This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.

1. By Septian
Reason To
Learn & use R

2. List of Content
What is R?01
Why learn & use R?02
Pros & Cons03
Summary04

3. R?
- Statisticians
- Data Scientist
- Data Analyst
R users
R is increasingly popular because of the
terminology of big data and data
visualization
Popular
R is a programming language that
focused on statistical computing
Language
R was released in 1995, created by
Ross Ihaka & Robert Gentleman
at the University of Auckland
Released
We can easily install R from :
https://cran.r-project.org
It’s free!
Open Source
Reference : wikipedia.com

4. Why we should learn R?
R Is Open-source and Freely Available
Unlike SAS or Matlab, you can freely install, use, update, clone, modify,
redistribute and resell R. This saves companies money, but it also
allows for easy upgrades, which is useful for a statistical programming
language.
R Is a Powerful, Scripting Language
As such, R can handle large, complex data sets. R is also the best
language to use for large, resource-intensive simulations, and it can be
used on high-performance computer clusters.
R Is Cross-platform Compatible
R can be run on Windows, Mac OS X, and Linux. It can also import
data from Microsoft Excel, Microsoft Access, MySQL, SQLite, Oracle,
and other programs.
R Is Highly Flexible and Evolving
Many new developments in statistics first appear as R packages.
Reference : simplilearn.com

5. R Popularity
• Tableau, PowerBI, Excel are easier to learn,
but have lower business capability.
• Phyton, SAS, Matlab have high data
science capability, but lack visualization for
business.
• R is the best data science, visualization,
interactive tools for business + it’s free!
Reference : business-science.io

6. Why we should use R?
R has brain
R implement Cutting-edge algorithms :
• High end machine learning (H2O)
• Deep learning (keras/tensorflow)
R has muscle
R has powerfull package for :
• Forcasting (timeseries)
• Connecting to other language (rJava, rcpp)
• Parallelizing operations (parallel, future)
• Handling big data (sparklyr)
R has heart
R is very data scientist friendly :
• Visualization (ggplot2)
Visualization (ggplot2)
R has community
You begin learning R because of its capability,
you stay with R because of its community.
Reference : business-science.io

7. Visualization in R
Reference : ggplot2.tidyverse.org

8. Example Visualization
Reference : lenkiefer.com

9. Top Tier Company Using R
Reference : datasciencecentral.com
For behavior analysis related to
status updates and profile pictures.
For advertising effectiveness and
economic forecasting.
For data visualization and
semantic clustering
Acquired Revolution R company and
use it for a variety of purposes.
For scaling and data scientist
Join R consortium group

10. Advantages
11
R is prominently known as the lingua franca of statistics. This is the
main reason as to why R is dominant among other programming
languages for developing statistical tools.
3 It is cross-platform which runs on many operating systems. It’s best
for Mac, Linux, and Microsoft Windows. And its Free!
2
R facilitates quality plotting and graphing. The popular libraries like
ggplot2 and plotly advocate for aesthetic and visually appealing
graphs that set R apart from other programming languages
4 In R, everyone is welcomed to provide bug fixes, code
enhancements, and new packages. So, R continuously growing!
Reference : javapoint.com

11. Disadvantages
11
R is a very complicated language, and it has a steep learning curve.
The people who don't have prior knowledge or programming
experience may find it difficult to learn R.
3
In R, objects are stored in physical memory. It is in contrast with
other programming languages like Python. R utilizes more memory
as compared to Python. It requires the entire data in one single
place which is in the memory
2 There’s no customer support of R Language whom you can
complain if something doesn’t work.
4
Reference : javapoint.com

12. Summary
Data Science is the most popular technology in the world today. Since it is
mostly comprised of statistics, R is the lingua franca of this field. We went
through the various points which delineate why learning R is the first choice for
mastering Data Science. In the end, we conclude that learning R will have
immense benefits that will provide you with the right tools to deal with data on a
large scale.
R will just not help you in the technical fields, it will also be a great help in your
business. It has become one of the most popular programming languages in
the industries.

13. Thank You