"convert_dia run using sequential and parallel loading returns same results"
"convert_dia summarizing using sequential and parallel loading returns same results"

"convert_enc run using sequential and parallel loading returns same results with original columns"
"convert_enc run using sequential and parallel loading returns same results"
"convert_enc summarizing using sequential and parallel loading returns same results"

## Description: Test convert_lab() function
if(OVERWRITE | !file.exists(paste0(folder_parse, "lab_conv.csv"))) {data.table::fwrite(c_s, paste0(folder_parse, "lab_conv.csv"))}
l_s <- data.table::fread(paste0(folder_parse, "lab_conv.csv"), na.strings = "NA", strip.white = FALSE, colClasses = apply(c_s, 2, class))

"convert_med run using sequential and parallel loading returns same results with original columns"
"convert_med run using sequential and parallel loading returns same results"
"convert_med summarizing using sequential and parallel loading returns same results"

Error in `1:max(which_rows[[x]])`: result would be too long a vector

## Description: Test convert_notes() function
d_fname <- list.files(folder_raw, full.names = TRUE)[grep("rad.txt", list.files(folder_raw), ignore.case = TRUE)]
d_p <- suppressMessages(load_notes(d_fname, type = "rad", nThread = 2))
anchors = c("Exam Code", "Ordering Provider", "HISTORY", "Associated Reports", "Report Below", "REASON", "REPORT",
"TECHNIQUE", "COMPARISON", "FINDINGS", "IMPRESSION", "RECOMMENDATION", "SIGNATURES", "report_end")
c_s <- suppressMessages(convert_notes(d_p, code = "rad_rep_txt", anchors = anchors, nThread = 2))
c_s <- suppressMessages(convert_notes(d_p, code = "rad_rep_txt", anchors = anchors, nThread = 1))
c_p <- suppressMessages(convert_notes(d_p, code = "rad_rep_txt", anchors = anchors, nThread = 2))
if(OVERWRITE | !file.exists(paste0(folder_parse, "notes_conv.csv"))) {data.table::fwrite(c_s, paste0(folder_parse, "notes_conv.csv"))}
l_s <- data.table::fread(paste0(folder_parse, "notes_conv.csv"), na.strings = "NA", strip.white = FALSE, colClasses = apply(c_s, 2, class))