D3-loom: D3 Plugin to Create A Loom Chart Layout (for D3-v4)

d3-loom

This is a d3 plugin to create a "loom" visualization. 

The words spoken by the Fellowship member during all 3 movies

The loom layout is meant to create a chart with a group of entities in the center and different group of entities on the outside. They are connected by strings where the thickness of the string on the outside represents the connection (i.e. value) of the inner and outer entity.

Or in the words of Robert Kosara

One chart to rule them all, one chart to find them; one chart to bring them all and in the darkness bind them

For example, in the above image, the inner entities are the characters of the Fellowship in the Lord of the Rings movies. The outer entities are the locations in Middle Earth where the movie takes place. The connection/value is the number of words spoken by each character at each location.

Since this layout was transformed from d3's chord diagram many of the below API references will be similar to those from the chord diagram for the loom and similar to the ribbon functions for the strings.

Installing

Download the latest build. d3-loom depends on d3, so be sure to include a script tag with the d3 library before including d3-loom.js. In a vanilla environment, a d3 global is exported:

<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="d3-loom.js"></script>

<script>

var loom = d3.loom();

</script>

If you use a package manager like npm or yarn, say

npm install d3-loom

or

yarn add d3-loom

to add d3-loom to your project. AMD, CommonJS, and vanilla environments are supported.

Note

One thing that I have not (yet) figured out is how to sort the outer groups/entities in such a way to automatically make a visually appealing split in 2 separate halves. This is only relevant when you specify an empty percentage, thus create a gap in the top and bottom. For now you will have to manually order the outer entities in such a way that when split into two groups, the total value of those two groups separately lies close to 50%. However, you don't need to have the exact number of entities on the left half as on the right. The program will try and find a split that separates all the entities in two groups to make them both as close to 50% as possible, but it will not reorder the outer entities to do so.

Feedback

This is my first attempt at a plugin and it has definitely not been tested well enough. I would therefore love to hear from you about any bugs or errors that you run into while trying to use the plugin. You can create an Issue right here, reach me on Twitter via @NadiehBremer or mail me on info at visualcinnamon.com

API Reference

# d3.loom()

Constructs a new loom generator with the default settings.

# loom(data)

Computes the loom layout for the specified data. The length of the returned array is the same as data, however, due to sorting of the strings, to reduce overlap, the ordering can be different than the initial data.

Typically a dataset for the loom contains 3 values; the outer entity, the inner entity and the value that connects these two (e.g. outer = location, inner = person, value = days that person stayed in the location):

var data = [
  {
    outer: "Amsterdam",
    inner: "Alexander",
    value: 679
  },
  {
    outer: "Bangkok",
    inner: "Brendan",
    value: 124
  },
  //...and so on
];

The return value of loom(data) is an array of looms, where each loom represents the connection between an entity in the center of the loom (the inner) and the entity on the outside of the loom (the outer) and is an object with the following properties:

  • inner - the inner subgroup
  • outer - the outer subgroup

Both the inner and outer subgroup are also objects. The inner has the following properties:

  • name - the name of the inner entity
  • offset - the horizontal offset of the inner string's endpoint from the center
  • x - the horizontal location of the inner entity
  • y - the vertical location of the inner entity

And the outer has the following properties:

  • groupStartAngle - the start angle of the outer group to which the specific string belongs
  • startAngle - the start angle of the string at the outer edge in radians
  • endAngle - the end angle of the string at the outer edge in radians
  • value - the numeric value of the string
  • index - the zero-based sorted index of the group
  • subindex - the zero-based sorted sub-index of the string within the group
  • innername - the name of the connected inner entity
  • outername - the name of the outer entity

The looms are passed to d3.string to display the relationships between the inner and outer entities.

The looms array also defines a two secondary arrays. The first, called groups, is an array representing the outer entities. The length of the array is the number of unique outer entities and is an object with the following properties:

  • startAngle - the start angle of the arc in radians
  • endAngle - the end angle of the arc in radians
  • value - the numeric value of the arc
  • index - the zero-based sorted index of the arc
  • outername - the name of the outer entity

The groups are passed to d3.arc to produce a donut chart around the circumference of the loom layout.

