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  1. Skills Directory
  2. R

R

Advanced

R is a software environment for statistical computing and graphics. R is commonly used for data analysis, statistical computing, machine learning algorithms and scientific research.  

This competency area includes advanced concepts of the R programming language such as functional programming, object-oriented programming, performance optimization, data manipulation, data visualization, machine learning, and statistical modeling. 

Key Competencies: 

  1. Functional Programming - Knowledge of advanced techniques such as higher-order functions, and function composition. Understanding concepts like map, reduce, filter, and anonymous functions can be beneficial.

  2. Object-Oriented Programming (OOP) - Knowledge of R's object-oriented features, such as defining and using classes, methods, and inheritance. Understanding S3, S4, and reference classes can be valuable.

  3. Performance Optimization - Knowledge of techniques like vectorization, efficient memory management, avoiding unnecessary loops, and utilizing specialized packages like data.table and dplyr for faster data manipulation.

  4. Data Manipulation - Explore advanced data manipulation techniques using packages like dplyr, tidyr, and data.table to handle missing values, reshape data, perform complex aggregations, join and merge datasets, and work with large datasets efficiently.

  5. Data Visualization - Knowledge of data visualization libraries such as ggplot2 and plotly and advanced techniques for creating complex and interactive visualizations, customizing themes and aesthetics, creating animations, and generating publication-quality plots.

  6. Machine Learning and Statistical Modeling - Explore packages such as caret, randomForest, glmnet, and xgboost for tasks like regression, classification, clustering, and feature selection.