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Introduction to R Studio

R Studio is an integrated development environment(IDE) for R. IDE is a GUI, where you can write your quotes, see the results and also see the variables that are generated during the course of programming. 

Introduction to R studio for beginners:

Rstudio is an open-source tool that provides Ide to use R language, and enterprise-ready professional software for data science teams to develop share the work with their team.



R Studio can be downloaded from its official Website (https://rstudio.com/) and instructions for installation are available on How to Install RStudio for R programming in Windows? 

After the installation process is over, the R Studio interface looks like: 



Features of R Studio

Set the working directory in R Studio

R is always pointed at a directory on our computer. We can find out which directory by running the getwd() function. Note: this function has no arguments. We can set the working directory manually in two ways: 

This will select the current directory, which you have chosen using this file browser as your working directory. Once you set the working directory, you are ready to program in R Studio.

Create an RStudio project

Step 1: Select the FILE option and select create option.

Step 2: Then select the New Project option.

Step 3: Then choose the path and directory name.

Finally, project are created in a specific location:

Navigating directories in R studio

Creating your first R script

Here we are adding two numbers in R studio. 

How to Perform Various Operations in RStudio

We’ll see some common tasks, their codes in R Studio

Installing R packages

Syntax:

install.packages('package_name')

Loading R package

Syntax:

library(package_name)

Help on an R package

help(package_name)

Conclusion

R Studio, a versatile R IDE, supports open-source and commercial usage on various platforms. It streamlines data science tasks, from project creation to package management, with a user-friendly interface. This article introduces R Studio’s essential features, empowering users to efficiently harness R’s capabilities for data analysis and programming.


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