R
PROGRAMMING
R PROGRAMMING
R is a language and environment for statistical computing and
graphics
R provides a wide variety of statistical and graphical techniques,
and is highly extensible.
One of R’s strengths is the ease with which well-designed
publication-quality plots can be produced, including mathematical
symbols and formulae where needed.
THE R ENVIRONMENT
R is an integrated suite of software facilities for data manipulation,
calculation and graphical display. It includes:
an effective data handling and storage facility,
a suite of operators for calculations on arrays, in particular matrices,
a large, coherent, integrated collection of intermediate tools for data analysis,
graphical facilities for data analysis and display either on-screen or on
hardcopy, and
a well-developed, simple and effective programming language which
includes conditionals, loops, user-defined recursive functions and input and
output facilities.
R is a programming language and free software developed by Ross
Ihaka and Robert Gentleman in 1993. R possesses an extensive
catalog of statistical and graphical methods. It includes machine
learning algorithms, linear regression, time series, statistical
inference to name a few.
Data analysis with R is done in a series of steps; programming,
transforming, discovering, modeling and communicate the results
Program: R is a clear and accessible programming tool
Transform: R is made up of a collection of libraries designed
specifically for data science
Discover: Investigate the data, refine your hypothesis and analyze
them
Model: R provides a wide array of tools to capture the right model
for your data
Communicate: Integrate codes, graphs, and outputs to a report
with R Markdown or build Shiny apps to share with the world
WHY USE R?
Data science is shaping the way companies run their businesses.
Without a doubt, staying away from Artificial Intelligence and
Machine will lead the company to fail.
R is a great tool to explore and investigate the data. Elaborate
analysis like clustering, correlation, and data reduction are done
with R. This is the most crucial part, without a good feature
engineering and model, the deployment of the machine learning
will not give
R Programming allows to integrate with other languages (C/C++,
Java, Python) and enables to communicate with many data sources:
ODBC-compliant databases (Excel, Access) and other statistical
packages (SAS, Stata, SPSS, Minitab).
It’s a platform-independent language. This means it can be applied
to all operating system
It’s an open-source free language. That means anyone can install it
in any organization without purchasing a license.
STATISTICAL FEATURES OF R
Basic Statistics: The most common basic statistics terms are the mean, mode,
and median. These are all known as “Measures of Central Tendency.” So using
the R language we can measure central tendency very easily.
Static graphics: R is rich with facilities for creating and developing
interesting static graphics. R contains functionality for many plot types
including graphic maps, mosaic plots, biplots
Probability distributions: Probability distributions play a vital role in
statistics and by using R we can easily handle various types of probability
distribution such as Binomial Distribution, Normal Distribution, Chi-squared
Distribution and many more.
PROGRAMMING FEATURES OF
R
R Packages: One of the major features of R is it has a wide
availability of libraries. R has CRAN(Comprehensive R Archive
Network), which is a repository holding more than 10, 0000
packages.
Distributed Computing: Distributed computing is a model in
which components of a software system are shared among multiple
computers to improve efficiency and performance.
APPLICATIONS OF R
We use R for Data Science. It gives us a broad variety of libraries related to
statistics. It also provides the environment for statistical computing and design.
R is used by many quantitative analysts as its programming tool. Thus, it helps
in data importing and cleaning.
R is the most prevalent language. So many data analysts and research
programmers use it. Hence, it is used as a fundamental tool for finance.
Tech giants like Google, Facebook, bing, Accenture, Wipro and many more
using R nowadays
THANK YOU!
