Let's play around a bit more with the dataset we built in Part 1 of this series.

Now we are going to compare data from more championships in Europe.

Let's check out the first divisions from the following countries:
- Germany (1. Bundesliga)
- England (Premier League)
- Spain (Primera División)
- Italy (Serie A)
- France (League 1)

If you want to replicate the following steps, I assume that you got all data from these championships using the code from Part 1.

I added a column "col" to every championship table. This column holds a character vector with a color name that's different for each championship:
Germany: Black
England: Blue
Spain: Red
Italy: Green
France: Grey

First, we create one big table with the data from all mentioned championships.
whole.tab <- rbind(GER.tab, UK.tab, ES.tab, IT.tab, FR.tab)

Now, let's have a look at the distribution of the teams from around Europe (using the previously mentioned color coding).

plot(whole.tab$Value, whole.tab$Goals.for, col = whole.tab$col, pch = 19, bty = "n", xlab = "Value", ylab = "Goals for")
whole.mod <- lm(Goals.for ~ Value, data = whole.tab)
abline(coef = coef(whole.mod), lty = "dashed")
whole.cor <- cor.test(whole.tab$Value, whole.tab$Goals.for)
title(sub = paste("r = ", round(whole.cor$estimate, 3), ", p = ", round(whole.cor$p.value, 8), sep = ""))


Isn't that nice - and so many colors :)

Guess, which team is represented by the red dot on the far right of the plot...

What I'm interested in is the following question: Can we compare the championships included in our dataset in terms of the Value-Goals correlation? Sure, that should work. I compute Pearson's correlations for each championship, put the correlation coefficients into a vector and then plot them.

tab.l <- list(GER.tab, UK.tab, ES.tab, IT.tab, FR.tab)
cors <- c()
for (tab in tab.l) {
    cors <- c(cors, cor.test(tab[,"Value"], tab[,"Goals.for"])$estimate) }
names(cors) <- c("GER", "UK", "ES", "IT", "FR")
dotplot(cors, xlab = "Value/Goals correlation coefficient (Pearson)", cex = 1.5)

cors.gp <- c()
for (tab in tab.l) {
    cors.gp <- c(cors.gp, cor.test(tab[,"Goals.for"], tab[,"Points"])$estimate) }
names(cors.gp) <- c("GER", "UK", "ES", "IT", "FR")
dotplot(cors.gp, xlab = "Goals/Points correlation coefficient (Pearson)", cex = 1.5)

The result:
Please note, that this is only a visual comparison of correlations between the value of a team and the amount of goals it scored so far in their national championships (with only 7 / 8 games into the season). This is by no means a water-proof statistical analysis! Nevertheless, let's merrily interpret this thing :)

In spain, the value-goals correlation seems is the highest one - closely followed by the Premier Leauge and the German Bundesliga. In Italy, however, the correlation between the value of a team and the goals it scored is very low. Only around 0.1! That's quite surprising for me.

I had an idea for another little analysis. The goal in soccer is to gain points in your national championship, right? You can only get points by scoring goals. But sometimes, if you score one or more goals, your opponent scores even more goals than yourself. Then, the goals you scored are "wasted" somehow (because you don't get any points for that game). So, as a measure of effectiveness, we can correlate the goals a team scored and the points it achieved so far. The higher this correlation is, the more effective is the team. Now let's have a look in which championship this correlation between scored goals and points is the highest:

cors.gp <- c()
for (tab in tab.l) {
    cors.gp <- c(cors.gp, cor.test(tab[,"Goals.for"], tab[,"Points"])$estimate) }
names(cors.gp) <- c("GER", "UK", "ES", "IT", "FR")
dotplot(cors.gp, xlab = "Goals/Points correlation coefficient (Pearson)", cex = 1.5)


Well, ain't that a surprise - the Germans are the most effective :) Of course, these correlation coefficients are much higher than the ones we observed in the value-goals correlations. In France, however, the number of goals is the least predictive for having many points.

Maybe, I will present some more analyses with the soccer dataset, soon. Or I'll present some other stuff I did, we'll see.






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Hi all, this is just an announcement.

I am moving Rcrastinate to a blogdown-based solution and am therefore leaving blogger.com. If you're interested in the new setup and how you could do the same yourself, please check out the all shiny and new Rcrastinate over at

http://rcrastinate.rbind.io/

In my first post over there, I am giving a short summary on how I started the whole thing. I hope that the new Rcrastinate is also integrated into R-bloggers soon.

Thanks for being here, see you over there.

Alright, seems like this is developing into a blog where I am increasingly investigating my own music listening habits.

Recently, I've come across the analyzelastfm package by Sebastian Wolf. I used it to download my complete listening history from Last.FM for the last ten years. That's a complete dataset from 2009 to 2018 with exactly 65,356 "scrobbles" (which is the word Last.FM uses to describe one instance of a playback of a song).
3

Giddy up, giddy it up

Wanna move into a fool's gold room

With my pulse on the animal jewels

Of the rules that you choose to use to get loose

With the luminous moves

Bored of these limits, let me get, let me get it like

Wow!

When it comes to surreal lyrics and videos, I'm always thinking of Beck. Above, I cited the beginning of the song "Wow" from his latest album "Colors" which has received rather mixed reviews. In this post, I want to show you what I have done with Spotify's API.

Click here for the interactive visualization

If you're interested in the visualisation of networks or graphs, you might've heard of the great package "visNetwork". I think it's a really great package and I love playing around with it. The scenarios of graph-based analyses are many and diverse: whenever you can describe your data in terms of "outgoing" and "receiving" entities, a graph-based analysis and/or visualisation is possible.
12

Here is some updated R code from my previous post. It doesn't throw any warnings when importing tracks with and without heart rate information. Also, it is easier to distinguish types of tracks now (e.g., when you want to plot runs and rides separately). Another thing I changed: You get very basic information on the track when you click on it (currently the name of the track and the total length).

Have fun and leave a comment if you have any questions.
3

So, Strava's heatmap made quite a stir the last few weeks. I decided to give it a try myself. I wanted to create some kind of "personal heatmap" of my runs, using Strava's API. Also, combining the data with Leaflet maps allows us to make use of the beautiful map tiles supported by Leaflet and to zoom and move the maps around - with the runs on it, of course.

So, let's get started. First, you will need an access token for Strava's API.

I've been using the ggplot2 package a lot recently. When creating a legend or tick marks on the axes, ggplot2 uses the levels of a character or factor vector. Most of the time, I am working with coded variables that use some abbreviation of the "true" meaning (e.g. "f" for female and "m" for male or single characters for some single character for a location: "S" for Stuttgart and "M" for Mannheim).

In my plots, I don't want these codes but the full name of the level.

It's been a while since I had the opportunity to post something on music. Let's get back to that.

I got my hands on some song lyrics by a range of artists. (I have an R script to download all lyrics for a given artist from a lyrics website.
4

Lately, I got the chance to play around with Shiny and Leaflet a lot - and it is really fun! So I decided to catch up on an old post of mine and build a Shiny application where you can upload your own GPX files and plot them directly in the browser.

Of course, you will need some GPX file to try it out. You can get an example file here (you gonna need to save it in a .gpx file with a text editor, though). Also, the Shiny application will always plot the first track saved in a GPX file.
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