The renv can be simplified to remove unnecessary packages (to speed up (re-)installation) and replaced with an exact step-by-step process for people to follow. Some participants struggled with package installation as it is currently written.

https://github.com/dlab-berkeley/R-Machine-Learning/blob/ee158d8506d68e72498a36d7484a403ed5b4b506/README.md?plain=1#L43-L75

Also, install_github() should be replaced with standard install.packages() where possible, e.g. remotes::install_github("tidymodels/discrim") can just be install.packages("discrim") since the standard v1.0.0 package has been released now.

This caused additional issues because it assumes they have already run install.packages("remotes") because it is not explicitly stated in the instructions, so add that if there's a good reason to keep any install_github calls around.

01-overview.rmd doesn't seem to install missing packages by default. I had to manually install tidymodels and and ck37r. Not sure if this is an issue on my end or with the code block.

@pssachdeva I just noticed this repo is missing binerhub links in the README: https://github.com/dlab-berkeley/R-Machine-Learning/blob/ee158d8506d68e72498a36d7484a403ed5b4b506/README.md?plain=1#L81-L87

Any chance you could add it and test it in time for today's workshop at 10am since we have folks from partner organizations who may need a binderized version?

Also, it would be great if you can add the iconified buttons like what's shown in R Fundamentals:

Thanks!

From the instructions it is unclear which step is suppose to be performed first:

1. The first line in Part1 renv::init()
2. The 5th step in installation instructions install.packages(c("tidyverse", "tidymodels", "here","pROC","glmnet", "ranger", "rpart", "xgboost","rpart.plot", "doParallel", "palmerpenguins", "ISLR2", "klaR", "stacks"))

Running the part 4 .RMD locally, I'm getting an error on line 197 with:

cl <- makeCluster(detectCores() - 1)
registerDoParallel(cl)

I believe this function optimizes a local environment for heavier computation? I've dug into the docs and arguments get deep into CS, beyond my scope at this time. It's possible to continue downstream in the workflow without these lines being run, which is what I've done so far.

The labels were backwards so it looked like as the penalty got larger, the coefficients get larger. The reverse is true.

On lines 357-364, R runs into an error where it says that not all variables are available despite it seeming like they are.

"Error: Not all variables in the recipe are present in the supplied training set: 'student', 'balance', 'income', 'default'.
Run rlang::last_error() to see where the error occurred."

In the installation instructions the step:

If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud by clicking this button: Binder_link_here

This leads you to a Jupyter environment that cannot run code in the .Rmd lesson files.

I suggest we remove this option.

While the README contains instructions on installing the necessary packages for the workshop, it might be useful to have an renv setup so that users could simply run a renv::init() at the beginning of the first notebook in order to make sure all necessary packages are installed.

On line 216, R says that the seed is not a valid integer. Reducing the seed to four numbers fixes this issue.

In Part2.md, line 122, the linear_reg model is missing parentheses when passing into the stan engine.

When I open this workshop in datahub via the gitpuller, and run the first line:

renv::init()

I get the error message:

> knitr::opts_chunk$set(echo = TRUE)
Error in loadNamespace(x) : there is no package called ‘knitr’

When I try to install knitr manually, it takes a very long time.

To show if balances are different for default status:
plot(default$balance ~ default$default)

Lines #315-340, this line of code should be in an R chunk but is not.

This needs to be fixed on its own and also because the ensembles model depends on it.

In lessons/Part3.Rmd, I don't think parsnip::set_model exists. Perhaps parsnip::set_mode should be used.

https://github.com/dlab-berkeley/R-Machine-Learning/blob/d120942132d6f7bdbd9c6d6c59cdad72db46bcb9/lessons/Part3.Rmd#L78

on line 345, there is a typo where instead of "recipe" the function says "ecipe".

