R-Programming

ABOUT R LANGUAGE

1. R is an interpreted computer programming language i.e line by line compilation.
2. It is also a software environment used to analyze statistical information, graphical representation, reporting, and data modeling.
3. The modern implementation of S is R, a part of the GNU free software project. S-PLUS, is a commercial product, formerly sold by TIBCO Software.
4. R is the implementation of the S programming language, which is combined with lexical scoping semantics.
5. Used for machine learning, data analytics, etc.
6. R has automatically created packages related to trending technology and algorithms.
7. R was developed by Ross Lhaka and Robert Gentleman. First version was released in 1992 and version 4.0.0 in 2020.

Features of R

1. Procedural programming and OOPS with generic functions.
2. Easily integrated with Hadoop and HDFS, C, C++, Python, Java, Fortran, and javascript.
3. Free to use.
4. 1500+ packages available for R on online repositories like CRAN, Github, etc.
5. Powerful graphics:- ggplot2 and plotly.
6. Interpreted language. No need for a compiler.
7. Cross platform support i.e can run on any OS and system.
8. Perform fast calculations i.e wide variety of complex operations can be possible.

MLR package for machine learning in R.

All Machine learning algorithms are available and provide tools that help with machine learning as well.

Why learn R???

1. Data analysis and statistical analysis as well as machine learning.
2. Create packages, functions, objects in R. Platform independent.
3. 2 Million jobs for R programmers worldwide for data analytics, business analytics, data visualization expert, business intelligence experts.
4. Data science industry using R like health, finance, banking, manufacturing and many more.

Installing R and RStudio

https://youtu.be/UqYLkc9NgGY

R Studio

It has 4 parts

1. Editor for writing the R script or program.
2. Global Environment where all the variables will be shown whichever you will create.
3. Console, terminal, background jobs where output of scripts will be shown, command line terminal and background tasks if any running.
4. Files, plots, packages, viewer will show files in root directory of R, plots will show all the graphs we will create, packages will contain all the packages available in R studio to install and update packages, Viewer
5. To execute script 'ctrl+enter'
6. Use script to save R programs and execute them.
7. Extension will be .R
8. Ctrl+R to run the script. Output will be shown on the console.
9. Edit -> Run all to run complete script in one go.
10. '#' to comment eg.

  #this is R studio

1. Sessioninfo() will tell us about the current session and packages attached to the session.

2. ?plot(x) get help about the function.

3. R is case sensitive language.

4. All functions are mostly in lowercase letters.

5. It's crantastic - Repository of packages.

6. R-bloggers - R programming help.

Clone the GitHub repository to your computer via RStudio

1. In RStudio, start a new Project:

2. File > New Project > Version Control > Git.

3. In “Repository URL”, paste the URL of your new GitHub repository. It will be something like this https://github.com/abc/myrepo.git.

4. Accept the default project directory name, e.g. myrepo, which coincides with the GitHub repo name.

5. Take charge of – or at least notice! – where the Project will be saved locally. A common rookie mistake is to have no idea where you are saving files or what your working directory is. Pay attention. Be intentional. Personally, I would do this in ~/tmp.

6. I suggest you check “Open in new session”, as that’s what you’ll usually do in real life.

7. Click “Create Project”.

8. You should find yourself in a new local RStudio Project that represents your test repo on GitHub. This should download the README.md file from GitHub.

9. Look in RStudio’s file browser pane for the README.md file.

Connect RStudio to Git and GitHub

THANK YOU