Wipe the slate clean and learn R from scratch
Introduction to R
R Environment Setup
Why Should I Learn R?
Applications of R
Future Scope with R
Introduction to Data Analysis with R
R Data Analytics Softwares
Data Types in R
Data Structures in R
R Vectors
R Lists
R Matrices
R Arrays
R Factors
R Control Statements
R Functions
R Vector Functions
R Numeric & Character Functions
R Matrix Functions
R Recursive Functions
Arguments in R
R vs Python
R vs SAS vs SPSS
Best Books for R
Level up to more exciting and challenging chapters
R Data Frames
Packages in R
Data Reshaping in R
Object Oriented Programming with R
Debugging in R
R Input/ Output Features
R String Manipulation
R Data Manipulation
R Descriptive Statistics
Contingency Tables in R
R Graphical Models
R Generalized Linear Models(GLM)
R Graphical Models Applications
R Graphical Analysis
Data Visualization with R
R Bar Charts
R Lattice Packages
Saving Graphs to Files with R
Performance Tuning in R
R Hypothesis Testing
R Linear Regression
R Nonlinear Regression
R Logistic Regression
R Decision Trees
R Random Forest
Master new skills and evolve as an expert
Introduction to RStudio
R Hadoop Integration
R Clustering
R Classification
SVM Training & Testing Models in R
R Bayesian Networks
R Bayesian Methods
R Bayesian Inferences
R Bayesian Network Applications
R Normal Distribution
R Binomial & Poisson Distribution
Importing Data in R
Exporting Data in R
R Predictive & Descriptive Analytics
R Survival Analysis
T-tests in R
ANOVA Test
R Chi-Square test
R Interview Questions- Part 1
R Interview Questions- Part 2
R Interview Questions- Part 3
R Quiz – Part 1
R Quiz – Part 2
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Exploring the Language
Let’s take a look at some facts about R and its philosophies.
R first appeared in 1993 as an implementation of the S programming language. The term R comes from the fact that both of its creators’ names begin with the initial of R. Version 3.5.1 rolled out in July of 2018; we call it Feather Spray. This is a programming language and a free software environment for statistical computing and graphics. You will often find statisticians and data miners using it to develop statistical software and for data analysis.
Apart from a command-line interface, some IDEs like the RStudio let us work with graphical front ends.
Ross Ihaka
Robert Gentleman