R Programming: Fundamentals and Language Essence

Overview

This two-day course introduces participants to the fundamental principles of R programming and the definition of the language. R is a programming language and a free environment for statistical computing and graphics, supported by the R Core Team and the R Foundation for Statistical Computing. The R system offers a wide range of statistical techniques and graphics, providing great flexibility in data analysis. It is widely used by statisticians and data analysts, is free, and—like many high-level languages—is relatively easy to learn while remaining powerful and expressive.

Objectives

• Learn the fundamental principles of R programming.
• Understand basic object types in R.
• Get acquainted with data object indexing, control structures, ranges, and functions.
• Understand the operation of the S3 method dispatching mechanism.
• Become familiar with commonly used base functions for data manipulation and numerical computations.

Target Audience

Statisticians, statistical programmers, data scientists, and analysts who wish to gain a solid understanding of the fundamental principles of the R language.

Keywords

R, R programming.

Instructor

The course is conducted by an expert with over 15 years of experience in R programming and data analysis using R. They hold an engineering degree in computer science and a master's degree in mathematics.

1. Hardware and Operating System

• Processor: Modern dual-core processor, 3GHz, 4MB cache memory.
• RAM: 16GB.
• Disk: 20GB free space on the system partition + an additional 20GB free space on any partition (before installing additional software).
• Operating System: MS Windows or Linux (preferred: Fedora, Redhat, or CentOS).

2. R Environment and IDE

R (https://cran.r-project.org/mirrors.html):

• MS Windows: base R, Rtools.
• Linux: R-core, R-devel, and R-core-devel.

IDE Environment: RStudio Desktop (free version).

3. R Packages

Required packages: "rlang", "lobstr", "ggplot2", "dplyr", "reshape2".

All applications should be in the latest stable available versions.

Introduction to R

A brief overview of the R software ecosystem and the integrated development environment RStudio (installation, launching, CRAN, and packages). Introduction to basic R commands, comparison of command lines with scripts, command history, workspace, and help system.

Definition of the R Language

• 1. Objects: basic types, attributes, and complex data structures (factor, matrix, data.frame).
• 2. Finite and infinite values, NaN; missing data (NA) and NULL objects.
• 3. Basic operations on atomic vectors: arithmetic, logical, and relational operators; recycling rules.
• 4. Indexing of vectors, lists, and multi-dimensional structures.
• 5. Functions.
• 6. Control structures (conditions and loops).
• 7. Object-oriented programming: R class system and dispatch mechanism (S3 classes).

Language Fundamentals

• 1. Data manipulation: sorting, ordering, counting occurrences, splitting, joining and merging, apply function family, and replication.
• 2. Numbers: pitfalls of floating-point numbers, basic mathematical functions, rounding, generating sequences, combinatorics, sampling, and generating random numbers.
• 3. Statistics: descriptive statistics and frequency tables.
• 4. Probability distributions: density functions, cumulative distribution functions, quantiles, and generating random variables for standard distributions.
• 5. Text: character strings and pattern matching.