With R, organizations can analyze large datasets directly from databases such as PostgreSQL, SQLite, and DuckDB without having to manually export the data first. In this blended learning course on R Databases, you’ll learn how to connect databases to R for efficient data analysis, SQL queries, automation, and Geo-ICT workflows.
R Databases focuses on linking the R programming language to databases for data analysis, automation, and reproducible workflows. Within Geo-ICT, this is important because geographic and administrative datasets are often no longer managed as separate files but are stored centrally in databases such as PostgreSQL, SQLite, DuckDB, or via ODBC connections.
With R, database data can be queried, filtered, analyzed, and combined with other data sources directly. Think of analyzing large tables, executing SQL queries, retrieving spatial data from PostgreSQL/PostGIS, or processing files with DuckDB without having to export everything beforehand. This creates an efficient workflow for data science, GIS, and Geo-ICT projects.
What makes R powerful is the combination of database connections, programmability, and analysis functionality. This allows recurring data processes to be automated and analyses to be performed in a reproducible manner. Within Geo-ICT, R is increasingly being used as a link between databases, GIS systems, reports, and data science workflows.
In this blended learning course, you will work with key database packages such as DBI, RPostgres, RSQLite, odbc, pool, and duckdb. You will learn to connect to various databases, execute SQL queries from within R, retrieve data efficiently, and set up database-driven analyses.
In addition, R offers extensive capabilities for combining database data with visualization, statistics, and spatial analysis. This makes this blended learning course particularly relevant for GIS specialists, data analysts, database administrators, and Geo-ICT professionals who want to work more efficiently with large datasets and central data sources.
In this blended learning course, you will be introduced to the key capabilities of R for working with databases. You will learn how to connect to databases, how to execute SQL queries from R, and how to efficiently retrieve data for further analysis. You will work with packages such as DBI, RPostgres, RSQLite, odbc, and duckdb.
The course focuses on working with PostgreSQL, SQLite, and DuckDB. You’ll learn how to access tables, write queries, process results, and integrate database data with R workflows. You’ll also learn how to use ODBC connections to connect to various database systems.
In addition, you’ll learn how to manage database connections in a clean and scalable way. Using packages like pool, you’ll discover how connections can be used more efficiently, for example in dashboards, reports, or recurring analyses. Attention is also given to performance, data volume, and preventing unnecessary data exports.
During the blended learning course, you’ll work with practical datasets and learn how to set up reproducible database-driven workflows in R. Upon completion, you’ll be able to independently connect databases to R and use data efficiently within analysis, GIS, and Geo-ICT projects.
Do you already have experience with R Spatial Basics, R Data Science, or R Visualization? Then this blended learning course is a logical next step, as databases often form the foundation for larger and more professional Geo-ICT workflows.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the technical fundamentals and the practical application of databases in R. In the online modules, you’ll learn how to connect databases, execute SQL queries, and process data efficiently using modern R packages.
You’ll discover how to work with PostgreSQL, SQLite, DuckDB, and ODBC connections. You’ll also learn how to combine database data with analysis, visualization, and reporting within R. Thanks to unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll immediately apply the theory to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to set up reliable database connections using packages such as DBI, RPostgres, RSQLite, odbc, pool, and duckdb.
The combination of online learning and interactive hands-on experience ensures that you not only learn how to connect to databases, but also how to effectively utilize these connections within professional data workflows. After completing the blended learning program, you will be able to analyze large datasets more efficiently and set up reproducible database-driven processes in R.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic understanding of SQL is helpful, but not required. During the blended learning course, SQL queries are covered step by step and applied in practical examples using databases and Geo-ICT datasets.
During the blended learning program, you will work with PostgreSQL, SQLite, and DuckDB, among other tools. You will also learn how to use ODBC connections to connect to other database systems.
By connecting databases directly to R, large datasets can be processed more efficiently without first having to export everything manually. This makes analyses faster, more reproducible, and more scalable within data science and Geo-ICT workflows.
Yes. Databases such as PostgreSQL/PostGIS are widely used in GIS software like QGIS and, to a lesser extent, ArcGIS. R can connect directly to these databases to analyze, automate, and visualize geographic data.
During the blended learning program, you will work with tools such as DBI, RPostgres, RSQLite, ODBC, pool, and duckdb for database connections, query management, and the efficient processing of large datasets within R.
