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cleanup

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dr. M.S. (Matthijs) Berends 2022-10-22 10:20:09 +02:00
parent d0b54f640c
commit d10651eb26
6 changed files with 17 additions and 24 deletions
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR Package: AMR
Version: 1.8.2.9031 Version: 1.8.2.9032
Date: 2022-10-21 Date: 2022-10-22
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

2
NEWS.md
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@ -1,4 +1,4 @@
# AMR 1.8.2.9031 # AMR 1.8.2.9032
This version will eventually become v2.0! We're happy to reach a new major milestone soon! This version will eventually become v2.0! We're happy to reach a new major milestone soon!

2
R/aa_globals.R
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@ -94,6 +94,8 @@ globalVariables(c(
"atc_group1", "atc_group1",
"atc_group2", "atc_group2",
"base_ab", "base_ab",
"ci_min",
"ci_max",
"code", "code",
"cols", "cols",
"count", "count",

29
R/add_custom_antimicrobials.R
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@ -112,25 +112,18 @@ add_custom_antimicrobials <- function(x) {
AMR_env$custom_ab_codes <- c(AMR_env$custom_ab_codes, x$ab) AMR_env$custom_ab_codes <- c(AMR_env$custom_ab_codes, x$ab)
class(AMR_env$AB_lookup$ab) <- "character" class(AMR_env$AB_lookup$ab) <- "character"
bind_rows <- import_fn("bind_rows", "dplyr", error_on_fail = FALSE) new_df <- AMR_env$AB_lookup[0, , drop = FALSE][seq_len(NROW(x)), , drop = FALSE]
if (is.null(bind_rows)) { rownames(new_df) <- NULL
# do the binding in base R list_cols <- vapply(FUN.VALUE = logical(1), new_df, is.list)
new_df <- AMR_env$AB_lookup[0, , drop = FALSE][seq_len(NROW(x)), , drop = FALSE] for (l in which(list_cols)) {
rownames(new_df) <- NULL # prevent binding NULLs in lists, replace with NA
list_cols <- vapply(FUN.VALUE = logical(1), new_df, is.list) new_df[, l] <- as.list(NA_character_)
for (l in which(list_cols)) {
# prevent binding NULLs in lists, replace with NA
new_df[, l] <- as.list(NA_character_)
}
for (col in colnames(x)) {
# assign new values
new_df[, col] <- x[, col, drop = TRUE]
}
AMR_env$AB_lookup <- unique(rbind(AMR_env$AB_lookup, new_df))
} else {
# otherwise use dplyr
AMR_env$AB_lookup <- unique(bind_rows(AMR_env$AB_lookup, x))
} }
for (col in colnames(x)) {
# assign new values
new_df[, col] <- x[, col, drop = TRUE]
}
AMR_env$AB_lookup <- unique(rbind(AMR_env$AB_lookup, new_df))
class(AMR_env$AB_lookup$ab) <- c("ab", "character") class(AMR_env$AB_lookup$ab) <- c("ab", "character")
message_("Added ", nr2char(nrow(x)), " record", ifelse(nrow(x) > 1, "s", ""), " to the internal `antibiotics` data set.") message_("Added ", nr2char(nrow(x)), " record", ifelse(nrow(x) > 1, "s", ""), " to the internal `antibiotics` data set.")
} }

1
R/rsi_calc.R
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@ -279,7 +279,6 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
col_results <- as.data.frame(as.matrix(table(values)), stringsAsFactors = FALSE) col_results <- as.data.frame(as.matrix(table(values)), stringsAsFactors = FALSE)
col_results$interpretation <- rownames(col_results) col_results$interpretation <- rownames(col_results)
col_results$isolates <- col_results[, 1, drop = TRUE] col_results$isolates <- col_results[, 1, drop = TRUE]
ddf <<- col_results
if (NROW(col_results) > 0 && sum(col_results$isolates, na.rm = TRUE) > 0) { if (NROW(col_results) > 0 && sum(col_results$isolates, na.rm = TRUE) > 0) {
if (sum(col_results$isolates, na.rm = TRUE) >= minimum) { if (sum(col_results$isolates, na.rm = TRUE) >= minimum) {
col_results$value <- col_results$isolates / sum(col_results$isolates, na.rm = TRUE) col_results$value <- col_results$isolates / sum(col_results$isolates, na.rm = TRUE)

1
inst/tinytest/test-zzz.R
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@ -34,7 +34,6 @@
import_functions <- c( import_functions <- c(
"%chin%" = "data.table", "%chin%" = "data.table",
"anti_join" = "dplyr", "anti_join" = "dplyr",
"bind_rows" = "dplyr",
"chmatch" = "data.table", "chmatch" = "data.table",
"cur_column" = "dplyr", "cur_column" = "dplyr",
"full_join" = "dplyr", "full_join" = "dplyr",