The document provides an introduction to R, a programming language widely used for statistical analysis and data visualization, including its features, history, and advantages. It outlines fundamental programming concepts, R's data structures, and the significance of R in business analytics and data science. Additionally, it covers how R integrates with various operating systems and its extensive community support.

1. Data Science with R
Unit II: Introduction to R & Programming Structures
M. Narasimha Raju
Dept. of Computer Science & Engineering
Shri Vishnu Engineering College for Women (A),

2. R- Software (Open Source - Free Download)
⚫R 4.0.5 for Windows
⚫https://cran.r-project.org/bin/windows/base/
⚫RStudio Desktop 1.4.1106
⚫https://www.rstudio.com/products/rstudio/download/
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3. Online Compilers
⚫https://replit.com/languages/rlang
⚫https://www.mycompiler.io/new/r
⚫https://onecompiler.com/r
⚫https://www.onlinegdb.com/online_r_interpreter
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4. R- Overview
⚫R is a programming language and software environment for
statistical analysis, graphics representation and reporting.
⚫R was created by Ross Ihaka and Robert Gentleman at the
University of Auckland, New Zealand, and is currently developed
by the R Development Core Team.
⚫R made its first appearance in 1993.
⚫The core of R is an interpreted computer language which allows
branching and looping as well as modular programming using
functions. R allows integration with the procedures written in the
C, C++, .Net, Python or FORTRAN languages for efficiency.
⚫R is freely available under the GNU General Public License, and
pre-compiled binary versions are provided for various operating
systems like Linux, Windows and Mac.
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5. Features of R
⚫R is a well-developed, simple and effective programming language
which includes conditionals, loops, user defined recursive functions and
input and output facilities.
⚫R has an effective data handling and storage facility,
⚫R provides a suite of operators for calculations on arrays, lists, vectors
and matrices.
⚫R provides a large, coherent and integrated collection of tools for data
analysis.
⚫R provides graphical facilities for data analysis and display either
directly at the computer or printing at the papers.
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6. Introduction to R
⚫R is a free open source software package that is increasingly finding use in business
analytics and is rapidly becoming the industry standard
⚫ Industry giants like Microsoft and IBM are funding heavily
⚫R can be defined as below:-
⚫ Programming language for graphics and statistical computations
⚫ Object-oriented: data/information stored as objects, operations on objects
⚫ Available freely under the GNU public license
⚫ Used in data mining and statistical analysis
⚫ Included time series analysis, linear and nonlinear modeling among others
⚫ Very active community and package contributions
⚫ Very little programming language knowledge necessary
⚫ Can be downloaded from http://www.r-project.org/
⚫ Latest Version:- R-3.6.0 for Windows (32/64 bit)

7. R-Studio
⚫Advantages
⚫ It can run on any operating system
⚫ It integrates seamlessly with Big Data systems like Hadoop, map-reduce
⚫ Provides excellent graphical outputs
⚫ It has a small core and thousands of contributed projects
⚫ One can easily migrated to commercially supported software like SPSS
⚫R-Studio
⚫ R Studio is an IDE for R with advanced and more user-friendly GUI.
⚫ R is the substrate on which we can mount various features using PACKAGES like RCMDR-
R
⚫ Commander or R-Studio.
⚫ R was started by Bell Laboratories as “S” for Fortran Library.
⚫ Latest version:- RStudio 1.2.1335 - Windows 7+ (64-bit)

