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Why R is the Most Suitable Programming Language for Encompassing Data Science Projects?

Why R is the Most Suitable Programming Language for Encompassing Data Science Projects?

Since the early 1990’s, when R was first conceptualized, it has been leading the show in the field of data science. In the past few years, however, the popularity of R has increased exponentially – thanks to the advancement in data analytics environment. From data scientists to statisticians and researchers, R has become a hot-favorite for all. And, why not? It’s a GNU package and a free software package for statistical computing.

In this era of evolution, finding the best tool to stay ahead of the curve is the need of the hour. For that and more, we have selected R and given below are the points proving why R is the best programming tool in the competitive environment of data science.

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R is a substitute for data science for non-technical data enthusiasts

Well, if you are aware of leading data science trends and programming languages, you will find two high-end data science tools – R and Python – they tend to be the topic of conversation for all data-related matter. Python is a top of the line programming language for software professionals who have a knack in mathematics, statistics and machine learning, but lacks big time in offering library support on subjects like Econometrics and a bunch of communication tools, including reporting.

Most of the consultants working in the field of data science belongs from business community, and have no particular interest in technical know-how about developing software and acing programming languages. Learning python would not be as much of help as it would be mastering R programming – R is a programming language that supports libraries for stats, machine learning and data science. Thus, R is the best fit for data science enthusiasts not belonging from technical background. Also, R offers support packages or libraries for Econometrics, Finance, etc. – all of this is widely used for data analytics.

For R language training in Delhi, drop by DexLab Analytics.

After Tidyverse, mastering R is easy

Previously, learning R was no mean feat. It was considered one of the toughest languages to learn and largely inconsistent; the reasons being structuring and formality. But the things started to change when Tidyverse was introduced – it’s a robust set of packages and tools that offers steady structural programming interface.

In fact, after the launch of ‘dplyr’ and ‘ggplot2’, curve complexities got reduced even more. Just like any other languages, R went on getting better with its programming interface and achieving more structural and consistent – thanks to Tidyverse – it turned out to be efficient as it includes support packages for visualization, modeling, manipulation, iteration and communication – all of these turned R a super easy language to ace on.

R is mostly used for business purposes

The biggest advantage of R as compared to other programming languages is its capability to create industry-ready reports and infographics, and ML-powered web applications. For business-related matter, no other tool is as efficient as R.

But have you wondered what makes R so popular among the business community? It’s the two special R-enabled frameworks – RMARKDOWN and Shiny.

RMARKDOWN helps in developing reconstructable reports, which are regarded as the stepping stone for building blogs, websites, presentations, books and journals. On the other hand, Shiny is a powerful framework for creating interactive web applications for R. It is handy and widely popular.

DexLab Analytics offers leading R programming courses in Gurgaon for all the data enthusiasts. Check out the course itinerary and decide for yourself.

 

The blog has been sourced from – 

www.technotification.com/2018/06/r-programming-data-science.html

 

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Open a World of Opportunities: Web Scraping Using PHP and Python

Open a World of Opportunities: Web Scraping Using PHP and Python

The latest estimates says, the total number of websites has crossed one billion mark; everyday a new site is being added and removed, but the record stays.

Having said that, just imagine how much data is floating around the web. The amount is so huge that it would be impossible for even hundreds of humans to digest all the information in a lifetime. To tackle such large amounts of data, you not only need to have easy access to all the information but should also process some scalable way to gather data in order to organize and analyze it. And that’s exactly where web data scraping comes into picture.

Web scraping, data mining, web data extraction, web harvesting or screen scraping – they all means the same thing – a technique in which a computer program fetches huge piles of data from a website and saves them in your computer, spreadsheet or database in a normal format for easy analysis.

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Web Scraping with Python and BeautifulSoup

In case, you are not satisfied with the internet sources of web scraping, you are most likely to develop your very own data scraping tools, which is quite easier. In this blog we will show you how to frame a web scraper with Python and very simple yet dynamic BeautifulSoup Library:

First, import the libraries we will use: requests and BeautifulSoup:

# Import libraries
import requests
from bs4 import BeautifulSoup

Secondly, point out the variable for the URL using request.get method and gain access to the HTML content right from this page:

import requests
URL = "http://www.values.com/inspirational-quotes"
r = requests.get(URL)
print(r.content)

Next, we will parse a webpage, and for that, we need to create a BeautifulSoup object:

import requests 
from bs4 import BeautifulSoup
URL = "http://www.values.com/inspirational-quotes"
r = requests.get(URL)

 # Create a BeautifulSoup object
soup = BeautifulSoup(r.content, 'html5lib')
print(soup.prettify())

Now, let’s extract some meaningful information from HTML content. Look at the HTML content of the webpage, which was printed using the soup.pretify()method..

table = soup.find('div', attrs = {'id':'container'})

Here, you will find each quote inside a div container, belonging to the class quote.

We will repeat the process with each div container, belonging to the class quote. For that, we will use findAll()method and repeat the process with each quote using variable row.

After which, we will create a dictionary, in which all the data about the quote will be saved in a list, and is called ‘quotes’.

    quote['lines'] = row.h6.text

Now, coming to the final step – write down the data to a CSV file, but how?

See below:

filename = 'inspirational_quotes.csv'
with open(filename, 'wb') as f:
    w = csv.DictWriter(f,['theme','url','img','lines','author'])
    w.writeheader()
    for quote in quotes:
        w.writerow(quote)

This type of web scraping is used on a small-scale; for larger scale, you can consider:

Scraping Websites with PHP and Curl

To connect to a large number of servers and protocols, and download pictures, videos and graphics from several websites, consider Scraping Websites with PHP and cURL.

<?php

function curl_download($Url){

    if (!function_exists('curl_init')){
        die('cURL is not installed. Install and try again.');
    }

    $ch = curl_init();
    curl_setopt($ch, CURLOPT_URL, $Url);
    curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
    $output = curl_exec($ch);
    curl_close($ch);

    return $output;

print curl_download('http://www.gutenberg.org/browse/scores/top');

?>

In a nutshell, the scopes of using web scraping for analyzing content and applying it to your content marketing strategies are vast like the horizon. Armed by endless types of data analysis, web scraping technology has proved to be a valuable tool for the content producers. So, when are you feeding yourself with web scraping technology?

Discover the perfect platform for excellent R programming using Python courses. For more information on R programming training institute drop by DexLab Analytics.

 
This post originally appeared ondzone.com/articles/be-leading-content-provider-using-web-scraping-php
 

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