1. Introduction to R : 

      Introduction to Algorithm and programming concepts.  What is R? – Why R? – Advantages of R  over Other Programming Languages - R Studio: R command Prompt, R script file, comments –  Handling Packages in R: Installing a R Package, Few commands to get started: installed.packages(),  packageDescription(), help(), find.package(), library() - Input and Output – Entering Data from  keyboard – Printing fewer digits or more digits – Special Values functions : NA, Inf and –inf. 

2. R Data Types: 

      R Data Types: Vectors, Lists, Matrices, Arrays, Factors, Data Frame – R - Variables: Variable  assignment, Data types of Variable, Finding Variable ls(), Deleting Variables - R Operators:    Arithmetic Operators, Relational Operators, Logical Operator, Assignment Operators, Miscellaneous  Operators - R Decision Making: if statement, if – else statement, if – else if statement, switch  statement – R Loops: repeat loop, while loop, for loop - Loop control statement: break statement, next  statement. Solving problems from Assignment sheet. 

3. Functions in R-Language:

     R-Function : function definition, Built in functions: mean(), paste(), sum(), min(), max(), seq(), user-  defined function, calling a function, calling a function without an argument, calling a function with    argument values - R-Strings – Manipulating  - R Vectors – Sequence vector, rep function, vector  access, vector names, vector math, vector recycling, vector element sorting - R List - Creating a List,  List Tags and Values, Add/Delete Element to or from a List, Size of List, Merging Lists, Converting  List to Vector - R Matrices – Accessing Elements of a Matrix, Matrix Computations: Addition,  subtraction, Multiplication and Division- R Arrays: Naming Columns and Rows, Accessing Array  Elements, Manipulating Array Elements, Calculation Across Array Elements - R Factors –creating  factors, generating factor levels gl().  

4. String Manipulation in R language:

    String fnctions : grep(), nchar() , paste(), sprintf(), substr(), strsplit(), regex() gregexpr(), toupper(), tolower(), paste()  

5. Bit-wise operators using R:

        bitwOr(value1,value2), bitwXor(value1,value2), bitwNot(valoe),      bitwAnd(value1,value2),bitwShiftL(value,shift), bitwShiftR(value,shift), Solving problems from    assignment sheet. 

6. Creating Data Frames and visualization of Data:

   Data Frames –Create Data Frame, Data Frame Access, Understanding Data in Data Frames: dim(), nrow(), ncol(), str(), Summary(), names(), head(), tail(), edit() functions - Extract Data from Data Frame, Expand Data Frame: Add Column, Add Row - Joining columns and rows in a Data frame rbind() and cbind() – Merging Data frames merge() – Melting and Casting data melt(), cast(). Loading and handling Data in R: Getting and Setting the Working Directory – getwd(), setwd(), dir() File Handling in R language, -CSV Files - Input as a CSV file, Reading a CSV File, Analyzing the CSV File: summary(), min(), max(), range(), mean(), median(), apply() - Writing into a CSV File – R -Excel File – Reading the Excel file.  

7. Installing RMySQL Package in R:

           Installing  RMySQL Package, Creating Database, table under MYSQL, Inserting data in a    table , Update and alter table, Display content of  table. 

8. Descriptive Statistics using R: 

     Descriptive Statistics: Data Range, Frequencies, Mode, Mean and Median: Mean Applying Trim        Option, Applying NA Option, Median - Mode - Standard Deviation – Correlation - Spotting Problems  in Data with Visualization: visually Checking Distributions for a single Variable - R –Pie Charts: Pie  Chart title and Colors – Slice Percentages and Chart Legend, 3D Pie Chart – R Histograms – Density  Plot - R – Bar Charts: Bar Chart Labels, Title and Colors. Line Chart, Scatterplot, Developing graphs,  Box Plot, Drawing line, circle, rectangle, triangle using R language . 

Core Components of an R Programming Syllabus:

• Introduction to R and R-Studio:

• Overview of R's role in data science and statistical computing.
• Installation and setup of R and RStudio.
• Understanding the R environment and basic syntax.

• R Basics and Data Types:

• Variables, operators, and basic data types (numeric, character, logical).
• Introduction to R's fundamental data structures: vectors, lists, matrices, arrays, and data frames.
• Factors and their use in categorical data.
• Handling dates and times in R.

• Control Flow and Functions:

• Conditional statements (if, else if, else).
• Looping constructs (for, while).
• Creating and utilizing user-defined functions.

• Data Manipulation and Management:

• Importing and exporting data from various sources (CSV, Excel, databases).
• Data cleaning and transformation techniques.
• Subsetting, indexing, and manipulating data structures.

• Data Visualization:

• Creating various plots and graphs (scatter plots, line graphs, histograms, bar charts, box plots).
• Introduction to advanced visualization packages like ggplot2.

• Basic Statistics and Exploratory Data Analysis:

• Calculating descriptive statistics (mean, median, mode, standard deviation, quantiles).
• Techniques for exploring and summarizing data.

• Packages and Libraries:

• Installing and managing R packages.
• Leveraging specialized packages for specific tasks (e.g., data manipulation, visualization, statistical modeling).

Advanced Topics (often included in more comprehensive courses):

• Statistical Modeling: Linear regression, logistic regression, hypothesis testing.
• Machine Learning: Introduction to supervised and unsupervised learning algorithms (e.g., decision trees, clustering).
• Database Connectivity: Connecting R to databases.
• Creating R Packages or Applications.