1649984580

# From2-array: Create A From2 Stream Based on an Array Of Source Values

from2-array

Create a from2 stream based on an array of source values

Useful when you want to create a Readable stream with some values you already have in an array

## example

``````var from = require('from2-array')
var through = require('through2')

from.obj([{
name:'a'
},{
name:'b'
},{
name:'c'
}]).pipe(through.obj(function(chunk, enc, cb){
console.log('found: ' + chunk.name)
cb()
}))``````

Author: Binocarlos
Source Code: https://github.com/binocarlos/from2-array

1666082925

## How to Create Arrays in Python

### In this tutorial, you'll know the basics of how to create arrays in Python using the array module. Learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

This tutorialvideo on 'Arrays in Python' will help you establish a strong hold on all the fundamentals in python programming language. Below are the topics covered in this video:
1:15 What is an array?
2:53 Is python list same as an array?
3:48  How to create arrays in python?
7:19 Accessing array elements
9:59 Basic array operations
- 10:33  Finding the length of an array
- 15:06  Removing elements
- 18:32  Array concatenation
- 20:59  Slicing
- 23:26  Looping

Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the `array module`. We won't cover NumPy arrays here.

1. Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
2. How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
3. Array methods for performing operations
1. Change an existing value
3. Remove a value
4. Conclusion

Let's get started!

## What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

### What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

### When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the `array module` in order to be used.

Arrays of the `array module` are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

## How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the `array module` which contains all the necassary functions.

There are three ways you can import the `array module`:

• By using `import array` at the top of the file. This includes the module `array`. You would then go on to create an array using `array.array()`.
``````import array

#how you would create an array
array.array()``````
• Instead of having to type `array.array()` all the time, you could use `import array as arr` at the top of the file, instead of `import array` alone. You would then create an array by typing `arr.array()`. The `arr` acts as an alias name, with the array constructor then immediately following it.
``````import array as arr

#how you would create an array
arr.array()``````
• Lastly, you could also use `from array import *`, with `*` importing all the functionalities available. You would then create an array by writing the `array()` constructor alone.
``````from array import *

#how you would create an array
array()``````

### How to Define Arrays in Python

Once you've imported the `array module`, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

``variable_name = array(typecode,[elements])``

Let's break it down:

• `variable_name` would be the name of the array.
• The `typecode` specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
• Inside square brackets you mention the `elements` that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing `variable_name = array(typecode)` alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

Tying everything together, here is an example of how you would define an array in Python:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])``````

Let's break it down:

• First we included the array module, in this case with `import array as arr `.
• Then, we created a `numbers` array.
• We used `arr.array()` because of `import array as arr `.
• Inside the `array()` constructor, we first included `i`, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with `H` for example, would mean that no negative values are allowed.
• Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of `i` typecode, meaning they were not integer values, you would get an error:

``````import array as arr

numbers = arr.array('i',[10.0,20,30])

print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer``````

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

``````from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])``````

The example above imported the `array module` via `from array import *` and created an array `numbers` of float data type. This means that it holds only floating point numbers, which is specified with the `'d'` typecode.

### How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in `len()` method.

It will return the integer number that is equal to the total number of elements in the array you specify.

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(len(numbers))

#output
# 3``````

In the example above, the array contained three elements – `10, 20, 30` – so the length of `numbers` is `3`.

### Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at `0`. It is important to remember that counting starts at `0` and not at `1`.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

``array_name[index_value_of_item]``

Here is how you would access each individual element in an array:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30``````

Remember that the index value of the last element of an array is always one less than the length of the array. Where `n` is the length of the array, `n - 1` will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of `-1`, the second to last element would have an index of `-2`, and so on.

Here is how you would get each item in an array using that method:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item

#output

#30
#20
#10``````

### How to Search Through an Array in Python

You can find out an element's index number by using the `index()` method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

``````import array as arr

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0``````

If there is more than one element with the same value, the index of the first instance of the value will be returned:

``````import array as arr

numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0``````

### How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the `print()` method. That method gives the following result:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])``````

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple `for` loop:

``````import array as arr

numbers = arr.array('i',[10,20,30])

for number in numbers:
print(number)

#output
#10
#20
#30``````

You could also use the `range()` function, and pass the `len()` method as its parameter. This would give the same result as above:

``````import array as arr

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
print(values[value])

#output

#10
#20
#30``````

### How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon `:`.

When using the slicing operator and you only include one value, the counting starts from `0` by default. It gets the first item, and goes up to but not including the index number you specify.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])``````

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])``````

## Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

### How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])``````

### How to Add a New Value to an Array

To add one single value at the end of an array, use the `append()` method:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])``````

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer``````

But what if you want to add more than one value to the end an array?

Use the `extend()` method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])``````

And what if you don't want to add an item to the end of an array? Use the `insert()` method, to add an item at a specific position.

The `insert()` function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])``````

### How to Remove a Value from an Array

To remove an element from an array, use the `remove()` method and include the value as an argument to the method.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])``````

With `remove()`, only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])``````

Only the first occurence of `10` is removed.

You can also use the `pop()` method, and specify the position of the element to be removed:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])``````

## Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the `array module`. Hopefully you found this guide helpful.

Thanks for reading and happy coding!

#python #programming

1670560264

## Understanding Arrays in Python

### Learn how to use Python arrays. Create arrays in Python using the array module. You'll see how to define them and the different methods commonly used for performing operations on them.

The artcile covers arrays that you create by importing the `array module`. We won't cover NumPy arrays here.

1. Introduction to Arrays
1. The differences between Lists and Arrays
2. When to use arrays
2. How to use arrays
1. Define arrays
2. Find the length of arrays
3. Array indexing
4. Search through arrays
5. Loop through arrays
6. Slice an array
3. Array methods for performing operations
1. Change an existing value
3. Remove a value
4. Conclusion

Let's get started!

## What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

### What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types. This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type. There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

### When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the `array module` in order to be used.

Arrays of the `array module` are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

## How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the `array module` which contains all the necassary functions.

There are three ways you can import the `array module`:

1. By using `import array` at the top of the file. This includes the module `array`. You would then go on to create an array using `array.array()`.
``````import array

#how you would create an array
array.array()
``````
1. Instead of having to type `array.array()` all the time, you could use `import array as arr` at the top of the file, instead of `import array` alone. You would then create an array by typing `arr.array()`. The `arr` acts as an alias name, with the array constructor then immediately following it.
``````import array as arr

#how you would create an array
arr.array()
``````
1. Lastly, you could also use `from array import *`, with `*` importing all the functionalities available. You would then create an array by writing the `array()` constructor alone.
``````from array import *

#how you would create an array
array()
``````

### How to Define Arrays in Python

Once you've imported the `array module`, you can then go on to define a Python array.