The other array, called, innergroups, is an array represting the inner entities. The length of the array is the number of unique inner entities and is an object with the following properties:

  • name - the name of the inner entity
  • offset - the horizontal offset of the inner string's endpoint from the center
  • x - the horizontal location of the inner entity
  • y - the vertical location of the inner entity

The innergroups are used to create the textual representation of the inner entities in the center of the loom layout.

# loom.padAngle([angle])

If angle is specified, sets the pad angle (i.e. the white space) between adjacent outer groups to the specified number in radians and returns this loom layout. If angle is not specified, returns the current pad angle, which defaults to zero.

# loom.inner([inner])

The inner represents the name/id/... textual value of the entities that will be placed in the center. If inner is specified, sets the inner accessor to the specified function and returns this string generator. If inner is not specified, returns the current inner accessor, which defaults to:

function inner(d) {
  return d.inner;
}

# loom.outer([outer])

The outer represents the name/id/... textual value of the entities that will be placed around the loom along a circle. If outer is specified, sets the outer accessor to the specified function and returns this string generator. If outer is not specified, returns the current outer accessor, which defaults to:

function outer(d) {
  return d.outer;
}

# loom.value([value])

The value represents the numeric value that is the connection between the inner and outer entity. It is the value that determines the width of the strings on the outside. If value is specified, sets the value accessor to the specified function and returns this string generator. If value is not specified, returns the current value accessor, which defaults to:

function value(d) {
  return d.value;
}

# loom.heightInner([height])

This height gives the vertical distance between the inner entities in the center. If height is specified, sets the heightInner to the specified number and returns this loom generator. If height is not specified, returns the current heightInner value, which defaults to 20 pixels.

# loom.widthInner([width])

This width gives the horizontal distance between the inner endpoints of the strings in the center. It is the value that determines the width of the gap that is created so the text of the inner entities does not overlap the strings. If width is specified, sets the widthInner to the specified function or number and returns this loom generator. However, note that this function receives a d value that contains the string of the entity in the center (the inner). You can therefore make the width depend on the length of the inner's string. If width is not specified, returns the current widthInner value, which defaults to 30 pixels.

# loom.emptyPerc([value])

This value gives the percentage of the circle that will be empty to create space in the top and bottom. If value is specified, sets the current emptyPerc to the specified number in the range [0,1] and returns this loom generator. If value is not specified, returns the current emptyPerc value, which defaults to 0.2.

# loom.sortGroups([compare])

If compare is specified, sets the group comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current group comparator, which defaults to null. If the group comparator is non-null, it is used to sort the outer groups/entities by their total value (i.e. the sum of all the inner strings). See also d3.ascending and d3.descending.

# loom.sortSubgroups([compare])

If compare is specified, sets the subgroup comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current subgroup comparator, which defaults to null. If the subgroup comparator is non-null, it is used to sort the subgroups (i.e. the separate strings) within each outer entity by their value. See also d3.ascending and d3.descending. This sorting applies to both the order of the strings on the outer edge and the vertical order of the inner entities. It is advised to supply a subGroup sorting whenever there is not already a sorting applied to the underlying data, otherwise the inner entities and the strings will be drawn in the exact order as they appear in the data, typically resulting in a lot of overlapping strings.

# loom.sortLooms([compare])

If compare is specified, sets the loom comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current loom comparator, which defaults to null. If the loom comparator is non-null, it is used to sort the strings by their value; this only affects the z-order of these inner strings (i.e. how they overlap). See also d3.ascending and [d3.descending].(https://github.com/d3/d3-array#descending).

# d3.string()

Creates a new string generator with the default settings.

# string(arguments…)

Generates a string for the given arguments. The arguments are arbitrary; they are simply propagated to the string's generator's accessor functions along with the this object. If the string generator has a context, then the string is rendered to this context as a sequence of path method calls and this function returns void. Otherwise, a path data string is returned.

Typically, only the radius, thicknessInner, and pullout should be adjusted when used on conjunction with the loom, because all the other accessors will work with the default settings.

