R

I had to compute an indicator this week. It had confidence intervals that relied on taking 100,000 samples from the indicator's approximate distribution. I had to repeat this over multiple GP practices and for twelve different demographic groups.

I love making plots in R with ggplot. However, there are always a few niggles that I forget about between plots. I wrote this post so that I have somewhere to look the next time I need to tweak a few things in my plots. I intend to come back and add updates in the future as I learn more things. If I keep coming back, I might also remember a few of these too.

The shiny app embedded below allows you to explore the number of packets you need to complete the Panini sticker album for this summer's Euro tournament. This builds on the results I presented in an earlier post and allows you to explore how many fewer packets you need to buy when you have more friends to swap with. You can also vary the number of runs performed because the model runs considerably slower with more swappers involved.

As a follow-up to my post about the Euro 2016 Panini Stickers, I've now completed the collection with the help of an online swapping site and by buying the last 39 stickers directly from Panini. I also managed to write a new simulator, this time with additional collectors involved.

This post deals with how to generate random numbers in R. It is good to know how to generate random numbers with a particular language or software package for at least one of the following three reasons: