Getting Started with R Programming
Check out these R tutorials and ease your way to become the next data scientist. Choose where to begin, learn at your own pace:
Unlock the latest R tutorials and learn R Programming from scratch
- Introduction to R
- R Comprehensive Guide For Beginners
- Learn R from Master Guide
- Features of R
- Pros and Cons of R
- R Environment Setup
- Why Should I Learn R?
- Applications of R
- Top R Projects
- R Installation
- Future Scope with R
- Introduction to Data Analysis with R
- R Data Analysis Tools
- 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 Python for Data Science
- R vs Python vs SAS
- Data Analytics Tools – R vs SAS vs SPSS
- Data Analytics with R, Tableau & Excel
- Best Books for R
Level up to more exciting and challenging R tutorials
- R Data Frames
- Packages in R
- R Packages List
- R Packages for Data Science
- R Factor Analysis
- 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
- Machine Learning for R
Master essential R Programming 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
- Role of R in Data Science
- R Interview Questions for Beginners
- R Interview Questions for Intermediates
- R Interview Questions for Experts
- R Quiz – Part 1
- R Quiz – Part 2
- R – 70+ Project Ideas & Datasets
- R Project – Sentiment Analysis
- R Project – Uber Data Analysis
- R Project – Credit Card Fraud Detection
- R Project – Customer Segmentation using Machine Learning
Practical Learning with R Projects
Strengthen your portfolio and get your dream Data Science job by implementing these real-world R projects. Display your true potential to recruiters and become the next data scientist.
Check out more cool technologies related to R Programming
Exploring the R Language
Let’s take a look at some facts about R programming and its philosophies.
What is R Programming?
R Programming language is used for performing statistical computing as well as graphical processing of data. It was developed by Ross Ihaka and Robert Gentleman at University of Auckland, New Zealand.
Various data scientists, statisticians and data analysts use the R programming language for analyzing data and carrying out statistical analysis using visualizations. With the help of R, you can perform data analysis on structured and unstructured data.
History of R Language
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