Do you want to make smart use of your data & be able to communicate your analytics effectively?
Then you’d better learn R!
Let us tell you few good reasons why you should learn R.
R for data science
1. The best choice for Statistical analysis
R is a programming language and software environment supported by the R Foundation for Statistical Computing that offers the most complete and up-to-date set of statistical & graphical methods. Being a vectorized language, R is a powerful and fast tool for data wrangling and analysis.
2. Astonishing data visualizations
R stands out for its graphical output. Packages like ggplot2 & plotly allow you to create stunning histograms, scatter-plots, box-plots, line charts, dot plots, heat maps, lollipops, tree-maps etc. either on-screen or hard-copy.
R welcomes everyone
3. Active & vast community
Nowadays, more than 2 million enthusiastic users are part of the active R community. Academics & professionals share, collaborate & support each other, through blogs, meetups & conferences etc. You can easily find answers to your problems from the community, and you can share your work, use or take over the one provided by others. You can also address bugs if you spot one in an open-source package you use. Users interact, collaborate, share problems and ideas, this is why R users have so much fun and the community keeps growing.
4. Free open source environment
R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License, and it relies on the continuous development of the R Foundation and on the free contribution provided by its community. R is transparent, flexible and easy to modify.
5. Trust in multi-domains
R has been adopted in a broad range of industries, including Biotech, Finance, Manufacturing, Journalism, E-commerce, Government, Healthcare, Research and High Technology industries. Major companies trust R to solve their complex issues - BBC, Facebook, Airbnb, Amazon, eBay, Google, Netflix, Twitter, SRF, UK and Swiss Government etc.
R helps you to publish & share with others
6. Reporting & Web application
Create custom reports, presentations and dynamic documents with R Markdown, or use Shiny as a framework to easily build interactive web applications. Either one or the other, in R you can quickly wrap your data around a storytelling and aesthetic visualizations, to give insights to people and decisions makers.
R is Flexible & even more
7. Comprehensive
CRAN (The Comprehensive R Archive Network) counts more than 10’000 packages that cover the full data science workflow, from technical to statistical, to graphical, etc. If a functionality is missing in a package, you can request it from the author or create your own package. The library keeps growing as the popularity and international collaboration get bigger.
5. Cross platform
R enjoys platform independence and runs on many operating systems, Linux, Windows, MacOS.
9. Compatible
In R you can build an interface with other languages that may be a better fit for a given problem to solve: write high performing code with C++, access objects with Java, bring more life to your Shiny App with JavaScript, deal with your big data using Apache Spark or Hadoop.
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