cachem | Key-value caches for R | Caching library

Support for proteome profiling has been provided in the package. To obtain the newest version the package should be installed from Github with the following command BiocManager::install("BioinformaticsFMRP/TCGAbiolinks") (see here).

Maybe indeed worth an answer for posterity...

Specify ggsave("test.png", dpi = 300, bg = "white")

Background (pun intended): the argument will be passed to grDevices::png via the ... argument. bg controls the background of the device.

All you need to do is change #textbox to .box in the tags of dashboardBody(). You were right there.

So why, why, why? The id textbox was really only encapsulating what span encapsulated. So you really were centering vertically the entire time. However, if you wanted to center the text in the entire box, you needed to go up a level. I used developer tools to find out what id or class would work.

Notice the highlight - that's what you're referring to with #textbox

Notice the highlight - that's what you're referring to with .box.

Alright, you asked about changing one specific box in the comments. So I've add the following:

Yes, you can. Alright, if you wanted to change the second box, for example look for something within the tags, classes and ids that isolates the box you're wanted to change. For the second box, I would look at the class col-sm-6 and the class box.

To set tags for this box and beyond you can set the HTML tags to .col-sm-6 + .col-sm-6 div.box{css here}. If you only wanted that box changed, then use the same method to flip the CSS back. This is what this might look like in dashboardbody(). (For this example, I changed the id in the second box in tabItem to "textbox2."):

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.

I shut down R, updated to R 4.0.3, and installed cachem from sources and this combination worked.