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R Programming Language

R Programming Language for Statistical Computing and Graphical Representation

  DATAhill Solutions Srinivas Reddy

   Development, Programming Languages

   Published on 02/2020

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  • Introduction to R Programming
    20:05
    Preview
  • R Installation & Setting R Environment
    50:16
  • Variables, Operators & Data types
    53:10
  • Structures
    47:08
    Preview
  • Vectors
    01:04:04
  • Vector Manipulation & Sub-Setting
    01:06:03
  • Constants
    41:38
  • RStudio Installation & Lists Part 1
    01:02:20
  • Lists Part 2
    47:44
  • List Manipulation, Sub-Setting & Merging
    45:01
  • List to Vector & Matrix Part 1
    49:52
  • Matrix Part 2
    44:02
  • Matrix Accessing
    48:26
  • Matrix Manipulation, rep function & Data Frame
    56:08
  • Data Frame Accessing
    54:01
  • Column Bind & Row Bind
    50:32
  • Merging Data Frames Part 1
    50:04
  • Merging Data Frames Part 2
    54:26
  • Melting & Casting
    52:55
  • Arrays
    43:50
    Preview
  • Factors
    50:53
  • Functions & Control Flow Statements
    40:27
  • Strings & String Manipulation with Base Package
    53:22
  • String Manipulation with Stringi Package Part 1
    58:33
  • String Manipulation with Stringi Package Part 2 & Date and Time Part 1
    48:13
  • Date and Time Part 2
    53:19
  • Data Extraction from CSV File
    42:02
  • Data Extraction from EXCEL File
    50:40
  • Data Extraction from CLIPBOARD, URL, XML & JSON Files
    50:04
  • Introduction to DBMS
    50:22
  • Structured Query Language, MySQL Installation & Normalization
    41:35
  • Data Definition Language Commands
    01:02:24
    Preview
  • Data Manipulation Language Commands
    47:29
  • Sub Queries & Constraints
    16:07
  • Aggregate Functions, Clauses & Views
    07:21
  • Data Extraction from Databases Part 1
    52:31
  • Data Extraction from Databases Part 2 & DPlyr Package Part 1
    52:39
  • DPlyr Package Part 2
    51:36
  • DPlyr Functions on Air Quality Data Set
    57:01
  • Plyr Package for Data Analysis
    46:51
  • Tidyr Package with Functions
    50:48
  • Factor Analysis
    57:11
  • Prob.Table & CrossTable
    50:22
  • Statistical Observations Part 1
    51:48
  • Statistical Observations Part 2
    40:35
  • Statistical Analysis on Credit Data set
    01:00:29
  • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
    59:20
  • Box Plots
    54:38
    Preview
  • Histograms & Line Graphs
    45:26
  • Scatter Plots & Scatter plot Matrices
    01:03:47
  • Low Level Plotting
    56:01
  • Bar Plot & Density Plot
    46:31
  • Combining Plots
    35:37
  • Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
    51:07
  • Mat Plot, ECDF & Box Plot with IRIS Data set
    01:02:55
  • Additional Box Plot Style Parameters
    01:01:41
  • Set.Seed Function & Preparing Data for Plotting
    01:09:42
  • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
    59:26
  • ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal
    54:42
  • Data Exploration and Visualization
    51:00
  • Machine Learning, Types of ML with Algorithms
    01:04:53
  • How Machine Solve Real Time Problems
    43:33
  • Nearest Neighbor(KNN) Classification
    01:07:45
  • KNN Classification with Cancer Data set Part 1
    01:03:15
  • KNN Classification with Cancer Data set Part 2
    43:12
  • Navie Bayes Classification
    43:53
  • Navie Bayes Classification with SMS Spam Data set & Text Mining
    58:43
  • WordCloud & Document Term Matrix
    56:39
  • Train & Evaluate a Model using Navie Bayes
    01:11:40
  • MarkDown using Knitr Package
    01:02:15
  • Decision Trees
    57:16
  • Decision Trees with Credit Data set Part 1
    47:03
  • Decision Trees with Credit Data set Part 2
    45:11
  • Support Vector Machine, Neural Networks & Random Forest
    46:50
  • Regression & Linear Regression
    44:04
  • Multiple Regression
    48:24
  • Generalized Linear Regression, Non Linear Regression & Logistic Regression
    35:37
  • Clustering
    29:04
  • K-Means Clustering with SNS Data Analysis
    01:06:18
  • Association Rules (Market Basket Analysis)
    39:33
  • Market Basket Analysis using Association Rules with Groceries Data set
    56:19
  • Python Libraries for Data Science
    22:32

Description

This course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.

Before proceeding with this course, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

Who this course is for:

  • All graduates and pursuing students.

What Will I Get ?

  • R Programming Language for Statistical Computing and Graphical Representation

Requirements

  • Before proceeding with this course, you should have a basic understanding of Computer Programming terminologies. A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learn
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R Programming Language
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