60 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r DSC-2025-20 | No Messy Data – Introduction to Data Cleaning and Pre-processing in R de Universität Bremen Fri, 24 Apr 2026 18:53:16 +0200 Fri, 24 Apr 2026 18:53:16 +0200 Universität Bremen content-676368 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676368 <p>Survey and other research data are often messy: variable names may be unclear, values missing, or the structure not suited for the analysis you have in mind. Before you can run meaningful analyses, you usually need to tidy up — and that’s exactly where data cleaning and pre-processing come in.</p> <p>Learning how to handle these early steps well is essential for understanding your data and producing reliable results. It’s also part of good scientific practice: preparing your data in a way that is transparent, understandable, and easy to reproduce later on.</p> <p>Understanding common pitfalls, best practices, and standard conventions for handling data not only helps avoid errors but also increases the credibility and trustworthiness of your research. By using <strong>R</strong> — a free, open-source and widely adopted tool in the research community — you can develop efficient, script-based workflows that are not only adaptable and transparent, but also reproducible and shareable. The<strong> tidyverse </strong>package provides a consistent and intuitive syntax for data manipulation, making it easy to learn and apply.</p> Content content-676369 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676369 <p><strong>By the end of the workshop, participants</strong> will understand why data cleaning is a crucial step for transparent and reproducible research, and be able to recognize typical issues in raw research data. They will gain hands-on experience transforming and structuring data into a format suitable for analysis. In addition, participants will understand how to document the data cleaning process clearly and reproducibly using R scripts.</p> Content content-676370 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676370 <p>The workshop combines <strong>basic theoretical input</strong> with plenty of <strong>hands-on exercises in R and tidyverse</strong>, so that you not only understand key concepts but also gain confidence in applying them in practice.</p> <ul> <li>Best practises in data cleaning for quality, transparency, and reproducibility</li> <li>Exploring and reducing data</li> <li>Creating and manipulating variables</li> <li>Working with variable and value labels</li> <li>Detecting and handling missing values</li> <li>Documenting data cleaning</li> </ul> Content content-676372 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676372 <p>This workshop is a beginners training. It’s aimed at researchers in the social sciences, health sciences, and humanities who are working with – or planning to work with – survey-based or other quantitative data, but have little or no prior experience in handling such data or using statistical software.<br /> <br /> A little programming experience in R or another language is an advantage, but not a requirement. All that's needed is a willingness to engage with R and take the first steps into scripting and coding.</p> Content content-676373 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676373 <p>Your own laptop and a stable Wifi connection (e.g. via <a class="external-link" href="https://www.uni-bremen.de/en/zfn/wifi/overview-wifi" target="_blank" title="Öffnet externen Link in neuem Fenster">eduroam</a>).</p> <p>Installation of <a class="external-link" href="https://cran.r-project.org/" target="_blank" title="Öffnet externen Link in neuem Fenster">R Version 4.5.0</a> and higher and <a class="external-link" href="https://www.rstudio.com/" target="_blank" title="Öffnet externen Link in neuem Fenster">RStudio Version 2025.05.1+513</a> and higher prior to the course. Both programs are free and open source.</p> Content content-676376 Fri, 06 Mar 2026 14:26:58 +0100 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2025-20-no-messy-data-introduction-to-data-cleaning-and-pre-processing-in-r#c676376 Content