course

R

Got a good head for numbers? Then R might be the language for you! It’s well-suited for business and statistical analysis, data analytics and scientific research — particularly because it makes it easy for you to visualize the data so that others can easily understand it. It lets you do cool stuff like draw interactive graphs and adjust their parameters along the axes, and create interactive applications.

What you'll learn

Variables
Declare and use variables to help your program store data.
Interactive programs
Make interactive programs that output information to the display and take user input.
Data types
Use different data types to manage information effectively.
Conditional logic
Implement conditional logic to set rules in your programs.
Functions
Declare and use functions to make your code reusable and compact.
Matrices, vectors and data frames
Handle datasets of different shapes and forms with matrices, vectors and data frames.
Work with external data
Write code that can import, read and group external data.
Graphs and charts
Produce graphs and charts to visualize data.
illustration

Blog

blog
Python Vs. R: Which Should Be The Go-To For Beginners Looking To Get Into Data Science?
In this week’s Ask SoloLearn, the SoloLearn community asked about the expanding and exciting field of data science. From Google Analytics that enables you to measure site traffic (and a hundred different variables) in real-time, to the COVID case dashboards from Johns Hopkins and others, data science is at the forefront of technology these days.
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blog
How Does A Data Scientist Use R?
If you’re interested in learning data science, then you’ve probably heard of R. It’s a programming language that’s popular with statisticians, data scientists, and anyone else who needs to analyze a large amount of data quickly.
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blog
What Is R, And How Is It Used?
One of the biggest demands in modern tech is for robust, dynamic programming languages that can aid in the evolving fields of data science and machine learning. Both of these areas have demonstrated incredible potential in shaping the way we collect, organize, analyze, and utilize data to improve research and innovations in a wide variety of areas.
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