R Programming Training

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R Programming

R Programming Training With Data Science

Master Data Analysis and Visualization with Our R Programming Course

Unlock the potential of R, the powerful language for statistical computing and data visualization. Whether you're a beginner looking to break into the world of data science or a professional aiming to expand your skillset, our R Programming Course is your gateway to mastering data analysis.

Course Highlights

  • Mode of Learning: Live Online Interactive Sessions
  • Instructors: Industry Experts with Proven Expertise in R Programming
  • Fee: Affordable Pricing With Easy Instalment options.
  • Certification: Receive a recognized certification upon completion
  • Free Trial Class: Evaluate the course before enrolling
  • Placement Assistance: Comprehensive support for job seekers
  • Internship: Work on real-time projects to gain practical experience

    • Syllabus

    THE ART OF R PROGRAMMING
      INTRODUCTION
      • Why Use R for Your Statistical Work?
      • Object-Oriented Programming
      • Functional Programming?
      • Functional Programming?
      INSTALLING R
      • Downloading R from CRAN
      • Installing from Source
      GETTING STARTED
        How to Run R
        • Interactive Mode
        • Batch Mode
      First R Session
        Introduction to Functions
        • Variable Scope
        • Default Arguments
        Preview of Some Important R Data Structures
        • Vectors, the R
        • Character Strings
        • Matrices
        • Lists
        • Arrays
        • Data Frames
        VECTORS
          Scalars, Vectors, Arrays, and Matrices
          • Adding and Deleting Vector Elements
          • Obtaining the Length of a Vector
          • Matrices and Arrays as Vectors
          Declarations
          Common Vector Operations
          • Vector Arithmetic and Logical Operations
          • Vector Indexing
          • Generating Useful Vectors with the : Operator
          • Generating Vector Sequences with seq()
          • Repeating Vector Constants with rep
          Vectorized Operations
          • Vector In, Vector Out
          • Vector In, Matrix Out
          NA and NULL Values
          • Using NA
          • Using NULL
          Filtering
          • Generating Filtering Indices
          • Filtering with the subset() Function
          • The Selection Function which
          A Vectorized if-then-else: The ifelse() Function
          • Extended Example: A Measure of Association
          • Extended Example: Recoding an Abalone Data Set
          Testing Vector Equality
          Vector Element Names
          More on c()
        MATRICES AND ARRAYS
          Creating Matrices
          • General Matrix Operations
          • Performing Linear Algebra Operations on Matrices
          • Matrix Indexing
          • Filtering on Matrices
          Applying Functions to Matrix Rows and Columns
          • Using the apply() Function
          • Extended Example: Finding Outliers
          • Adding and Deleting Matrix Rows and Columns
          • Changing the Size of a Matrix
          More on the Vector/Matrix Distinction
          Avoiding Unintended Dimension Reduction
          Naming Matrix Rows and Columns
          Higher-Dimensional Arrays
        LISTS
          Creating Lists
          General List Operations
          • List Indexing
          • Adding and Deleting List Elements
          • Getting the Size of a List
          Accessing List Components and Values
          Applying Functions to Lists
          • Using the lapply() and sapply() Functions
        ARRAYS
        • Naming Columns and Rows
        • Accessing Array Elements
        • Check if an Item Exists
        • Amount of Rows and Columns
        • Array Length
        • Manipulating Array Elements
        • Calculations Across Array Elements
        DATA FRAMES
          Creating Data Frames
          • Accessing Data Frames
          Other Matrix-Like Operations
          • Extracting Subdata Frames
          • More on Treatment of NA Values
          • Using the rbind() and cbind() Functions and Alternatives .
          • Applying apply()
          Merging Data Frames
          • Extended Example: An Employee Database
          Applying Functions to Data Frames
          • Using lapply() and sapply() on Data Frames
        FACTORS AND TABLES
          Factors and Levels
          Common Functions Used with Factors
          • The tapply() Function
          • The split() Function
          • The by() Function
          Working with Tables
          • Matrix/Array-Like Operations on Tables
          • Extended Example: Extracting a
          Other Factor- and Table-Related Functions
          • The aggregate() Function
