R Programming Training by Experts

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Our Training Process

R Programming - Syllabus, Fees & Duration

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

    Download Syllabus - R Programming
    This syllabus is not final and can be customized as per needs/updates
 
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R Programming Jobs in Houston

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R Programming Internship/Course Details

R Programming internship jobs in Houston
R Programming Because of its open source credibility, R programming is quickly becoming most in expert in the field of analytics. There is a significant shortage of experts with R programming skills on the market, which brings attention to pursue. While teaching R Programming in the classroom, our Nestsoft trainers discuss their previous and current project experiences with candidates, allowing them to gain exposure to real-world business experience. It is a simple programming language than, other programming languages, would have no requirements. We offer training who do not have a background in statistics. In R, there are a variety of great packages that can aid in a fast data analysis. The course provides students hands-on experience with a variety of R programming principles. However, reasonable experience is required for this. . Nestsoft offer the best R programming training, starting with the fundamentals and advancing to complex analytics concepts.

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