R is one of the leading languages for data analysis, statistics and data science, but many beginners find the first steps unnecessarily painful. This course gently guides participants over that initial hurdle. Through hands-on work in R and RStudio, we build up from the very basics to a modern, tidyverse-based workflow for data wrangling, visualisation and simple modelling.
General course information
| Course dates | 27 July - 4 August, 2026 (1.5-week course, 7 study days) |
|---|---|
| Course fee | 700 EUR |
| Course format | Summer course |
| Study field | Statistics and Data Science (with applications across social sciences, economics, life sciences and other empirical fields) Practical data analysis and reproducible research using R (R, RStudio, tidyverse, ggplot2, quarto) |
| Language | English |
| Study group | Upper secondary school students, bachelor's , master's, PhD students, lifelong learners |
| Assessment / ECTS | Pass/Fail (3 ECTS) |
| Location | Tartu University of Tartu Delta Centre, Narva mnt 18 |
| Course leader and lecturer | Indrek Seppo is data analyst and experienced R instructor (SEPPO AI OÜ in cooperation with the University of Tartu). |
Course description
R is one of the most powerful tools for data analysis and data science, but it is often perceived as having a steep learning curve. This introductory, hands-on course is designed to make that first step as smooth and enjoyable as possible.
We start from the very basics of R and RStudio (objects, data types, data frames, getting data in and out) and move towards a modern workflow using the tidyverse.
Participants will learn how to:
transform and summarise data with dplyr;
create clear and beautiful graphics using the grammar of graphics with ggplot2;
work with tidy data and move between long and wide formats;
write simple functions to avoid repetition;
use quarto to produce reproducible reports that combine text, code and results.
Throughout the course, every concept is immediately practised on real-world datasets.
Study information
Please note this is a preliminary programme. The final schedule will be sent to the participants two weeks before the course starts.
The course is planned as a 7-day intensive course, with 4 academic contact hours per day
(1 academic hour = 45 minutes; ≈ 3 clock hours of contact per day).
Example daily timetable (each day):
10:00–11:30 – Guided practical / lab (2 academic hours)
11:45–13:15 – Guided practical / lab (2 academic hours)
Saturday, 25 July or Sunday, 26 July: Arrival
Day 1: Monday, 27 July
Getting started
Registration and info session at 9:00 in the morning.
Introduction to the course, R and RStudio
Objects, data types, data frames
Working directory, help system
Getting data in and out of R
Day 2: Tuesday, 28 July
First steps in analysis and graphics
Logical operators and basic data manipulation
Introduction to the grammar of graphics with ggplot2 – first plots
Day 3: Wednesday, 29 July
Powerful visualisations
Grammar of graphics continued
Working with factor variables
Refining and customising plots
Day 4: Thursday, 30 July
Tidy data and data wrangling
Tidy vs wide data: the concept of tidy data
Data wrangling with dplyr: selecting, filtering, mutating
Day 5: Friday, 30 July
Summaries and reporting
Group summaries and descriptive statistics by groups
Introduction to RMarkdown – from script to reproducible report
Saturday, 1 August: free day
Sunday, 2 August: free day
Day 6: Monday, 3 August
Functions and joins
Lists and writing simple functions in R
Joining and combining datasets (left join, inner join, etc.)
Day 7: Tuesday, 4 August
Modelling and a taste of machine learning
Basics of data modelling in R (e.g. linear regression)
A small taste of machine learning in R (e.g. train/test split, simple model comparison)
FINAL DAY
Participants are expected to work independently 1–3 additional hours per day. All independent work is clearly guided and aligned with the daily topics.
After successfully completing the course, the student:
- can work confidently in R and RStudio, including importing data, managing objects and using the help system;
- understands and uses core R data structures (vectors, factors, data frames, lists) in practical data analysis;
- writes simple functions in R to automate repetitive tasks and improve code clarity;
- understands the grammar of graphics and can create, customise and interpret data visualisations using ggplot2;
- understands the concept of tidy data and can transform data between wide and long formats;
- uses dplyr for data wrangling, including filtering, selecting, transforming and summarising data, also by groups;
- creates reproducible analyses and reports using RMarkdown, combining narrative text, code and output;
- applies basic modelling techniques in R (e.g. simple regression or classification) and interprets the main results;
- is able to continue learning R independently after the course.
Course registration info
- Application period: 20 March – 20 April
- Notification of acceptance: accepted participants will be informed after the application period, by 30 April at the latest
- Deadline for paying the course fee: 31 May
- Confirmation of courses taking place: 5 June
- UniTartu Summer School: 27 July – 7 August
Only fully completed applications, including all required annexes, received by the deadline (20 April) will be considered for selection.
Applicants must submit the following:
- Online application form
(application period: 20 March–20 April 2026) - Motivation letter (maximum 1 page, 3000 characters), explaining:
- your motivation to participate,
- your expectations for the programme,
- how the summer course relates to your studies and interests,
- how you plan to use the knowledge and experience gained in the future
- Transcript of academic records
- Copy of your passport
- Proof of the application fee payment (25 EUR)
The participants of the UniTartu Summer School courses are required to pay:
- The application fee of 25 EUR must be paid by the application deadline (20 April) at the latest.
The application fee is non-refundable. - The course fee is 700 EUR.
Includes: Study materials, academic work with lecturers, Certificate of completion, cultural events in the evenings
Not included: meals, transportation and accommodation
Please note that the course fee is payable only after you have been accepted into the course. Once accepted, you will receive a confirmation of acceptance together with an invoice. The course fee can only be paid based on the invoice issued to you.
By paying the application fee, course fee and cultural events fee, you accept the terms and conditions information document. You are required to tick the box in the credit card payment form to confirm you have read and agree to terms and conditions. If you choose to pay by bank transfer, you will be informed of the same conditions.
Please note that by paying the fees, you are considered to have accepted the Terms and Conditions.
