Sampada VardheWhat is the R programming language?
R is the most widely used scripting language for statistical computing, graphics, data analytics, and scientific research. R was coined at the Bell Laboratories by John Chambers in 1976. R was an extension and implementation of the S programming language. R was developed by Ross Ihaka and Robert Gentlemen in the Department of Statistics at the University of Auckland, New Zealand in 1992. The first version was released in 1995 and a stable beta version was released in 2000. R is widely used by statisticians, data miners, Data Scientists as statistical software. An effective data handling and storage tool. Calculations can be done on spreadsheets and arrays. R is an integrated collection of statistical analysis tools. R has many users-approximately 3 million users in the US alone. For people who are not aware of R programming – A tool that is appealing as an IT skill in a programmer’s resume. R Language is used in various professions like research, business intelligence, business analytics, and statistical reporting.70% of US companies use this software. R is used for Data analysis, programming, transforming, modeling, communicating the results.
Why R?
1.R programming is used for Statistical Analysis, Data Analysis, and Machine Learning.
2.R is open source for predictive analysis and data visualizations. It is free, easily installed, and usable with training.
3.Today R can be used in any domain like banking, finance support, weather forecasting, health care, social media, manufacturing, E-commerce, IT, research and academic, etc.
4.Features of R: Interpreted Language, Learning is very easy, Graphical supports, huge community, better job prospects, better data handling, support matrix arithmetic.
5.R is essentially a coding language for statistics.
6.R has many statistical packages that have sophisticated graphical capabilities.
7.Runs on all platforms.
5 reasons why you should learn R programming?
- R is stunning at Statistical computing and Data visualization – There are so many built-in functions and tools that make these statistical tasks very simple. Whether you are writing statistical reports for school or work R is your go-to.
- Packages – Many Packages in R that are not available in other coding languages! What is a Package? A collection of reusable R functions and sample data. For example, there is tidyverse package that has built-in functions to help clean data.
- Career– Knowing R can be a stepping stone for making a career as a data scientist/Data Analyst and help in the analytical aspect. So using R Data Scientist/Data Analyst clean data, run machine learning models, and make business decisions. So, definitely recommended learning R as a career aspect.
- R community – there is a BIG R Community. Community -A place where you can give and receive help for coding. For example, there is Stack overflow– a Q&A platform for coding questions. Twitter – #rstats community very good for coding questions. CRAN repository– A collection of packages for data analysis. Have a big community can help with your R learning experience. There are 4000 packages on CRAN.
- DATA is BIG part of the world now and it’s important to understand it, especially with the coronavirus pandemic and you see data everywhere. So it’s always important to understand data and R can help you with that. Whether statistical analysis, Data Visualizations.
Ultimately you should definitely learn R. And if you have not started learning the above reasons give you the push to start learning.
Survey of Kagglers finds that Python and R are the most used:
R usage by industry:
A large number of companies use R for Data Science:
- Facebook – Behaviour analysis, Sentiment analysis
- Google – Advertising effectiveness.
- Microsoft
- Uber – Statistical analysis
- Airbnb – Scale data science.
- IBM
- ANZ – Credit risk modeling
- HP
- Ford
Every industry today is needing people who can work with data e.g. Big Tech, finance, big-pharma, insurance, fashion, sports so that means every industry is in need of R programmers.
Finding out what motivates you will help you reach your goal without being bored. So pick an area that you will be interested in, such as:
- Data Science / Data Analysis
- Data visualization
- Predictive modeling/machine learning
- Statistics
- Reproducible reports
- Dashboard reports
R is a free software environment for statistical computing, graphical support, machine learning, and data science. R programming is adapting across the various parallels of the industry. R is the most versatile and efficient statistical tool that can fit every need of the user.
Summary
R is the best tool to explore and work with the data. We can do prediction, clustering, find correlation in the data, and also do data reduction. Good feature engineering is very important for the machine learning model to perform best and give meaningful results. So you can have a look at MKSSS AIT’s Certification on R Language. With the help of our training you will get expertise in:
- R programming and familiar with the R environment.
- Vectors
- Matrices and arrays
- Lists
- Data frames
- Factors
- Control Statements
- Word with a variety of input and output files
- Data Manipulation
- Statistic Functions
- General Statistics
- Probability
- Data Visualizations
