This post introduces the R package signnet, available on CRAN, which implements several methods to analyze signed networks. Signed networks include two types of relations, positive and negative ones (say friends and enemies). They are fundamentally different from traditional networks and require a very different set of tools to analyse ... [Read more...]

There are many network repositories out there that offer a large variety of amazing free data. (See the awesome network analysis list on github for an overview.) The problem is, that network data can come in many formats. Either in plain text as edgelist or adjacency matrix, or in a ... [Read more...]

A new version of the snahelper package is now available on CRAN. If you do not now the package: So far, it included one RStudio addin that provided a GUI to analyze and visualize networks. Check out the introductory post for more details. This major... [Read more...]

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I recently came across the article “How Mapping Shots In The NBA Changed It Forever” and although I am not a big basketball fan, I was impressed by the visualizations. I actually bought the book “Sprawlball” by Kirk Goldsberry afterwards, where this was taken from. I can only recommend it, ... [Read more...]

This post introduces the new R package graphlayouts which is available on CRAN since a few days. We will use network data from the Game of Thrones TV series (seemed timely at the time of writing) to illustrate the core layout algorithms of the package. Most of the algorithms use ... [Read more...]

The ggraph package provides a ggplot-like grammar for plotting graphs and as such you can produce very neat network visualizations. But as with ggplot, it takes a while to get used to the grammar. There are already a few amazing Rstudio Addins that assist you with ggplot (for example ggplotAssist ... [Read more...]

This is the third post of a series on the concept of “network centrality” with applications in R and the package netrankr. The last part introduced the concept of neighborhood-inclusion and its implications for centrality. In this post, we extend the concept to a broader class of dominance relations by ... [Read more...]

This is the second post of a series on the concept of “network centrality” with applications in R and the package netrankr. The first part briefly introduced the concept itself, relevant R package, and some reoccurring issues for applications. This post will discuss some theoretical foundations and common properties of ... [Read more...]

This is the first post of a series on the concept of “network centrality” with applications in R and the package netrankr. There is already a rudimentary tutorial for the package, but I wanted to extend it to a broader tutorial for network centrality. The main focus of the blog ... [Read more...]

This post is dedicated to the beautiful chaos created by double pendulums. I have seen a great variety of animated versions, implemented with different tool but never in R. Thanks to the amazing package gganimate, it is actually not that hard to produce them in R.

library(tidyverse)
library(gganimate)

I am not ... [Read more...]

I recently picked up a copy of my favorite game Stardew Valley again. If you don’t know the game, I can highly recommend it! You inherit a pixel farm and you are in charge of everything. Crops, animals, fishing, mining and never forget to socialize. My plan was to ... [Read more...]

This is the second part of Analyzing the Greatest Strikers in Football. In the first part, we created the function get_goals() which allows us to conveniently scrape detailed information of players career goals from transfermarkt.co.uk. In this part, we are going to explore the data.

library(tidyverse) # for data wrangling
library(lubridate) # for date formats
library(ggimage)   # adding images to ggplot
library(patchwork) # attaching ggplot objects
library(viridis)   # viridis color schemes

We ... [Read more...]

I do not always come up with new ideas for my blog, but rather get inspired by the great work of others. In this case, it was a reddit post by u/Cheapo_Sam, who charted world footballs greatest goal scorers in a marvelous way. According to the post, the ... [Read more...]

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I academically grew up among graph drawers, that is, computer scientists and mathematicians interested in deriving two-dimensional depictions of graphs. One may despicably call it pixel science, yet a lot of hard theoretical work is put into producing pretty graph layouts. Although I am not at all an expert in ... [Read more...]

This is a short post on how to quickly calculate the Fiedler vector for large graphs with the igraph package.

#used libraries
library(igraph)    # for network data structures and tools
library(microbenchmark)    # for benchmark results

Fiedler Vector with eigen My goto approach at the start was using the eigen() function to compute the whole spectrum of the Laplacian Matrix.

g <- sample_gnp(n = 100,p = 0.1,directed = FALSE,loops = FALSE)
M <- laplacian_matrix(g,sparse = FALSE)
spec <- eigen(M)
comps <- sum(round(spec$values,8)==0)
fiedler <- spec$vectors[,comps-1]

While this is easy ... [Read more...]

This is the last part of the mini series Analysing NBA Player data. The first part was concerned with scraping and cleaning player statistics from any NBA season. The second part showed how to use principal component analysis and k means clustering to “revolutionize” player positions. Which kind of failed. ... [Read more...]

This is the second post of my little series Analyzing NBA player data. The first part was concerned with scraping and cleaning player statistics from any NBA season. This post is dealing with gaining some inside in the player stats. In particular, clustering players according to their stats to produce ... [Read more...]

As a football (soccer) data enthusiast, I have always been jealous of the amount of available data for American sports. While much of the interesting football data is proprietary, you can can get virtually anything of interest for the NBA, MLB, NFL or NHL. I have decided to move away ... [Read more...]