Below are some of the Core Features of R.
1. Handling Data:
Data Frames : R utilizes data.frames to maintain and manipulate data tables, much like spreadsheets or SQL tables.
Vectors & Lists: This is the core data structure in R, which can effectively handle numeric, character and any other types of data.
2. Statistical Analysis:
R has a lot of pre-programmed statistical tests and models for things like:
Descriptive statistics
Hypothesis testing (t-tests, chi-square tests, etc.)
Regression analysis: linear, logistic, etc.
Time series analysis
Bayesian analysis
Base R itself supports many statistical functions and the extended packages push the capabilities further.
3. Data Visualization:
R is very strong in creating high-quality plots and graphs.
ggplot2: arguably the most fashionable package for proper, eloquent graphics construction.
Shiny: A Relsonic to build shiny, fully interactive web applications directly in R.
4. Machine Learning:
Libraries like caret, random Forest, xgboost and e1071 provide support for machine learning operations such as classification, regression, clustering, and more.
5. Package Ecosystem:
CRAN: The Comprehensive R Archive Network is a repository for several thousands of R packages that extend R's functionality in different fields, such as bioinformatics, finance, social science, etc.
Bioconductor: An open source repository of packages for bioinformatics and computational biology.
GitHub: Several R packages are also found on GitHub for further personalization.
6. Reproducibility & Reporting:
R Markdown: A tool for weaving together R code and narrative text to generate dynamic documents, reports, slideshows, and even web applications.
R Notebooks: Interactive R Markdowns which allow users to execute code chunks, with results appearing in real time.
Useful Link : A Comprehensive Guide to Installing R Project on Your System
1. Handling Data:
Data Frames : R utilizes data.frames to maintain and manipulate data tables, much like spreadsheets or SQL tables.
Vectors & Lists: This is the core data structure in R, which can effectively handle numeric, character and any other types of data.
2. Statistical Analysis:
R has a lot of pre-programmed statistical tests and models for things like:
Descriptive statistics
Hypothesis testing (t-tests, chi-square tests, etc.)
Regression analysis: linear, logistic, etc.
Time series analysis
Bayesian analysis
Base R itself supports many statistical functions and the extended packages push the capabilities further.
3. Data Visualization:
R is very strong in creating high-quality plots and graphs.
ggplot2: arguably the most fashionable package for proper, eloquent graphics construction.
Shiny: A Relsonic to build shiny, fully interactive web applications directly in R.
4. Machine Learning:
Libraries like caret, random Forest, xgboost and e1071 provide support for machine learning operations such as classification, regression, clustering, and more.
5. Package Ecosystem:
CRAN: The Comprehensive R Archive Network is a repository for several thousands of R packages that extend R's functionality in different fields, such as bioinformatics, finance, social science, etc.
Bioconductor: An open source repository of packages for bioinformatics and computational biology.
GitHub: Several R packages are also found on GitHub for further personalization.
6. Reproducibility & Reporting:
R Markdown: A tool for weaving together R code and narrative text to generate dynamic documents, reports, slideshows, and even web applications.
R Notebooks: Interactive R Markdowns which allow users to execute code chunks, with results appearing in real time.
Useful Link : A Comprehensive Guide to Installing R Project on Your System