worldfootballR
osrm
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worldfootballR | osrm | |
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7 | 3 | |
408 | 226 | |
- | 1.8% | |
9.0 | 4.3 | |
3 months ago | about 1 year ago | |
R | R | |
- | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
worldfootballR
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[OC] Attacking Productivity: Who is Over-performing this Season and Who has been Lucky?
I found this the other day though, where there is an R package with what looks like a good amount of data. So, when I'm ready I might explore this as this might be the best approach to pull in a lot more players more easily.
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Daily Discussion
https://jaseziv.github.io/worldfootballR/ works really well with publicly available data and does most of the data scraping for you, but if you wanted to access paid stuff then you’ll need something else.
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[OC] A Data Dive into Spurs (lack of) Sub Usage (2nd Least Sub Minutes in League Play)
Data is from FotMob and grabbed via worldfootballR. Highly recommend to anyone looking to play around with soccer data, it's super well documented (as is everything in SportsDataverse). It doesn't have player location and all the advanced stuff but has a lot of rich shot data + match stats/events. worldfootballR has a bunch of fb-ref, understat, and transfermarket data as well.
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data sets about Scottish football
There’s an R package called worldfootballR that can be used to extract data from FBref, Transfermarkt, Understat and FotMob. Most of those sites don’t carry much data about Scottish football but FotMob have some really useful shot location data with xG and xGOT values. Here’s the link to the package: https://github.com/JaseZiv/worldfootballR
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[Q] Looking for downloadable football (soccer) statistics
The worldfootballr R package can help you download from some of the big ones.
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[OC] Liverpool and Real Madrid's paths through the knock out stages to the Champions League final
Source:WorldfootballR package
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[OC] Liverpool Substitutions Using worldfootballR and GT
Data extracted using worldfootballR
osrm
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Using R to Cluster Points by Road Networks
OSRM: A super fast and easy to use routing engine that runs on OSM data. You only need to run 5 lines of code to (1) download a .pbf from Geofabrik, (2-5) download the OSRM docker image and pre-process the OSM data. There are also 3 profiles predefined that you can use: car, bike, foot (e.g. foot.lua). It basically hosts a local server. I find the easiest way is to combine it with the osrm R package. I have seen you also need to adjust for the elevation. I think I have seen some custom LUA profiles that also account for DTM derived elevation changes as an additional weight.
- how to extract shortest path between two nodes from a given base road network?
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Connecting Points on a Map
- https://github.com/riatelab/osrm/issues/41
What are some alternatives?
dplyr - dplyr: A grammar of data manipulation
wooldridge - The official R data package for "Introductory Econometrics: A Modern Approach". A vignette contains example models from each chapter.
blogdown - Create Blogs and Websites with R Markdown
expedition-diaries - Expedition Diaries' website back-end and front-end
ggplot2 - An implementation of the Grammar of Graphics in R
cppRouting - Algorithms for Routing and Solving the Traffic Assignment Problem
wesanderson - A Wes Anderson color palette for R
sfnetworks - Tidy Geospatial Networks in R
epanet2toolkit - An R package for calling the Epanet software for simulation of piping networks.
soccerdata - ⛏⚽ Scrape soccer data from Club Elo, ESPN, FBref, FiveThirtyEight, Football-Data.co.uk, FotMob, Sofascore, SoFIFA, Understat and WhoScored.
DontBlameTheData - Repository for the backend of dontblamethedata.com
bruceR - 📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.