8. The rise of Popularity of R

9. R-Basic
⚫R Command Prompt
⚫ $ R
⚫ > myString <- "Hello, World!"
⚫ > print ( myString)
⚫ [1] “Hello, World!”
⚫R Script File
⚫ # My first program in R Programming
⚫ myString <- "Hello, World!"
⚫ print ( myString)
⚫ Save the above code in a file test.R and
execute
⚫Comments
⚫# My first program in R
Programming
⚫# No multi-line comments at a
time
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10. Data type of the R-object
⚫We may like to store information of various data types like
character, integer, floating point, double floating point, Boolean etc.
⚫Based on the data type of a variable, the operating system allocates
memory and decides what can be stored in the reserved memory.
⚫In contrast to other programming languages like C and java in R,
the variables are not declared as some data type.
⚫The variables are assigned with R-Objects and the data type of the
R-object becomes the data type of the variable.
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11. Data type of the R-object
⚫The variables are assigned with R-Objects and the data type
of the R-object becomes the data type of the variable.
⚫There are many types of R-objects.
⚫Vectors
⚫Lists
⚫Matrices
⚫Arrays
⚫Factors
⚫Data Frames
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12. vector object - Six data types of atomic vectors
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13. Vector object
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14. Vectors
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15. Lists
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16. Matrix
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17. Array
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18. Factor
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19. Data Frames
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20. How to Run R
⚫R is an extremely versatile open source programming language for
statistics and data science.
⚫R operates in two modes: interactive and batch
⚫Interactive Mode
⚫The window in which all this appears is called the R console.
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21. Batch Mode
⚫convenient to automate R sessions
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22. A First R Session
⚫The standard assignment
operator in R is <-.
⚫We can also use =, but this is
discouraged, as it does not
work in some special situations.
⚫The c stands for concatenate.
⚫> q <- c(x,x,8)
⚫sets q to (1,2,4,1,2,4,8)
⚫> x
[1] 1 2 4
⚫> x[3]
[1] 4
⚫index or subscript R vectors
are indexed starting from 1,
not 0
⚫> x <- c(1,2,4)
⚫> x[2:3]
[1] 2 4
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23. R’s internal data sets
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24. Introduction to Functions
⚫A function is a group of instructions that takes inputs, uses them to
compute other values, and returns a result.
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26. Preview of Some Important R Data Structures
⚫Vectors, the R Workhorse
⚫Scalars
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27. Character Strings
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28. Matrices
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29. Lists
⚫Like an R vector, an R list is a container for values, but its contents can be
items of different data types.
⚫(C/C++ programmers will note the analogy to a C struct.)
⚫List elements are accessed using two-part names, which are indicated with
the dollar sign $ in R.
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30. Data Frames
⚫A data frame in R is a list, with
each component of the list
being a vector corresponding
to a column in our “matrix” of
data.
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31. Classes
⚫R is an object-oriented language.
⚫Objects are instances o f classes.
⚫Classes are a bit more abstract than
the data types you’ve met so far.
⚫A commonly used generic function
is summary().
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32. Getting Help
⚫ The help() Function
⚫ To get information on the seq() function
⚫ > help(seq)
⚫ > ?seq
⚫ help.search("multivariate normal")
⚫ ??"multivariate normal“
⚫ Help for Other Topics
⚫ help(package=MASS)
⚫ Help for Batch Mode
⚫ R CMD command –help
⚫ Help on the Internet
⚫ The R Project’s own manuals are available from
the R home page
http://www.r-project.org/. Click Manuals.
⚫ Various R search engines are listed on the R home
page. Click Search.
⚫ The Comprehensive R Archive Network (CRAN), at
http://cran.r-project.org/, is a repository of user-
contributed R code and thus makes for a good
Google search term.
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33. Datasets & Variables
⚫A collection of related sets of information
⚫Composed of different elements, but can be operated as a single unit
⚫Contents of table or matrix of data, where every column denotes a variable, and
each row corresponds an element or member of the collection of data
⚫Variable Types:
⚫ Numerical
⚫ Continuous
⚫ Discrete
⚫ Categorical
⚫ Regular Categorical or Nominal
⚫ Ordinal
⚫ Interval
⚫ Ratio

34. Numerical Data
⚫Observations can take any value in a set of real numbers
⚫Can add, subtract, take averages
Example:
⚫Discrete - Numerical values with jumps; can take only certain
number of values (finite or countably infinite)
⚫Ex:- No. of items bought at a market, Population counts, Census
⚫Continuous - Opposite of discrete; infinite possible values
⚫Ex:- Height, Weight, Time, Government spending, Fluid
measurements (milk, water)

35. Categorical Data
⚫ Observations that form categories
⚫ CANNOT add, subtract, take averages
⚫ NOMINAL or Regular Categorical - One or more categories, no order
⚫ Nominal scales are used for labeling variables, with out any quantitative values. We
cannot tell one value is better than other
⚫ Example:- What is your gender:- m-male, f-female
⚫ Example:- What is your hair color:- 1. black, 2. brown, 3.white, 4.gray, 5.other
⚫ ORDINAL - Levels have a natural order
⚫ With ordinal scales, it is the order of the values is what's important and significant, but the
difference between each one is not really known
⚫ Example:- How do you feel today:-
1.very unhappy 2.unhappy 3.ok 4.happy 5. very happy
⚫ Example:- How satisfied are you with our service?
1.very satisfied 2. some what satisfied 3.neutral 4.somwwhat unsatisfied 5.very unsatisfied

36. Interval
⚫Interval scaled are numerical scale in which we know not only the
order, but also the exact distance between the values
⚫Example:- A+ grade: 91-100, A grade: 81-90, B+ grade: 71-80, B grade: 61-
70, C-grade:51-60, Fail: <60
⚫ Celsius Temp Example:- the difference between 60 and 50 degree is a
measurable 10 degree, as is the difference between 80 and 70 degrees
⚫RATIO
⚫Ratio scales tell us about the order or the exact value between units
⚫Example:- a device provides two examples of ratio scales (height and
weight)

37. Data Science with R
Unit I: Introduction to R & Programming Structures
M. Narasimha Raju
Dept. of Computer Science & Engineering
Shri Vishnu Engineering College for Women (A),
Thank
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