# string.radius([radius])

If radius is specified, sets the radius accessor to the specified function and returns this string generator. If radius is not specified, returns the current radius value, which defaults to 100 pixels.

The radius represents the inner radius of the loom and is typically set to a single number. It is advised to always set this value different from the default, depending on the space available within your svg.

# string.thicknessInner([thickness])

If thickness is specified, sets the thicknessInner to the specified number and returns this string generator. If thickness is not specified, returns the current thicknessInner value, which defaults to 0 pixels. The thicknessInner defines the "height", or thickness, that the strings will have at their endpoints next to the inner entities.

# string.pullout([pullout])

If pullout is specified, sets the pullout to the specified number and returns this string generator. If pullout is not specified, returns the current pullout value, which defaults to 50 pixels. The pullout defines how far the two circle halves will be placed outward horizontally.

# string.inner([inner])

If inner is specified, sets the inner accessor to the specified function and returns this string generator. If inner is not specified, returns the current source accessor, which defaults to:

function inner(d) {
  return d.inner;
}

When the string generator is used in conjunction with the loom, the resulting loom array will contain an inner object. Thus this accessor function does not have to be set separately, but just use the default.

# string.outer([outer])

If outer is specified, sets the outer accessor to the specified function and returns this string generator. If outer is not specified, returns the current outer accessor, which defaults to:

function outer(d) {
  return d.outer;
}

When the string generator is used in conjunction with the loom, the resulting loom array will contain an outer object. Thus this accessor function does not have to be set separately, but just use the default.

# string.groupStartAngle([angle])

If angle is specified, sets the start angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current start angle accessor, which defaults to:

function groupStartAngle(d) {
  return d.groupStartAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. This separate assessor is needed to make sure that even when an emptyPerc is set, all the strings belonging to the same outer group will be drawn at the same side. It's best make this assessor similar in "function" to the startAngle below (i.e. if a constant is added onto the startAngle to rotate the whole, then the same constant should be added to the groupStartAngle). When the string generator is used in conjunction with the loom, the resulting loom array will contain an groupStartAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.startAngle([angle])

If angle is specified, sets the start angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current start angle accessor, which defaults to:

function startAngle(d) {
  return d.startAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. When the string generator is used in conjunction with the loom, the resulting loom array will contain an startAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.endAngle([angle])

If angle is specified, sets the end angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current end angle accessor, which defaults to:

function endAngle(d) {
  return d.endAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. When the string generator is used in conjunction with the loom, the resulting loom array will contain an endAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.x([x])

If x is specified, sets the x accessor to the specified function and returns this string generator. If x is not specified, returns the current x accessor, which defaults to:

function x(d) {
  return d.x;
}

The x defines the horizontal location where the inner entities are placed, typically in the center of the loom. When the string generator is used in conjunction with the loom, the resulting loom array will contain an x value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.y([y])

If y is specified, sets the y accessor to the specified function and returns this string generator. If y is not specified, returns the current y accessor, which defaults to:

function y(d) {
  return d.y;
}

The y defines the vertical location where the inner entities are placed. They are typically placed in a column like fashion in the center, one above the other. When the string generator is used in conjunction with the loom, the resulting loom array will contain an y value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.offset([offset])

If offset is specified, sets the offset accessor to the specified function and returns this string generator. If offset is not specified, returns the current offset accessor, which defaults to:

function offset(d) {
  return d.offset;
}

The offset defines the horizontal space between the inner end points of the strings, so that the text of the inner entities does not overlap the strings. It is typically set through the widthInner accessor of the loom and propagates through to the string function. Therefore, when the string generator is used in conjunction with the loom, the resulting loom array will contain an offset value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.context([context])

If context is specified, sets the context and returns this string generator. If context is not specified, returns the current context, which defaults to null. If the context is not null, then the generated string is rendered to this context as a sequence of path method calls. Otherwise, a path data string representing the generated string is returned. See also d3-path.


For an extensive explanation of the effects of all the settings decribed farther below, please see the blog/tutorial I wrote about the loom here.


