmTe/7uFV2ZB/ir\nDGwGQqrQxRd7ODnzM7/k/ITE/MwvgJCYn/kFEBLzM78AQmJ+5hdASMzP/AIIifmZXwAhMT/z\nCyAk5md+AYTE/MwvwMuQLr/cw8mZn/kl5/cypI0bPZyc+Zlfcn4vQwISBiEBAggJEEBIgABC\nAgQQEiCAkAABhAQIICRAACEBAggJEEBIgABCAgQQEiCAkAABhAQIMBvStucvPDL74BP/Xhh2\nXwv35//nmZi/4C/dG2TXbHPbhvA7l/fPy2p28w6v5jd5/o4ZSt0cftvg+Uea3+j5R5pM5vzN\nhjRJZZ7Qp1O6OjuspBapg20jTMy/U9XudEH3WqruqtL7FtVIOWvkseqEfI/mN3n+trV5Vctc\nyCbPP9L8Rs8/wmRC5282pJembLY+fn2Yeq70vhZZ5uYvci7gXQPU0NL72quntS7sp+7waH6T\n5287t86tZS5kk+cfaX6j5x9hMqHz9+Q50j1qWOkN0xeS5QN1asl4oWpjb35OrV/kyfymz/8p\n9eak8AvZ9PnvPb/XIUmdvychTVFhX1tbZNx16dWPbah4b3l/UiNLxhPUWGfbRi31ZH7D57+y\n2iW6zIVs+PzLzW/2/MtPJnX+XoRUdIKaWXrLff5X9bmK9xc1ctiFzVSrtSW3h6ipzravmuHJ\n/GbPv7DT4ZvLXshmz7/8/GbPv/xkUufvRUjjVO+wW3fPXJ2/eHhq2kdmJs+x/kV2X1N6u496\n1dlerv7hyfxmz3+8eleXvZDNnn/5+c2ef/nJpM7fg5Amq2O3lLvzZtXD0PRFq59vWHthyc3i\nf5FD1TRP5g8xc/5fZV2hKwjJyPlHmD/E3H//vSaTOn/zIU1U7SL8YL4VKtfcEharViVj0w/t\n9p4/xMj5F7VuvE1r7x7aRZo/xOh///DJAvvQbpzquDnC3RtVVYOLqKNKWi5+stnW4IsN4fOH\nGDn/ParEZcX3mTz/SPOHGP3vHz6Z1PmbDuladeq2SPe/qlqbW8TWNLW1eLxQtbU3v6TWM/fy\nd/j8IUbOv/AyxwmqzWVTi+8zef6R5g8x+t8/fDKp8zcbUuFQ1a30O8hPT/pN63lf2sP5ddVE\nA/PP/cL+uP5c1alkft1ePWMtbICRb0hGmN/o+buKH1qZP/8I8xs9/7KTiZ6/2ZDGq9R+zns0\nnBNpqubbX1qbnt67bYo6e7eB+e9RTU674KRsVeebkvn1ouqp51zTTnUw8RaZCPMbPX9X8YVs\n/vwjzG/0/MtOJnr+ZkMaU/wQuZt9yzmRz4a2PCT90K7TjDywWjKq3aFp1dvf5j5Dcf9F6uX9\namU2uWm7R/MbPX9X2ZBMnn+E+Y2ef9nJRM+fv0YBCCAkQAAhAQIICRBASIAAQgIEEBIggJAA\nAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAA\nAYQECCAkQAAhAQIICRBASIAAQgIEEBIggJAAAYQECCCkoPpJnbP3XZ+rwR4sBDZC8qsFqoOz\nfVapFfY2Pyv79/DP7yukZeqP8VweyiIkvyqsmbbF3l6Wop6wtzNV1zKf3/XfJXv/EULyDiH5\n1nlqhr1p3Dm3v70dq+7d158gJO8Qkm89pK6xPq5Ud/auY9/s4PwO7jm98zLqDPhGlzy0K5jY\nIqv+yG25DbUT0o/9cqsc95Y1vsf99fHTvFp9siEk31qiWlofn1BzJquvtd6SVqNQ68dTa10y\npm9mziclIV2qGo26vulJNRpqO6QuecdeeX5a6kdaL56oTpg2bdoKT08hiRCSf9VNWat1/6p7\nFqvJWs9Q51ltZXTLtz7xZdVWxSHNUq23a51/nGqo7ZDULUVaT1NnaR7aGUZI/jVAvaB1nR5a\nH2Y1dI16SOvh6sN1tnPUqlBIg9Rr9q7vhEJqsMfaFFXP04RkGCH511NqmPX4brzWfWsW6lZq\nqdbtVLG5oZBaqg32rttDIbmviB+dqQnJMELyrx9UMz1ZLdB6ilqwLqWedU8jNWOma3MopIbp\n7r45DXXpq3at0zQhGUZIPtZU/Xie/RLDN2r8C2qQdUdrNa/kkxG/Ig12PkVI5hGSj12unjrE\nebBWp/sw9Yy1HaauK/mkG9JA9bp9453yIa1QF5hdbXIjJB97XrVUD9iDfjmN1c/WdlF6xnv2\n7W3PF4c0U7XdofXO9uVD2qLae7LoJEVIPrY2Ramv7MHjSrVw7nkyPaXbjdeflXN0yfeRBqvG\no69vdlKNxnqvkHQHdeFtdyzyYt3JiJD8rJU6tMjeLlPqKveezwcenlnz6Ctml76zYfwRmfVG\nbExvrfcOaVmvmim8s8EUQkoIX6gLvV5CkiOkgFtnf9jR1f7eLTxESAF39dFX33FlQ9WjyOuF\nJDlCCrg3uuZlHtR2wm6v15HsCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEIC\nBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEICBBASIICQAAGEBAggJEAAIQECCAkQQEiAAEIC\nBPw/POz8ER+LG3cAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “Weight vs Milage”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the input values.\n",
    "input <- mtcars[,c('wt','mpg')]\n",
    "\n",
    "\n",
    "\n",
    "# Plot the chart for cars with weight between 2.5 to 5 and mileage between 15 and 30.\n",
    "plot(x = input$wt,y = input$mpg,\n",
    "   xlab = \"Weight\",\n",
    "   ylab = \"Milage\",\n",
    "   xlim = c(2.5,5),\n",
    "   ylim = c(15,30),\t\t \n",
    "   main = \"Weight vs Milage\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEG analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(DESeq2)\n",
    "library(ggplot2)\n",
