R is a programming language for statistical computing and graphics.It used for data analysis, data manipulation, graphics, statistical computing and statistical analysis.
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Introduction to R
R basics and syntax
RStudio environment
R data types
R data structures
Data Manipulation with R
Data frames and matrices
Data cleaning and filtering
Data aggregation and summarization
Reshaping data
Data Visualization with R
Basic graphics with base R
Advanced graphics with ggplot2
Interactive graphics with Shiny
Best practices in data visualization
Statistical Analysis with R
Descriptive statistics
Inferential statistics
Hypothesis testing
Regression analysis
Machine Learning with R
Supervised learning
Unsupervised learning
Classification and regression
Dimensionality reduction
Text Analysis with R
Text preprocessing
Text mining techniques
Sentiment analysis
Topic modeling
Web Scraping with R
HTML and CSS basics
Web scraping libraries
Regular expressions
Handling scraped data
Working with APIs in R
JSON and XML data formats
Accessing APIs with R
OAuth authentication
Handling API responses
Debugging and Profiling in R
Debugging techniques
Profiling code performance
Memory management
Code optimization
Parallel Computing with R
Multithreading and parallelism
Distributed computing with SparkR
GPU computing with R
Performance considerations
Building R Packages
Package structure and organization
Documentation and testing
Version control with Git
Publishing and sharing packages
Applications of R
Data science workflows
Bioinformatics and genomics
Finance and economics
Social sciences and humanities
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