Wissensvermittlung, Nachwuchsförderung und der fachliche Austausch auf Augenhöhe sind für uns Herzensangelegenheiten. Wir sind der Meinung: Geteilte Innovationsfreude ist doppelte Innovationsfreude.
Deshalb mischen sich codecentric-Mitarbeiter und -Mitarbeiterinnen gerne unter die Community – ob als Gastgeber, Redner oder Organisatoren diverser Veranstaltungen. Triff uns auf einem der folgenden Events!
Agenda: 6 pm: Get together 6.30 pm: Talk by Daniel Paulus, Lead Software Engineer at the Sauce Labs 7.00 pm: Q&A 7.30 – 9 pm: Networking, Catering About the talk: The most challenging aspect of running tests on a real ...
R-spatial: new developments for vector, raster, and spatiotemporal data.
Two projects that were funded by the R Consortium (sf: Simple features for R, and stars: spatiotemporal tidy arrays for R) aim at modernizing the way spatial data is handled in R. Simple feature access is an ISO standard for vector data (points, lines, polygons). Spatiotemporal arrays comprise time series for vector data, raster data (such as satellite imagery), and time series of rasters. Motivations for this modernization include the desire for compatibility with the tidyverse, improving interfaces to external dependencies (GDAL, GEOS, spatial databases), the need to analyze increasingly large datasets, and extending the classes of problems that R can deal with.
Edzer Pebesma leads the spatio-temporal modelling laboratory at the institute for geoinformatics. He holds a PhD in geosciences, and is interested in spatial statistics, environmental modelling, geoinformatics and GI Science, semantic technology for spatial analysis, optimizing environmental monitoring, but also in e-Science and reproducible research. He is one of the authors of Applied Spatial Data Analysis with R (second edition), Co-Editor-in-Chief for the Journal of Statistical Software and for Computers & Geosciences, and associate editor for Spatial Statistics. He believes that research is useful in particular when it helps solving real-world problems.
Automatically archiving reproducible studies with Docker
Reproducibility of computations is crucial in an era where data is born digital and analysed algorithmically. Most studies however only publish the results, often with figures as important interpreted outputs. But where do these figures come from? R offers excellent tools to create reproducible works, i.e. Sweave and RMarkdown. Several approaches to capture the workspace environment in R have been made, working around CRAN’s deliberate choice not to provide explicit versioning of packages and their dependencies. They preserve a collection of packages locally (packrat, pkgsnap, switchr/GRANBase) or remotely (MRAN timemachine/checkpoint), or install specific versions from CRAN or source (requireGitHub, devtools). A user can manually re-create a specific environment, but this is a cumbersome task. We introduce a new possibility to preserve a runtime environment including both, packages and R, by adding an abstraction layer in the form of a container, which can execute a script or run an interactive session. The package containerit automatically creates such containers based on Docker. Docker is a solution for packaging an application and its dependencies, but shows to be useful in the context of reproducible research. The package creates a container manifest, the Dockerfile, which is usually written by hand, from sessionInfo(), R scripts, or RMarkdown documents.