    "library(htmltools)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>ensgene</th><th scope=col>SRR1039508</th><th scope=col>SRR1039509</th><th scope=col>SRR1039512</th><th scope=col>SRR1039513</th><th scope=col>SRR1039516</th><th scope=col>SRR1039517</th><th scope=col>SRR1039520</th><th scope=col>SRR1039521</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ENSG00000000003</td><td>723</td><td>486</td><td>904</td><td>445</td><td>1170</td><td>1097</td><td>806</td><td>604</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ENSG00000000005</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>   0</td><td>   0</td><td>  0</td><td>  0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ENSG00000000419</td><td>467</td><td>523</td><td>616</td><td>371</td><td> 582</td><td> 781</td><td>417</td><td>509</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ENSG00000000457</td><td>347</td><td>258</td><td>364</td><td>237</td><td> 318</td><td> 447</td><td>330</td><td>324</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ENSG00000000460</td><td> 96</td><td> 81</td><td> 73</td><td> 66</td><td> 118</td><td>  94</td><td>102</td><td> 74</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ENSG00000000938</td><td>  0</td><td>  0</td><td>  1</td><td>  0</td><td>   2</td><td>   0</td><td>  0</td><td>  0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 9\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & ensgene & SRR1039508 & SRR1039509 & SRR1039512 & SRR1039513 & SRR1039516 & SRR1039517 & SRR1039520 & SRR1039521\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & ENSG00000000003 & 723 & 486 & 904 & 445 & 1170 & 1097 & 806 & 604\\\\\n",
       "\t2 & ENSG00000000005 &   0 &   0 &   0 &   0 &    0 &    0 &   0 &   0\\\\\n",
       "\t3 & ENSG00000000419 & 467 & 523 & 616 & 371 &  582 &  781 & 417 & 509\\\\\n",
       "\t4 & ENSG00000000457 & 347 & 258 & 364 & 237 &  318 &  447 & 330 & 324\\\\\n",
       "\t5 & ENSG00000000460 &  96 &  81 &  73 &  66 &  118 &   94 & 102 &  74\\\\\n",
       "\t6 & ENSG00000000938 &   0 &   0 &   1 &   0 &    2 &    0 &   0 &   0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 9\n",
       "\n",
       "| <!--/--> | ensgene &lt;chr&gt; | SRR1039508 &lt;dbl&gt; | SRR1039509 &lt;dbl&gt; | SRR1039512 &lt;dbl&gt; | SRR1039513 &lt;dbl&gt; | SRR1039516 &lt;dbl&gt; | SRR1039517 &lt;dbl&gt; | SRR1039520 &lt;dbl&gt; | SRR1039521 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | ENSG00000000003 | 723 | 486 | 904 | 445 | 1170 | 1097 | 806 | 604 |\n",
       "| 2 | ENSG00000000005 |   0 |   0 |   0 |   0 |    0 |    0 |   0 |   0 |\n",
       "| 3 | ENSG00000000419 | 467 | 523 | 616 | 371 |  582 |  781 | 417 | 509 |\n",
       "| 4 | ENSG00000000457 | 347 | 258 | 364 | 237 |  318 |  447 | 330 | 324 |\n",
       "| 5 | ENSG00000000460 |  96 |  81 |  73 |  66 |  118 |   94 | 102 |  74 |\n",
       "| 6 | ENSG00000000938 |   0 |   0 |   1 |   0 |    2 |    0 |   0 |   0 |\n",
       "\n"
      ],
      "text/plain": [
       "  ensgene         SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516\n",
       "1 ENSG00000000003 723        486        904        445        1170      \n",
       "2 ENSG00000000005   0          0          0          0           0      \n",
       "3 ENSG00000000419 467        523        616        371         582      \n",
       "4 ENSG00000000457 347        258        364        237         318      \n",
       "5 ENSG00000000460  96         81         73         66         118      \n",
       "6 ENSG00000000938   0          0          1          0           2      \n",
       "  SRR1039517 SRR1039520 SRR1039521\n",
       "1 1097       806        604       \n",
       "2    0         0          0       \n",
       "3  781       417        509       \n",
       "4  447       330        324       \n",
       "5   94       102         74       \n",
       "6    0         0          0       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "countsName <- \"https://bioconnector.github.io/workshops/data/airway_scaledcounts.csv\"\n",
    "download.file(countsName, destfile = \"airway_scaledcounts.csv\", method = \"auto\")\n",