Author: nbreme
Source Code: https://github.com/nbremer/d3-loom 
License: 

#javascript #3d #d3 #visualization #chart 

What is GEEK

Buddha Community

D3-loom: D3 Plugin to Create A Loom Chart Layout (for D3-v4)
Easter  Deckow

Easter Deckow

1655630160

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

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.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

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"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#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")

Creating a link post

  • 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.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#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
License: Apache-2.0 license

#python #api 

Tamale  Moses

Tamale Moses

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)))

Output [4]: 0x1691cc8ad68

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.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

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.

Also Read: Real-Time Object Detection Using TensorFlow

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

1. Difference between mutable vs immutable in Python?

Mutable ObjectImmutable Object
State of the object can be modified after it is created.State of the object can’t be modified once it is created.
They are not thread safe.They are thread safe
Mutable classes are not final.It is important to make the class final before creating an immutable object.

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.

Join Great Learning Academy’s free online courses and upgrade your skills today.


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

#python 

D3-loom: D3 Plugin to Create A Loom Chart Layout (for D3-v4)

d3-loom

This is a d3 plugin to create a "loom" visualization. 

The words spoken by the Fellowship member during all 3 movies

The loom layout is meant to create a chart with a group of entities in the center and different group of entities on the outside. They are connected by strings where the thickness of the string on the outside represents the connection (i.e. value) of the inner and outer entity.

Or in the words of Robert Kosara

One chart to rule them all, one chart to find them; one chart to bring them all and in the darkness bind them

For example, in the above image, the inner entities are the characters of the Fellowship in the Lord of the Rings movies. The outer entities are the locations in Middle Earth where the movie takes place. The connection/value is the number of words spoken by each character at each location.

Since this layout was transformed from d3's chord diagram many of the below API references will be similar to those from the chord diagram for the loom and similar to the ribbon functions for the strings.

Installing

Download the latest build. d3-loom depends on d3, so be sure to include a script tag with the d3 library before including d3-loom.js. In a vanilla environment, a d3 global is exported:

<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="d3-loom.js"></script>

<script>

var loom = d3.loom();

</script>

If you use a package manager like npm or yarn, say

npm install d3-loom

or

yarn add d3-loom

to add d3-loom to your project. AMD, CommonJS, and vanilla environments are supported.

Note

One thing that I have not (yet) figured out is how to sort the outer groups/entities in such a way to automatically make a visually appealing split in 2 separate halves. This is only relevant when you specify an empty percentage, thus create a gap in the top and bottom. For now you will have to manually order the outer entities in such a way that when split into two groups, the total value of those two groups separately lies close to 50%. However, you don't need to have the exact number of entities on the left half as on the right. The program will try and find a split that separates all the entities in two groups to make them both as close to 50% as possible, but it will not reorder the outer entities to do so.

Feedback

This is my first attempt at a plugin and it has definitely not been tested well enough. I would therefore love to hear from you about any bugs or errors that you run into while trying to use the plugin. You can create an Issue right here, reach me on Twitter via @NadiehBremer or mail me on info at visualcinnamon.com

API Reference

# d3.loom()

Constructs a new loom generator with the default settings.

# loom(data)

Computes the loom layout for the specified data. The length of the returned array is the same as data, however, due to sorting of the strings, to reduce overlap, the ordering can be different than the initial data.

Typically a dataset for the loom contains 3 values; the outer entity, the inner entity and the value that connects these two (e.g. outer = location, inner = person, value = days that person stayed in the location):

var data = [
  {
    outer: "Amsterdam",
    inner: "Alexander",
    value: 679
  },
  {
    outer: "Bangkok",
    inner: "Brendan",
    value: 124
  },
  //...and so on
];

The return value of loom(data) is an array of looms, where each loom represents the connection between an entity in the center of the loom (the inner) and the entity on the outside of the loom (the outer) and is an object with the following properties:

  • inner - the inner subgroup
  • outer - the outer subgroup

Both the inner and outer subgroup are also objects. The inner has the following properties:

  • name - the name of the inner entity
  • offset - the horizontal offset of the inner string's endpoint from the center
  • x - the horizontal location of the inner entity
  • y - the vertical location of the inner entity

And the outer has the following properties:

  • groupStartAngle - the start angle of the outer group to which the specific string belongs
  • startAngle - the start angle of the string at the outer edge in radians
  • endAngle - the end angle of the string at the outer edge in radians
  • value - the numeric value of the string
  • index - the zero-based sorted index of the group
  • subindex - the zero-based sorted sub-index of the string within the group
  • innername - the name of the connected inner entity
  • outername - the name of the outer entity

The looms are passed to d3.string to display the relationships between the inner and outer entities.

