Программирование в R

2026/2027

ID 1123316

Статус: Маго-лего

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Охват аудитории: для всех кампусов НИУ ВШЭ

Преподаватели: Барыкин Ярослав Андреевич, Соколов Борис Олегович

Язык: русский

Кредиты: 3

Контактные часы: 28

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Аннотация

R is a programming language specifically designed for doing advanced statistical analysis. It is highly popular among data scientists, since it allows to solve essentially all types of tasks that analysts may encounter in real-life industrial or scientific applications: from data preparation and cleaning to estimation of complicated statistical models to presentation of results in an effective and human-friendly way. This course introduces fundamentals of R programming and reviews most widely used base R commands for exploratory data analysis and visualization. You will also learn how to prepare you data for analysis and then explore it using tidyverse and ggplot2 libraries, as well as how to communicate your results using Rmarkdown tools. It is expected that you understand some basic statistical concepts, such as variable, distribution, mean, variance, and correlation, but if not, this should not be a big issue, since you will have an opportunity to learn them when playing around real-world examples of applied data analysis with R and also exercises throughout the course.

Цель освоения дисциплины

The key objective of this class is to help students to master basic skills of using R for data manipulation and exploratory data analysis.

Планируемые результаты обучения

be able to install R and Rstudio on your PC/laptop
be able to perform simple and complicated mathematical and logical operations using R
be able to understand basic principles of programming in R and recognize key R data types and data classes
be able to import external data sets into R
be able to clean, recode, transform, subset, and merge your data using base R tools and tidyverse
be able to perform exploratory data analysis in R: frequencies, shares, means, variances, correlations, etc.
be able to create effective data visualizations using base R and ggplot2
be able to summarize outputs of your analysis in tabular forms
be able to write your own simple R functions, profile and debug your code.
be able to prepare html and pdf reports on your analyses using Rmarkdown

Содержание учебной дисциплины

Day 1: Getting started with R
Day 2: Exploratory data analysis using base R
Day 3: Exploratory data analysis using base R – 2
Day 4: Exploratory data analysis using tidyverse
Day 5: Data visualization using ggplot2 and ggpubr
Day 6: Miscellaneous topics
Day 7: Preparing Analytical Reports Using R markdown

Элементы контроля

Class activity
Active involvement in discussions, correct responses to my questions and smart questions to me, presentations of your homework, etc. Please notice that in the first place I will evaluate the quality of your participation, not frequency (although one smart comment on the final day of the course will definitely not earn you an excellent grade for this component).
Home assignment 2
after some (not all) lectures you will be asked to complete written home assignments. Most of those assignments will be like “please complete the following programming tasks, report the results, and make short written comments explaining what you have done ” or “please answer some questions covering the content of the last lecture”. Those assignments will be relatively short and simple (1-2 pages) and are expected to be submitted before the start of the next class day (i.e., 18:10 next Thursday relatively to the day of assignment).
Final exam
you will be asked to complete several programming and analytical tasks on a real-world data set (see example below). The DEADLINE for submitting your final exam paper is Tuesday, OCTOBER 25, 18:10 (please notice that the date is preliminary can be changed).
Home assignment 3
after some (not all) lectures you will be asked to complete written home assignments. Most of those assignments will be like “please complete the following programming tasks, report the results, and make short written comments explaining what you have done ” or “please answer some questions covering the content of the last lecture”. Those assignments will be relatively short and simple (1-2 pages) and are expected to be submitted before the start of the next class day (i.e., 18:10 next Thursday relatively to the day of assignment).
Home assignment 1
after some (not all) lectures you will be asked to complete written home assignments. Most of those assignments will be like “please complete the following programming tasks, report the results, and make short written comments explaining what you have done ” or “please answer some questions covering the content of the last lecture”. Those assignments will be relatively short and simple (1-2 pages) and are expected to be submitted before the start of the next class day (i.e., 18:10 next Thursday relatively to the day of assignment).

Промежуточная аттестация

2026/2027 1st module
0.2 * Home assignment 2 + 0.2 * Home assignment 3 + 0.25 * Final exam + 0.2 * Home assignment 1 + 0.15 * Class activity

Список литературы

Авторы

Соколов Борис Олегович

Программа дисциплины