Big nations – better players?

Wow, it’s been almost a year since the last post. Here’s some non-science for getting me in shape.

It’s been a couple of weeks since the FIFA World Cup final. Amazing falls of favorites and rises of underdogs are what people always look for in such events. Croatia, who had barely qualified for the tournament showed a great performance until the very last game, losing only to the young France team. Discussing the team’s chances one can’t help but compare their countries’ population sizes. Indeed, isn’t it amazing that Croatia, a nation of 4.1 million people, could leave behind England (55 million), Russia (144 million), Argentina (44 million), and Nigeria (186 million)? Or is it not? Below is some semi-serious attempt to analyze population – football performance relationship. Continue reading “Big nations – better players?”

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My first Shiny app: fitting sigmoid curves

Since the grad school I have been using R for data analysis and (mainly) for preparation of nice plots. As with LaTeX, it was a pretty steep learning curve. But after a year or so I managed to become comfortable with writing a new script for each new experiment. After some time, however, I started feeling that it wasn’t enough. Many experiments were almost identical, so breeding 99%-similar scripts didn’t seem to be an efficient way of handling data. That’s when I first started to think about making ‘reusable’ apps for specific purposes. Continue reading “My first Shiny app: fitting sigmoid curves”