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R is a language and environment for statistical computing and graphics. It is a powerful language in the field of data science. You can perform complex data manipulations, build machine-learning models, and create stunning data visualizations with R.
Our R tutorial is designed to make your data science journey smooth. It covers everything you need to know, from the basics of R programming to advanced techniques.
Page Index
R Introduction
R Flow Control
R Functions
R Data Structure
R Object & Class
R Graphs & Charts
R Advanced Topics
R Application
R is primarily used in the field of data science. Some areas where R is used are:
- Data Analysis: R is a great choice for data science related tasks such as data wrangling, visualization, and analysis, thanks to its extensive libraries like dplyr, tidyr, ggplot2, Shiny, etc.
- Statistical Modeling: R provides extensible statistical and graphical techniques making it easy for researchers and statisticians.
- Machine Learning: R's growing machine learning libraries and packages help data scientists build complex models and algorithms for machine learning.
How to Learn R?
R Programming can be useful for professionals seeking a career in data analysis, statistics, or data visualization. Here's how to get started with R:
Learn R basics: The first step in learning R is to be comfortable with its syntax and fundamentals. It is important to be able to read and write R to get started with it.
Practice coding: After getting acquainted with the fundamentals, it is important to practice them. Start with simple exercises and work your way up to more complex challenges.
Build projects: The best way to learn R is by building projects. Think of projects that interest you and try to build them using R. Start with basic projects and move on to more advanced ones.
Best Resources to Learn R
- Datamentor.io for a step-by-step guide to R.
- Official R documentation for reference and advanced topics.