The general syntax for creating an array looks like this:

``````variable_name = array(typecode,[elements])
``````

Let's break it down:

• `variable_name` would be the name of the array.
• The `typecode` specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
• Inside square brackets you mention the `elements` that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing `variable_name = array(typecode)` alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

Tying everything together, here is an example of how you would define an array in Python:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])
``````

Let's break it down:

• First we included the array module, in this case with `import array as arr `.
• Then, we created a `numbers` array.
• We used `arr.array()` because of `import array as arr `.
• Inside the `array()` constructor, we first included `i`, for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with `H` for example, would mean that no negative values are allowed.
• Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of `i` typecode, meaning they were not integer values, you would get an error:

``````import array as arr

numbers = arr.array('i',[10.0,20,30])

print(numbers)

#output

#Traceback (most recent call last):
# File "/Users/dionysialemonaki/python_articles/demo.py", line 14, in <module>
#   numbers = arr.array('i',[10.0,20,30])
#TypeError: 'float' object cannot be interpreted as an integer
``````

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

``````from array import *

#an array of floating point values
numbers = array('d',[10.0,20.0,30.0])

print(numbers)

#output

#array('d', [10.0, 20.0, 30.0])
``````

The example above imported the `array module` via `from array import *` and created an array `numbers` of float data type. This means that it holds only floating point numbers, which is specified with the `'d'` typecode.

### How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in `len()` method.

It will return the integer number that is equal to the total number of elements in the array you specify.

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(len(numbers))

#output
# 3
``````

In the example above, the array contained three elements – `10, 20, 30` – so the length of `numbers` is `3`.

### Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number.

Indexing in Python, and in all programming languages and computing in general, starts at `0`. It is important to remember that counting starts at `0` and not at `1`.

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

``````array_name[index_value_of_item]
``````

Here is how you would access each individual element in an array:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers[0]) # gets the 1st element
print(numbers[1]) # gets the 2nd element
print(numbers[2]) # gets the 3rd element

#output

#10
#20
#30
``````

Remember that the index value of the last element of an array is always one less than the length of the array. Where `n` is the length of the array, `n - 1` will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of `-1`, the second to last element would have an index of `-2`, and so on.

Here is how you would get each item in an array using that method:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers[-1]) #gets last item
print(numbers[-2]) #gets second to last item
print(numbers[-3]) #gets first item

#output

#30
#20
#10
``````

### How to Search Through an Array in Python

You can find out an element's index number by using the `index()` method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

``````import array as arr

numbers = arr.array('i',[10,20,30])

#search for the index of the value 10
print(numbers.index(10))

#output

#0
``````

If there is more than one element with the same value, the index of the first instance of the value will be returned:

``````import array as arr

numbers = arr.array('i',[10,20,30,10,20,30])

#search for the index of the value 10
#will return the index number of the first instance of the value 10
print(numbers.index(10))

#output

#0
``````

### How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the `print()` method. That method gives the following result:

``````import array as arr

numbers = arr.array('i',[10,20,30])

print(numbers)

#output

#array('i', [10, 20, 30])
``````

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple `for` loop:

``````import array as arr

numbers = arr.array('i',[10,20,30])

for number in numbers:
print(number)

#output
#10
#20
#30
``````

You could also use the `range()` function, and pass the `len()` method as its parameter. This would give the same result as above:

``````import array as arr

values = arr.array('i',[10,20,30])

#prints each individual value in the array
for value in range(len(values)):
print(values[value])

#output

#10
#20
#30
``````

### How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon `:`.

When using the slicing operator and you only include one value, the counting starts from `0` by default. It gets the first item, and goes up to but not including the index number you specify.

``````
import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#get the values 10 and 20 only
print(numbers[:2])  #first to second position

#output

#array('i', [10, 20])
``````

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#get the values 20 and 30 only
print(numbers[1:3]) #second to third position

#output

#rray('i', [20, 30])
``````

## Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

### How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#change the first element
#change it from having a value of 10 to having a value of 40
numbers[0] = 40

print(numbers)

#output

#array('i', [40, 20, 30])
``````

### How to Add a New Value to an Array

To add one single value at the end of an array, use the `append()` method:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40)

print(numbers)

#output

#array('i', [10, 20, 30, 40])
``````

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 to the end of numbers
numbers.append(40.0)

print(numbers)

#output

#Traceback (most recent call last):
#  File "/Users/dionysialemonaki/python_articles/demo.py", line 19, in <module>
#   numbers.append(40.0)
#TypeError: 'float' object cannot be interpreted as an integer
``````

But what if you want to add more than one value to the end an array?

Use the `extend()` method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integers 40,50,60 to the end of numbers
#The numbers need to be enclosed in square brackets

numbers.extend([40,50,60])

print(numbers)

#output

#array('i', [10, 20, 30, 40, 50, 60])
``````

And what if you don't want to add an item to the end of an array? Use the `insert()` method, to add an item at a specific position.

The `insert()` function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

#add the integer 40 in the first position
#remember indexing starts at 0

numbers.insert(0,40)

print(numbers)

#output

#array('i', [40, 10, 20, 30])
``````

### How to Remove a Value from an Array

To remove an element from an array, use the `remove()` method and include the value as an argument to the method.

