pmap | pmap.c : implementation of something like Solaris

Edited to change the regex and show my tidyr/dplyr solution

I am looking for an efficient way (preferably purrr) way to handle a lot searching and counting regex patterns in a large dataframe.

Here is a simple example of what I'm trying to achieve.

Say I have a data frame of sentences:

I am trying to compile a tool for my project. I have solved all the problems so far, but i can't find the solution for this problem.

If anyone could give me help, i would appreciate it, Thank you.

NtlScriptEncrypter.cpp

I have some data that I am trying to apply a function over. It goes to a URL, collects the JSON data and then stores it into a folder on my computer.

I apply the following code:

I have a bit of code that goes through a number of columns containing dates and selects the earliest date from the options to populate a new column with. To do this I was using the dplyr::rowwise function.

Unfortunately, the data set is quite big and comes at a time cost in obtaining an output. Here is an example of my initial approach.

I have two lists foo and bar, where length(foo) > length(bar). I want to apply a function length(bar) times to each element of bar and store the output of each application in is own list and store all of the applications of the function to each element of bar in their own lists. This nested list output structure is important as I am passing it to functions that require nested lists. Examples of what I have and desired are in the minimal example.

While this works nicely with nested for-loops, I've been trying to accomplish this with purrr's map functions. I've managed to do this by creating (a) a list of length(bar) where each element is foo, (b) passing this new list and bar to an anonymous function in purrr::pmap(), and then (c) passing this to an anonymous function in purrr:map().

While this works, it seems very antithetical to the purpose of purrr:

• Instead of .x, .y, and ~ syntax, I'm defining anonymous functions.
• Instead of passing my raw lists (which vary in length), I'm converting one to a nested list to match the other's length. This can be memory-intensive, slow, etc.
• I'm working with nested lists rather than flatter lists/dataframes I then partition into my desired data structure.

Is there an alternative way of working with different-length lists in purrr than my aproach? How might I modify my code (minimal example below) to better-exploit the syntax of purrr? One idea (Handling vectors of different lengths in purrr) is to use cross() or some equivalent to generate a single object for passing to pmap(), but I'm not how how to then generate the nested list structure.

Following on the renaming request #67453183 I want to do the same for formats using the dictionary, because it won't bring together columns of distinct types.

I have a series of data sets and a dictionary to bring these together. But I'm struggling to figure out how to automate this. > Suppose this data and dictionary (actual one is much longer, thus I want to automate):

I want to generate a dataframe where each row is given by a function of the cross product of several properties. This can be done imperatively:

May be this question termed as part-3 of this question.

I know that arguments in lambda functions when used in tidyverse especially purrr functions are written as -

• . when there is only 1 argument
• .x & .y when there are 2
• OR ..1, ..2 so on when there are > 2 arguments

In the linked question, I learnt that if all the arguments have to be simultaneously passed we may use ellipsis i.e. ....

But my question, why such ... work only when wrapped inside a c() and doesn't work when used as such. In the below two syntaxes, second one works while first one doesn't?

I have been exploring the various application of using pmap function and its variations recently and I am particularly interested in using c(...) to pass all the arguments into. The following data set belongs to another question that we discussed earlier today with a number of very knowledgeable users. We were supposed to repeat the values in weight column based on values in Days column along their respective rows to get the following output: