Basics of R programming (1)

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R PROGRAMMING

• ARAVALI COLLEGE OF ENGINEERING MANAGEMENT
• (ACEM, FARIDABAD)

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Program Name B.TECH CSESemester VIICourse
Name R PROGRAMMINGCourse Code
OEC-CS-701(III)Faculty Name RASHIKA
SINGHDesignation ASSISTANT PROFESSORDepartment
__CSE_____
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UNIT No.1

• INTRODUCTION
• Getting R
• R Version
• 32-bit versus 64-bit
• The R Environment
• Command Line Interface
• RStudio
• Revolution Analytics
• Learning Outcome
• Familiarize themselves with R and the RStudio IDE
  

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R PROGRAMMING

• R is a software environment which is used to
  analyze statistical information and graphical
  representation.
• R allows us to do modular programming using
  functions.
•  This programming language was named R, based on
  the first name letter of the two authors (Robert
  Gentleman and Ross Ihaka).

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• "R is an interpreted computer programming
  language which was created by Ross Ihaka and
  Robert Gentleman at the University of Auckland,
  New Zealand." 
• The R Development Core Team currently develops R.
  It is also a software environment used to
  analyze statistical information, graphical
  representation, reporting, and data modeling.
• R is the implementation of the S
  programming language, which is combined
  with lexical scoping semantics.
• R not only allows us to do branching and looping
  but also allows to do modular programming using
  functions. R allows integration with the
  procedures written in the C, C, .Net, Python,
  and FORTRAN languages to improve efficiency.
• In the present era, R is one of the most
  important tool which is used by researchers, data
  analyst, statisticians, and marketers for
  retrieving, cleaning, analyzing, visualizing, and
  presenting data.

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History of R Programming

• The history of R goes back about 20-30 years ago.
  R was developed by Ross lhaka and Robert
  Gentleman in the University of Auckland, New
  Zealand, and the R Development Core Team
  currently develops it.
• This programming language name is taken from the
  name of both the developers.
• The first project was considered in 1992. The
  initial version was released in 1995, and in
  2000, a stable beta version was released.

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The following table shows the release date,
version, and description of R language
Version-Release Date Description
0.49 1997-04-23 First time R's source was released, and CRAN (Comprehensive R Archive Network) was started.
0.60 1997-12-05 R officially gets the GNU license.
0.65.1 1999-10-07 update.packages and install.packages both are included.
1.0 2000-02-29 The first production-ready version was released.
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1.4 2001-12-19 First version for Mac OS is made available.

2.0 2004-10-04 The first version for Mac OS is made available.
2.1 2005-04-18 Add support for UTF-8encoding, internationalization, localization etc.
2.11 2010-04-22 Add support for Windows 64-bit systems.
2.13 2011-04-14 Added a function that rapidly converts code to byte code.
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2.14 2011-10-31 Added some new packages.
2.15 2012-03-30 Improved serialization speed for long vectors.
3.0 2013-04-03 Support for larger numeric values on 64-bit systems.
3.4 2017-04-21 The just-in-time compilation (JIT) is enabled by default.
3.5 2018-04-23 Added new features such as compact internal representation of integer sequences, serialization format etc.
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Features of R programming

• It is a simple and effective programming language
  which has been well developed.
• It is data analysis software.
• It is a well-designed, easy, and effective
  language which has the concepts of user-defined,
  looping, conditional, and various I/O facilities.
• It has a consistent and incorporated set of tools
  which are used for data analysis.
• For different types of calculation on arrays,
  lists and vectors, R contains a suite of
  operators.
• It provides effective data handling and storage
  facility.
• It is an open-source, powerful, and highly
  extensible software.
• It provides highly extensible graphical
  techniques.
• It allows us to perform multiple calculations
  using vectors.
• R is an interpreted language.

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Why use R Programming?
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• R is important for Data Science?
• R plays a very important role in Data Science,
  you will be benefited with following operations
  in R.
• You can run your code without any compiler  R is
  an interpreted language. Hence we can run code
  without any compiler. R interprets the code and
  makes the development of code easier.
• Many calculations done with vectors  R is a
  vector language, so anyone can add functions to a
  single Vector without putting in a loop. Hence, R
  is powerful and faster than other languages.
• Statistical Language  R used in biology,
  genetics as well as in statistics. R is a turning
  complete language where any type of task can
  perform.
• 2. R is Good for Business?
• R will just not help you in the technical fields,
  it will also be a great help in your business.
• Here, the major reason is that R is open-source,
  therefore it can be modified and redistributed as
  per the users need. It is great for
  visualization and has far more capabilities as
  compared to other tools.
• For data-driven businesses, lack of Data
  Scientists is a huge concern. Companies are using
  R programming as their core platform and are
  recruiting trained R programmers.

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• 3. R is a gateway to Lucrative Career
• R language is used extensively in Data Science.
  This field offers some of the
• highest-paying jobs in the world today. 
• 4. Open-source
• R is an open-source language. It is maintained
  by a community of active users and
• you can avail R for free. You can modify various
  functions in R and make your own
• packages. Since R is issued under the General
  Public Licence (GNU), there are no
• restrictions on its usage.
• 5. Popularity
• R has become one of the most popular programming
  languages in the industries.
• Conventionally, R was mostly used in academia but
  with the emergence of Data
• Science, the need for R in the industries became
  evident. R is used at Facebook for social
• network analysis. It is being used at Twitter for
  semantic analysis as well as visualizations.
• 6. Robust Visualization Library
• R comprises of libraries like ggplot2,
  plotly that offer aesthetic graphical plots to
  its
• users. R is most widely recognized for its
  stunning visualizations which gives it an
• edge over other Data Science programming
  languages.

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7. With R, you can develop amazing Web-Apps R
provides you with the ability to build aesthetic
web-applications. Using the R Shiny package, you
can develop interactive dashboards straight from
the console of your R IDE. Using this, you can
embed your visualizations and enhance the
storytelling of your data analysis through
aesthetic visualizations. 8. A go-to language
for Statistics and Data Science R is the
standard language for Statistics and Data
Science. R was developed for statistics, by
statisticians. It has been in use even before the
word Data Science was coined. Statisticians
and Data Scientists are most familiar with R
than any other programming language. R
facilitates various statistical operations
through its thousands of packages. 9. R is being
used in almost every industry R is one of the
most widely used programming languages in the
world today. It is used in almost every industry,
ranging from finance, banking to medicine and
manufacturing. R is used for portfolio
management, risk analytics in finance and banking
industries. It is used for carrying out an
analysis of drug discovery and genomic analysis
in bioinformatics. R is also used to implement
various statistical measures to optimize
industrial processes.
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Pros and Cons of R Programming Language
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PROS/ADVATAGES OF R PROGRAMMING

• 1. Open Source
• R is an open-source programming language. This
  means that anyone can work with R without any
  need for a license or a fee. Furthermore, you can
  contribute towards the development of R
  by customizing its packages, developing new ones
  and resolving issues.
• 2. Exemplary Support for Data Wrangling
• R provides exemplary support for data wrangling.
  The packages like dplyr, readr are capable of
  transforming messy data into a structured form.
• 3. The Array of Packages
• R has a vast array of packages. With over 10,000
  packages in the CRAN repository, the number is
  constantly growing. These packages appeal to all
  the areas of industry.
• 4. Quality Plotting and Graphing
• 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.
• 5. Highly Compatible
• R is highly compatible and can be paired with
  many other programming languages like C, C,
  Java, and Python. It can also be integrated with
  technologies like Hadoop and various other
  database management systems as well.

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• 6. Platform Independent
• R is a platform-independent language. It is a
  cross-platform programming language, meaning that
  it can be run quite easily on Windows, Linux, and
  Mac.
• 7. Eye-Catching Reports
• With packages like Shiny and Markdown, reporting
  the results of an analysis is extremely easy with
  R. You can make reports with the data, plots and
  R scripts embedded in them. You can even make
  interactive web apps that allow the user to play
  with the results and the data.
• 8. Machine Learning Operations
• R provides various facilities for carrying out
  machine learning operations like classification,
  regression and also provides features for
  developing artificial neural networks.
• 9. Statistics
• 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.
• 10. Continuously Growing
• R is a constantly evolving programming language.
  It is a state of the art technology that provides
  updates whenever any new feature is added.

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Disadvantages of R Programming

• 1. Weak Origin
• R shares its origin with a much older programming
  language S. This means that its base package
  does not have support for dynamic or 3D graphics.
  With common packages of R like Ggplot2 and
  Plotly, it is possible to create dynamic, 3D as
  well as animated graphics.
• 2. Data Handling
• In R, the physical memory stores the objects.
  This is in contrast to other languages like
  Python. Furthermore, R utilizes more memory as
  compared with Python. Also, R requires the entire
  data in one single place, that is, in the memory.
  Therefore, it is not an ideal option when dealing
  with Big Data. However, with data management
  packages and integration with Hadoop possible,
  this is easily covered.
• 3. Basic Security
• R lacks basic security. This feature is an
  essential part of most programming languages like
  Python. Because of this, there are several
  restrictions with R as it cannot be embedded into
  a web-application.

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• 4. Complicated Language
• R is not an easy language to learn. It has a
  steep learning curve. Due to this, people who do
  not have prior programming experience may find it
  difficult to learn R.
• 5. Lesser Speed
• R packages and the R programming language is much
  slower than other languages like MATLAB and Python
  .
• 6. Spread Across various Packages
• The algorithms in R are spread across different
  packages. Programmers without prior knowledge of
  packages may find it difficult to implement
  algorithms.

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R VERSIONS

• RStudio Server enables users and administrators
  to have very fine-grained control over which
  versions of R are used in various contexts.
  Capabilities include
• Administrators can install several versions of R
  and specify a global default version as well as
  per-user or per-group default versions.
• Users can switch between any of the available
  versions of R as they like.
• Users can specify that individual R projects
  remember their last version of R and always use
  that version until explicitly migrated to a new
  version.

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• On Windows, RStudio uses the system's current
  version of R by default. When R is installed on
  Windows it writes the version being installed to
  the Registry as the "current" version of R (the
  specific registry keys written are
  described here). This is the version of R which
  RStudio runs against by default.
• You can override which version of R is used via
  General panel of the RStudio Options dialog. This
  dialog allows you to specify that RStudio should
  always bind to the default 32 or 64-bit version
  of R, or to specify a different version
  altogether

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Note that by holding down the Control key during
the launch of RStudio you can cause the R version
selection dialog to display at startup.
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32-BIT VERSUS 64-BIT

• we have two R binaries
• /APPS/32/bin/R /APPS/64/bin/R If you are on 32
  bit, then you automatically get the first when
  you just say R. If you are on 64 bit, then you
  automatically get the second when you just say R.
• On 64 bit you can run the first, if you so
  desire, but you must invoke it using the full
  path name /APPS/32/bin/R. It will work in
  emulation mode.
• If you never load your own C code into R
  with dyn.load(sharedlibraryname) and never use
  libraries other than those that those installed
  by the system administrators and you load
  withlibrary(packagename), then you should have no
  problems. Just say R with no additional fuss to
  invoke R, and it will work.
• Otherwise, you will have to be aware of What
  architecture you are running on.What architecture
  the R binary you are r

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APPLICATIONS OF R

• 1. Finance
• Data Science is most widely used in the financial
  industry.
• R is the most popular tool for this role. This is
  because R provides an advanced statistical suite
  that is able to carry out all the
  necessary financial tasks.
• With the help of R, financial institutions are
  able to perform downside risk measurement, adjust
  risk performance and utilize visualizations
  like candlestick charts, density plots, drawdown
  plots, etc.
• 2. Banking
• Just like financial institutions, banking
  industries make use of R for credit risk modeling
  and other forms of risk analytics.
• Bank of America makes use of R for financial
  reporting. With the help of R, the data
  scientists at BOA are able to analyze financial
  losses and make use of Rs visualization tools.

