R Programming Complete Certification Training

R concepts, coding examples. Data structure, loops, functions, packages, plots/charts, data/files, decision-making in R.

What you'll learn

Deep practical knowledge of R programming language

Become a Data Scientist, Data Engineer, Data Analyst or Consultant

Fundamentals and setup of R Language

Get familiar with RStudio

Variables and Data Types

Input-Output Features in R

Operators in R

Data Structure in R

Vectors, Lists and their application

R Programs for Lists and Vectors in RStudio

Matrix and application of Matrices in R with R Programs

Arrays with R Programs for Arrays in RStudio

Data Frames and R Programs for Data Frame in RStudio

Factors, application of Factors, R Programs for Factors in RStudio

Decision-making in R, types of decision-making statements with R Programs

Loops in R, flowcharts and programs for loops in R

Functions in R

Strings in R

Packages in R

Data and File Management in R

Plotting in R (graphs, charts, plots, histograms)

Write complex R programs for practical industry scenarios

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the R Programming course by Uplatz.R is a programming language that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible.While there is something called the S language which is often the vehicle of choice for research in statistical methodology, on the other hand R provides an Open Source route to participation in that activity. R is nothing but an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.R can be considered as an integrated version of a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S.This R Programming course by Uplatz is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. Even if you are a beginner, this R course is the perfect place to start. If you are trying to understand the R programming language as a beginner, this R Programming course will provide you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise. This R tutorial will provide you an opportunity to take a deep-dive into R programming and build your R skills from scratch. To get the most out of the R programming training, you would need to practice as you proceed with the tutorials. After successful completion of the R Programming training course you will be able to:Master the use of the R and RStudio interactive environmentExpand R by installing R packagesExplore and understand how to use the R documentationRead Structured Data into R from various sourcesUnderstand the different data types in RUnderstand the different data structures in RR programming constructs - variables, functions, string manipulation, loops, etc.Conduct decision-making using RAble to do Data and file management in RPackages in RPlotting and Visualization in Rand more…R Programming - Course Syllabus1. Fundamentals of R LanguageIntroduction to RHistory of RWhy R programming LanguageComparison between R and PythonApplication of R2. Setup of R LanguageLocal Environment setupInstalling R on WindowsInstalling R on LinuxRStudioWhat is RStudio?Installation of RStudioFirst Program - Hello World3. Variables and Data TypesVariables in RDeclaration of variableVariable assignmentFinding variableData types in RData type conversionR programs for Variables and Data types in RStudio4. Input-Output Features in Rscan() functionreadline() functionpaste() functionpaste0() functioncat() functionR Programs for implementing these functions in RStudio5. Operators in RArithmetic OperatorsRelational OperatorsLogical OperatorsAssignment OperatorsMiscellaneous OperatorsR Programs to perform various operations using operators in RStudio6. Data Structure in R (part-I)What is data structure?Types of data structureVector- What is a vector in R?- Creating a vector- Accessing element of vector- Some more operations on vectors- R Programs for vectors in RStudioApplication of Vector in RList- What is a list in R?- Creating a list- Accessing element of list- Modifying element of list- Some more operations on listR Programs for list in RStudio7. Data Structure in R (part-II)Matrix or Matrices- What is matrix in R?- Creating a matrix- Accessing element of matrix- Modifying element of matrix- Matrix OperationsR Programs for matrices in RStudioApplication of Matrices in RArrays- What are arrays in R?- Creating an array- Naming rows and columns- Accessing element of an array- Some more operations on arraysR Programs for arrays in RStudio8. Data Structure in R (part-III)Data frame- What is a data frame in R?- Creating a data frame- Accessing element of data frame- Modifying element of data frame- Add the new element or component in data frame- Deleting element of data frame- Some more operations on data frameR Programs for data frame in RStudioFactors- Factors in R- Creating a factor- Accessing element of factor- Modifying element of factorR Programs for Factors in RStudioApplication of Factors in R9. Decision Making in RIntroduction to Decision makingTypes of decision-making statementsIntroduction, syntax, flowchart and programs for- if statement- if…else statement- if…else if…else statement- switch statement10. Loop control in RIntroduction to loops in RTypes of loops in R- for loop- while loop- repeat loop- nested loopbreak and next statement in RIntroduction, syntax, flowchart and programs for- for loop- while loop- repeat loop- nested loop11. Functions in RIntroduction to function in RBuilt-in FunctionUser-defined FunctionCreating a FunctionFunction ComponentsCalling a FunctionRecursive FunctionVarious programs for functions in RStudio12. Strings in RIntroduction to string in R- Rules to write R Strings- Concatenate two or more strings in R- Find length of String in R- Extract Substring from a String in R- Changing the case i.e. Upper to lower case and lower to upper caseVarious programs for String in RStudio13. Packages in RIntroduction to Packages in RGet the list of all the packages installed in RStudioInstallation of the packagesHow to use the packages in RUseful R Packages for Data ScienceR program for package in RStudio14. Data and File Management in RGetting and Setting the Working DirectoryInput as CSV FileAnalysing the CSV FileWriting into a CSV FileR programs to implement CSV file15. Plotting in R (Part-I)Line graphScatterplotsPie Charts3D Pie Chart16. Plotting in R (Part-II)Bar / line chartHistogramBox plot

