Confident work in RStudio - You will learn to work comfortably in R and RStudio: run code, write scripts, use the console, and keep your workspace organized, so everyday analytical tasks become faster and easier.
Reliable data operations - You will master selecting elements, filtering rows, and creating new columns, so you can prepare datasets for analysis on your own without fixing spreadsheets manually or repeating clicks.
Better object control - You will learn how to inspect data structure and class, manage objects in memory, and save or reload results, which helps you stay in control of your project and avoid workspace clutter.
Efficient import and export - You will learn to import and export text data and work with data frames, lists, and matrices, so you can move data smoothly between files and your analytical environment.
Comfort with vectors - You will understand R data types, conversions, logical, text, and numeric vectors, as well as missing values, factors, and dates, so you can prepare variables correctly for later analysis.
Faster data summaries - You will learn practical ways to calculate means, sums, and frequency tables, and to summarize grouped data, helping you draw conclusions from raw datasets much more quickly.
Effective use of dplyr and data.table - You will practice using dplyr and data.table for filtering, grouping, sorting, sampling, and joining datasets, so you can choose tools that are both convenient and efficient for your work.
Independent skill growth - You will learn how to find, install, and load packages and use R help resources, so after the course you can solve many issues on your own and keep expanding your analytical toolkit.
R is positioned first as an open source analytical environment and as a programming language with its own working model. The section covers installation, the role of RStudio and orientation in its interface, giving the technical foundation needed for later work with code and data.
Working in the console and in scripts is combined with practical data manipulation tasks, so operational habits develop together with syntax. The section includes workspace and working directory management, object persistence, package handling, help usage, and conditional selection of rows or creation of columns.
Basic statistical summaries in R are introduced through measures and outputs commonly used at the start of exploratory work. Participants calculate mean and sum, then build frequency tables to describe distributions and obtain compact numerical views of the data under analysis.
Core inspection and housekeeping functions such as class(), str(), dim() and head() are used to verify object structure and quickly assess data content. The section also includes rm(), which supports workspace cleanup and helps manage objects during iterative work with scripts and datasets.
Reliable exchange of files is treated as a necessary part of everyday analytical work in R. The section focuses on moving data between the environment and external sources, so imported datasets keep a usable structure and exported results can be further processed or shared without loss of clarity.
At the vector level, participants work with the fundamental data structure behind many R operations. The material covers type checking and conversion, hierarchy of types, text processing with string tools, logical and numeric vectors, factors, dates and practical handling of missing values.
Matrices, lists and data frames form the basis for storing structured information in R, so the section explains when each object type is appropriate. It also introduces the *apply family for repetitive operations and covers import from and export to text-based data files.
dplyr and data.table are used here to show two common approaches to working with tabular data in R. Alongside the tidy data concept, the section addresses browsing data frames, handling missing values and selecting rows through filtering or sampling depending on the analytical need.
Creating variables, grouping records and arranging rows are presented as core steps in reshaping data for further analysis. The section also covers summaries, joins between datasets and practical comparison of execution speed across packages when the same task is implemented in different ways.
The closing part consolidates the full workflow of data handling in R, from environment setup and object management to transformation and basic analysis. It also helps connect base mechanisms with dplyr and data.table so participants can place individual techniques in one coherent process.
Basic computer skills - You should be comfortable using your operating system, launching programs, working with files and folders, and installing simple applications on your own computer.
Working with data files - You should understand what data files are and how to use them, for example opening, saving, and organizing datasets in folders so you can work smoothly during the exercises.
Basic logical thinking - You should be comfortable with simple relationships, conditions, and step-by-step sequences, because during the course you will filter data and create variables.
Basics of data work - You should recognize basic terms such as column, row, value, and table so you can follow examples on filtering, summaries, and combining data in R more easily.