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• 3. Healthcare
• Genetics, Bioinformatics, Drug Discovery,
  Epidemiology are some of the fields in healthcare
  that make heavy usage of R. With the help of R,
  these companies are able to crunch data and
  process information, providing an essential
  backdrop for further analysis and data
  processing.
• 4. Social Media
• For many beginners in Data Science and R, social
  media is a data playground. Sentiment Analysis
  and other forms of social media data mining are
  some of the important statistical tools that are
  used with R.
• Social Media is also a challenging field for Data
  Science because the data prevalent on social
  media websites is mostly unstructured in nature.
  R is used for social media analytics, for
  segmenting potential customers and targeting them
  for selling your products.

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• 5. E-Commerce
• The e-commerce industry is one of the most
  important sectors that utilize Data Science. R is
  one of the standard tools that is being used in
  e-commerce.
• Since these internet-based companies have to deal
  with various forms of data, structured and
  unstructured, as well as from varying data
  sources like spreadsheets and databases (SQL
  NoSQL), R proves to be an effective choice for
  these industries.
• 6. Manufacturing
• Manufacturing companies like Ford, Modelez, and
  John Deere use R to analyze customer sentiment.
  This helps them optimize their product according
  to trending consumer interests and also to match
  their production volume to varying market demand.
  They also use R to minimize their production
  costs and maximize profits.

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Real-Life Use Cases of R Language

• Facebook  Facebook uses R to update status and
  its social network graph. It is also used for
  predicting colleague interactions with R.
• Ford Motor Company  Ford relies on Hadoop. It
  also relies on R for statistical analysis as well
  as carrying out data-driven support for decision
  making.
• Google  Google uses R to calculate ROI on
  advertising campaigns and to predict economic
  activity and also to improve the efficiency of
  online advertising.
• Foursquare  R is an important stack behind
  Foursquares famed recommendation engine.
• John Deere  Statisticians at John Deere use R
  for time series modeling and also geospatial
  analysis in a reliable and reproducible way. The
  results are then integrated with Excel and SAP.
• Microsoft  Microsoft uses R for the Xbox
  matchmaking service and also as a statistical
  engine within the Azure ML framework.
• Mozilla  It is the foundation behind the Firefox
  web browser and uses R to visualize web activity.

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FUTURE SCOPE OF R
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• 1. Data Scientist
• The profession of Data Scientist is the most
  demanding job role. A Data Scientist is supposed
  to extract data, transform it into a structured
  format, perform analysis and forecast future
  insights. For this purpose, R is the most ideal
  tool as it provides efficient data handling
  capability as well as a robust set of analysis
  and machine learning tools. 
• 2. Business Analyst
• A Business Analyst has to develop solutions that
  are technical in nature for the various business
  problems. They are required to seek solutions,
  advance the efforts of the company as well as
  fulfill the requirements of the business. For
  this purpose, R provides various business
  intelligence tools through its extensive
  packages. 

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• 3. Data Analyst
• A Data Analyst is responsible for extracting and
  analyzing data. This task requires extensive
  usage of Rs statistical libraries to deliver
  accurate results so that the companies can make
  careful data-driven decisions. 
• 4. Data Visualization Expert
• R is most popular for its visualization
  libraries. Due to this reason, Data Visualization
  experts in R programming are in-demand in the
  industries. The various packages of R
  likeggplot2, plotly, etc provide visually
  appealing graphs and plots to their users.
• 5. Quantitative Analyst
• Quantitative Analysts are engaged in the
  financial and banking industries. These
  industries have to deal with all types of data
  and R provides an ideal solution to their various
  data problems. 

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Aravali College of Engineering And
Management Jasana, Tigaon Road, Neharpar,
Faridabad, Delhi NCR Toll Free Number 91-
8527538785 Website  www.acem.edu.in