Overview

Section 1: Introduction to R Programming

Lecture 1 Introduction to R Programming

Section 2: Setup of R Language

Lecture 2 Setup of R Language

Section 3: Variables and Data Types

Lecture 3 Variables and Data Types - part 1

Lecture 4 Variables and Data Types - part 2

Section 4: Input-Output Features

Lecture 5 Input-Output Features - part 1

Lecture 6 Input-Output Features - part 2

Section 5: Operators in R

Lecture 7 Operators in R - part 1

Lecture 8 Operators in R - part 2

Section 6: Vectors - Data Structure

Lecture 9 Vectors - Data Structure - part 1

Lecture 10 Vectors - Data Structure - part 2

Section 7: List - Data Structure

Lecture 11 List - Data Structure - part 1

Lecture 12 List - Data Structure - part 2

Section 8: Matrix - Data Structure

Lecture 13 Matrix - Data Structure - part 1

Lecture 14 Matrix - Data Structure - part 2

Section 9: Arrays - Data Structure

Lecture 15 Arrays - Data Structure - part 1

Lecture 16 Arrays - Data Structure - part 2

Section 10: Data Frame - Data Structure

Lecture 17 Data Frame - Data Structure - part 1

Lecture 18 Data Frame - Data Structure - part 2

Lecture 19 Data Frame - Data Structure - part 3

Section 11: Factors - Data Structure

Lecture 20 Factors - Data Structure - part 1

Lecture 21 Factors - Data Structure - part 2

Section 12: Decision Making in R

Lecture 22 Decision Making in R - part 1

Lecture 23 Decision Making in R - part 2

Section 13: Loops in R

Lecture 24 Loops in R - part 1

Lecture 25 Loops in R - part 2

Lecture 26 Loops in R - part 3

Section 14: Functions in R

Lecture 27 Functions in R - part 1

Lecture 28 Functions in R - part 2

Section 15: Strings in R

Lecture 29 Strings in R - part 1

Lecture 30 Strings in R - part 2

Section 16: Packages in R

Lecture 31 Packages in R

Section 17: Data and File Management in R

Lecture 32 Data and File Management in R - part 1

Lecture 33 Data and File Management in R - part 2

Section 18: Charts, Plots, Histograms in R

Lecture 34 Line chart in R

Lecture 35 Scatterplot in R

Lecture 36 Pie chart in R

Lecture 37 Bar chart in R

Lecture 38 Histogram in R

Lecture 39 Boxplots in R

Section 19: End of Course Quiz

Section 20: Coding Exercises

R Developers & Data Developers,Data Scientists - R, Python,Newbies and beginners aspiring for a career in programming & statistical analysis,Data Engineers and Statistical Analysts,R & Python Programmers,Technical & Analytics Consultants,Anyone wishing to learn data science and machine learning,Lead R Developers,R Modelling Analysts,Data Software Developers,Financial and Marketing Analysts,Software Engineers,Web Application Developers,Business Analysts and Consultants,Data Science and Machine Learning enthusiasts