future.apply | R package : future.apply - Apply Function | Architecture library

I have the following data frame that contains points that originate from different samples. Each point has a type. I need to calculate, for each point belonging to a given sample of a given type (for instance for "Sample_1" type "A") how many points of another type are around it in a given cutoff. My current implementation uses "future.apply" and I was wondering if there is a more efficient way to solve this problem. The example here is limited and should run quickly, the real problem is composed of several thousands of lines and it's much slower. In the end I store the results in a list. This list has, for each element with "type" in "starting_point", the number of elements of type "target_point" in a threshold of 40.

I'm currently trying to write a function that filters some rows of a disk.frame object using regular expressions. I, unfortunately, run into some issues with the evaluation of my search string in the filter function. My idea was to pass a regular expression as a string into a function argument (e.g. storm_name) and then pass that argument into my filtering call. I used the %like% function included in {data.table} for filtering rows.

My problem is that the storm_name object gets evaluated inside the disk.frame. However, since the storm_name is only included in the function environment, but not in the disk.frame object, I get the following error:

I am a beginner on mlr3 and am facing problems while running AutoFSelector learner associated to glmnet on a classification task containing >2000 numeric variables. I reproduce this error while using the simpler mlr3 predefined task Sonar. For note, I am using R version 4.1.2 (2021-11-01)on macOS Monterey 12.1. All required packages have been loaded on CRAN.

WHAT I WANT: I'm trying to fit a GAM model for classification using tidymodels on a given data.

SO FAR: I'm able to fit a logit model.

Couldn't find an exact match for this question so here goes. I'm trying to style the automatically-generated cross-reference caption label font, e.g. 'Table 1' or 'Figure 2' for a PDF output generated from a RMD file within RStudio Server. Currently, I'm only able to style the actual caption text.

I've set up as my RMD file as follows:

I use a custom step function that I have built for a package of mine that works just fine, it is in a local copy of the package (Not yet submitted to CRAN) link to function here: step_hai_fourier

Here is the session info (we can see healthyR.ai 0.0.2.9000 is loaded):

I am building a custom recipes function and getting an error when I try to prep() the recipe. I get the following error:

I am just wondering if this is a serious tradeoff one should consider. Let's say you have a dataframe in R and want to perform an operation on each observation (row). I know it is already a delicate issue to iterate over the rows, so I was just wondering which of the three option:

• Normal for loop over each row
• Split dataframe into a list of nrow elements and apply operation on each element and bind the result together
• Do the same as above in parallel

Without any benchmarking or so, this is basically what I am asking in pseudocode:

I'm following Jan Kirenz tutorial for classification using Tidymodels. Everything so far has gone well until I try to evaluate the model using the function fit_resamples(). I keep getting the error message Error in UseMethod("required_pkgs") : no applicable method for 'required_pkgs' applied to an object of class "workflow" .

The code he uses in that section is: