Основы синтаксиса R

2026/2027

Статус: Маго-лего

Кто читает: Департамент образовательных программ

Где читается: Институт образования

Когда читается: 1 модуль

Охват аудитории: для своего кампуса

Преподаватели: Юсупова Элен Магомедовна

Язык: английский

Кредиты: 3

Контактные часы: 6

Full Syllabus Ask Question

Abstract

This course is designed to help students with no prior computer programming experience learn to think computationally and write code to solve problems using R-language. This course will cover the basics of computing and procedural programming, including mathematical, relational, and logical operators, variables and variable types, the basics of style and commenting, iterative solutions, arrays, matrices and their applications, sorting and searching algorithms, elements of string processing, structures, ways to correctly store and represent information. Each topic is illustrated with a set of real-world examples. In line with modern data analysis practices, the course also teaches students to use AI tools responsibl: to critically evaluate, explain, and debug AI-generated code rather than relying on it uncritically.

Learning Objectives

The goal of the course is to introduce students to fundamentals in using R. The primary objective of the course is providing students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal for analyzing the data during their education and later, in their career. The secondary objective is to show examples and real researches in which programming skills were applied in order to speed up the data processing

Expected Learning Outcomes

Students know the basic types of objects used in R
Students can perform basic mathematical and logical operations with basic types of objects in R
Students know the structure and types of loops in R
Students can carry out a full cycle of data pre-processing operations in R
Students can use basic R tools to visualize data
Students can create data frames and load data frames into R.
Students can operate the data frame data type in R: add and delete columns in a data frame, filter and aggregate data frames, merge data frames and reshape a data frame (convert from wide to long representation and vice versa).
Students can explain—in clear terms—the steps, functions, and output of R code for standard data analysis tasks covered in the curriculum: data preparation, conditional logic, loops, functions and packages, tidyverse workflow, summary statistics, and data visualization.
Students can critically evaluate, debug, and explain AI-generated R code, distinguishing correct from incorrect or suboptimal solutions
Students can locate, interpret, and evaluate real-world applications of R packages, comparing community-sourced solutions with AI-generated alternatives for the same task
Students can produce clear, accurate instructional material on an R topic for new users, critically verifying and correcting AI-generated explanations for correctness and clarity

Course Contents

Introduction to R and R-Studio software, acquaintance with the logic of the R language
Basic types of R objects
Basic operations on R objects
Loops in R
Pre-processing data in R
Introduction to descriptive statistics in R
Basics of data visualization in R
Data Frames

Assessment Elements

Exam (R project)
Students find or collect data and conduct independent research using R. They defend one final visualization that summarizes the key results of their work. The written submission is a prerequisite for the defense; the final grade is assigned only after the oral defense, where students explain their code (including any AI-generated parts), justify data-processing decisions, and answer follow-up questions on R syntax, functions, and logic.
Tests
To help you review and consolidate the material, a short mini-test will be available in the LMS after each course topic. These tests are designed for self-assessment and to help you track your progress. Their cumulative score contributes to the final grade, carrying a weight of 0.2. It is important to note that these tests are non-blocking, and to provide an opportunity for improvement, you will be able to retake each test up to two times, with your best attempt being recorded for your final score.
Package Discovery
Students select a specific R package (or a published R-based research) and identify how it is used in real-world projects or studies. Students then compare a community-sourced solution with an AI-generated alternative for the same task, evaluating differences in approach, correctness, and clarity. The assignment develops the ability to critically assess AI-suggested code against established practices used by the R community.
Mini-guide
Students prepare a short instructional guide on a selected R topic (a function, package, or concept covered in the course), intended for their peers. As part of the assignment, students request an AI-generated explanation of the topic, then critically review, correct, and expand it, ensuring accuracy and clarity for a student audience. The task reinforces the ability to explain and verify AI-generated educational content, not just code.

Interim Assessment

2026/2027 1st module
0.4 * Exam (R project) + 0.2 * Tests + 0.2 * Mini-guide + 0.2 * Package Discovery

Bibliography

Recommended Core Bibliography

An introduction to R : a programming environment for data analysis and graphics, Venables, W. N., 2009
R в действии : анализ и визуализация данных в программе R, Кабаков, Р. И., 2014

Recommended Additional Bibliography

R Cookbook : Proven recipes for data analysis, statistics, and graphics, Teetor, P., 2011
Введение в статистическое обучение с примерами на языке R, Джеймс, Г., 2016

Authors

Abdurakhmanova Elen Magomedovna

Course Syllabus