The looms array also defines a two secondary arrays. The first, called groups, is an array representing the outer entities. The length of the array is the number of unique outer entities and is an object with the following properties:

  • startAngle - the start angle of the arc in radians
  • endAngle - the end angle of the arc in radians
  • value - the numeric value of the arc
  • index - the zero-based sorted index of the arc
  • outername - the name of the outer entity

The groups are passed to d3.arc to produce a donut chart around the circumference of the loom layout.

The other array, called, innergroups, is an array represting the inner entities. The length of the array is the number of unique inner entities and is an object with the following properties:

  • name - the name of the inner entity
  • offset - the horizontal offset of the inner string's endpoint from the center
  • x - the horizontal location of the inner entity
  • y - the vertical location of the inner entity

The innergroups are used to create the textual representation of the inner entities in the center of the loom layout.

# loom.padAngle([angle])

If angle is specified, sets the pad angle (i.e. the white space) between adjacent outer groups to the specified number in radians and returns this loom layout. If angle is not specified, returns the current pad angle, which defaults to zero.

# loom.inner([inner])

The inner represents the name/id/... textual value of the entities that will be placed in the center. If inner is specified, sets the inner accessor to the specified function and returns this string generator. If inner is not specified, returns the current inner accessor, which defaults to:

function inner(d) {
  return d.inner;
}

# loom.outer([outer])

The outer represents the name/id/... textual value of the entities that will be placed around the loom along a circle. If outer is specified, sets the outer accessor to the specified function and returns this string generator. If outer is not specified, returns the current outer accessor, which defaults to:

function outer(d) {
  return d.outer;
}

# loom.value([value])

The value represents the numeric value that is the connection between the inner and outer entity. It is the value that determines the width of the strings on the outside. If value is specified, sets the value accessor to the specified function and returns this string generator. If value is not specified, returns the current value accessor, which defaults to:

function value(d) {
  return d.value;
}

# loom.heightInner([height])

This height gives the vertical distance between the inner entities in the center. If height is specified, sets the heightInner to the specified number and returns this loom generator. If height is not specified, returns the current heightInner value, which defaults to 20 pixels.

# loom.widthInner([width])

This width gives the horizontal distance between the inner endpoints of the strings in the center. It is the value that determines the width of the gap that is created so the text of the inner entities does not overlap the strings. If width is specified, sets the widthInner to the specified function or number and returns this loom generator. However, note that this function receives a d value that contains the string of the entity in the center (the inner). You can therefore make the width depend on the length of the inner's string. If width is not specified, returns the current widthInner value, which defaults to 30 pixels.

# loom.emptyPerc([value])

This value gives the percentage of the circle that will be empty to create space in the top and bottom. If value is specified, sets the current emptyPerc to the specified number in the range [0,1] and returns this loom generator. If value is not specified, returns the current emptyPerc value, which defaults to 0.2.

# loom.sortGroups([compare])

If compare is specified, sets the group comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current group comparator, which defaults to null. If the group comparator is non-null, it is used to sort the outer groups/entities by their total value (i.e. the sum of all the inner strings). See also d3.ascending and d3.descending.

# loom.sortSubgroups([compare])

If compare is specified, sets the subgroup comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current subgroup comparator, which defaults to null. If the subgroup comparator is non-null, it is used to sort the subgroups (i.e. the separate strings) within each outer entity by their value. See also d3.ascending and d3.descending. This sorting applies to both the order of the strings on the outer edge and the vertical order of the inner entities. It is advised to supply a subGroup sorting whenever there is not already a sorting applied to the underlying data, otherwise the inner entities and the strings will be drawn in the exact order as they appear in the data, typically resulting in a lot of overlapping strings.

