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Rstats 101

Learn R Programming Tips & Tricks for Statistics and Data Science

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What is R?

rstats101 · June 7, 2021 ·

R is a programming language originally developed for doing statistical computing. It is similar to the S programming language that was developed at Bell Laboratories (formerly AT&T).

As the R Project site says

R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc

R Download

Check out R Project website to learn more about R programming language. R is available for most operating systems like UNIX/Linux, MacOS and Windows.

You can Download R and install from here https://cloud.r-project.org/ or one of the servers that is located close to you from here https://cran.r-project.org/mirrors.html

R Packages from CRAN

The Comprehensive R Archive Network or CRAN also maintains a lots of open source R libraries for using R available for download.

CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use the CRAN mirror nearest to you to minimize network load.

If you have more questions about R, check out the R FAQs at CRAN.

https://cran.r-project.org/doc/FAQ/R-FAQ.html

RStudio IDE

RStudio: tidyverse
RStudio: tidyverse
RStudio is one of the most popular IDE for R and one can download RStudio IDE for free from https://www.rstudio.com/products/rstudio/

It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management.

In addition to RStudio IDE, RStudio also a contributes to many popular open source R packages like tidyverse suit of packages which include dplyr, tidyr and ggplot2.

Another great thing about R is that it has a phenomenal and most welcoming community and they make it easy to learn R & use R for a variety of things in Statistics, Data Science, and Machine Learning, not just statistical computing.

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