codetools | using github.com/enteprise api | REST library

This problem has been fixed in the developmental version of {parsnip} (>0.1.7). You can install it by running remotes::install_github("tidymodels/parsnip").

First things first, Lambda and S3 need to be in the same region.

Secondly, it looks like you're not the bucket owner (you haven't created the bucket yourself by looking at the template).

This means, the bucket you're using to retrieve the Lambda source code from is (I suppose coming from the workshop), and they decided to create that bucket in the region us-east-1. Enforcing you to also deploy your stack in the region us-east-1 (if you want to follow the workshop).

But what if you really wanted to deploy this stack to eu-west-1?

That would mean you need to create a bucket in region eu-west-1 with and copy the objects from the workshop bucket into your newly created bucket and update your CloudFormation template to point and retrive the Lambda source code from your newly created bucket (note you might need to name the bucket differently as bucket names are globally shared).

I hope this is a bit clear.

@importFrom recipes prep bake had to be added to the .R file

Errors like this in Shiny apps are almost always a result of missing packages, which the logs confirm.

Turns out in this case I think the missing package is kernlab, which I only found by reading the documentation given here: https://topepo.github.io/caret/train-models-by-tag.html#Support_Vector_Machines. It's a suggested package, not imported, so the command suggested in the comments by heds1 would sort this out.

You definitely were passing the vector of variables correctly to the recipe -- no problem there!

You were running into other problems with your model fitting. An xgboost model requires all predictors to be numeric, so if you convert something like zip code to factors, you need to then use step_dummy(). If you have something of high cardinality like zip codes, you probably will need to handle new levels or unknown levels as well.

The reason you are experiencing an error is that you have created a predictor variable from the outcome. When it comes time to predict on new data, the outcome is not available; we are predicting the outcome for new data, not assuming that it is there already.

This is a fairly strong assumption of the tidymodels framework, for either modeling or preprocessing, to protect against information leakage. You can read about this a bit more here.

It's possible you already know about these resources, but if you are working with time series models, I'd suggest checking out these resources:

• Resampling for time series
• Using timetk for time series preprocessing
• Using modeltime for time series modeling

I think this is ultimately a small bug in renv. Here's my guess at what's happening:

1. While this project has been initialized as an renv project, it does not have a lockfile for some reason. (Perhaps renv::activate() was called to initialize renv without explicitly creating a lockfile?)

2. The project has an renv autoloader; this is from a script at renv/activate.R. That script is configured to load renv 0.11.0.

3. When the project is loaded, renv finds that renv 0.12.0 is installed in the project library, not the expected version 0.11.0. This causes the warning to be emitted. (Perhaps renv was updated in that project previously?)

So, ultimately, the warning is misleading here -- the request for renv 0.11.0 comes directly from the autoloader, not from the lockfile (which does not exist). As for why the lockfile does not exist, I'm not sure -- but it most likely implies the project was initialized via renv::activate(), and not by renv::init().

All that said -- you can safely re-generate the lockfile via renv::snapshot().

The error here occurs because on step_string2factor() during tuning, the recipe starts trying to handle variables that don't have any roles, like species and owner.

Try setting the role for all of your nominal variables before picking out the outcomes and predictors.

I think that the issue might be where your Rmd is located. The working directory automatically gets changed to that location when knitting. You could either change the root directory (Setting work directory in knitr using opts_chunk$set(root.dir = ...) doesn't work), or simply use paths that first go "up" to the main directory of your R project ("../figure.png").