worldfootballR VS ggplot2

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.

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.

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.

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

The worldfootballr R package can help you download from some of the big ones.

Source:WorldfootballR package

Data extracted using worldfootballR

ggplot2

I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package.

Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2.

I used the ggplot2 package in R to create these figures.

This might not be at the top of your list, but science fiction often presents advanced data analysis and visualization technologies. Open source data analysis tools such as Python's Pandas and R's ggplot2 have revolutionized the field, making complex data manipulation and visualization accessible to all. In the science fiction novel The Martian, astronaut Mark Watney uses a variety of data analysis and visualization tools to survive on Mars. He uses Python's Pandas to clean and organize data, and he uses R's ggplot2 to create visualizations of his data. These tools allow him to make sense of the vast amounts of data and help him to make critical decisions about his survival.

Sorry about the slow reply, but the auto-moderator seems to be deleting my comments (for some unknown reason). I will try once more: the geom_tile function in ggplot2.

Visual cues are a much better idea, thanks! Unfortunately, I don't know how to do that in ggplot2, either (I created these figures in R).