For Jan...

I've got no data for "S.H.I.E.L.D." :(

Maybe, but just maybe, "Firefly" gets the way-to-early-cancelled bonus by the voting community.

Today, let us have a look at movies. The Internet Movie Database (IMDb) has some data dumps available on their website. It's a subset of the information available on the IMDb site, but it's more than enough. I will spare you my code to convert these data dumps in R dataframes, because the code is boring and complicated (unfortunately, the data dumps are not too nice to read automatically).

I just wanna show you what you can do with these dumps.

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.

Alright, now we have all the data we need in one dataframe. To make this code work, I assume you ran the code from Part 1. We need the dataframe big.tab.

All the data presented here is based on the data from 18/10/2012. You can run an analysis with the actual data or I can do it at some point later in the season.

Let's plot some stuff. How about the old german saying about soccer "Geld schießt keine Tore" (Money doesn't score goals)? Let's look into this.

Teams with more money always win, right? At least they have a bigger chance of having success in their national championship. Let's look into that...

NOTE: If you are not interested in the details of programming with R or getting data from the internet with it, you want to skip this post and have a look at Part 2.

First, we have to get some data. transfermarkt.de is a german site hosting statistics of all kinds of stuff around soccer.
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