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30])
``````

With `remove()`, only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

``````
import array as arr

#original array
numbers = arr.array('i',[10,20,30,10,20])

numbers.remove(10)

print(numbers)

#output

#array('i', [20, 30, 10, 20])
``````

Only the first occurence of `10` is removed.

You can also use the `pop()` method, and specify the position of the element to be removed:

``````import array as arr

#original array
numbers = arr.array('i',[10,20,30,10,20])

#remove the first instance of 10
numbers.pop(0)

print(numbers)

#output

#array('i', [20, 30, 10, 20])
``````

## Conclusion

And there you have it - you now know the basics of how to create arrays in Python using the `array module`. Hopefully you found this guide helpful.

You'll start from the basics and learn in an interacitve and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

Original article source at https://www.freecodecamp.org

#python

1655630160

## Installation

Install via pip:

``\$ pip install pytumblr``

Install from source:

``````\$ git clone https://github.com/tumblr/pytumblr.git
\$ cd pytumblr
\$ python setup.py install``````

## Usage

### Create a client

A `pytumblr.TumblrRestClient` is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

``````client = pytumblr.TumblrRestClient(
'<consumer_key>',
'<consumer_secret>',
'<oauth_token>',
'<oauth_secret>',
)

client.info() # Grabs the current user information``````

Two easy ways to get your credentials to are:

1. The built-in `interactive_console.py` tool (if you already have a consumer key & secret)
2. The Tumblr API console at https://api.tumblr.com/console
3. Get sample login code at https://api.tumblr.com/console/calls/user/info

### Supported Methods

#### User Methods

``````client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post``````

#### Blog Methods

``````client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog``````

#### Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

• state - a string, the state of the post. Supported types are published, draft, queue, private
• tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
• tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
• date - a string, the customized GMT that you want
• format - a string, the format that your post is in. Support types are html or markdown
• slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

``````#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
tweet="Woah this is an incredible sweet post [URL]",
data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
caption="## Mega sweet kittens")``````

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

``````#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")``````

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

``````#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")``````

• title - a string, the title of post that you want. Supports HTML entities.
• url - a string, the url that you want to create a link post for.
• description - a string, the desciption of the link that you have
``````#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
description="Search is pretty cool when a duck does it.")``````

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

``````#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])``````

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

``````#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")``````

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

``````#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")``````

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

``````client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")``````

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

``client.reblog(blogName, id=125356, reblog_key="reblog_key")``

Deleting a post

Deleting just requires that you own the post and have the post id

``client.delete_post(blogName, 123456) # Deletes your post :(``

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

``client.create_text(blogName, tags=['hello', 'world'], ...)``

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

``data = client.notes(blogName, id='123456')``

The results include a timestamp you can use to make future calls.

``data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])``

#### Tagged Methods

``````# get posts with a given tag
client.tagged(tag, **params)``````

### Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need `pyyaml` installed to run it, but then it's just:

``\$ python interactive-console.py``

and away you go! Tokens are stored in `~/.tumblr` and are also shared by other Tumblr API clients like the Ruby client.

### Running tests

The tests (and coverage reports) are run with nose, like this:

``python setup.py test``

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr

1669003576

## Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python.

## Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

### Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

### Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

### List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

• Lists
• Sets
• Dictionaries
• User-Defined Classes (It purely depends upon the user to define the characteristics)

Objects of built-in type that are immutable are:

• Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
• Strings
• Tuples
• Frozen Sets
• User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

## Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

• Identity – This refers to the address that the object refers to in the computer’s memory.
• Type – This refers to the kind of object that is created. For example- integer, list, string etc.
• Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

## Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities

``````cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]
``````

# Printing the elements from the list cities, separated by a comma & space

``````for city in cities:
print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(cities)))

Output [2]: 0x1691d7de8c8
``````

#Adding a new city to the list cities

``````cities.append(‘Chennai’)
``````

#Printing the elements from the list cities, separated by a comma & space

``````for city in cities:
print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(cities)))

Output [4]: 0x1691d7de8c8
``````

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.

#Creating a Tuple with variable name ‘foo’

``````foo = (1, 2)
``````

#Changing the index[0] value from 1 to 3

``````foo[0] = 3

TypeError: 'tuple' object does not support item assignment
``````

## Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

``````weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’
``````

# Printing the elements of tuple weekdays

``````print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(weekdays)))

Output [2]: 0x1691cc35090
``````

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

``````weekdays  +=  ‘Pythonday’,
``````

#Printing the elements of tuple weekdays

``````print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(weekdays)))

``````

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender

``````person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
``````

#printing the tuple

``````print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

``````

#printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(person)))

Output [2]: 0x1691ef47f88
``````

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

``````person[0][1] = 4
``````

#printing the updated tuple

``````print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])
``````

#printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(person)))

Output [4]: 0x1691ef47f88
``````

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

``````list1 = [(1, 2, 3), (4, 5, 6)]
``````

#printing the list

``````print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

``````

#printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	``````

#changing object reference at index 0

``````list1[0] = (7, 8, 9)
``````

#printing the list

``Output [3]: [(7, 8, 9), (4, 5, 6)]``

#printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(list1)))

Output [4]: 0x1691d5b13c8
``````

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’

x = 10

#printing the value of ‘x’

``````print(x)

Output [1]: 10
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(x)))

Output [2]: 0x538fb560

``````

#creating an object of integer type with value 10 and reference variable name ‘y’

``````y = 10
``````

#printing the value of ‘y’

``````print(y)

Output [3]: 10
``````

#Printing the location of the object created in the memory address in hexadecimal format

``````print(hex(id(y)))

Output [4]: 0x538fb560
``````

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick checkPython Data Structures

### Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

## Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

## FAQs

#### 2. What are the mutable and immutable data types in Python?

• Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

• Some immutable data types are:

int, float, decimal, bool, string, tuple, range.

#### 3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

#### 4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

#### 5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

#### 6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

Original article source at: https://www.mygreatlearning.com

1669188856

## What Is R Programming Language? introduction & Basics

In this R article, we will learn about What Is R Programming Language? introduction & Basics. R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++, and Fortran codes are preferred.

Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicating the results

• Program: R is a clear and accessible programming tool
• Transform: R is made up of a collection of libraries designed specifically for data science
• Discover: Investigate the data, refine your hypothesis and analyze them
• Model: R provides a wide array of tools to capture the right model for your data
• Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world.

### What is R used for?

• Statistical inference
• Data analysis
• Machine learning algorithm

As conclusion, R is the world’s most widely used statistics programming language. It’s the 1st choice of data scientists and supported by a vibrant and talented community of contributors. R is taught in universities and deployed in mission-critical business applications.

## R-environment setup

Windows Installation – We can download the Windows installer version of R from R-3.2.2 for windows (32/64)

As it is a Windows installer (.exe) with the name “R-version-win.exe”. You can just double click and run the installer accepting the default settings. If your Windows is a 32-bit version, it installs the 32-bit version. But if your windows are 64-bit, then it installs both the 32-bit and 64-bit versions.

After installation, you can locate the icon to run the program in a directory structure “R\R3.2.2\bin\i386\Rgui.exe” under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.

## R basic Syntax

R Programming is a very popular programming language that is broadly used in data analysis. The way in which we define its code is quite simple. The “Hello World!” is the basic program for all the languages, and now we will understand the syntax of R programming with the “Hello world” program. We can write our code either in the command prompt, or we can use an R script file.

### R command prompt

Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
\$R
This will launch R interpreter and you will get a prompt > where you can start typing your program as follows −

``````>myString <- "Hello, World"
>print (myString)
[1] "Hello, World!"``````

Here the first statement defines a string variable myString, where we assign a string “Hello, World!” and then the next statement print() is being used to print the value stored in myString variable.

