Shiny + archivist = reproducible interactive exploration


Shiny is a great tool for interactive exploration (and not only for that). But, due to its architecture, all objects/results that are generated are stored in a separate R process so you cannot access them easily from your R console.

In some cases you may wish to retrieve a model or a plot that you have just generated. Or maybe just wish to store all R objects (plots, data sets, models) that have been ever generated by your Shiny application. Or maybe you would like to do some further tuning or validation of a selected model or plot. Or maybe you wish to collect and compare all lm() models ever generated by your app? Or maybe you would like to have an R code that will recover given R object in future.

So, how to do this?

Czytaj dalej Shiny + archivist = reproducible interactive exploration

All your models belong to us: how to combine package archivist and function trace()

Let’s see how to collect all linear regression models that you will ever create in R.

It’s easy with the trace() function. A really powerful, yet not that popular function, that allows you to inject any R code in any point of a body of any function.
Useful in debugging and have other interesting applications.
Below I will show how to use this function to store a copy of every linear model that is created with lm(). In the same way you may store copies of plots/other models/data frames/anything.

To store a persistent copy of an object one can simply use the save() function. But we are going to use the archivist package instead. It stores objects in a repository and give you some nice features, like searching within repository, sharing the repository with other users, checking session info for a particular object or restoring packages to versions consistent with a selected object.

To use archivist with the trace() function you just need to call two lines. First one will create an empty repo, and the second will execute ‘saveToLocalRepo()’ at the end of each call to the lm() function.

Now, at the end of every lm() function the fitted model will be stored in the repository.
Let’s see this in action.

All models are stored as rda files in a disk based repository.
You can load them to R with the asearch() function.
Let’s get all lm objects, apply the AIC function to each of them and sort along AIC.

The aread() function will download the selected model.

Now you can just create model after model and if needed they all can be restored.

Read more about the archivist here: http://pbiecek.github.io/archivist/.

Why should you backup your R objects?

There is a saying that there are two groups of people: those who are already doing backups and those who will. So, how this is linked with reproducible research and R?

If your work is to analyze data then you often face a need to restore/recreate/update results that you have generated some time ago.
You may think ,,I have a knitr reports for everything!”. That’s great! It will save you a lot of troubles. But to have 100% of warranty for exactly same results you need to have exactly the same environment and same versions of packages.

Do you know how many R packages have been updated during last 12 months?

I took list of top 20 R packages from here, scrap dates of their current and older CRAN releases from here and generate a plot with dates of submissions to CRAN sorted along date of last submission.

Czytaj dalej Why should you backup your R objects?