Is there a tutorial for Shiny, with reactive dataframe -> tidymodels -> ggplot?

[pathos]

I've been learning a lot through troubleshooting with tidymodels through a shiny app.

Essentially, I have a reactive dataframe, df() per usual in tutorials and guides. The dataframe is reactive because the user uploads the data, using fileInput() in ui.R.

This makes rendering ggplots a bit trickier after processing the data with recipes. I'm currently stuck at setting up the recipes for tidymodels to work with. Recipes doesn't seem to accept df().

I did find, so far is that I need to set formula explicitly using as.formula() so it would be recipe(as.formula(paste(input$independent_variable ~', .)) instead of the usual recipe(y ~ .)

So, I'm wondering if there is such a tutorial.

Thanks

[Max]

Can you please provide a minimal reprex (reproducible example)? The goal of a reprex is to make it as easy as possible for me to recreate your problem so that I can fix it: please help me help you!

If you've never heard of a reprex before, start by reading "What is a reprex", and follow the advice further down that page.

[pathos]

Thanks for your reply.

Not exactly minimal reprex I think, but I took a bit of time to reduce it down from the original and the dashboard is in showcase mode, so all of the code is presented there. I also think that the included parts are relevant. Here is the link for it: enet_dashboard

Although the error is more generic on the website, the error I get when running from R Studio is Error: cannot coerce type 'closure' to vector of type 'character'

The part I'm having trouble with is the Plotly output gg_enet in the 'Model' tab, more specifically the #MODEL part and below in server.R.

To clarify, I've been trying to narrow it down, and worked out that at least until this part, it's ok:

folds = dff() |>
      sliding_period(lookback = input$lookback, # if Inf, then it's chain
                     period = input$period,
                     index = {{ var_time_sym }},
                     #assess_stop = 1L, # include how many 'periods' in the future
                     every = input$every, # group how many 'periods' together
                     step = input$step, # how many 'periods' * 'every' to move the window (?)
                     skip = input$skip)

(That index = {{ var_time_sym }} part took a good amount of time to figure out)

Then I un-commented the part after that, which is the #MODEL part (starting with defining fit_enet), and that part is where I'm having difficulties, even though it works without reactive programming part with user input from the UI.

---

In case you need a sample .csv file to upload into the dashboard, here's some code to generate it:

df <- data.frame(yearr = sample(2015:2021, 2190, replace = TRUE),
                 monthh = sample(1:12, 2190, replace = TRUE),
                 dayy = sample(1:29, 2190, replace = TRUE)) |>
  mutate(datee = ymd(paste(yearr, monthh, dayy)),
         weekk = week(datee),
         yy  = sample(10000:20000, 2190, replace = TRUE) + (yearr^2) + (monthh^2) + (weekk^2),
         dummyy = as.factor(round(sample(0:1, 2190, replace = TRUE)))) |>
  filter(!is.na(datee)) |>
  arrange(-desc(datee))

write_csv(df, 'sample_data.csv')

After upload, change Select predicted variable to yy, Select time variable to datee, and click on the 'model' tab.