1.2 Web Scraping Can Be Ugly
Depending on what web sites you want to scrape the process can be involved and quite tedious. Many websites are very much aware that people are scraping so they offer Application Programming Interfaces (APIs) to make requests for information easier for the user and easier for the server administrators to control access. Most times the user must apply for a “key” to gain access.
In this R tutorial, you will learn R programming from basic to advance. This tutorial is ideal for both beginners and advanced programmers. R is the world's most widely used programming language for statistical analysis, predictive modeling and data science. It's popularity is claimed in many recent surveys and studies. $(webscrapingexample) pip install -r setup.py Import Required Modules Within the folder we created earlier, create a webscrapingexample.py file and include the following code snippets. Sep 04, 2017 $(webscrapingexample) pip install -r setup.py Import Required Modules Within the folder we created earlier, create a webscrapingexample.py file and include the following code snippets. 3 Tools for Webscraping 3.1 rvest How can you select elements of a website in R?Thervest package is the workhorse toolkit. The workflow typically is as follows:3 1. Read a webpage using the function readhtml. This function will download the HTML and store it so that rvest can navigate it. Select the elements you want using the function htmlnodes. In R, the merge command is a great way to match two data frames together. Just read the two data frames into R mydata1 = read.csv(path1, header=T).
For premium sites, the key costs money. Some sites like Google and Wunderground (a popular weather site) allow some number of free accesses before they start charging you. Even so the results are typically returned in XML or JSON which then requires you to parse the result to get the information you want. In the best situation there is an R package that will wrap in the parsing and will return lists or data frames.
Here is a summary:
First. Always try to find an R package that will access a site (e.g. New York Times, Wunderground, PubMed). These packages (e.g. omdbapi, easyPubMed, RBitCoin, rtimes) provide a programmatic search interface and return data frames with little to no effort on your part.
If no package exists then hopefully there is an API that allows you to query the website and get results back in JSON or XML. I prefer JSON because it’s “easier” and the packages for parsing JSON return lists which are native data structures to R. So you can easily turn results into data frames. You will ususally use the rvest package in conjunction with XML, and the RSJONIO packages.
If the Web site doesn’t have an API then you will need to scrape text. This isn’t hard but it is tedious. You will need to use rvest to parse HMTL elements. If you want to parse mutliple pages then you will need to use rvest to move to the other pages and possibly fill out forms. If there is a lot of Javascript then you might need to use RSelenium to programmatically manage the web page.
There is a good number of programming languages out there being used for web scraping. Python & R are among the most widely used languages in data science. Each of these has its unique features, upsides, as well as downsides, and in this web scraping Python Vs R comparison article, we’ll be helping you weigh your options.
What Is Web scraping?
Before we look at what each of these programming languages can and cannot do, let us first understand what web scraping is.
Web scraping is also known as web harvesting or web data extraction. The name defines it appropriately, as it implies extracting data from websites.
There are different web scraping software that can access the world wide web directly, by means of a web browser. They can also gain access by using the Hypertext Transfer Protocol.
Web scraping can be done manually, although the term itself generally refers to an automated process facilitated by bots or web crawlers.
It is basically lifting data from the web and copying it into a central local database or spreadsheet. The information that has been lifted will be saved for future retrieval or analysis.
Fetching the data is the first step, which involves downloading the webpage. The next step is the extraction process.
The content from the webpage can either be reformatted or copied onto a spreadsheet.
Generally speaking, web scrapers search for and extract information from a webpage to be used for other purposes.
A common example of a web scraping practice is searching for and copying contact information of local or international businesses. This is known as contact scraping.
Other web scraping purposes include product price search and comparison, product reviews, current market/stock prices, etc.
Is Web scraping Illegal?
Searching for and lifting information from web pages may seem like something an internet fraudster would be involved in. As true as this may be, there are a lot of web scrapers who have legitimate intentions.
