Ah, probability! Generalizing the famous Monty Hall problem. Here I generalize this fun probability game a bit and I’ve put the results into an R Shiny web application. In the classic Monty Hall problem, which originates from the old game show, Let’s Make a Deal (hosted by Monty Hall), there are three doors. Behind one of these doors is a car. Behind the other two stand goats. Monty Hall tells you to choose a door. You hope to win the car obviously, by randomly choosing the correct door. After you make your choice, the door remains closed. Monty Hall then opens one of the other two doors, always to reveal a goat. After all, if you have not chosen the correct door, and the car is behind one of the other two, what point is there in Monty showing you the car? That would end the game. After he shows you a goat, he gives you a simple choice. Do you want to stick to your guns and open the door you originally chose or do you want to switch to the other unopened door? Does it make a difference in your chances of winning? It certainly does.
I’ve uploaded a new R Shiny web application. This app shows plots of bootstrapped, 50-year moving average correlations between tree growth and both temperature and precipitation at various sites. But that nature of the data is not the topic here. Some of my apps can be kind of complex and large. However, I decided to throw this one together as a simpler example. The matrices are formatted and stored in an R workspace file. There is one menu for selecting which image to plot.
I’ve uploaded a new R Shiny web application. The app shows daily precipitation for different locations around Alaska as far back as 1950. Statisticians and useRs may find the app interesting because of its customization features for controlling the look and feel of the R base graphics used to produce a customizable chart. UseRs of the Shiny package may find the customized CSS of the app interesting as well. I went with a darker theme this time. And if you find historical Alaska daily precipitation interesting, there is of course that. The emphasis here is on customization. This app only begins to scratch the surface, but it represents a good first example of making various R base graphics plotting arguments, among other things, available to the useR via a Shiny app.
I’ve uploaded a new R Shiny web application. The app examines trends in sea ice concentration and extreme wind events over time. Since both variables, mean monthly sea ice concentration and proportion of days in a month defined as exhibiting extreme winds, are on the same scale ranging from zero to one, I plot both time series together to make it easier to see if and when there is low ice concentration but high winds.
I have an example here of how to use the base R
parallel package within a Shiny web application. In general, I do not put content into a Shiny app which requires a large amount of processing. I’d prefer the user visiting the app url to have to wait for nothing. I see Shiny apps more as presentation tools than as data processing tools. Nevertheless, performing parallel processing inside of reactive expressions really throws the shackles off and redefines what I previously was seeing as some of the “limitations” of Shiny.
I have released a preliminary version of a new R Shiny web application. This app is focused on exploration of 2-km downscaled temperature and precipitation climate model output over Alaska and western Canada for historical and projected time periods. The data are monthly variables at a decadal time scale (decadal mean temperature and precipitation). The values corresponding to each community are those of the 2-km grid cell in which a community was located. Bear in mind that this app is a work in progress and should not be treated as a finalized, vetted product or dataset. More details can be found on the app About tab.
Now for the fun stuff!…