2025/2026
Basics of R Syntax
Type:
Mago-Lego
Delivered by:
Department of Educational Programmes
When:
1 module
Open to:
students of one campus
Language:
English
Contact hours:
6
Course Syllabus
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.
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.
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
- Data Frames
- Pre-processing data in R
- Introduction to descriptive statistics in R
- Basics of data visualization in R
Assessment Elements
- TestsTo 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.
- R projectStudents have to complete an individual assignment using R Markdown. The primary goal is to apply the full basic analysis workflow, from loading and preprocessing a dataset to calculating key descriptive statistics and presenting the results. The project includes the following steps: 1. Load and briefly describe your selected dataset (provided by the instructor or chosen independently). 2. Perform basic preprocessing: check the structure, identify and handle missing values, convert data types, rename variables if needed. 3. Calculate and interpret key descriptive statistics: mean, median, standard deviation, minimum, maximum, and frequencies for categorical variables. 4. Briefly comment on your findings (2–3 sentences for main results) in the final document. Your work should be presented in an R Markdown file (.Rmd) with clear and logical explanations of each analysis step. You are encouraged to use any functions and packages covered throughout the course.
- ExamThe final exam is an oral assessment, approximately 15 minutes per student. During the exam, the student will be shown 3 to 5 R code snippets and asked to explain what each code does, including its steps, purpose, key functions, and expected output. The student will then be presented with a practical coding task: they must write R code to solve a given problem (such as data analysis, data cleaning, or visualization). The student is allowed to use internet resources to search for documentation, examples, or syntax; however, evaluation focuses on their ability to find relevant solutions, adapt them appropriately, and demonstrate a clear understanding of the code, rather than simply copy-pasting. Additional questions may cover terminology, R language concepts, function logic, package features, and data handling strategies (for example: “What is a factor? Why handle missing values? What’s the difference between for and while loops? How does the pipe operator work?”). Students may also be asked to revise or improve given code, comment on errors, or suggest alternative approaches. The exam is designed to assess both theoretical knowledge and practical skills, as well as critical thinking, the ability to explain solutions, and real-world application of R programming.
Bibliography
Recommended Core Bibliography
- An introduction to R : a programming environment for data analysis and graphics, Venables, W. N., 2009
- Long, J. D., & Teetor, P. (2019). R Cookbook : Proven Recipes for Data Analysis, Statistics, and Graphics: Vol. Second edition. O’Reilly Media.
- R Cookbook : Proven recipes for data analysis, statistics, and graphics, Teetor, P., 2011
- R в действии : анализ и визуализация данных в программе R, Кабаков, Р. И., 2014
Recommended Additional Bibliography
- Введение в статистическое обучение с примерами на языке R, Джеймс, Г., 2016