R Programming Online Live Class by Experts

Our Training/Internship Process

R Programming Online Live Classes in Tucson | Free Demo Training

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
 
10000+
20+
50+
25+

R Programming Jobs in Tucson

Enjoy the demand

Find jobs related to R Programming in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Tucson, chennai and europe countries. You can find many jobs for freshers related to the job positions in Tucson.

  • R Programmer
  • Data Scientist
  • Software Engineer
  • Software Technologist
  • R - Shiny Programmer
  • Analytics Engineer
  • R Programming Trainer

R Programming Internship/Course Details

R Programming internship jobs in Tucson
R Programming There is a significant shortage of experts with R programming skills on the market, which brings attention to pursue. Nestsoft is the excellent R programming Training in kerala . The course is designed with statistics students in consideration. We train skilled experts how to use the R programming language in statistical analysis, data visualisation, machine learning, and data mining, among other things. . Learn R programming online to enhance your professional capabilities and learn how to employ the language for statistical computation and graphics. Many large companies, including prominent banks, IT, retail, healthcare, pharmaceutical, supply chain, and logistics industries, adopt R. Nestsoft offer the best R programming training, starting with the fundamentals and advancing to complex analytics concepts. R is a computer language that can be used for statistical analysis, reporting, and graphics. We offer training who do not have a background in statistics.

Meet a Few of our Industry Experts 🚀 Your Pathway to IT Career

Aneesh

Mobile: +91 9895490866
Location: Online (Tucson, Usa)
Qualification: B. Tech

Experience: Handson experience with odoo v13 14 15 16 capable of designing developing and maintaining enterprise application   more..

Tejaswini

Mobile: +91 89210 61945
Location: Online (Tucson, Usa)
Qualification: Mtech

Experience: I have 4 5 years Experience as software test engineer I have experience in Manual testing and automation testing and  more..

romesh

Mobile: +91 91884 77559
Location: Online (Tucson, Usa)
Qualification: MCA

Experience: I am working as a mean stack developer in E-Tech Services pvt ltd since 11 January 2022 Application for Meanstack  more..

Aditya

Mobile: +91 94975 90866
Location: Online (Tucson, Usa)
Qualification: Diploma in Engineering

Experience: English content curriculum developer IELTS & OET trainer proof reading soft skills training reasoning & verbal ability for competitive exams  more..

Sadaf

Mobile: +91 8301010866
Location: Online (Tucson, Usa)
Qualification: 10th qualified from IGCSE board and IB diploma program graduate

Experience: I have experieinced IGCSE since I myself graduated from igcse school and I did 12th from IB board I have  more..

Anish

Mobile: +91 91884 77559
Location: Online (Tucson, Usa)
Qualification: MCA

Experience: Software development Html Css Python   more..

Ishfaq

Mobile: +91 91884 77559
Location: Online (Tucson, Usa)
Qualification: MCA

Experience: 2 years of experience Two Years Experience in computer literacy customer service computer security data collection and equipment repairs installation  more..

Vishant

Mobile: +91 98474 90866
Location: Online (Tucson, Usa)
Qualification: BCA

Experience: Adobe Photoshop Adobe ai Adobe xd Figma Ads banner design UI design  more..

Adarsh

Mobile: +91 94975 90866
Location: Online (Tucson, Usa)
Qualification: Bsc computer science

Experience: Writing test cases manual testing automation testing J meter API testing Postman bugzilla jira  more..

Anuj

Mobile: +91 94975 90866
Location: Online (Tucson, Usa)
Qualification: MBA

Experience: I have 2 year experience as IT Analyst in HCL technologies I am pursuing my Full stack developer course from  more..

Neetu

Mobile: +91 89210 61945
Location: Online (Tucson, Usa)
Qualification: MCA

Experience: Data Structures algorithms c++ SQL DBMS Golang Object oriented programming Operating system Computer networking  more..

Kowthalam

Mobile: +91 94975 90866
Location: Online (Tucson, Usa)
Qualification: Btech

Experience: Full stack developer HTML CSS Javascript Java Sql  more..

Sonali

Mobile: +91 94975 90866
Location: Online (Tucson, Usa)
Qualification: B.tech

Experience: 6 years experience in php   more..

YASHDEEP

Mobile: +91 89210 61945
Location: Online (Tucson, Usa)
Qualification: BTECH

Experience: django python Java c css  more..

Rashee

Mobile: +91 89210 61945
Location: Online (Tucson, Usa)
Qualification: B. Tech

Experience: HTM CSS JavaScript ReactJS Redux MongoDB WordPress Blender Adobe Photoshop Illustrator Canva Web Developer Intern Assist Design Automation Pvt Ltd  more..

Rohit

Mobile: +91 91884 77559
Location: Online (Tucson, Usa)
Qualification: Bachelor of engineering

Experience: Java selenium testng api testing Java selenium Maven postman MySQL agile waterfall test scripts jira Sdlc stlc etc I have  more..

Manoj

Mobile: +91 8301010866
Location: Online (Tucson, Usa)
Qualification: Bachelors

Experience: I am Manoj P having a total of 3 5 years of hands-on experience in software testing Key Skills :  more..

Hariharasudhan

Mobile: +91 91884 77559
Location: Online (Tucson, Usa)
Qualification: Bachelor of Engineering

Experience: Having 4 8 Years of Experience in Software Testing in that both Manual and Automation Experience in automation frameworks including  more..

Success Stories

The enviable salary packages and track record of our previous students are the proof of our excellence. Please go through our students' reviews about our training methods and faculty and compare it to the recorded video classes that most of the other institutes offer. See for yourself how TechnoMaster is truly unique.

Photos of Training / Internships

Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson
Internship/projects in tucson

Trained more than 10000+ students who trust Nestsoft TechnoMaster

Get Your Personal Trainer