    "\n",
    "countData <- read.csv('airway_scaledcounts.csv', header = TRUE, sep = \",\")\n",
    "head(countData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>dex</th><th scope=col>celltype</th><th scope=col>geo_id</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>SRR1039508</td><td>control</td><td>N61311 </td><td>GSM1275862</td></tr>\n",
       "\t<tr><td>SRR1039509</td><td>treated</td><td>N61311 </td><td>GSM1275863</td></tr>\n",
       "\t<tr><td>SRR1039512</td><td>control</td><td>N052611</td><td>GSM1275866</td></tr>\n",
       "\t<tr><td>SRR1039513</td><td>treated</td><td>N052611</td><td>GSM1275867</td></tr>\n",
       "\t<tr><td>SRR1039516</td><td>control</td><td>N080611</td><td>GSM1275870</td></tr>\n",
       "\t<tr><td>SRR1039517</td><td>treated</td><td>N080611</td><td>GSM1275871</td></tr>\n",
       "\t<tr><td>SRR1039520</td><td>control</td><td>N061011</td><td>GSM1275874</td></tr>\n",
       "\t<tr><td>SRR1039521</td><td>treated</td><td>N061011</td><td>GSM1275875</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 4\n",
       "\\begin{tabular}{llll}\n",
       " id & dex & celltype & geo\\_id\\\\\n",
       " <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t SRR1039508 & control & N61311  & GSM1275862\\\\\n",
       "\t SRR1039509 & treated & N61311  & GSM1275863\\\\\n",
       "\t SRR1039512 & control & N052611 & GSM1275866\\\\\n",
       "\t SRR1039513 & treated & N052611 & GSM1275867\\\\\n",
       "\t SRR1039516 & control & N080611 & GSM1275870\\\\\n",
       "\t SRR1039517 & treated & N080611 & GSM1275871\\\\\n",
       "\t SRR1039520 & control & N061011 & GSM1275874\\\\\n",
       "\t SRR1039521 & treated & N061011 & GSM1275875\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 4\n",
       "\n",
       "| id &lt;chr&gt; | dex &lt;chr&gt; | celltype &lt;chr&gt; | geo_id &lt;chr&gt; |\n",
       "|---|---|---|---|\n",
       "| SRR1039508 | control | N61311  | GSM1275862 |\n",
       "| SRR1039509 | treated | N61311  | GSM1275863 |\n",
       "| SRR1039512 | control | N052611 | GSM1275866 |\n",
       "| SRR1039513 | treated | N052611 | GSM1275867 |\n",
       "| SRR1039516 | control | N080611 | GSM1275870 |\n",
       "| SRR1039517 | treated | N080611 | GSM1275871 |\n",
       "| SRR1039520 | control | N061011 | GSM1275874 |\n",
       "| SRR1039521 | treated | N061011 | GSM1275875 |\n",
       "\n"
      ],
      "text/plain": [
       "  id         dex     celltype geo_id    \n",
       "1 SRR1039508 control N61311   GSM1275862\n",
       "2 SRR1039509 treated N61311   GSM1275863\n",
       "3 SRR1039512 control N052611  GSM1275866\n",
       "4 SRR1039513 treated N052611  GSM1275867\n",
       "5 SRR1039516 control N080611  GSM1275870\n",
       "6 SRR1039517 treated N080611  GSM1275871\n",
       "7 SRR1039520 control N061011  GSM1275874\n",
       "8 SRR1039521 treated N061011  GSM1275875"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metaDataName <- \"https://bioconnector.github.io/workshops/data/airway_metadata.csv\"\n",
    "download.file(metaDataName, destfile = \"airway_metadata.csv\", method = \"auto\")\n",
    "\n",
    "metaData <- read.csv('airway_metadata.csv', header = TRUE, sep = \",\")\n",
    "metaData"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct DESEQDataSet Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "converting counts to integer mode\n",
      "\n",
      "Warning message in DESeqDataSet(se, design = design, ignoreRank):\n",
      "“some variables in design formula are characters, converting to factors”\n"
     ]
    }
   ],
   "source": [
    "dds <- DESeqDataSetFromMatrix(countData=countData, \n",
    "                              colData=metaData, \n",
