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unit tests

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This commit is contained in:
dr. M.S. (Matthijs) Berends
2023-02-12 17:10:48 +01:00
parent 68abb00c59
commit 45a9697c84
23 changed files with 438 additions and 406 deletions
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20
R/mo.R
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@ -325,7 +325,8 @@ as.mo <- function(x,
result_mo <- NA_character_
} else {
result_mo <- AMR_env$MO_lookup$mo[match(top_hits[1], AMR_env$MO_lookup$fullname)]
AMR_env$mo_uncertainties <- rbind2(AMR_env$mo_uncertainties,
AMR_env$mo_uncertainties <- rbind2(
AMR_env$mo_uncertainties,
data.frame(
original_input = x_search,
input = x_search_cleaned,
@ -335,14 +336,17 @@ as.mo <- function(x,
minimum_matching_score = ifelse(is.null(minimum_matching_score), "NULL", minimum_matching_score),
keep_synonyms = keep_synonyms,
stringsAsFactors = FALSE
))
)
)
# save to package env to save time for next time
AMR_env$mo_previously_coerced <- unique(rbind2(AMR_env$mo_previously_coerced,
AMR_env$mo_previously_coerced <- unique(rbind2(
AMR_env$mo_previously_coerced,
data.frame(
x = paste(x_search, minimum_matching_score),
mo = result_mo,
stringsAsFactors = FALSE
)))
)
))
}
# the actual result:
as.character(result_mo)
@ -797,14 +801,14 @@ print.mo_uncertainties <- function(x, ...) {
}
cat(word_wrap("Matching scores are based on the resemblance between the input and the full taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.\n\n", add_fn = font_blue))
add_MO_lookup_to_AMR_env()
col_red <- function(x) font_rose_bg(font_black(x, collapse = NULL), collapse = NULL)
col_orange <- function(x) font_orange_bg(font_black(x, collapse = NULL), collapse = NULL)
col_yellow <- function(x) font_yellow_bg(font_black(x, collapse = NULL), collapse = NULL)
col_green <- function(x) font_green_bg(font_black(x, collapse = NULL), collapse = NULL)
if (has_colour()) {
cat(word_wrap("Colour keys: ",
col_red(" 0.000-0.499 "),
@ -814,7 +818,7 @@ print.mo_uncertainties <- function(x, ...) {
add_fn = font_blue
), font_green_bg(" "), "\n", sep = "")
}
score_set_colour <- function(text, scores) {
# set colours to scores
text[scores >= 0.7] <- col_green(text[scores >= 0.7])