## R data-types

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.

In contrast to other programming languages like C and java in R, the variables are not declared as some data type. 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 −

• Vectors
• Lists
• Matrices
• Arrays
• Factors
• Data Frames

### Vectors

``````#create a vector and find the elements which are >5
v<-c(1,2,3,4,5,6,5,8)
v[v>5]

#subset
subset(v,v>5)

#position in the vector created in which square of the numbers of v is >10 holds good
which(v*v>10)

#to know the values
v[v*v>10]``````

Output: [1] 6 8 Output: [1] 6 8 Output: [1] 4 5 6 7 8 Output: [1] 4 5 6 5 8

### Matrices

A matrix is a two-dimensional rectangular data set. It can be created using a vector input to the matrix function.

``````#matrices: a vector with two dimensional attributes
mat<-matrix(c(1,2,3,4))

mat1<-matrix(c(1,2,3,4),nrow=2)
mat1``````

Output:     [,1] [,2] [1,]    1    3 [2,]    2    4

``````mat2<-matrix(c(1,2,3,4),ncol=2,byrow=T)
mat2``````

Output:       [,1] [,2] [1,]    1    2 [2,]    3    4

``````mat3<-matrix(c(1,2,3,4),byrow=T)
mat3

#transpose of matrix
mattrans<-t(mat)
mattrans

#create a character matrix called fruits with elements apple, orange, pear, grapes
fruits<-matrix(c("apple","orange","pear","grapes"),2)
#create 3×4 matrix of marks obtained in each quarterly exams for 4 different subjects
X<-matrix(c(50,70,40,90,60, 80,50, 90,100, 50,30, 70),nrow=3)
X

#give row names and column names
rownames(X)<-paste(prefix="Test.",1:3)
subs<-c("Maths", "English", "Science", "History")
colnames(X)<-subs
X``````

Output:       [,1]  [1,]    1  [2,]    2  [3,]    3  [4,]    4 Output:      [,1] [,2] [,3] [,4]  [1,]    1    2    3    4 Output:      [,1] [,2] [,3] [,4]  [1,]   50   90   50   50  [2,]   70   60   90   30  [3,]   40   80  100   70 Output:   Maths English Science History  Test. 1    50      90      50      50  Test. 2    70      60      90      30  Test. 3    40      80     100      70

### Arrays

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 dimensions. In the below example we create an array with two elements which are 3×3 matrices each.

``````#Arrays
arr<-array(1:24,dim=c(3,4,2))
arr

#create an array using alphabets with dimensions 3 rows, 2 columns and 3 arrays
arr1<-array(letters[1:18],dim=c(3,2,3))

#select only 1st two matrix of an array
arr1[,,c(1:2)]

#LIST
X<-list(u=2, n='abc')
X
X\$u``````
`` [,1] [,2] [,3] [,4]``
`` [,1] [,2] [,3] [,4]``
`` [,1] [,2]``
`` [,1] [,2]``

### Dataframes

Data frames are tabular data objects. Unlike a matrix in a data frame, each column can contain different modes of data. The first column can be numeric while the second column can be character and the third column can be logical. It is a list of vectors of equal length.

``````#Dataframes
students<-c("J","L","M","K","I","F","R","S")
Subjects<-rep(c("science","maths"),each=2)
marks<-c(55,70,66,85,88,90,56,78)
data<-data.frame(students,Subjects,marks)
#Accessing dataframes
data[[1]]

data\$Subjects
data[,1]``````

Output: [1] J L M K I F R S Levels: F I J K L M R S Output:   data\$Subjects   [1] science science maths   maths   science science maths   maths     Levels: maths science

### Factors

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.

Factors are created using the factor() function. The nlevels function gives the count of levels.

``````#Factors
x<-c(1,2,3)
factor(x)

#apply function
data1<-data.frame(age=c(55,34,42,66,77),bmi=c(26,25,21,30,22))
d<-apply(data1,2,mean)
d

#create two vectors age and gender and find mean age with respect to gender
age<-c(33,34,55,54)
gender<-factor(c("m","f","m","f"))
tapply(age,gender,mean)

``````

Output: [1] 1 2 3 Levels: 1 2 3 Output:  age  bmi 54.8 24.8 Output:  f  m         44 44

## R Variables

A variable provides us with named storage that our programs can manipulate. A variable in R can store an atomic vector, a group of atomic vectors, or a combination of many R objects. A valid variable name consists of letters, numbers, and the dot or underlines characters.

### Rules for writing Identifiers in R

1. Identifiers can be a combination of letters, digits, period (.), and underscore (_).
2. It must start with a letter or a period. If it starts with a period, it cannot be followed by a digit.
3. Reserved words in R cannot be used as identifiers.

#### Valid identifiers in R

total, sum, .fine.with.dot, this_is_acceptable, Number5

#### Invalid identifiers in R

tot@l, 5um, _fine, TRUE, .0ne

### Best Practices

Earlier versions of R used underscore (_) as an assignment operator. So, the period (.) was used extensively in variable names having multiple words. Current versions of R support underscore as a valid identifier but it is good practice to use a period as word separators.
For example, a.variable.name is preferred over a_variable_name or alternatively we could use camel case as aVariableName.

### Constants in R

Constants, as the name suggests, are entities whose value cannot be altered. Basic types of constant are numeric constants and character constants.

Numeric Constants

All numbers fall under this category. They can be of type integer, double or complex. It can be checked with the typeof() function.
Numeric Constants followed by L are regarded as integers and those followed by i are regarded as complex.

``````> typeof(5)
> typeof(5L)
> typeof(5L)``````

[1] “double” [1] “double” [[1] “double”

Character Constants

Character constants can be represented using either single quotes (‘) or double quotes (“) as delimiters.

``````> 'example'
> typeof("5")``````

[1] "example" [1] "character"

## R Operators

Operators – Arithmetic, Relational, Logical, Assignment, and some of the Miscellaneous Operators that R programming language provides.

There are four main categories of Operators in the R programming language.