    "                              design=~dex, tidy = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run DESEQ function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "\n",
      "estimating dispersions\n",
      "\n",
      "gene-wise dispersion estimates\n",
      "\n",
      "mean-dispersion relationship\n",
      "\n",
      "final dispersion estimates\n",
      "\n",
      "fitting model and testing\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dds <- DESeq(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Take a look at the results table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>row</th><th scope=col>baseMean</th><th scope=col>log2FoldChange</th><th scope=col>lfcSE</th><th scope=col>stat</th><th scope=col>pvalue</th><th scope=col>padj</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>ENSG00000000003</td><td>747.1941954</td><td>-0.35070302</td><td>0.1682457</td><td>-2.0844697</td><td>0.03711747</td><td>0.1630348</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>ENSG00000000005</td><td>  0.0000000</td><td>         NA</td><td>       NA</td><td>        NA</td><td>        NA</td><td>       NA</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>ENSG00000000419</td><td>520.1341601</td><td> 0.20610777</td><td>0.1010592</td><td> 2.0394752</td><td>0.04140263</td><td>0.1760317</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>ENSG00000000457</td><td>322.6648439</td><td> 0.02452695</td><td>0.1451451</td><td> 0.1689823</td><td>0.86581056</td><td>0.9616942</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>ENSG00000000460</td><td> 87.6826252</td><td>-0.14714205</td><td>0.2570073</td><td>-0.5725210</td><td>0.56696907</td><td>0.8158486</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>ENSG00000000938</td><td>  0.3191666</td><td>-1.73228897</td><td>3.4936010</td><td>-0.4958463</td><td>0.62000288</td><td>       NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & row & baseMean & log2FoldChange & lfcSE & stat & pvalue & padj\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & ENSG00000000003 & 747.1941954 & -0.35070302 & 0.1682457 & -2.0844697 & 0.03711747 & 0.1630348\\\\\n",
       "\t2 & ENSG00000000005 &   0.0000000 &          NA &        NA &         NA &         NA &        NA\\\\\n",
       "\t3 & ENSG00000000419 & 520.1341601 &  0.20610777 & 0.1010592 &  2.0394752 & 0.04140263 & 0.1760317\\\\\n",
       "\t4 & ENSG00000000457 & 322.6648439 &  0.02452695 & 0.1451451 &  0.1689823 & 0.86581056 & 0.9616942\\\\\n",
       "\t5 & ENSG00000000460 &  87.6826252 & -0.14714205 & 0.2570073 & -0.5725210 & 0.56696907 & 0.8158486\\\\\n",
       "\t6 & ENSG00000000938 &   0.3191666 & -1.73228897 & 3.4936010 & -0.4958463 & 0.62000288 &        NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 7\n",
       "\n",
       "| <!--/--> | row &lt;chr&gt; | baseMean &lt;dbl&gt; | log2FoldChange &lt;dbl&gt; | lfcSE &lt;dbl&gt; | stat &lt;dbl&gt; | pvalue &lt;dbl&gt; | padj &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | ENSG00000000003 | 747.1941954 | -0.35070302 | 0.1682457 | -2.0844697 | 0.03711747 | 0.1630348 |\n",
       "| 2 | ENSG00000000005 |   0.0000000 |          NA |        NA |         NA |         NA |        NA |\n",
       "| 3 | ENSG00000000419 | 520.1341601 |  0.20610777 | 0.1010592 |  2.0394752 | 0.04140263 | 0.1760317 |\n",
       "| 4 | ENSG00000000457 | 322.6648439 |  0.02452695 | 0.1451451 |  0.1689823 | 0.86581056 | 0.9616942 |\n",
       "| 5 | ENSG00000000460 |  87.6826252 | -0.14714205 | 0.2570073 | -0.5725210 | 0.56696907 | 0.8158486 |\n",
       "| 6 | ENSG00000000938 |   0.3191666 | -1.73228897 | 3.4936010 | -0.4958463 | 0.62000288 |        NA |\n",
       "\n"
      ],
      "text/plain": [
