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Course Outline

Objectives


• Learn the core principles of R programming.
• Understand basic types of R objects.
• Gain familiarity with data object indexing, control structures, scoping, and functions.
• Understand how S3 method dispatch works.
• Become familiar with commonly used base functions for data manipulation and numerical computation.

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


Keywords
R, R programming.


Instructor
This training is led by an expert with over 15 years of experience in R programming and data analysis using
R. He holds a Bachelor of Engineering degree in Computer Science and Master of Science in Mathematics.

Content


Day 1


An Introduction to R


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


R Language Definition
1. Objects: basic types, attributes, and compound data structures (factor, matrix, data.frame).
2. Finite, infinite, and NaN values; 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 multidimensional structures.
5. Functions.
6. Control structures (conditionals and loops).
7. Object-oriented programming: the R class system and dispatch mechanism (S3 classes).

Day 2


Language Essentials


1. Data manipulation: sorting, ordering, counting occurrences, subsetting, binding and merging, the
apply family, and replication.
2. Numbers: floating-point pitfalls, basic mathematical functions, rounding, generating sequences, combinatorics,
sampling, and random number generation.
3. Statistics: descriptive statistics and frequency tables.
4. Probability distributions: density, cumulative distribution, quantile functions, and random variate
generation for standard distributions.
5. Text: character strings and pattern matching.

Prerequisites
Software
1. HW (at minimum):
(a) processor: 2-cores modern processor, 3GHz, 4MB cache,
(b) RAM: 16GB,
(c) disc: 20GB free space on OS partition + additional 20GB free space on any partition. This
is the space before installation of additional R related software listed below.
2. OS: MS Windows or Linux (preferred) Fedora, Redhat, or CentOS.
3. R (https://cran.r-project.org/mirrors.html).
In case of MS Windows OS, the following components should be installed: base R, Rtools.
In case of Linux OS, the following components should be installed: R-core, R-devel and R-core-devel.
4. RStudio IDE (https://posit.co/download/rstudio-desktop/) - free version.
5. R packages: "rlang", "lobstr", "ggplot2", "dplyr", "reshape2".
All applications should be in latest stable, available releases.

Copyright ©2026 Wojciech Wójciak. All Rights reserved.
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Requirements

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

 14 Hours

Number of participants


Price Per Participant (Exc. Tax)

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