1. Arithmetic Operators
2. Relational Operators
3. Logical Operators
4. Assignment Operators
5. Mixed Operators

``````x <- 35
y<-10``````

x+y       > x-y     > x*y       > x/y      > x%/%y     > x%%y   > x^y   [1] 45      [1] 25    [1] 350    [1] 3.5      [1] 3      [1] 5 [1]2.75e+15

### Logical Operators

The below table shows the logical operators in R. Operators & and | perform element-wise operation producing result having a length of the longer operand. But && and || examines only the first element of the operands resulting in a single length logical vector.

``````a <- c(TRUE,TRUE,FALSE,0,6,7)
b <- c(FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
a&b
[1] FALSE TRUE FALSE FALSE TRUE TRUE
a&&b
[1] FALSE
> a|b
[1] TRUE TRUE FALSE TRUE TRUE TRUE
> a||b
[1] TRUE
> !a
[1] FALSE FALSE TRUE TRUE FALSE FALSE
> !b
[1] TRUE FALSE TRUE FALSE FALSE FALSE``````

## R functions

Functions are defined using the function() directive and are stored as R objects just like anything else. In particular, they are R objects of class “function”. Here’s a simple function that takes no arguments simply prints ‘Hi statistics’.

``````#define the function
f <- function() {
print("Hi statistics!!!")
}
#Call the function
f()``````

Output: [1] "Hi statistics!!!"

Now let’s define a function called standardize, and the function has a single argument x which is used in the body of a function.

``````#Define the function that will calculate standardized score.
standardize = function(x) {
m = mean(x)
sd = sd(x)
result = (x – m) / sd
result
}
input<- c(40:50) #Take input for what we want to calculate a standardized score.
standardize(input) #Call the function``````

Output:   standardize(input) #Call the function   [1] -1.5075567 -1.2060454 -0.9045340 -0.6030227 -0.3015113 0.0000000 0.3015113 0.6030227 0.9045340 1.2060454 1.5075567

## Loop Functions

R has some very useful functions which implement looping in a compact form to make life easier. The very rich and powerful family of applied functions is made of intrinsically vectorized functions. These functions in R allow you to apply some function to a series of objects (eg. vectors, matrices, data frames, or files). They include:

1. lapply(): Loop over a list and evaluate a function on each element
2. sapply(): Same as lapply but try to simplify the result
3. apply(): Apply a function over the margins of an array
4. tapply(): Apply a function over subsets of a vector
5. mapply(): Multivariate version of lapply

There is another function called split() which is also useful, particularly in conjunction with lapply.

## R Vectors

A vector is a sequence of data elements of the same basic type. Members in a vector are officially called components. Vectors are the most basic R data objects and there are six types of atomic vectors. They are logical, integer, double, complex, character, and raw.

``````The c() function can be used to create vectors of objects by concatenating things together.
x <- c(1,2,3,4,5) #double
x #If you use only x auto-printing occurs
l <- c(TRUE, FALSE) #logical
l <- c(T, F) ## logical
c <- c("a", "b", "c", "d") ## character
i <- 1:20 ## integer
cm <- c(2+2i, 3+3i) ## complex
print(l)
print(c)
print(i)
print(cm)

You can see the type of each vector using typeof() function in R.
typeof(x)
typeof(l)
typeof(c)
typeof(i)
typeof(cm)``````

Output: print(l) [1] TRUE FALSE   print(c)   [1] "a" "b" "c" "d"   print(i)   [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20   print(cm)   [1] 2+2i 3+3i Output: typeof(x) [1] "double"   typeof(l)   [1] "logical"   typeof(c)   [1] "character"   typeof(i)   [1] "integer"   typeof(cm)   [1] "complex"

### Creating a vector using seq() function:

We can use the seq() function to create a vector within an interval by specifying step size or specifying the length of the vector.

``````seq(1:10) #By default it will be incremented by 1
seq(1, 20, length.out=5) # specify length of the vector
seq(1, 20, by=2) # specify step size``````

Output: > seq(1:10) #By default it will be incremented by 1 [1] 1 2 3 4 5 6 7 8 9 10 > seq(1, 20, length.out=5) # specify length of the vector [1] 1.00 5.75 10.50 15.25 20.00 > seq(1, 20, by=2) # specify step size [1] 1 3 5 7 9 11 13 15 17 19

### Extract Elements from a Vector:

Elements of a vector can be accessed using indexing. The vector indexing can be logical, integer, or character. The [ ] brackets are used for indexing. Indexing starts with position 1, unlike most programming languages where indexing starts from 0.

### Extract Using Integer as Index:

We can use integers as an index to access specific elements. We can also use negative integers to return all elements except that specific element.

``````x<- 101:110
x[1]   #access the first element
x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements
x[5:10]        #Extract all elements from 5th to 10th
x[c(-5,-10)] #Extract all elements except 5th and 10th
x[-c(5:10)] #Extract all elements except from 5th to 10th
``````

Output:   x[1] #Extract the first element   [1] 101   x[c(2,3,4,5)] #Extract 2nd, 3rd, 4th, and 5th elements   [1] 102 103 104 105   x[5:10] #Extract all elements from 5th to 10th   [1] 105 106 107 108 109 110   x[c(-5,-10)] #Extract all elements except 5th and 10th   [1] 101 102 103 104 106 107 108 109   x[-c(5:10)] #Extract all elements except from 5th to 10th   [1] 101 102 103 104

### Extract Using Logical Vector as Index:

If you use a logical vector for indexing, the position where the logical vector is TRUE will be returned.

``````x[x < 105]
x[x>=104]``````

Output:   x[x < 105] [1] 101 102 103 104 x[x>=104]   [1] 104 105 106 107 108 109 110

### Modify a Vector in R:

We can modify a vector and assign a new value to it. You can truncate a vector by using reassignments. Check the below example.

``````x<- 10:12
x[1]<- 101 #Modify the first element
x
x[2]<-102 #Modify the 2nd element
x
x<- x[1:2] #Truncate the last element
x
``````

Output:   x   [1] 101 11 12   x[2]<-102 #Modify the 2nd element   x   [1] 101 102 12   x<- x[1:2] #Truncate the last element   x   [1] 101 102

### Arithmetic Operations on Vectors:

We can use arithmetic operations on two vectors of the same length. They can be added, subtracted, multiplied, or divided. Check the output of the below code.

``````# Create two vectors.
v1 <- c(1:10)
v2 <- c(101:110)

# Vector subtraction.
sub.result <- v2-v1
print(sub.result)
# Vector multiplication.
multi.result <- v1*v2
print(multi.result)
# Vector division.
divi.result <- v2/v1
print(divi.result)
``````

Output:   print(add.result)   [1] 102 104 106 108 110 112 114 116 118 120   print(sub.result)   [1] 100 100 100 100 100 100 100 100 100 100   print(multi.result)   [1] 101 204 309 416 525 636 749 864 981 1100   print(divi.result)   [1] 101.00000 51.00000 34.33333 26.00000 21.00000 17.66667 15.28571 13.50000 12.11111 11.00000

### Find Minimum and Maximum in a Vector:

The minimum and the maximum of a vector can be found using the min() or the max() function. range() is also available which returns the minimum and maximum in a vector.

``````x<- 1001:1010
max(x) # Find the maximum
min(x) # Find the minimum
range(x) #Find the range``````

Output:   max(x) # Find the maximum   [1] 1010   min(x) # Find the minimum   [1] 1001   range(x) #Find the range   [1] 1001 1010

## R Lists

The list is a data structure having elements of mixed data types. A vector having all elements of the same type is called an atomic vector but a vector having elements of a different type is called list.
We can check the type with typeof() or class() function and find the length using length()function.

``````x <- list("stat",5.1, TRUE, 1 + 4i)
x
class(x)
typeof(x)
length(x)
``````

Output:   x   [[1]]   [1] "stat"   [[2]]   [1] 5.1   [[3]]   [1] TRUE   [[4]]   [1] 1+4i   class(x)   [1] “list”   typeof(x)   [1] “list”   length(x)   [1] 4

You can create an empty list of a prespecified length with the vector() function.

``````x <- vector("list", length = 10)
x``````

Output:   x   [[1]]   NULL   [[2]]   NULL   [[3]]   NULL   [[4]]   NULL   [[5]]   NULL   [[6]]   NULL   [[7]]   NULL   [[8]]   NULL   [[9]]   NULL   [[10]]   NULL

### How to extract elements from a list?

Lists can be subset using two syntaxes, the \$ operator, and square brackets []. The \$ operator returns a named element of a list. The [] syntax returns a list, while the [[]] returns an element of a list.

``````# subsetting
l\$e
l["e"]
l[1:2]
l[c(1:2)] #index using integer vector
l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last.
l[c(T,F,F,F,F)] # logical index to access elements``````

Output: > l\$e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l["e"] \$e [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] 1 0 0 0 0 0 0 0 0 0 [2,] 0 1 0 0 0 0 0 0 0 0 [3,] 0 0 1 0 0 0 0 0 0 0 [4,] 0 0 0 1 0 0 0 0 0 0 [5,] 0 0 0 0 1 0 0 0 0 0 [6,] 0 0 0 0 0 1 0 0 0 0 [7,] 0 0 0 0 0 0 1 0 0 0 [8,] 0 0 0 0 0 0 0 1 0 0 [9,] 0 0 0 0 0 0 0 0 1 0 [10,] 0 0 0 0 0 0 0 0 0 1 > l[1:2] [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[c(1:2)] #index using integer vector [[1]] [1] 1 2 3 4 [[2]] [1] FALSE > l[-c(3:length(l))] #negative index to exclude elements from 3rd up to last. [[1]] [1] 1 2 3 4 [[2]] [1] FALSE l[c(T,F,F,F,F)] [[1]] [1] 1 2 3 4

### Modifying a List in R:

We can change components of a list through reassignment.

``````l[["name"]] <- "Kalyan Nandi"
l``````

Output: [[1]] [1] 1 2 3 4 [[2]] [1] FALSE [[3]] [1] “Hello Statistics!” \$d function (arg = 42) { print(“Hello World!”) } \$name [1] “Kalyan Nandi”

## R Matrices

In R Programming Matrix is a two-dimensional data structure. They contain elements of the same atomic types. A Matrix can be created using the matrix() function. R can also be used for matrix calculations. Matrices have rows and columns containing a single data type. In a matrix, the order of rows and columns is important. Dimension can be checked directly with the dim() function and all attributes of an object can be checked with the attributes() function. Check the below example.

``````Creating a matrix in R

m <- matrix(nrow = 2, ncol = 3)
dim(m)
attributes(m)
m <- matrix(1:20, nrow = 4, ncol = 5)
m``````

Output:   dim(m)   [1] 2 3   attributes(m)   \$dim   [1] 2 3   m <- matrix(1:20, nrow = 4, ncol = 5)   m   [,1] [,2] [,3] [,4] [,5]   [1,] 1 5 9 13 17   [2,] 2 6 10 14 18   [3,] 3 7 11 15 19   [4,] 4 8 12 16 20

Matrices can be created by column-binding or row-binding with the cbind() and rbind() functions.

``````x<-1:3
y<-10:12
z<-30:32
cbind(x,y,z)
rbind(x,y,z)``````

Output:   cbind(x,y,z)   x y z   [1,] 1 10 30   [2,] 2 11 31   [3,] 3 12 32   rbind(x,y,z)   [,1] [,2] [,3]   x 1 2 3   y 10 11 12   z 30 31 32

By default, the matrix function reorders a vector into columns, but we can also tell R to use rows instead.

``````x <-1:9
matrix(x, nrow = 3, ncol = 3)
matrix(x, nrow = 3, ncol = 3, byrow = TRUE)
``````

Output   cbind(x,y,z)   x y z   [1,] 1 10 30   [2,] 2 11 31   [3,] 3 12 32   rbind(x,y,z)   [,1] [,2] [,3]   x 1 2 3   y 10 11 12   z 30 31 32

## R Arrays

In R, Arrays are the data types that can store data in more than two dimensions. An array can be created using the array() function. It takes vectors as input and uses the values in the dim parameter to create an array. If you create an array of dimensions (2, 3, 4) then it creates 4 rectangular matrices each with 2 rows and 3 columns. Arrays can store only data type.

### Give a Name to Columns and Rows:

We can give names to the rows, columns, and matrices in the array by setting the dimnames parameter.

``````v1 <- c(1,2,3)
v2 <- 100:110
col.names <- c("Col1","Col2","Col3","Col4","Col5","Col6","Col7")
row.names <- c("Row1","Row2")
matrix.names <- c("Matrix1","Matrix2")
arr4 <- array(c(v1,v2), dim=c(2,7,2), dimnames = list(row.names,col.names, matrix.names))
arr4
``````

Output: , , Matrix1 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110 , , Matrix2 Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

### Accessing/Extracting Array Elements:

``````# Print the 2nd row of the 1st matrix of the array.
print(arr4[2,,1])
# Print the element in the 2nd row and 4th column of the 2nd matrix.
print(arr4[2,4,2])
# Print the 2nd Matrix.
print(arr4[,,2])``````

Output: > print(arr4[2,,1]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 2 100 102 104 106 108 110 > > # Print the element in the 2nd row and 4th column of the 2nd matrix. > print(arr4[2,4,2]) [1] 104 > > # Print the 2nd Matrix. > print(arr4[,,2]) Col1 Col2 Col3 Col4 Col5 Col6 Col7 Row1 1 3 101 103 105 107 109 Row2 2 100 102 104 106 108 110

## R Factors

Factors are used to represent categorical data and can be unordered or ordered. An example might be “Male” and “Female” if we consider gender. Factor objects can be created with the factor() function.

``````x <- factor(c("male", "female", "male", "male", "female"))
x
table(x)``````

Output:   x   [1] male female male male female   Levels: female male   table(x)   x   female male     2      3

By default, Levels are put in alphabetical order. If you print the above code you will get levels as female and male. But if you want to get your levels in a particular order then set levels parameter like this.

``````x <- factor(c("male", "female", "male", "male", "female"), levels=c("male", "female"))
x
table(x)``````

Output:   x   [1] male female male male female   Levels: male female   table(x)   x   male female    3      2

## R Dataframes

Data frames are used to store tabular data in R. They are an important type of object in R and are used in a variety of statistical modeling applications. Data frames are represented as a special type of list where every element of the list has to have the same length. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows. Unlike matrices, data frames can store different classes of objects in each column. Matrices must have every element be the same class (e.g. all integers or all numeric).

### Creating a Data Frame:

Data frames can be created explicitly with the data.frame() function.

``````employee <- c('Ram','Sham','Jadu')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2016-11-1','2015-3-25','2017-3-14'))
employ_data <- data.frame(employee, salary, startdate)
employ_data
View(employ_data)``````

Output: employ_data employee salary startdate 1 Ram 21000 2016-11-01 2 Sham 23400 2015-03-25 3 Jadu 26800 2017-03-14   View(employ_data)

### Get the Structure of the Data Frame:

If you look at the structure of the data frame now, you see that the variable employee is a character vector, as shown in the following output:

``str(employ_data)``

Output: > str(employ_data) 'data.frame': 3 obs. of 3 variables: \$ employee : Factor w/ 3 levels "Jadu","Ram","Sham": 2 3 1 \$ salary : num 21000 23400 26800 \$ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

Note that the first column, employee, is of type factor, instead of a character vector. By default, data.frame() function converts character vector into factor. To suppress this behavior, we can pass the argument stringsAsFactors=FALSE.

``````employ_data <- data.frame(employee, salary, startdate, stringsAsFactors = FALSE)
str(employ_data)``````

Output: 'data.frame': 3 obs. of 3 variables: \$ employee : chr "Ram" "Sham" "Jadu" \$ salary : num 21000 23400 26800 \$ startdate: Date, format: "2016-11-01" "2015-03-25" "2017-03-14"

## R Packages

The primary location for obtaining R packages is CRAN.

You can obtain information about the available packages on CRAN with the available.packages() function.
a <- available.packages()

head(rownames(a), 30) # Show the names of the first 30 packages
Packages can be installed with the install.packages() function in R.  To install a single package, pass the name of the lecture to the install.packages() function as the first argument.
The following code installs the ggplot2 package from CRAN.
install.packages(“ggplot2”)
You can install multiple R packages at once with a single call to install.packages(). Place the names of the R packages in a character vector.
install.packages(c(“caret”, “ggplot2”, “dplyr”))

Installing a package does not make it immediately available to you in R; you must load the package. The library() function is used to load packages into R. The following code is used to load the ggplot2 package into R. Do not put the package name in quotes.
library(ggplot2)
If you have Installed your packages without root access using the command install.packages(“ggplot2″, lib=”/data/Rpackages/”). Then to load use the below command.
library(ggplot2, lib.loc=”/data/Rpackages/”)
After loading a package, the functions exported by that package will be attached to the top of the search() list (after the workspace).
library(ggplot2)

search()

## R – CSV() files

In R, we can read data from files stored outside the R environment. We can also write data into files that will be stored and accessed by the operating system. R can read and write into various file formats like CSV, Excel, XML, etc.

### Getting and Setting the Working Directory

We can check which directory the R workspace is pointing to using the getwd() function. You can also set a new working directory using setwd()function.

``````# Get and print current working directory.
print(getwd())

# Set current working directory.
setwd("/web/com")

# Get and print current working directory.
print(getwd())``````

Output: [1] "/web/com/1441086124_2016" [1] "/web/com"

### Input as CSV File

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.

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.

Following is a simple example of read.csv() function to read a CSV file available in your current working directory −

``````data <- read.csv("input.csv")
print(data)``````
``  id,   name,    salary,   start_date,     dept``

## R- Charts and Graphs

### R- Pie Charts

Pie charts are created with the function pie(x, labels=) where x is a non-negative numeric vector indicating the area of each slice and labels= notes a character vector of names for the slices.

#### Syntax

The basic syntax for creating a pie-chart using the R is −

pie(x, labels, radius, main, col, clockwise)

Following is the description of the parameters used −

• x is a vector containing the numeric values used in the pie chart.
• labels are used to give a description of the slices.
• radius indicates the radius of the circle of the pie chart. (value between −1 and +1).
• main indicates the title of the chart.
• col indicates the color palette.
• clockwise is a logical value indicating if the slices are drawn clockwise or anti-clockwise.

#### Simple Pie chart

``````# Simple Pie Chart
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie(slices, labels = lbls, main="Pie Chart of Countries")``````

3-D pie chart

The pie3D( ) function in the plotrix package provides 3D exploded pie charts.

``````# 3D Exploded Pie Chart
library(plotrix)
slices <- c(10, 12, 4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
pie3D(slices,labels=lbls,explode=0.1,
main="Pie Chart of Countries ")``````

### R -Bar Charts

A bar chart represents data in rectangular bars with a 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 the bar chart, each of the bars can be given different colors.

