Rcrastinate: 2012

Creating PDFs and websites with the "knitr" package

Just a fast note: I came across the R-package "knitr" which enables you to generate PDF files by mixing LaTeX and R code in one document. The result looks very nice and is great to create documentations, manuals and so on. I find knitr much easier to use than the quite popular Sweave (but I guess this has to do with personal preferences). In fact, knitr uses Sweave and expands it.

Also, you can create HTML files using R Markdown documents. Just imagine you write an article or create a website with statistical analyses or programming stuff in R.

Oct

Josh vs. himself (or: Firefly > all)

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.

Oct

Going to the Movies...

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.

Oct

Soccer is all about money (?) - Part 3: More plots & analyses

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.

Oct

Soccer is all about money (?) - Part 2: Simple analyses

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.

Oct

Soccer is all about money (?) - Part 1: Getting the Data

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.

Sep

Let's go! (and Disclaimer)

Let's get that started ...

Fun stuff with R coming soon!

Disclaimer beforehand:

The analyses I'll present are not meant to be taken too seriously in a scientific way. I just wanna show what you can do with R as a programming language, basic statistics and different kinds of visualizations. Also, I'm not going to proofread my posts several times. So maybe sometimes my English is not too good, please excuse errors.