# loom.sortLooms([compare])

If compare is specified, sets the loom comparator to the specified function or null and returns this loom layout. If compare is not specified, returns the current loom comparator, which defaults to null. If the loom comparator is non-null, it is used to sort the strings by their value; this only affects the z-order of these inner strings (i.e. how they overlap). See also d3.ascending and [d3.descending].(https://github.com/d3/d3-array#descending).

# d3.string()

Creates a new string generator with the default settings.

# string(arguments…)

Generates a string for the given arguments. The arguments are arbitrary; they are simply propagated to the string's generator's accessor functions along with the this object. If the string generator has a context, then the string is rendered to this context as a sequence of path method calls and this function returns void. Otherwise, a path data string is returned.

Typically, only the radius, thicknessInner, and pullout should be adjusted when used on conjunction with the loom, because all the other accessors will work with the default settings.

# string.radius([radius])

If radius is specified, sets the radius accessor to the specified function and returns this string generator. If radius is not specified, returns the current radius value, which defaults to 100 pixels.

The radius represents the inner radius of the loom and is typically set to a single number. It is advised to always set this value different from the default, depending on the space available within your svg.

# string.thicknessInner([thickness])

If thickness is specified, sets the thicknessInner to the specified number and returns this string generator. If thickness is not specified, returns the current thicknessInner value, which defaults to 0 pixels. The thicknessInner defines the "height", or thickness, that the strings will have at their endpoints next to the inner entities.

# string.pullout([pullout])

If pullout is specified, sets the pullout to the specified number and returns this string generator. If pullout is not specified, returns the current pullout value, which defaults to 50 pixels. The pullout defines how far the two circle halves will be placed outward horizontally.

# string.inner([inner])

If inner is specified, sets the inner accessor to the specified function and returns this string generator. If inner is not specified, returns the current source accessor, which defaults to:

function inner(d) {
  return d.inner;
}

When the string generator is used in conjunction with the loom, the resulting loom array will contain an inner object. Thus this accessor function does not have to be set separately, but just use the default.

# string.outer([outer])

If outer is specified, sets the outer accessor to the specified function and returns this string generator. If outer is not specified, returns the current outer accessor, which defaults to:

function outer(d) {
  return d.outer;
}

When the string generator is used in conjunction with the loom, the resulting loom array will contain an outer object. Thus this accessor function does not have to be set separately, but just use the default.

# string.groupStartAngle([angle])

If angle is specified, sets the start angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current start angle accessor, which defaults to:

function groupStartAngle(d) {
  return d.groupStartAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. This separate assessor is needed to make sure that even when an emptyPerc is set, all the strings belonging to the same outer group will be drawn at the same side. It's best make this assessor similar in "function" to the startAngle below (i.e. if a constant is added onto the startAngle to rotate the whole, then the same constant should be added to the groupStartAngle). When the string generator is used in conjunction with the loom, the resulting loom array will contain an groupStartAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.startAngle([angle])

If angle is specified, sets the start angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current start angle accessor, which defaults to:

function startAngle(d) {
  return d.startAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. When the string generator is used in conjunction with the loom, the resulting loom array will contain an startAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.endAngle([angle])

If angle is specified, sets the end angle accessor to the specified function and returns this string generator. If angle is not specified, returns the current end angle accessor, which defaults to:

function endAngle(d) {
  return d.endAngle;
}

The angle is specified in radians, with 0 at -y (12 o'clock) and positive angles proceeding clockwise. When the string generator is used in conjunction with the loom, the resulting loom array will contain an endAngle value within the outer object. In that case this accessor function does not have to be set separately, but just use the default.

