rda | Rissanen Data Analysis : Examining Dataset Characteristics

I've modified the code a little and have used dictionary for easier access and it is working great.

1.: setting a seed at the beginning is enough to obtain different results at each loop iteration. Edited to illustrate it with the example below

2.: for example:

INTERNAL$a = b creates a local copy of INTERNAL in your function, and modifies that. Since you want to modify the global copy, you could use

This error shows up when the path does not have the exact file specified in the argument.

The command here::i_am() (and the package here in general) does not change the main path R is working with (i.e. in getwd()), it just simplifies calling any file from the main folder by using here("file") instead of stating the full path in the argument (e.g., "./path/to/file").

In this case, the correct argument would be read_json(here::here("config.json"))

The problem here is that renv::load() is masking base::load(). In general, you should not call library(renv) in your scripts -- instead, you should prefix any renv APIs you want to use with renv::.

Alternatively, explicitly call base::load() to ensure the right version of load() is resolved.

1. I do not see a problem with the line that you indicated. The TypeError: non-boolean (Missing) used in boolean context occurs because of line 115 of your function: bn_complete[t] = ifelse(B_Emg[t] < BMIN | B_Emg[t] > 0, BMIN, B_Emg[t])

There is some operator precedence issues and issues with using missing. I believe this may be closer to what you intend.

If found a solution:

In R (and many other languages) you'll be using a form of for-loop. In R there are several wrappers for loops with specific outcome in the *apply family. Here's a short (incomplete) list of the *apply family and their input/output:

• lapply -> list output
• sapply -> List or atomic (integer vector, numeric vector etc.)
• mapply -> Similar to sapply but can take more than 1 input to go over (so if you have 2 simultanious things to loop over for example)
• tapply -> loop over groups defined by INDEX
• apply -> Loop over an array (either rows or columns) return matrix/vector

And so on.

I am guessing that your example is incomplete, but I'll show 3 examples to get you started. One using a for-loop, one using lapply and one using mapply.

A for-loop is the classic method (found in most programming languages). It works by having a for(---) where --- is replaced by something to iterate over. This could be error_list or it could be a numeric vector seq(1, n) or 1:n. Here you have more than 1 thing to iterate over, so a numeric vector makes sense (and we use this to subset the data)