       "  row             baseMean    log2FoldChange lfcSE     stat       pvalue    \n",
       "1 ENSG00000000003 747.1941954 -0.35070302    0.1682457 -2.0844697 0.03711747\n",
       "2 ENSG00000000005   0.0000000          NA           NA         NA         NA\n",
       "3 ENSG00000000419 520.1341601  0.20610777    0.1010592  2.0394752 0.04140263\n",
       "4 ENSG00000000457 322.6648439  0.02452695    0.1451451  0.1689823 0.86581056\n",
       "5 ENSG00000000460  87.6826252 -0.14714205    0.2570073 -0.5725210 0.56696907\n",
       "6 ENSG00000000938   0.3191666 -1.73228897    3.4936010 -0.4958463 0.62000288\n",
       "  padj     \n",
       "1 0.1630348\n",
       "2        NA\n",
       "3 0.1760317\n",
       "4 0.9616942\n",
       "5 0.8158486\n",
       "6        NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- results(dds)\n",
    "head(results(dds, tidy=TRUE)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of differential gene expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 25258 with nonzero total read count\n",
      "adjusted p-value < 0.1\n",
      "LFC > 0 (up)       : 1563, 6.2%\n",
      "LFC < 0 (down)     : 1188, 4.7%\n",
      "outliers [1]       : 142, 0.56%\n",
      "low counts [2]     : 9971, 39%\n",
      "(mean count < 10)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "summary(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort summary list by p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "log2 fold change (MLE): dex treated vs control \n",
       "Wald test p-value: dex treated vs control \n",
       "DataFrame with 6 rows and 6 columns\n",
       "                 baseMean log2FoldChange     lfcSE      stat      pvalue\n",
       "                <numeric>      <numeric> <numeric> <numeric>   <numeric>\n",
       "ENSG00000152583   954.771        4.36836 0.2371268   18.4220 8.74490e-76\n",
       "ENSG00000179094   743.253        2.86389 0.1755693   16.3120 8.10784e-60\n",
       "ENSG00000116584  2277.913       -1.03470 0.0650984  -15.8944 6.92855e-57\n",
       "ENSG00000189221  2383.754        3.34154 0.2124058   15.7319 9.14433e-56\n",
       "ENSG00000120129  3440.704        2.96521 0.2036951   14.5571 5.26424e-48\n",
       "ENSG00000148175 13493.920        1.42717 0.1003890   14.2164 7.25128e-46\n",
       "                       padj\n",
       "                  <numeric>\n",
       "ENSG00000152583 1.32441e-71\n",
       "ENSG00000179094 6.13966e-56\n",
       "ENSG00000116584 3.49776e-53\n",
       "ENSG00000189221 3.46227e-52\n",
       "ENSG00000120129 1.59454e-44\n",
       "ENSG00000148175 1.83034e-42"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- res[order(res$padj),]\n",
    "head(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalized counts comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gt1RIsgbSI7KX19WItl3psEX0fi6i+H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\nMpNMAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “ENSG00000148175”"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,3))\n",
    "\n",
    "plotCounts(dds, gene=\"ENSG00000152583\", intgroup=\"dex\")\n",
    "plotCounts(dds, gene=\"ENSG00000179094\", intgroup=\"dex\")\n",
    "plotCounts(dds, gene=\"ENSG00000116584\", intgroup=\"dex\")\n",
    "plotCounts(dds, gene=\"ENSG00000189221\", intgroup=\"dex\")\n",
    "plotCounts(dds, gene=\"ENSG00000120129\", intgroup=\"dex\")\n",
    "plotCounts(dds, gene=\"ENSG00000148175\", intgroup=\"dex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volcano Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Jmg2AEAAMgExQ4AAEAmKHYAAAAyQbED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\nvAQHO3/1lf/SpV7q72MuXuwBQW2ITlfXCnt7s4aGYZs3s3x9YydN