How do I save multiple formulas in a R Package? Currently I have to run the as.formula code in order to retrieve the models, would be great if I can package them all in R.
I have saved package data in rda but don't know how to deal with "formula" class. Thanks!
Update: Saved a list of formulas in a list as rda file, now I can call them from the package now. Only the formulas are needed, not the environment in my case.
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I have been working on a ML project for which that work (done inside an R-project) resulted in some ML models (built with caret) ALONG WITH code that uses those models for additional analysis.
As the next phase, I am "deploying" these models by creating an R-package that my collaborators can use for analysis of new data, where that analysis includes USING the trained ML models. This package includes functions that generate reports, where, embedded in that report is the application of the trained ML models against the new data sets.
I am trying to identify the "right" way to include those trained models in the package. (Note, currently each model is saved in its own .rds file).
I want to be able to use those models inside of package functions.
I also want to consider the possibility of "updating" the models to a new version at a later date.
So ... should I:
Include the .rda files in inst/exdata
Include as part of sysdata.rda
Put them in an external data package (which seems reasonable, except almost all examples in tutorials expect a data package to
include data.frame-ish objects.)
With regard to that third option ... I note that these models likely imply that there are some additional "NAMESPACE" issues at play, as the models will require a whole bunch of caret related stuff to be useable. Is that NAMESPACE modification required to be in the "data" package or the package that I am building that will "use" the models?
My first intention is to go for 1. There is no need to go for other formats as PMML as you only want to run it within R. So I consider Rda as natively best. As long, as your models are not huge, it should be fine to share with collaborators (but maybe not for a CRAN package). I see, that 3. sounds convenient but why seperate models and functions? Freshly trained models then would come with a new package version, as you anayway would need to go with a data package. I dont see gaining much this way, but I have not much experiance with data packages.
I am currently building a R package and I want to use a trained model in one of my R script. Is it possible to load the model (saved in .rds form)?
Yes, it's exactly the way you described it. Save object with saveRDS function and load it with readRDS. You have to remember to load every package you used for the model, and to prepare data in exact way for prediction.
I built a model in R that I now need to use in SAS. I'm aware of R packages like haven that can write SAS-readable data files, but I can't find anything that can convert a model from R to SAS. Is this possible or am I wasting my time?
I have created a 300000 x 7 numeric matrix in R and I want to work with it in both R and Matlab. However, I'm not able to create a file well readeable for Matlab.
When using the command save(), with file=xx.csv, it recognizes 5 columns instead; with extension .txt all data is opened in a single column instead.
I have also tried with packages ff and ffdf to manage this big data (I guess the problem of R identifying rows and column when saving is related somehow to this), but I don't know how to save it in a readable format for Matlab afterwards.
An example of this dataset would be:
output <- matrix(runif(2100000, 1, 1000), ncol=7, nrow=300000)
If you want to work both with R and Matlab, and you have a matrix as big as yours, I'd suggest using the R.matlab package. The package provides methods readMat and writeMat. Both methods read/write the binary format that is understood by Matlab (and through R.matlab also by R).
Install the package by typing
install.packages("R.matlab")
Subsequently, don't forget to load the package, e.g. by
library(R.matlab)
The documentation of readMat and writeMat, accessible through ?readMat and ?writeMat, contains easy usage examples.
I'm using memisc to read in a massive SPSS file in order to reshape it. This seems to work very well.
However, I'd also like to be able to output the results to an SPSS-readable file that retains labels and descriptions--after all, this is the advantage of using the data-set construct memisc has set up.
I've searched the memisc documentation for any mention of an export, save, or output function. I've also tried passing a memisc data set to write.foreign (from the foreign package).
Why am I doing this in the first place? Reshaping massive data files in SPSS is a pain. R makes it easy. But the folks I'm doing this for want to maintain the labels and descriptions.
Thanks!