search for: keeptab

...etter! Sounds like the adjustments you devised, especially keeping the multicore approach for `make_clean_names()` and ensuring that `ID_Key` values remain intact, were the missing components you needed to fit it into your workflow. I believe the warning message regarding `dcast()` occurs because `keeptabs` is a `tbl_df` from the tidyverse rather than a base `data.frame` or `data.table`. The `data.table` implementation of `dcast()` expects a `data.table` or `data.frame`. When it detects a `tbl_df`, it tries to redirect to `reshape2::dcast()`, but since that appears to be deprecated, it will fail in...

...rces = input_files, ????????????format = 'csv', ????????????unify_schema = TRUE, ????????????col_types = schema( ????????????"ID_Key" = string(), ????????????"column1" = string(), ????????????"column2" = string() ????????????) ??????) |> as_tibble() ?????? ??keeptabs <- split(temp, temp$ID_Key) if(isTRUE(multicore)){ keeptabs <- mclapply(1:length(keeptabs), function(i) crewjanitormakeclean(keeptabs[[i]],c("column1","column2")), mc.cores = numcores) }else{ keeptabs <- lapply(1:length(keeptabs), function(i) crewj...

How is the server configured to handle memory distribution for individual users. I see it has over 700GB of total system memory, but how much can be assigned it each individual user? AAgain - just curious, and wondering how much memory was assigned to your instance when you were running R. regards, Gregg On Wednesday, December 11th, 2024 at 9:49 AM, Deramus, Thomas Patrick <tderamus at

...aframes based on Key_ID temp <- open_dataset( sources = input_files, format = 'csv', unify_schema = TRUE, col_types = schema( "ID_Key" = string(), "column1" = string(), "column1" = string() ) ) |> as_tibble() keeptabs <- split(temp, temp$ID_Key) I used a multicore framework to distribute the `sum` functions across each Key_ID when a multicore argument is enabled. ?????? if(isTRUE(multicore)){ output <- mclapply(1:length(modtabs), function(i) crewjanitormakeclean(modtabs[[i]],c("string_co...