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R Programming

Ratings (4.5)
100 Hours
Live Classes
Training mode
Online or Offline by Instructor
Hands-on Labs
Real-World Project
Digital Notes
Doubt Clear Session

About the Course

R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics, and data analysis.

Training Objective

R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software.

Job Roles of Course
  1. Data Scientist
  2. Data Architect
  3. Geo Statisticians
  4. R programmer
  5. Quantitative Analysis with R
  6. Data Visualization Analyst
  7. Data Analyst


Introduction of R Programming

  1. Introduction and history of R programming
  2. Basic fundamentals
  3. Installation and use of software
  4. R environment
  5. Options and function
  6. Different windows and support system
  7. Use of R as a calculator
  8. Syntax, Identifier, Keyword
  9. Basic Operators
  10. Introduction to R Studio and different platforms (VS code, Jupyter Notebook etc.)

  1. Data Types
    • Numeric
    • Integer
    • Complex
    • Double Character
    • String
    • Logical (Boolean) etc.
  2. Data Structure
    • Vector, Array, Matrix
    • Data Frame
    • List and Factor
    • Tables etc.
  3. Operations on different data structures: indexing, subsetting, and manipulation.

  1. Input data manually
  2. taking input from user
  3. Reading and writing data from different file formats (CSV, Excel, text files).

  1. Handling missing data
    • identifying, removing, or imputing missing values
    • Data reshaping, merging, and transforming
    • Calculating basic Statistics results (Central Tendency and Dispersions)

  1. Creation of String
  2. length
  3. Whitespace characters
  4. Concatenation
  5. Subsetting and Manipulation
  6. Case Conversion of strings

  1. Conditional Statements
    • if, if else Statements
    • if else if else Statements etc.
  2. Looping Structures
    • for loop
    • while loop
  3. Loop Control Statements
    • Break, Next
    • Vectorized Operations vs. Loops

  1. Function type
  2. Function argument
  3. Writing user defined function
  4. utility and operations of data visualization (dot chart, bar chart, frequency chart etc.)

Able to use R Package and Automatic Learning tool:

  1. Introduction to R packages
  2. use and functions (dplyr, ggplot2, tidyr, lubridate, readxl etc.)
  3. Introduction about Swirl Packages and its use.

Able to automate PDF related activities.

  1. Using higher order functions
  2. anonymous functions
  3. and functional idioms in R
  4. Using the apply family functions (apply, lapply, sapply, tapply)

Able to automate Word related activities.

  1. Techniques for debugging R code effectively
  2. Profiling code for performance optimization

Able to send bulk email by using UiPath Application as well as different activities also.

  1. Efficient programming techniques and best practices for faster code execution
  2. Identifying and addressing performance bottlenecks.

Able to scrap data from any website and store into any database like excel.

  1. S4 and reference classes
    • understanding and implementing object oriented programming paradigms in R
    • Creating and using classes and methods in R

Extract data from multiple PDF files.

  1. Parallel computing with R
    • using parallel
    • snow, and other packages for parallel processing
    • Leveraging multicore architectures for improved performance.

Run process through UiPath cloud so that user can use it from anywhere.

  1. Working with big data
    • handling large datasets in R (using packages like data.table)
  2. Advanced statistical analysis
    • exploring additional statistical models and techniques.

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