Small or new businesses use it to gather information in the cheapest, most effective way possible. Larger companies also scrape to gather information for their own legitimate benefits, even though most of them don’t like having bots scraping through their own sites.
Looking at it from a general point of view, assuming all intent remains legitimate, then web scraping is a legit means of getting data. However, the courts and law enforcement agencies continue to keep a close eye on web scraping activities.
In the beginning, web scraping was what you could call a “nuisance”, thanks to all the bots scraping all the information they could find from websites. In the year 2000 however, that nuisance led to a preliminary injunction filed by eBay against Bidder’s Edge. eBay claimed that the use of bots against their consent violated Trespass to Chattels law.
Eventually, it all ended with an out-of-court settlement, nonetheless, the “legal ball” against web scraping had been set rolling.
Another scraping-related legal issue came about in 2001 when a travel agency sued one of its competitors for scraping prices from their website, which the competitor used to set its own prices.
It was ruled that even though the scraping did not go down well with the complainant, the act was not enough to be termed “unauthorized access” according to federal hacking laws.
During the coming years, courts ruled several times that a simple “do not scrape us” warning on a website’s terms of service page was not a “legally binding” agreement.
For such terms to be enforced, the user must clearly agree to the said terms.
These rulings by the federal courts were basically “green lights” for scrapers to continue with their web scraping activities.
Things were to change in 2009 when Facebook won a copyright suit against a web scraper. This was one of the first web scraping lawsuits won by a complainant.
There was a follow-up of lawsuits filed against numerous web scrapers around America, who’s activities could be termed a violation of copyright, and potentially cause significant financial damages to the complainant.
The fair use clause, which companies used to defend the act of web scraping had been re-adjusted, and as low as 4.5% of information scraped from a website could no longer be considered to be “fair use”.
Languages For Web Scraping
There are a number of web scraping languages available. For the sake of this article, we will be looking at Python and R.
Web Scraping With Python
Python is one of the best web scraping languages you can find.
Its high-level programming functionality allows users to develop desktop GUI applications and other web applications. It also allows you to focus on the core uses of the application as it handles all other basic programming tasks.
It has very simple syntax rules which allow the user to maintain a readable codebase.
Python has many useful features, some of which will be listed out below.
It is easy to use: Python is easy to code since you are not required to insert semi-colons or braces. It is ideal for beginners or those who are new to the practice of web scraping.
Large Collection of Libraries: Python is packed with libraries, including Matlolotlib, Pandas, and Numpy. This makes it ideal for both the extraction and manipulation of data.
Dynamically typed: Using python webscraping, there will be no need for you to define data types for variables. It allows you directly use the variables at any point required.
The syntax is easy to understand: The Python syntax is very easy to understand, this is because reading a Python code is just as simple as reading a sentence in the English language.
Being very expressive and easy-to-read, the user can easily tell the difference between several scope/blocks in the code.
Small codes can be used for large tasks: The purpose of web scraping, besides sourcing and lifting information, is also to do so in a time-saving manner. If you spend too much time writing the code, then the whole purpose has been defeated.
Using web scraping in Python, you can write small codes and use them to execute voluminous tasks. This will help users save time.
Community: Many beginners can get stuck halfway into writing code. If that be the case, you can visit Python’s online community and ask for help in any area where you might have some trouble.
Web Scraping With R
Just like Python, R is a web scraping programming language used by statisticians and data hunters to compute, collect, and analyze data.
R has become a very popular language thanks to the quality of plots that the user can work out. These include symbols in mathematics and other statistical formulae.
R is packed with a wide variety of functions that make data mining tasks simple.
R packages include RVest and RCrawler, both of which are used for data mining.
Basically, this is how R web scraping works.
First, you access a web page using R. The next step is to instruct R where to look, and the data to look for on your desired web page.
Lastly, when the required data has been found, use R to convert it into a usable format. The Rest package can do this for you.
Here’s what R can be used for –
- R can be used for machine learning
- It can be used for data visualization and analysis
- It can be used in some areas of scientific computing
Similarities Between Web Scraping With R And Python
Python and R have quite a few similarities, as well as differences. Let’s first take a look at some of these similarities.
