Elite

R Programming

About Course

  • In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code
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Why learn R Programming

  • With R, you can develop amazing Web-App-
  • R provides you with the ability to build aesthetic web-applications. Using the R Shiny package, you can develop interactive dashboards straight from the console of your R IDE.
  • Robust Visualization Library-R is most widely recognized for its stunning visualizations which gives it an edge over other Data Science programming languages.
  • A go-to language for Statistics and Data Science
  • R is the standard language for Statistics and DataScience. R facilitates various statistical operations through its thousands of packages.

What will I learn

  • Describe the R programming language and its programming environment.
  • Explain the fundamental concepts associated with programing in R including
  • functions,variables,datatypes,pipes and vectors
  • Configure statistical programming software
  • Make use of R loop functions and debugging tools

Why ?

  • R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations
  • You can run your code without any compiler
  • R is open-source, therefore it can be modified and redistributed as per the user’s need. It is great for visualization and has far more capabilities as compared to other tools.
  • R is a gateway to Lucrative Career-R language is used extensively in Data Science. This field offers some of the highest-paying jobs in the world today.

Tags

  • Data Science
  • Machine Learning
  • R Programming
  • Exploratory Data Analysis
  • Rstudio
  • Data Analysis
  • Data Cleansing
  • Cluster Analysis

Topics for this course

    1. Data Types and Variables
    2. Numbers
    3. Attributes
    4. Vectors
    5. Evaluation
    6. Printing
    7. Data Frame
    8. Objects
    9. R Vectors
    10. Matrix Dimensions, Matrix Design
    11. C Bind and R Bind
    12. Lists

 

    1. Introduction To R Programming
    2. Installing R and R studio
    3. Packages
    4. Input/Output
    5. R interfaces
    6. R Library
    1. Reading/Writing Data
    2. Writing data
    3. Reshaping Data
    4. Reading data files with tables
    5. SORTING DATA
    6. Data Aggregation
    7. Files connection
    1. For
    2. Repeat
    3. While
    4. Next
    5. Return
    6. Function Arguments
    1. Lappy
    2. Tappy
    3. Split
    4. Mappy
    5. Apply
    1. Dates in R
    2. Times in R
    3. Operation on Dates and Time on R
    1. Creating/Generating Randon Number Sampling
    2. Creating a graph
    3. Bar Plot
    4. Density Plot
    5. Dot Plot
    6. Line charts
    7. Pie charts
    8. Box plot
    9. Scatter Plot
    10. Histogram
    11. Graphical Parameters
    12. Lattice graphs
    13. Combining Plot
    14. ggplots graph
    15. Correlograms

Target Audience

  • who want to improve their programming skills by applying industry best practices in their daily work.
  • Individuals looking to learn R Programming
  • Project Managers, Software architects and Development Team Leaders, who want to implement or improve a software development process within a project, and who want to define a project or company-wide set of recommended tools and best practices.
  • Project Managers, Software architects and Development Team Leaders, who want to implement or improve a software development process within a project, and who want to define a project or company-wide set of recommended tools and best practices.
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