In lines 251-256 of 04-decision-trees.rmd these two plots run into errors. I think it's because tree_fit_viz_metr and tree_fit_viz_mat are not defined already. Here is the relevant code:

`# Metrics
(tree_fit_viz_metr + labs(title = "Non-tuned")) / (visualize_class_eval(tree_fit_tuned) + labs(title = "Tuned"))

Confusion matrix

(tree_fit_viz_mat + labs(title = "Non-tuned")) / (visualize_class_conf(tree_fit_tuned) + labs(title = "Tuned"))`

Solutions for 04_regularization should include:

Import data

penguins <- palmerpenguins::penguins %>%
filter(!is.na(bill_length_mm))

Set seed

set.seed(23)

Perform split

penguin_split <- penguins %>% initial_split(prop = 0.80)
penguins_train <- training(penguin_split)
penguins_test <- testing(penguin_split)

Participants will run into error if null values in bill_length_mm are not dropped

Workshop title should be "R-Introduction-to-Machine-Learning-with-tidymodels"

on line 67, set_engine has an extraneous "l" which leads to an error

Error message

Error: The inputted candidates argument was not generated with the appropriate control

• settings. Please see ?control_stack

I delivered the workshop with these current materials, and things went pretty smoothly, but it's too much material for two 3 hour workshops.

I'd recommend splitting this workshop into either 3 or 4 2-hour workshops. This could look something like:

• Part 1: introduction and regression
• Part 2: preprocessing
• Part 3: regularization and cross-validation
• Part 4: more models

Perhaps Parts 1/2 could be merged into a single workshop.

I'd also recommend beefing up the "more models" section to more appropriately fill a 2 hour slot: more details on logistic regression, naive bayes, and random forest

Tidyverse cannot find the naive_Bayes function, leading to an error in the Part 3 .rmd file on lines 300-305.

install.packages("glmnet")

Line 70 in Part3.md should be set_mode rather than set_model.

On lines 381-389, R cannot find fit_members() function unless explicitly installing stacks package. We might need a section for this in the notebook.

Pipes (%>%) in the lessons do not consistently have spaces before them. The tidyverse style guide calls for spaces before pipe operators, which we should adhere to.

Typo: 03-Lasso, ln: 254
"initial"

'docs' folder doesn't exist in the R-Machine-Learning-main project, so there's an error that pops up when you open it. doesn't impact running code

• Overview-1.RMD
  needed to:

install.packages(‘rlang’)

before installation of all packages chunk, or else ggplot2 would not load.

• 04- Decision Trees, ln. 196
  Error in makePSOCKcluster(names = spec, ...) : Cluster setup failed. 4 of 4 workers failed to connect.

Error did not break the .RMD workflow.

• 05 - Random Forest
  update_model() function, same error as above

• 06- xgboost
  update_model(), same error, ln. 351

• 09 - hlclust

ln. 37:
Error: Problem with mutate() input ..1. x there is no package called ‘BBmisc’ℹ Input ..1 is across(is.numeric, BBmisc::normalize)

`install.packages("BBmisc")

library(BBmisc)

and np.

_

unregister_dopar doesn't seem to be a function from any loaded library.

Error in unregister_dopar() : could not find function "unregister_dopar"

https://github.com/dlab-berkeley/R-Machine-Learning/blob/5f627e0268ed7ac2998f96bea28ab1e40e10886f/lessons/Part3.Rmd#L373

Perhaps that function came from this Stack Overflow answer.

In lessons/Part3.Rmd, it's mentioned that the finalized LASSO model is fit with the training set, but in Line 158, it seems like a typo that it's fitted with data_test instead of data_train.

https://github.com/dlab-berkeley/R-Machine-Learning/blob/a12e89b65cbfd1356d2df04c736d4b2a914820fe/lessons/Part3.Rmd#L158

It may help students and instructors if we include the "Participants Instructions" closer to the top of the read me, in large script with a message something like:

"If you are working in a local computing environment, be sure to prepare for the workshop by running all the code in the 01.Overview.RMD before our first meeting. Doing this could take a few minutes, and is needed for you to run the workshop's code on your own computer."

Under the section Regularization under linear regression --> Ridge Regression

It would be helpful to add a short summary of the packages and functions introduced with their respective uses at the end of the third module (pre-processing)!