- They are both open-source programming languages
- They both have large & assistance-based communities
- They both have new libraries and tools consistently being added to their catalog
- They both find and extract data from online sources
Differences Between Web Scraping With R And Python
As R and Python have similarities, so do they have differences.
Here are their major differences –
- R is more focused on statistical analysis, while Python is for general purposes
- R is more functional, while Python is more object-oriented
- R has more in-built data analysis, while Python depends on packages
- statsmodels in Python and other packages provide decent coverage for statistical methods, while R has a larger ecosystem
- web scraping in Python is much easier than R
Web Scraping Tools
Web Scraping With Beautiful Soup
For those who don’t know, Beautiful Soup is a Python library designed to extract data from HTML, XML, and other similar markup languages.
In a case where you find a webpage containing the desired data, but does not provide a means of downloading the data directly, Beautiful Soup can be used to extract content from the webpage, take off the HTML markup, and store the data.
Basically, it is a web scraping tool that cleans up and parses extracted web documents.
Beautiful Soup installation is very easy using Pip or any other Python installer. You can consult a Python module installation tutorial if you need extra help.
Web Scraping With Scrapy
For those new to the word, Scrapy is a free and open-source web-crawling framework written in the Python programming language. It was initially designed for web scraping purposes, but it can also be used to pull out data with the use of APIs. It can also be used as a general-purpose web-crawler.
As at today, Scrapy is being maintained by a web scraping and development company called ScrapingHub LTD.
The Scrapy framework is built around “spiders”, which are assigned a specific set of tasks.
Building and scaling large crawling projects is made easy using Scrapy, as it lets developers reuse their code.
It also acts as a web crawling shell, which is basically a platform that developers can use to test their thesis on a site’s behavior.
There are many popular companies that make use of Scrapy. These include Parse.ly, Say one Technologies, Lyst, and Po Medialab.
Pros & Cons Of Web Scraping With R
The major pros –
1. Open Source
Being an open-source programming language, users are not required to pay a fee, neither do they require a license.
Users can also customize their packages, hence contributing to its development.
2. Good Support for Data Wrangling
With R, you can re-arrange messy data into a more organized form. Packages like dplyr can help you achieve this.
3. Wide Array of Packages
R has over 10,000 packages in the CRAN repository.
4. Quality Plotting and Graphing
R libraries such as ggplot2 and plotly have graphs that are visually appealing.
Now the major cons –
1. Weak Origin
Web Scraping With Raspberry Pi
R’s origin is shared with the old “S” programming language. Meaning that its base package doesn’t offer support for 3D or dynamic graphics.
2. Uses Too Much Memory
In comparison to Python, R uses a lot more memory, which makes it an unsuitable option when handling large data.
3. Complicated Language
Web Scraping With Ruby
For beginners, they would find the R language much harder to learn.
Pros & Cons Of Web Scraping With Python
The major pros –
1. Versatile & Easy To Use
Beginners will find the Python language very easy to learn.
2. Community Support
Python provides users with an online community that can be consulted if they face any challenges.
3. Open Source
Users can download Python for free. No license is required.
4. Many Libraries
Python offers users many libraries. Including web/game development and machine learning.
Now the major cons –
1. Speed
Being an interpreted language, Python is slower than some other languages.
2. Threading Issues
Thanks to the Global Interpreter Lock (GIL), you will experience poor threading with Python.
3. It Isn’t Mobile Friendly
Python as a programming language doesn’t have Android or iOS support.
Conclusion
So who wins the web scraping battle, Python or R?
If you’re looking for an easy-to-read programming language with a vast collection of libraries, then go for Python. Keep in mind though, there is no iOS or Android support for it.
On the other hand, if you need a more data-specific language, then R may be your best bet.
We hope this Python Vs R web scraping article has been helpful. Share it on social media if you find it helpful and see you next time!