What we’re about
R is an open source programming language for statistical computing and data science - from data mining, cleaning, wrangling, exploration, machine learning, and all the way to communication. R is commonly used in both academia, in fields like computational biology and applied statistics, and in commercial areas such as healthcare, quantitative finance, geospatial applications, and business intelligence.
The language's strengths lie in its powerful built-in tools for inferential statistics, compact modeling syntax, data visualization capabilities, report generation, interactive web apps and seamless integration with persistent data stores - everything from databases to flat files. Being open-source, R enjoys continual advancements through add-on packages, thereby maintaining its relevance with the ever-evolving landscape.
R's learning curve can be intimidating, making initial strides in understanding and using R potentially daunting. To counter this, the Berlin R Users Group is committed to fostering a community of R practitioners to exchange ideas, inspire newcomers, and encourage the use of R in ground-breaking research and practical applications.
We strive for diversity and inclusivity, extending an invitation to individuals of all races, genders, and sexual orientations. We align with and support the principles outlined in the Berlin Code of Conduct and the R Consortium Code of Conduct. Please join us, irrespective of your background.
Upcoming events (1)
See all- #47 Spatial Data Science with R: {sf}, {stars}, and other packagesHumboldt University of Berlin, Berlin, BE
This time we will have Edzer Pebesma, who is the developer/maintainer of many relevant R packages for spatial stuff.
He will introduce the relatively newer stack of R-spatial packages that includes sf, stars, s2, gstat, spdep and spatialreg. He will discuss the general way how we can represent spatial data in R, and point out a number of top mistakes naive data scientists will make when starting to work with spatial data: computing wrong measures from geographic coordinates, assuming the Earth is flat, ignoring the support of areal data, and failing to handle data cubes. He will also go into the challenge of using R to analyse data cubes that are too large to download, such as continental or global satellite data from Sentinel-2, or CMIP-6 climate forecast data. The talk will make frequent reference to the online book on Spatial Data Science, found at https://r-spatial.org/book/
This won't be a lecture. We will have the opportunity to hear first-hand information of the latest developments in the field, and to have an open conversation.
Agenda:
18:30-19:00 Arrival and networking
19:00-19:02 Welcoming remarks
19:02-20:30 Talk + Discussions
20:30 Moving to a nearby pizza place or BiergardenHow to find us:
Humboldt-Universität zu Berlin
Unter den Linden 6, 10117 Berlin
Hauptgebäude, 1. Obergeschoss
Raumbezeichnung: Hörsaal 2097