          • The cut() Function
        R PROGRAMMING STRUCTURES
          Control Statements
          • Loops
          • Looping Over Non vector Sets
            • if-else
          Arithmetic and Boolean Operators and Values
          Default Values for Arguments
          Return Values
          • Deciding Whether to Explicitly Call return()
          • Returning Complex Objects
          Functions Are Objects
          Environment and Scope Issues
          The Top-Level Environment
          • The Scope Hierarchy
          • More on ls()
          • Functions Have (Almost) No Side Effects
          No Pointers in R
          Writing Upstairs
          • Writing to Nonlocals with the Super assignment Operator
          • Writing to Nonlocals with assign()
          When Should You Use Global Variables?
          Replacement Functions
          • What’s Considered a Replacement Function?
          Tools for Composing Function Code
          • Text Editors and Integrated Development Environments
          The edit() Function
          Writing Your Own Binary Operations
          Anonymous Functions
        DOING MATH AND SIMULATIONS IN R
          Math Functions
          • Extended Example
          • Cumulative Sums and Products
          • Minima and Maxima
          Functions for Statistical Distributions
          Sorting
          Linear Algebra Operations on Vectors and Matrices
          • Extended Example: Vector Cross Product
          • Set Operations
          Simulation Programming in R
          • Built-In Random Variate Generators
          • Obtaining the Same Random Stream in Repeated Runs
        INPUT/OUTPUT
          Accessing the Keyboard and Monitor
          • Using the scan() Function
          • Using the readline() Function
          • Printing to the Screen
          Reading and Writing Files
          • Reading a Data Frame or Matrix from a File
          • Reading Text Files
          • Introduction to Connections
          • Extended Example
          • Accessing Files on Remote Machines via URLs
          • Writing to a File
          • Getting File and Directory Information
        STRING MANIPULATION
          An Overview of String-Manipulation Functions
          • grep()
          • nchar()
          • paste()
          • sprintf()
          • substr
          • strsplit()
          • regexpr()
          Regular Expressions
          • Extended Example
        R DATA INTERFACES
          R - CSV Files
          • Reading a CSV File
          • Analyzing the CSV File
          • Writing into a CSV File
          R - Excel Files
          • Install xlsx Package
          • Reading the Excel File
          R - Binary Files
          • Writing the Binary File
          • Reading the Binary File
          R - XML Files
          • Reading XML File
          • XML to Data Frame
          R - JSON Files
          • Install rjson Package
          • Read the JSON File
          • Convert JSON to a Data Frame
          R - Database
          • RMySQL Package
          • Connecting R to MySql
          • Querying the Tables
          • Query with Filter Clause
          • Updating Rows in the Tables
          • Inserting Data into the Tables
          • Creating Tables in MySql
          • Dropping Tables in MySql
        GRAPHICS
          Creating Graphs
          • The Workhorse of R Base Graphics: The plot() Function
          • R - Pie Charts
          • R - Bar Charts
          • R - Boxplots
          • R - Histograms
          • R - Line Graphs
          • R - Scatterplots
          • Starting a New Graph While Keeping the Old Ones
          • Extended Example
          • Adding Points: The points() Function
          • Adding a Legend: The legend() Function
          • Adding Text: The text() Function
          • Pinpointing Locations: The locator() Function
          • Restoring a Plot
          • Customizing Graphs
          • Changing Character Sizes: The cex
          • Changing the Range of Axes: The xlim and ylim Options
          • Graphing Explicit Functions
          • Extended Example
          Saving Graphs to Files
          • R Graphics Devices
          • Saving the Displayed Graph
          • Closing an R Graphics Device
          Creating Three-Dimensional Plots
        R Statistics
          R Statistics Intro
          R Data Set
          R Max and Min
          R Mean Median Mode
          R Percentiles
        INSTALLING AND USING PACKAGES
          Package Basics
          Loading a Package from Your Hard Drive
          Downloading a Package from the Web
          Installing Packages Automatically
          Installing Packages Manually
          Listing the Functions in a Package

      Duration: 20 Hrs
      Attend a Free Trial Class
      Syllabus -> R Programming