Section 1
##### What is R?

Section 2
##### Basic Operations in R

7

Expressions: Basic Idea

8

Constant Values: Numeric and Non-Numeric

9

Arithmetic: Operations and BODMAS

10

Conditions: Equality, Greater Than, Less Than, etc

11

Function Calls: Introduction to R Functions

12

Symbols and Assignment

13

Keywords: NA, Inf, NaN, NULL, TRUE, FALSE

14

Naming a Variable: Generally accepted conventions

Section 3
##### Data Types and Data Structures

Section 4
##### Subsetting in R

17

Vector Subsetting

18

c() function: Creation of Vectors

19

Using rep() and seq() functions

20

Using factor() to covert vectors to factors

21

Using data.frame() to create data frames

22

Meta data access: dimnames(), rownames(), colnames()

23

Using matrix() to create matrices

24

Using array() to create arrays

25

Subsetting data frames: row subset, column subset, using subset() function

26

Assigning to a subset

27

Using is.na() to detect NA

28

Subsetting factors

Section 5
##### Additional Topics on Data structures

29

The recycling rule: Uneven arithmetic operation on vectors

30

Type coercion: Character to Numeric

31

Automatic Type coercion

32

Coercing factors: Using as.factor() function

33

Changing factor levels

34

Attributes: attribute(), attr(), names() functions

35

Classes: Idea of OOP in R

36

Dates: As a special class

37

Formulas: As a special class

38

Exploring Objects: summary(), str(), dim() functions

39

Generic functions

Section 6
##### Data Import and Export

40

Text formats: Reading Delimited Files

41

read.table() function

42

Using read.fwf() function for fixed width files

43

Using readLines() for reading lines

44

Using write.csv() function to store data as CSV files

45

Reading Excel file: Package XLConnect

46

Reading SPSS file: Package Foreign

47

Reading SAS data file: Package sas7bdat

48

Database connection: The ideas of ODBC connecting in Windows

49

RODBC package: Create and Query database from R

50

Basic SQL

Section 7
##### Control Structures and User Defined Functions

51

Conditional Statements

52

If statement: The Structure

53

If Else statement: The Structure

54

Ifelse() function

55

Iteration

56

The for loop

57

The while loop

58

The repeat statement

59

lapply() function

60

sapply() function

61

apply() function

62

User defined function

63

Variable scooping: Global and Local Variables

64

Using user defined functions inside function definition

Section 8
##### Data Visualisation: Charting with R

65

The plot function

66

plot.new() function: Generating new plot object

67

plot.window() function: Creating window

68

points() function: Plotting points

69

axis() function: Generating Axis

70

box() function: Creating enclosure

71

title() function: Assigning title

72

par() function: Fixing plotting parameters

73

lines() function: Adding connector lines

74

Multi figure layout: Creating multiple charts in the same window

75

hist() function: Plotting histograms

76

Kernel Density Plot: The non-parametric probability distribution

77

Comparing Groups via Kernel Density: Comparing two different probability distributions

78

Simple Bar Plot: Visualizing categorical data

79

Staked Bar Plot: Understating category composition

80

Grouped Bar Plot

81

Line Charts

82

Pie Charts

83

Boxplots: Understanding data distributions and outliers

84

Using Google Chart Tools with R (Package googleVis)

85

Geo Charts

86

Motion Charts

Section 9
##### Visualisation on R using Google Vis

Section 10
##### Visualization in R using GGPLOT2

SAS vs R

A Short Description of Both Software Suites

SAS and R, both are important data analytics tools used in today’s tech world. Both tools are extensively used by Data Scientists and Data Analysts. Making a choice between SAS and R has been a longstanding debate in the world of Data Science.

Statistical Analysis System (SAS) language is a programming language that is used to read in data from spreadsheets and databases and output the results of statistical analysis in tables and graphs and as RTF, HTML, and PDF docs. SAS is commonly used for financial analytics capabilities. SAS is easy to learn, and it offers great technical support. It can be considered as an expensive alternative to R;

R is mostly used by the research community, professors and researchers, among other faculties. Since, R is an open-source tool, you can get the latest version as soon as it is released. R is mainly used for statistical analysis, graphical representations, and reporting. Here, we take a simplified yet concise look at the various features, functions, and strengths and weaknesses of each of these tools.

Features of SAS and R

Parameters of Comparison

Ease of Learning

SAS is very good when it comes to picking a new tool to learn without any prior programming language experience and thus can be an excellent experience for beginners.

R is bit tougher to learn as compared to SAS. It is not a high-level programming language and hence even a small mistake can turn out to be a huge problem.

Managing Data

In terms of handling and managing data, SAS is in a better position since the data is increasing at a huge pace day by day and SAS is better at handling data. Furthermore, R works only on RAM, and increasing the RAM as and when the data increases is not a feasible option.

Graphics

Graphics is a very important aspect of any Data Science or Data Analytics capabilities. Ability to visualize and analyze data is a crucial part. R is the winner in this area, thanks to the availability of various packages like ggplot, Latice, and RGIS.

SAS is not great at graphical capabilities. Though Base SAS has some graphical capabilities.

Working with Big Data

While working with Big Data, R has some very good features which can be utilized by Big Data, Data Science, and Data Analytics communities. If you are looking for deploying analytics at scale for Machine Learning capabilities, then R is the language to choose. Of late, SAS is taking fast strides to execute analytics also. But still, SAS lags R when it comes to integrating successfully with Big Data tools like Hadoop and others.

Industry Deployment

Since R is an open-source programming language, it can be used by anybody. It thus finds a widespread usage among small and medium enterprises. SAS, on the other hand, is extremely costly and is useful for large organizations. SAS is mostly used for data warehousing, data quality, data analytics, and reporting capabilities.

Cost

There are numerous packages in R which provide advanced graphical capabilities. It incorporates the latest features quickly as the packages get added on by programmers across the world. Currently, R is in popular demand. Although, SAS has been the market leader in corporate jobs, it is very expensive for start-ups.

Service Support

R has the biggest online community but without customer service support which makes it difficult for people to tackle any technical issue. Whereas, SAS has dedicated customer service, along with its community. Hence, installation and other technical challenges get easily sorted.

The choice between SAS and R always depends on organizational requirements. Large-scale organizations usually opt SAS over R, while the start-ups prefer the latter option.