One of the fastest growing programming languages on the planet, R is definitely a good language to learn, especially for those going into the statistics or machine learning areas of data science.
But what exactly is R and why should you learn it? Let’s take a look under the hood of the programming language driving the statistical wing of data science in 202.
What is R?
An open source programming language that’s purpose built for statistical analysis and data visualization, R was developed back in 1992. With more than 18,000 packages available through the R repository (known as CRAN, or the comprehensive R archive network), there’s no wonder its ecosystem is so vast – there are an estimated 2 million R users in the world today
Who created R?
It was created at the University of Auckland, New Zealand by Ross Ihaka and Robert Gentleman. The name ‘R’ comes from the first letter of each of their names, while also being a play on the name of the programming language S, created in the 1970s.
Is R similar to Python?
Both are open source programming languages supported by large communities with a large variety of tools and libraries.
Python is more of a general, all-round language that’s easier to get to grips with, while R is slightly more complex and is more focused on statistical analysis. It was purpose built by statisticians for statistical models and analytics.
R is designed to import data from excel CSV and text files, whereas Python supports all kinds of data formats. And while Python is more versatile when it comes to web scraping, modern R packages like Rvest are starting to catch up, with basic web scraping functionality.
You can explore data in Python using Pandas – Python’s data analysis library – but R is much more effective when it comes to data exploration. It’s built to analyze large data sets at speed, with a large number of options when it comes to exploring data.
R is also far more useful when it comes to data visualization. While Python has Matplotlib and Seaborn statistical graphs and charts, R was built with visualization in mind, enabling you to easily translate data into basic charts and plots, along with more complex scatter plots.
What can you do with it?
The key use of R is to handle large amounts of complex data. It can be used to create customized data models, to cleanse and prep data, to evaluate machine learning translations and deep learning algorithms and, most notably, to create data visualizations.
R is an interpreted language. This means you can run code without any compiler, as R interprets the code making development easier. It’s also a vector based language, which means it deals with vectors instead of base elements. This makes it easier to carry out mathematical functions, as you can perform them on an entire list as if they were a single object.
Why is R an important language to learn?
For anyone looking to work with statistics and big data, learning R is essential. It’s a very powerful programming language, capable of handling huge amounts of data and is also highly extensible, enabling the user to create new data structures, operations, notations and new regimes of control. It’s also very adaptable – you can use R in tandem with other data science programming languages, including Python, C++ and Java.
R’s interactive nature and the sheer amount of customizable tools available make it an excellent language for specialized data modelling and machine learning. Another one of R’s big strengths is its community. The larger and stronger a community, the more support is available for newcomers and experienced programmers alike. R’s community is renowned for being particularly supportive and inclusive, which is particularly reassuring for those looking for a new language to learn.
Not only is R useful from a statistical point of view, it’s also an excellent language to learn from a business perspective. This is because of its excellent data visualization capabilities. Data visualization is key when it comes to taking large amounts of information to create a compelling, easy to understand narrative that can shape the strategy of a business. And businesses are moving towards open source platforms with the tools and technologies to handle massive amounts of data.
This all explains why R is one of the most popular languages for data science, with 31% of data scientists regularly using it.
What career is learning R good for?
The most popular jobs for skilled R programmers are Data Analyst, Statistician, Data Visualization Analyst and Data Scientist. Other career paths that require a good understanding of R include Machine Learning Engineer, Data Architect and Database Administrator.
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