TPDeramus commented on issue #39038:
URL: https://github.com/apache/arrow/issues/39038#issuecomment-1836971381
Further, the use of `concat_tables()`
library(arrow)
library(tidyverse)
library(fastDummies)
temp <- open_csv_dataset(sources = cohort_csvs) %>% compute()
Subs <- data.frame(temp %>% distinct(key) %>% collect())
for (Subnum in 1:dim(Subs)[1]) {
out <-
data.frame(temp %>% filter(key == Subs[Subnum, ]) %>% collect())
out[is.na(out)] <- 'NA'
out$tags <- 'NA'
out <-
dummy_cols(
out,
select_columns = "terms",
remove_selected_columns = FALSE,
omit_colname_prefix = TRUE
)
out <-
dummy_cols(
out,
select_columns = "tags",
remove_selected_columns = FALSE,
omit_colname_prefix = TRUE
)
if (Subnum == 1){
Out_table <- arrow_table(out)
} else {
Out_table <- concat_tables(Out_table, arrow_table(out))
}
Seems to proceed without any errors.
Though I am uncertain this will provide what I need in the long run if there
are still `NA` values in the table (which should be swapped to 0 without
pulling into memory if possible).
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