Let us suppose, we have a vector of maximum temperatures (in degree Celsius) for seven days as follows.

``````max.temp <- c(22, 27, 26, 24, 23, 26, 28)
barplot(max.temp)``````

Some of the frequently used ones are, “main” to give the title, “xlab” and “ylab” to provide labels for the axes, names.arg for naming each bar, “col” to define color, etc.

We can also plot bars horizontally by providing the argument horiz=TRUE.

``````# barchart with added parameters
barplot(max.temp,
main = "Maximum Temperatures in a Week",
xlab = "Degree Celsius",
ylab = "Day",
names.arg = c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat"),
col = "darkred",
horiz = TRUE)``````

Simply doing barplot(age) will not give us the required plot. It will plot 10 bars with height equal to the student’s age. But we want to know the number of students in each age category.

This count can be quickly found using the table() function, as shown below.

``````> table(age)
age
16 17 18 19
1  2  6  1``````

Now plotting this data will give our required bar plot. Note below, that we define the argument “density” to shade the bars.

``````barplot(table(age),
main="Age Count of 10 Students",
xlab="Age",
ylab="Count",
border="red",
col="blue",
density=10
)``````

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.

R creates histogram using hist() function. This function takes a vector as an input and uses some more parameters to plot histograms.

#### Syntax

The basic syntax for creating a histogram using R is −

hist(v,main,xlab,xlim,ylim,breaks,col,border)

Following is the description of the parameters used −

• v is a vector containing numeric values used in the histogram.
• main indicates the title of the chart.
• col is used to set the color of the bars.
• border is used to set the border color of each bar.
• xlab is used to give a description of the x-axis.
• xlim is used to specify the range of values on the x-axis.
• ylim is used to specify the range of values on the y-axis.
• breaks are used to mention the width of each bar.

### Example

A simple histogram is created using input vector, label, col, and border parameters.

The script given below will create and save the histogram in the current R working directory.

``````# Create data for the graph.
v <-  c(9,13,21,8,36,22,12,41,31,33,19)

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

# Create the histogram.
hist(v,xlab = "Weight",col = "yellow",border = "blue")

# Save the file.
dev.off()``````

### Range of X and Y values

To specify the range of values allowed in X axis and Y axis, we can use the xlim and ylim parameters.

The width of each bar can be decided by using breaks.

``````# Create data for the graph.
v <- c(9,13,21,8,36,22,12,41,31,33,19)

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

# Create the histogram.
hist(v,xlab = "Weight",col = "green",border = "red", xlim = c(0,40), ylim = c(0,5),
breaks = 5)

# Save the file.
dev.off()``````

## R vs SAS – Which Tool is Better?

The debate around data analytics tools has been going on forever. Each time a new one comes out, comparisons transpire. Although many aspects of the tool remain subjective, beginners want to know which tool is better to start with.
The most popular and widely used tools for data analytics are R and SAS. Both of them have been around for a long time and are often pitted against each other. So, let’s compare them based on the most relevant factors.

1. Availability and Cost: SAS is widely used in most private organizations as it is a commercial software. It is more expensive than any other data analytics tool available. It might thus be a bit difficult buying the software if you are an individual professional or a student starting out. On the other hand, R is an open source software and is completely free to use. Anyone can begin using it right away without having to spend a penny. So, regarding availability and cost, R is hands down the better tool.
2. Ease of learning: Since SAS is a commercial software, it has a whole lot of online resources available. Also, those who already know SQL might find it easier to adapt to SAS as it comes with PROC SQL option. The tool has a user-friendly GUI. It comes with an extensive documentation and tutorial base which can help early learners get started seamlessly. Whereas, the learning curve for R is quite steep. You need to learn to code at the root level and carrying out simple tasks demand a lot of time and effort with R. However, several forums and online communities post religiously about its usage.
3. Data Handling Capabilities: When it comes to data handling, both SAS and R perform well, but there are some caveats for the latter. While SAS can even churn through terabytes of data with ease, R might be constrained as it makes use of the available RAM in the machine. This can be a hassle for 32-bit systems with low RAM capacity. Due to this, R can at times become unresponsive or give an ‘out of memory’ error. Both of them can run parallel computations, support integrations for Hadoop, Spark, Cloudera and Apache Pig among others. Also, the availability of devices with better RAM capacity might negate the disadvantages of R.
4. Graphical Capabilities: Graphical capabilities or data visualization is the strongest forte of R. This is where SAS lacks behind in a major way. R has access to packages like GGPlot, RGIS, Lattice, and GGVIS among others which provide superior graphical competency. In comparison, Base SAS is struggling hard to catch up with the advancements in graphics and visualization in data analytics. Even the graphics packages available in SAS are poorly documented which makes them difficult to use.
5. Advancements in Tool: Advancements in the industry give way to advancements in tools, and both SAS and R hold up pretty well in this regard. SAS, being a corporate software, rolls out new features and technologies frequently with new versions of its software. However, the updates are not as fast as R since it is open source software and has many contributors throughout the world. Alternatively, the latest updates in SAS are pushed out after thorough testing, making them much more stable, and reliable than R. Both the tools come with a fair share of pros & cons.
6. Job Scenario: Currently, large corporations insist on using SAS, but SMEs and start-ups are increasingly opting for R, given that it’s free. The current job trend seems to show that while SAS is losing its momentum, R is gaining potential. The job scenario is on the cusp of change, and both the tools seem strong, but since R is on an uphill path, it can probably witness more jobs in the future, albeit not in huge corporates.
7. Deep Learning Support: While SAS has just begun work on adding deep learning support, R has added support for a few packages which enable deep learning capabilities in the tool. You can use KerasR and keras package in R which are mere interfaces for the original Keras package built on Python. Although none of the tools are excellent facilitators of deep learning, R has seen some recent active developments on this front.
8. Customer Service Support and Community: As one would expect from full-fledged commercial software, SAS offers excellent customer service support as well as the backing of a helpful community. Since R is free open-source software, expecting customer support will be hard to justify. However, it has a vast online community that can help you with almost everything. On the other hand, no matter what problem you face with SAS, you can immediately reach out to their customer support and get it solved without any hassles.

Final Verdict
As per estimations by the Economic Times, the analytics industry will grow to \$16 billion till 2025 in India. If you wish to venture into this domain, there can’t be a better time. Just start learning the tool you think is better based on the comparison points above.

Original article source at: https://www.mygreatlearning.com