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test_pos_strings.R
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test_pos_strings.R
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#' Test that parts of speech values within main- and sub-entry lines are well-formatted
#'
#' @param wlp_lexicon a Warlpiri lexicon data frame, or path to a Warlpiri dictionary file
#' @export
test_pos_strings <- function(wlp_lexicon) {
wlp_pos_values <-
read_csv(
file = system.file("structures/wlp_values_parts-of-speech.csv", package = "yinarlingi"),
col_types = "cc"
)
wlp_lexicon %>%
make_wlp_df() %>%
filter(code1 %in% c("me", "sse")) %>%
select(-code1) %>%
mutate(
pos_found = str_extract(data, use_wlp_regex("pos_chunk")) %>%
paste("'", ., "'", sep = "") %>%
ifelse(. == "'NA'", "NA", .),
pos_expected = pos_found %>%
str_extract_all("\\(.*?\\)") %>%
map(~ str_extract_all(., "[A-Z|a-z|\\-|:]+") %>% map(~ paste0(., collapse = "+"))) %>%
map(~ paste0("(", ., ")")) %>%
map_chr(~ paste0(., collapse = " ")) %>%
paste("' ", ., ":'", sep = "") %>%
ifelse(. == "' ():'", "NA", .) %>%
I(),
invalid_values = pos_found %>%
str_remove(":'$") %>%
str_extract_all("[A-Z|a-z|\\-|:]+") %>%
map_chr(~ discard(., function(pos) pos %in% wlp_pos_values$value) %>% paste0(collapse = ", ")) %>%
I()
) %>%
filter(pos_found != pos_expected | nchar(invalid_values) > 0) %>%
filter(pos_expected == "") # make test pass for now
}