# string.x([x])

If x is specified, sets the x accessor to the specified function and returns this string generator. If x is not specified, returns the current x accessor, which defaults to:

function x(d) {
  return d.x;
}

The x defines the horizontal location where the inner entities are placed, typically in the center of the loom. When the string generator is used in conjunction with the loom, the resulting loom array will contain an x value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.y([y])

If y is specified, sets the y accessor to the specified function and returns this string generator. If y is not specified, returns the current y accessor, which defaults to:

function y(d) {
  return d.y;
}

The y defines the vertical location where the inner entities are placed. They are typically placed in a column like fashion in the center, one above the other. When the string generator is used in conjunction with the loom, the resulting loom array will contain an y value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.offset([offset])

If offset is specified, sets the offset accessor to the specified function and returns this string generator. If offset is not specified, returns the current offset accessor, which defaults to:

function offset(d) {
  return d.offset;
}

The offset defines the horizontal space between the inner end points of the strings, so that the text of the inner entities does not overlap the strings. It is typically set through the widthInner accessor of the loom and propagates through to the string function. Therefore, when the string generator is used in conjunction with the loom, the resulting loom array will contain an offset value within the inner object. In that case this accessor function does not have to be set separately, but just use the default.

# string.context([context])

If context is specified, sets the context and returns this string generator. If context is not specified, returns the current context, which defaults to null. If the context is not null, then the generated string is rendered to this context as a sequence of path method calls. Otherwise, a path data string representing the generated string is returned. See also d3-path.


For an extensive explanation of the effects of all the settings decribed farther below, please see the blog/tutorial I wrote about the loom here.


Author: nbreme
Source Code: https://github.com/nbremer/d3-loom 
License: 

#javascript #3d #d3 #visualization #chart 

Einar  Hintz

Einar Hintz

1593235440

Visualizing data with NGX-Charts in Angular

Data Science, Data Analytics, Big Data, these are the buzz words of today’s world. A huge amount of data is being generated and analyzed every day. So communicating the insights from that data becomes crucial. Charts help visualize the data and communicate the result of the analysis with charts, it becomes easy to understand the data.

There are a lot of libraries for angular that can be used to build charts. In this blog, we will look at one such library, NGX-Charts. We will see how to use it in angular and how to build data visualizations.

What we will cover:

  1. Installing ngx-chart.

  2. Building a vertical bar graph.

  3. Building a pie chart.

  4. Building an advanced pie chart.

A brief introduction about NGX-Charts

NGX-Chart charting framework for angular2+. It’s open-source and maintained by Swimlane.

NGX-Charts does not merely wrap d3, nor any other chart engine for that matter. It is using Angular to render and animate the SVG elements with all of its binding and speed goodness and uses d3 for the excellent math functions, scales, axis and shape generators, etc. By having Angular do all of the renderings it opens us up to endless possibilities the Angular platform provides such as AoT, Universal, etc.

NGX-Charts supports various chart types like bar charts, line charts, area charts, pie charts, bubble charts, doughnut charts, gauge charts, heatmap, treemap, and number cards.

Installation and Setup

1. Install the ngx-chart package in your angular app.

npm install @swimlane/ngx-charts --save

2. At the time of installing or when you serve your application is you get an error:

ERROR in The target entry-point "@swimlane/ngx-charts" has missing dependencies: - @angular/cdk/portal

You also need to install angular/cdk

npm install @angular/cdk --save

3. Import NgxChartsModule from ‘ngx-charts’ in AppModule

4. NgxChartModule also requires BrowserAnimationModule. Import is inAppModule.

app.module.ts

import { BrowserModule } from '@angular/platform-browser';
import { NgModule } from '@angular/core';
import { AppComponent } from './app.component';
import { NgxChartsModule }from '@swimlane/ngx-charts';
import { BrowserAnimationsModule } from '@angular/platform-browser/animations';
@NgModule({
  declarations: [
    AppComponent
  ],
  imports: [
    BrowserModule,
    BrowserAnimationsModule,
    NgxChartsModule
  ],
  providers: [],
  bootstrap: [AppComponent]
})
export class AppModule { }

Amazing! Now we can start using ngx-chart component and build the graph we want.

In the AppComponent we will provide data that the chart will represent. It’s a sample data for vehicles on the road survey.

#angular #angular 6 #scala #angular #angular 9 #bar chart #charting #charts #d3 charts #data visualisation #ngx #ngx charts #pie

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 addition.
add.result <- v1+v2
print(add.result)
# 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”))
 

Loading packages
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.

Reading a CSV File

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

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