This course will introduce participants with the analysis of network data in R. The course will cover data structures for network analysis in R; how to create network objects and explore their attributes; calculation of network-, node-, and edge-level statistics; detection and assessment of network clusters; statistical modeling of network data; and network visualization. The course will be slightly biased towards network analyses in the life sciences, but the contents learned here can be easily generalized to different fields. Each day, participants will have theoretical lectures followed by hands-on live coding sessions where they will see what they learned in practice, with frequent practice problems.

The course is targeted to researchers who would like to learn how to use R to analyze networks from data. Participants need to have a working knowledge of R (R syntax, commonly used functions, basic data structures such as data frames, vectors, matrices and their manipulation, and the ggplot2 plotting system). Familiarity with the {igraph} package is helpful, but not essential.

Those thinking of taking this course should have a minimum of intermediate familiarity with statistical analysis (linear modelling, etc), as well as aptitude with the R coding language and the tidyverse. Beginners with R and those unfamiliar or uncomfortable with data manipulation are discouraged from attending, as this may impede your ability to make the most of the course.

Monday (2-7 PM Berlin time) - Networks in data

Theory:
● Introduction to graph theory
● Network data representations
● Types of networks
● Topological properties of biological networks

Practice
● Creating and exploring igraph objects
● Exploring node and edge attributes
● Operations on graphs
● Interoperability of {igraph} and other packages

Tuesday (2-7 PM Berlin time) - Descriptive analyses of networks

Theory
● Network statistics
● Centrality measures
● Edge statistics
● Global network statistics
● Assessing network cohesion
● Graph clustering

Practice
● Calculating network-, node- and edge-level statistics
● Community detection and assessment

Wednesday (2-7 PM Berlin time) -Network models and statistical testing

Theory
● Random graphs, small-worlds, and preferential attachment models
● Hypothesis testing with network data
● Network simulations and permutations

Practice
● Simulating networks
● Assessing the significance of graph properties
● Modeling dynamic network processes

Thursday (2-7 PM Berlin time) -Network visualization

Theory:
● Network layouts
● The grammar of graphics with network data
● Best practices in network data visualization

Practice:
● Tidy network data analysis with {tidygraph}
● Network visualization with {ggraph} and {ggnetwork}
● Interactive network visualization

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.